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Wang SL, Ng TF, Mohamed K, Dzulkifly S, Li X, Leong YH. Polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs) prediction model based on limited peat samples using an evolved artificial neural network. CHEMOSPHERE 2024; 362:142683. [PMID: 38908451 DOI: 10.1016/j.chemosphere.2024.142683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Revised: 05/20/2024] [Accepted: 06/20/2024] [Indexed: 06/24/2024]
Abstract
Polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs) are involuntary by-products of incomplete combustion and are highly toxic to humans and the environment. The Malaysian peat is often acidic or extremely acidic having high levels of chlorine and/or other organic acids that act as catalysts or precursors in PCDD/Fs formation. This study aims to predict PCDD/Fs emissions in peat soil using an artificial neural network (ANN) approach based on limited emission data and selected physico-chemical properties. The ANN's prediction performance is affected by uncertainties in its initial connection weights. To improve prediction performance, an optimisation algorithm, termed differential evolution (DE), is used to optimise the ANN's initial connection weights and bias. The study adopts several ANNs with fixed architecture to predict PCDD/Fs emissions, each consisting of a multilayer perceptron (MLP) with a backpropagation algorithm. Eight input variables and one output variable were adopted to train and test various neural network architectures using real-world datasets. The model optimisation procedure was conducted to ascertain the network architecture with the best predictive accuracy. The evolved ANN based on 5 hidden neurons, with the assistance of self-adaptive ensemble-based differential evolution with enhanced population sizing (SAEDE-EP), successfully produced the lowest MSEtest (6.1790 × 10-3) and highest R2 (0.97447) based on the mean among the other HNs. An evolutionary-optimised ANN-based methodology is a viable solution to predict PCDD/Fs in peat soil. It is cost-effective for pollution control, environmental monitoring and capable of aiding authorities prevent PCDD/Fs exposure, e.g., during a fire.
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Affiliation(s)
- Shir Li Wang
- Faculty of Computing and Meta-Technology, Universiti Pendidikan Sultan Idris, 35900, Tanjong Malim, Perak, Malaysia; Data Intelligent and Knowledge Management (DILIGENT), Universiti Pendidikan Sultan Idris, 35900, Tanjong Malim, Perak, Malaysia
| | - Theam Foo Ng
- Centre for Global Sustainability Studies, Universiti Sains Malaysia, 11800, Penang, Malaysia
| | - Khairulmazidah Mohamed
- National Poison Centre, Universiti Sains Malaysia, 11800, Penang, Malaysia; Faculty of Applied Sciences, Universiti Teknologi MARA, 40450, Shah Alam, Selangor, Malaysia
| | - Sumayyah Dzulkifly
- Faculty of Computing and Meta-Technology, Universiti Pendidikan Sultan Idris, 35900, Tanjong Malim, Perak, Malaysia
| | - Xiaodong Li
- State Key Laboratory of Clean Energy Utilization, Yuquan Campus of Zhejiang University, Hangzhou, Zhejiang Province, 310027, PR China
| | - Yin-Hui Leong
- National Poison Centre, Universiti Sains Malaysia, 11800, Penang, Malaysia.
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2
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Li C, Jiang Z, Li W, Yu T, Wu X, Hu Z, Yang Y, Yang Z, Xu H, Zhang W, Zhang W, Ye Z. Machine learning-based prediction of cadmium pollution in topsoil and identification of critical driving factors in a mining area. ENVIRONMENTAL GEOCHEMISTRY AND HEALTH 2024; 46:315. [PMID: 39001912 DOI: 10.1007/s10653-024-02087-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2024] [Accepted: 06/18/2024] [Indexed: 07/15/2024]
Abstract
Mining activities have resulted in a substantial accumulation of cadmium (Cd) in agricultural soils, particularly in southern China. Long-term Cd exposure can cause plant growth inhibition and various diseases. Rapid identification of the extent of soil Cd pollution and its driving factors are essential for soil management and risk assessment. However, traditional geostatistical methods are difficult to simulate the complex nonlinear relationships between soil Cd and potential features. In this study, sequential extraction and hotspot analyses indicated that Cd accumulation increased significantly near mining sites and exhibited high mobility. The concentration of Cd was estimated using three machine learning models based on 3169 topsoil samples, seven quantitative variables (soil pH, Fe, Ca, Mn, TOC, Al/Si and ba value) and three quantitative variables (soil parent rock, terrain and soil type). The random forest model achieved marginally better performance than the other models, with an R2 of 0.78. Importance analysis revealed that soil pH and Ca and Mn contents were the most significant factors affecting Cd accumulation and migration. Conversely, due to the essence of controlling Cd migration being soil property, soil type, terrain, and soil parent materials had little impact on the spatial distribution of soil Cd under the influence of mining activities. Our results provide a better understanding of the geochemical behavior of soil Cd in mining areas, which could be helpful for environmental management departments in controlling the diffusion of Cd pollution and capturing key targets for soil remediation.
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Affiliation(s)
- Cheng Li
- Institute of Karst Geology, CAGS/Key Laboratory of Karst Dynamics, MNR & GZAR/International Research Center on Karst Under the Auspices of UNESCO, Guilin, 541004, Guangxi, People's Republic of China
- Technical Innovation Center of Mine Geological Environmental Restoration Engineering in Southern Karst Area, Ministry of Natural Resources, Nanning, 530028, People's Republic of China
- Pingguo Guangxi, Karst Ecosystem, National Observation and Research Station, Pingguo, 531406, Guangxi, People's Republic of China
| | - Zhongcheng Jiang
- Institute of Karst Geology, CAGS/Key Laboratory of Karst Dynamics, MNR & GZAR/International Research Center on Karst Under the Auspices of UNESCO, Guilin, 541004, Guangxi, People's Republic of China
- Pingguo Guangxi, Karst Ecosystem, National Observation and Research Station, Pingguo, 531406, Guangxi, People's Republic of China
| | - Wenli Li
- Institute of Karst Geology, CAGS/Key Laboratory of Karst Dynamics, MNR & GZAR/International Research Center on Karst Under the Auspices of UNESCO, Guilin, 541004, Guangxi, People's Republic of China
- Pingguo Guangxi, Karst Ecosystem, National Observation and Research Station, Pingguo, 531406, Guangxi, People's Republic of China
| | - Tao Yu
- School of Earth Sciences and Resources, China University of Geosciences, Beijing, 100083, People's Republic of China
| | - Xiangke Wu
- Mineral Resource Reservoir Evaluation Center of Guangxi, Nanning, 530023, People's Republic of China
| | - Zhaoxin Hu
- Institute of Karst Geology, CAGS/Key Laboratory of Karst Dynamics, MNR & GZAR/International Research Center on Karst Under the Auspices of UNESCO, Guilin, 541004, Guangxi, People's Republic of China
- Pingguo Guangxi, Karst Ecosystem, National Observation and Research Station, Pingguo, 531406, Guangxi, People's Republic of China
| | - Yeyu Yang
- Institute of Karst Geology, CAGS/Key Laboratory of Karst Dynamics, MNR & GZAR/International Research Center on Karst Under the Auspices of UNESCO, Guilin, 541004, Guangxi, People's Republic of China
- Technical Innovation Center of Mine Geological Environmental Restoration Engineering in Southern Karst Area, Ministry of Natural Resources, Nanning, 530028, People's Republic of China
- Pingguo Guangxi, Karst Ecosystem, National Observation and Research Station, Pingguo, 531406, Guangxi, People's Republic of China
| | - Zhongfang Yang
- School of Earth Sciences and Resources, China University of Geosciences, Beijing, 100083, People's Republic of China.
| | - Haofan Xu
- School of Environmental and Chemical Engineering, Foshan University, Foshan, 528000, Guangdong, People's Republic of China
| | - Wenping Zhang
- Institute of Karst Geology, CAGS/Key Laboratory of Karst Dynamics, MNR & GZAR/International Research Center on Karst Under the Auspices of UNESCO, Guilin, 541004, Guangxi, People's Republic of China
- Pingguo Guangxi, Karst Ecosystem, National Observation and Research Station, Pingguo, 531406, Guangxi, People's Republic of China
| | - Wenjie Zhang
- Technical Innovation Center of Mine Geological Environmental Restoration Engineering in Southern Karst Area, Ministry of Natural Resources, Nanning, 530028, People's Republic of China
| | - Zongda Ye
- Technical Innovation Center of Mine Geological Environmental Restoration Engineering in Southern Karst Area, Ministry of Natural Resources, Nanning, 530028, People's Republic of China
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Haider S, Ullah S, Kazi M, Qamar F, Siddique T, Anwer R, Khan SA, Salman S. Ion-Exchange Resin/Carrageenan-Copper-Based Nanocomposite: Artificial Neural Network, Advanced Thermodynamic Profiling, and Anticoagulant Studies. ACS OMEGA 2024; 9:23873-23891. [PMID: 38854529 PMCID: PMC11154903 DOI: 10.1021/acsomega.4c01540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 04/21/2024] [Accepted: 04/29/2024] [Indexed: 06/11/2024]
Abstract
Carrageenan (CG) and ion exchange resins (IERs) are better metal chelators. Kappa (κ) CG and IERs were synthesized and subjected to copper ion (Cu2+) adsorption to obtain DMSCH/κ-Cu, DC20H/κ-Cu, and IRP69H/κ-Cu nanocomposites (NCs). The NCs were studied using statistical physics formalism (SPF) at 315-375 K and a multilayer perceptron with five input nodes. The percentage of Cu2+ uptake efficiency was used as an outcome variable. Via the grand canonical ensemble, SPF gives models for both monolayer and multilayer sorption layers. For in vitro anticoagulant activity (ACA), the activated partial thromboplastin time were calculated using 100 μL of rabbit plasma incubated at 37 °C. After 2 min, 100 L of 0.025 M CaCl2 was added, and the clotting time was recorded for each group (n = 6). The results demonstrated that the key covariables for the adsorption process were pH and concentration. The results of artificial neural network models were comparable with the experimental findings. The error rates varied between 4.3 and 1.0%. The prediction analysis results ranged from 43.6 to 89.2. The ΔG and ΔS values for IRP69H/κ-Cu obtained were -18.91 and -16.32 and 26.21 and 22.74 kJ/mol for the temperatures 315 and 345 K, respectively. Adsorbate species were perpendicular to the adsorbent surfaces, notwithstanding the apparent importance of macro- and micropore volumes. These adsorbents typically fluctuate with temperature changes and contain one or more layers of sorption. Negative and positive sorption energies correspond to endothermic and exothermic processes. The biosorption energy (E1 and E2) values in this experiment have a value of less than 23 kJ mol-1. Complex SPF models' energy distributions validate surface properties and interactions with adsorbates. At a concentration of 100 μg/mL, DC20H/κ-Cu2+ exhibited an ACA of only 8 s. These NCs demonstrated better greater ACA with the order DC20H/κ < DMSCH/κ < IRP69H/κ. More research is needed to rule out the chemical processes behind the ACA of CG/IER-Cu NCs.
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Affiliation(s)
- Sana Haider
- Department
of Pharmacy, University of Peshawar, Peshawar 25120, Pakistan
| | - Sami Ullah
- Department
of Pharmacy, University of Peshawar, Peshawar 25120, Pakistan
| | - Mohsin Kazi
- Department
of Pharmaceutics, College of Pharmacy, King
Saud University, P.O. Box 2457, Riyadh 11451, Saudi Arabia
| | - Fouzia Qamar
- Department
of Biology, Lahore Garrison University, Main Campus, Lahore 54000, Pakistan
| | - Tariq Siddique
- Faculty
of Pharmacy, Ibadat International University, Islamabad 44000, Pakistan
| | - Rubia Anwer
- Faculty
of Pharmacy, Ibadat International University, Islamabad 44000, Pakistan
| | - Saeed Ahmad Khan
- Sharjah
Institute of Medical Research, Dubai 500001, United Arab Emirates
- Department
of Pharmacy, Kohat University of Science
and Technology, Kohat 26000, Pakistan
| | - Saad Salman
- Department
of Pharmacy, CECOS University of IT and
Emerging Sciences, Hayatabad,
Peshawar, Khyber Pakhtunkhwa 25000, Pakistan
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Zhang X, Lu C, Tian J, Zeng L, Wang Y, Sun W, Han H, Kang J. Artificial intelligence optimization and controllable slow-release iron sulfide realizes efficient separation of copper and arsenic in strongly acidic wastewater. J Environ Sci (China) 2024; 139:293-307. [PMID: 38105056 DOI: 10.1016/j.jes.2023.05.038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 05/16/2023] [Accepted: 05/28/2023] [Indexed: 12/19/2023]
Abstract
Iron sulfide (FeS) is a promising material for separating copper and arsenic from strongly acidic wastewater due to its S2- slow-release effect. However, uncertainties arise because of the constant changes in wastewater composition, affecting the selection of operating parameters and FeS types. In this study, the aging method was first used to prepare various controllable FeS nanoparticles to weaken the arsenic removal ability without affecting the copper removal. Orthogonal experiments were conducted, and the results identified the Cu/As ratio, H2SO4 concentration, and FeS dosage as the three main factors influencing the separation efficiency. The backpropagation artificial neural network (BP-ANN) model was established to determine the relationship between the influencing factors and the separation efficiency. The correlation coefficient (R) of overall model was 0.9923 after optimizing using genetic algorithm (GA). The BP-GA model was also solved using GA under specific constraints, predicting the best solution for the separation process in real-time. The predicted results show that the high temperature and long aging time of FeS were necessary to gain high separation efficiency, and the maximum separation factor can reached 1,400. This study provides a suitable sulfurizing material and a set of methods and models with robust flexibility that can successfully predict the separation efficiency of copper and arsenic from highly acidic environments.
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Affiliation(s)
- Xingfei Zhang
- School of Minerals Processing and Bioengineering, Central South University, Changsha 410083, China
| | - Chenglong Lu
- Centre for Mined Land Rehabilitation, Sustainable Minerals Institute, The University of Queensland, Brisbane 4072, Australia
| | - Jia Tian
- School of Minerals Processing and Bioengineering, Central South University, Changsha 410083, China
| | - Liqiang Zeng
- School of Minerals Processing and Bioengineering, Central South University, Changsha 410083, China
| | - Yufeng Wang
- School of Minerals Processing and Bioengineering, Central South University, Changsha 410083, China
| | - Wei Sun
- School of Minerals Processing and Bioengineering, Central South University, Changsha 410083, China
| | - Haisheng Han
- School of Minerals Processing and Bioengineering, Central South University, Changsha 410083, China.
| | - Jianhua Kang
- School of Minerals Processing and Bioengineering, Central South University, Changsha 410083, China
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Tian Y, Liu Q, Ji Y, Dang Q, Sun Y, He X, Liu Y, Su J. Prediction of sulfate concentrations in groundwater in areas with complex hydrogeological conditions based on machine learning. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 923:171312. [PMID: 38423319 DOI: 10.1016/j.scitotenv.2024.171312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 02/16/2024] [Accepted: 02/25/2024] [Indexed: 03/02/2024]
Abstract
The persistent and increasing levels of sulfate due to a variety of human activities over the last decades present a widely concerning environmental issue. Understanding the controlling factors of groundwater sulfate and predicting sulfate concentration is critical for governments or managers to provide information on groundwater protection. In this study, the integration of self-organizing map (SOM) approach and machine learning (ML) modeling offers the potential to determine the factors and predict sulfate concentrations in the Huaibei Plain, where groundwater is enriched with sulfate and the areas have complex hydrogeological conditions. The SOM calculation was used to illustrate groundwater hydrochemistry and analyze the correlations among the hydrochemical parameters. Three ML algorithms including random forest (RF), support vector machine (SVM), and back propagation neural network (BPNN) were adopted to predict sulfate levels in groundwater by using 501 groundwater samples and 8 predictor variables. The prediction performance was evaluated through statistical metrics (R2, MSE and MAE). Mine drainage mainly facilitated increase in groundwater SO42- while gypsum dissolution and pyrite oxidation were found another two potential sources. The major water chemistry type was Ca-HCO3. The dominant cation was Na+ while the dominant anion was HCO3-. There was an intuitive correlation between groundwater sulfate and total dissolved solids (TDS), Cl-, and Na+. By using input variables identified by the SOM method, the evaluation results of ML algorithms showed that the R2, MSE and MAE of RF, SVM, BPNN were 0.43-0.70, 0.16-0.49 and 0.25-0.44. Overall, BPNN showed the best prediction performance and had higher R2 values and lower error indices. TDS and Na+ had a high contribution to the prediction accuracy. These findings are crucial for developing groundwater protection and remediation policies, enabling more sustainable management.
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Affiliation(s)
- Yushan Tian
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Quanli Liu
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Yao Ji
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Qiuling Dang
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Yuanyuan Sun
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Xiaosong He
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Yue Liu
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China.
| | - Jing Su
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China.
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Hasan MM, Ng KTW, Ray S, Assuah A, Mahmud TS. Prophet time series modeling of waste disposal rates in four North American cities. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024:10.1007/s11356-024-33335-5. [PMID: 38632194 DOI: 10.1007/s11356-024-33335-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 04/11/2024] [Indexed: 04/19/2024]
Abstract
In this study, three different univariate municipal solid waste (MSW) disposal rate forecast models (SARIMA, Holt-Winters, Prophet) were examined using different testing periods in four North American cities with different socioeconomic conditions. A review of the literature suggests that the selected models are able to handle seasonality in a time series; however, their ability to handle outliers is not well understood. The Prophet model generally outperformed the Holt-Winters model and the SARIMA model. The MAPE and R2 of the Prophet model during pre-COVID-19 were 4.3-22.2% and 0.71-0.93, respectively. All three models showed satisfactory predictive results, especially during the pre-COVID-19 testing period. COVID-19 lockdowns and the associated regulatory measures appear to have affected MSW disposal behaviors, and all the univariate models failed to fully capture the abrupt changes in waste disposal behaviors. Modeling errors were largely attributed to data noise in seasonality and the unprecedented event of COVID-19 lockdowns. Overall, the modeling errors of the Prophet model were evenly distributed, with minimum modeling biases. The Prophet model also appeared to be versatile and successfully captured MSW disposal rates from 3000 to 39,000 tons/month. The study highlights the potential benefits of the use of univariate models in waste forecast.
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Affiliation(s)
- Mohammad Mehedi Hasan
- Faculty of Engineering and Applied Science, Environmental Systems Engineering, University of Regina, 3737 Wascana Parkway, Regina, Saskatchewan, S4S 0A2, Canada
| | - Kelvin Tsun Wai Ng
- Faculty of Engineering and Applied Science, Environmental Systems Engineering, University of Regina, 3737 Wascana Parkway, Regina, Saskatchewan, S4S 0A2, Canada.
| | - Sagar Ray
- Faculty of Engineering and Applied Science, Environmental Systems Engineering, University of Regina, 3737 Wascana Parkway, Regina, Saskatchewan, S4S 0A2, Canada
| | - Anderson Assuah
- University College of the North, Box 3000, 436 - 7th Street East, The Pas, Manitoba, R9A 1M7, Canada
| | - Tanvir Shahrier Mahmud
- Faculty of Engineering and Applied Science, Environmental Systems Engineering, University of Regina, 3737 Wascana Parkway, Regina, Saskatchewan, S4S 0A2, Canada
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Zhao B, Yu Z, Wang H, Shuai C, Qu S, Xu M. Data Science Applications in Circular Economy: Trends, Status, and Future. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:6457-6474. [PMID: 38568682 DOI: 10.1021/acs.est.3c08331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/17/2024]
Abstract
The circular economy (CE) aims to decouple the growth of the economy from the consumption of finite resources through strategies, such as eliminating waste, circulating materials in use, and regenerating natural systems. Due to the rapid development of data science (DS), promising progress has been made in the transition toward CE in the past decade. DS offers various methods to achieve accurate predictions, accelerate product sustainable design, prolong asset life, optimize the infrastructure needed to circulate materials, and provide evidence-based insights. Despite the exciting scientific advances in this field, there still lacks a comprehensive review on this topic to summarize past achievements, synthesize knowledge gained, and navigate future research directions. In this paper, we try to summarize how DS accelerated the transition to CE. We conducted a critical review of where and how DS has helped the CE transition with a focus on four areas including (1) characterizing socioeconomic metabolism, (2) reducing unnecessary waste generation by enhancing material efficiency and optimizing product design, (3) extending product lifetime through repair, and (4) facilitating waste reuse and recycling. We also introduced the limitations and challenges in the current applications and discussed opportunities to provide a clear roadmap for future research in this field.
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Affiliation(s)
- Bu Zhao
- School for Environment and Sustainability, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Zongqi Yu
- College of Literature, Science, and the Arts, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Hongze Wang
- School of Professional Studies, Columbia University, New York, New York 10027, United States
| | - Chenyang Shuai
- School of Management Science and Real Estate, Chongqing University, Chongqing, 40004, China
| | - Shen Qu
- School of Management and Economics, Beijing Institute of Technology, Beijing, 100081, China
- Center for Energy & Environmental Policy Research, Beijing Institute of Technology, Beijing, 100081, China
| | - Ming Xu
- School of Environment, Tsinghua University, Beijing, 100084, China
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Martínez-Cao C, Sánchez-Lasheras F, García-Fernández A, González-Blanco L, Zurrón-Madera P, Sáiz PA, Bobes J, García-Portilla MP. PsiOvi Staging Model for Schizophrenia (PsiOvi SMS): A New Internet Tool for Staging Patients with Schizophrenia. Eur Psychiatry 2024; 67:e36. [PMID: 38599765 PMCID: PMC11059252 DOI: 10.1192/j.eurpsy.2024.17] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 01/22/2024] [Accepted: 01/31/2024] [Indexed: 04/12/2024] Open
Abstract
BACKGROUND One of the challenges of psychiatry is the staging of patients, especially those with severe mental disorders. Therefore, we aim to develop an empirical staging model for schizophrenia. METHODS Data were obtained from 212 stable outpatients with schizophrenia: demographic, clinical, psychometric (PANSS, CAINS, CDSS, OSQ, CGI-S, PSP, MATRICS), inflammatory peripheral blood markers (C-reactive protein, interleukins-1RA and 6, and platelet/lymphocyte [PLR], neutrophil/lymphocyte [NLR], and monocyte/lymphocyte [MLR] ratios). We used machine learning techniques to develop the model (genetic algorithms, support vector machines) and applied a fitness function to measure the model's accuracy (% agreement between patient classification of our model and the CGI-S). RESULTS Our model includes 12 variables from 5 dimensions: 1) psychopathology: positive, negative, depressive, general psychopathology symptoms; 2) clinical features: number of hospitalizations; 3) cognition: processing speed, visual learning, social cognition; 4) biomarkers: PLR, NLR, MLR; and 5) functioning: PSP total score. Accuracy was 62% (SD = 5.3), and sensitivity values were appropriate for mild, moderate, and marked severity (from 0.62106 to 0.6728). DISCUSSION We present a multidimensional, accessible, and easy-to-apply model that goes beyond simply categorizing patients according to CGI-S score. It provides clinicians with a multifaceted patient profile that facilitates the design of personalized intervention plans.
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Affiliation(s)
- Clara Martínez-Cao
- Department of Psychiatry, University of Oviedo, Oviedo, Spain
- Health Research Institute of the Principality of Asturias (ISPA), Oviedo, Spain
- Institute of Neurosciences of the Principality of Asturias (INEUROPA), University of Oviedo, Oviedo, Spain
| | - Fernando Sánchez-Lasheras
- Department of Mathematics, University of Oviedo, Oviedo, Spain
- Institute of Space Sciences and Technologies of Asturias (ICTEA), University of Oviedo, Oviedo, Spain
| | - Ainoa García-Fernández
- Department of Psychiatry, University of Oviedo, Oviedo, Spain
- Health Research Institute of the Principality of Asturias (ISPA), Oviedo, Spain
- Institute of Neurosciences of the Principality of Asturias (INEUROPA), University of Oviedo, Oviedo, Spain
| | - Leticia González-Blanco
- Department of Psychiatry, University of Oviedo, Oviedo, Spain
- Health Research Institute of the Principality of Asturias (ISPA), Oviedo, Spain
- Institute of Neurosciences of the Principality of Asturias (INEUROPA), University of Oviedo, Oviedo, Spain
- Health Service of the Principality of Asturias (SESPA), Oviedo, Spain
- Centro de Investigación Biomédica en Red, Salud Mental (CIBERSAM), Madrid, Spain
| | - Paula Zurrón-Madera
- Department of Psychiatry, University of Oviedo, Oviedo, Spain
- Health Research Institute of the Principality of Asturias (ISPA), Oviedo, Spain
- Institute of Neurosciences of the Principality of Asturias (INEUROPA), University of Oviedo, Oviedo, Spain
- Health Service of the Principality of Asturias (SESPA), Oviedo, Spain
| | - Pilar A. Sáiz
- Department of Psychiatry, University of Oviedo, Oviedo, Spain
- Health Research Institute of the Principality of Asturias (ISPA), Oviedo, Spain
- Institute of Neurosciences of the Principality of Asturias (INEUROPA), University of Oviedo, Oviedo, Spain
- Health Service of the Principality of Asturias (SESPA), Oviedo, Spain
- Centro de Investigación Biomédica en Red, Salud Mental (CIBERSAM), Madrid, Spain
| | - Julio Bobes
- Department of Psychiatry, University of Oviedo, Oviedo, Spain
- Health Research Institute of the Principality of Asturias (ISPA), Oviedo, Spain
- Institute of Neurosciences of the Principality of Asturias (INEUROPA), University of Oviedo, Oviedo, Spain
- Health Service of the Principality of Asturias (SESPA), Oviedo, Spain
- Centro de Investigación Biomédica en Red, Salud Mental (CIBERSAM), Madrid, Spain
| | - María Paz García-Portilla
- Department of Psychiatry, University of Oviedo, Oviedo, Spain
- Health Research Institute of the Principality of Asturias (ISPA), Oviedo, Spain
- Institute of Neurosciences of the Principality of Asturias (INEUROPA), University of Oviedo, Oviedo, Spain
- Health Service of the Principality of Asturias (SESPA), Oviedo, Spain
- Centro de Investigación Biomédica en Red, Salud Mental (CIBERSAM), Madrid, Spain
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Ajami Yazdi A, Ebrahimian Pirbazari A, Esmaeili Khalil Saraei F, Esmaeili A, Ebrahimian Pirbazari A, Akbari Kohnehsari A, Derakhshesh A. Design of 2D/2D β-Ni(OH) 2/ZnO heterostructures via photocatalytic deposition of nickel for sonophotocatalytic degradation of tetracycline and modeling with three supervised machine learning algorithms. CHEMOSPHERE 2024; 352:141328. [PMID: 38296215 DOI: 10.1016/j.chemosphere.2024.141328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 11/29/2023] [Accepted: 01/27/2024] [Indexed: 03/10/2024]
Abstract
Due to the expansive use of tetracycline antibiotics (TCs) to treat various infectious diseases in humans and animals, their presence in the environment has created many challenges for human societies. Therefore, providing green and cost-effective solutions for their effective removal has become an urgent need. Here, we will introduce 2D/2D p-n heterostructures that exhibit excellent sonophotocatalytic/photocatalytic properties for water-soluble pollutant removal. In this contribution, for the first time, β- Ni(OH)2 nanosheets were synthesized through visible-light-induced photodeposition of different amounts of nickel on ZnO nanosheets (β-Ni(x)/ZNs) to fabricate 2D/2D p-n heterostructures. The PXRD patterns confirmed the formation of wurtzite phase for ZNs and the hexagonal crystal structure of β-Ni(OH)2. The FESEM and TEM micrographs showed that the β-Ni(OH)2 sheets were dispersed on the surface of ZNs and formed 2D/2D p-n heterojunction in β-Ni(x)/ZNs samples. With the photodeposition of β-Ni(OH)2 nanosheets on ZNs, the surface area, pore volume, and pore diameter of β-Ni(x)/ZNs heterostructures have increased compared to ZNs, which can have a positive effect on the sonophotocatalytic/photocatalytic performance of ZNs. The degradation experiments showed that β-Ni(0.1)/ZNs and β-Ni(0.4)/ZNs have the highest degradation percentage in photocatalytic (51 %) and sonophotocatalytic (71 %) degradation of TC, respectively. Finally, the sonophotocatalytic/photocatalytic degradation process of TC was systematically validated through modeling with three powerful and supervised machine learning algorithms, including Support Vector Regression (SVR), Artificial Neural Networks (ANNs), and Stochastic Gradient Boosting (SGB). Five statistical criteria including R2, SAE, MSE, SSE, and RMSE were calculated for model validation. It was observed that the developed SGB algorithm was the most reliable model for predicting the degradation percent of TC. The results revealed that using fabricated 2D/2D p-n heterojunctions (β-Ni(x)/ZNs) is more sustainable than the conventional ZnO photocatalytic systems in practical applications.
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Affiliation(s)
- Aghil Ajami Yazdi
- Hybrid Nanomaterials & Environment Lab, Fouman Faculty of Engineering, College of Engineering, University of Tehran, Fouman, 43581- 39115, Iran; Data Mining Research Group, Fouman Faculty of Engineering, College of Engineering, University of Tehran, Fouman, 43581-39115, Iran
| | - Azadeh Ebrahimian Pirbazari
- Hybrid Nanomaterials & Environment Lab, Fouman Faculty of Engineering, College of Engineering, University of Tehran, Fouman, 43581- 39115, Iran.
| | - Fatemeh Esmaeili Khalil Saraei
- Data Mining Research Group, Fouman Faculty of Engineering, College of Engineering, University of Tehran, Fouman, 43581-39115, Iran.
| | - Amin Esmaeili
- Department of Chemical Engineering, College of Engineering Technology, University of Doha for Science and Technology, 24449, Arab League St, Doha, Qatar
| | | | - Atena Akbari Kohnehsari
- Hybrid Nanomaterials & Environment Lab, Fouman Faculty of Engineering, College of Engineering, University of Tehran, Fouman, 43581- 39115, Iran; Data Mining Research Group, Fouman Faculty of Engineering, College of Engineering, University of Tehran, Fouman, 43581-39115, Iran
| | - Ali Derakhshesh
- Data Mining Research Group, Fouman Faculty of Engineering, College of Engineering, University of Tehran, Fouman, 43581-39115, Iran
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10
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Liu W, Sun M, He Y, Guo Q, Zhang L, Wang Z, Zhang G. Quality Evaluation of Asparagus officinalis by Profile of Amino Acids and Mineral Elements in Different Parts Combined with Chemometrics Methods. Chem Biodivers 2024; 21:e202301754. [PMID: 38348931 DOI: 10.1002/cbdv.202301754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 02/13/2024] [Indexed: 02/29/2024]
Abstract
Asparagus officinalis has a homologous value in medicine and vegetables. Its immature stem, commonly called asparagus, is a central edible part. Asparagus skin and leaf also contain rich nutrients. However, these parts are often discarded. This study investigated amino acid and mineral elements in immature stem, skinless asparagus, asparagus skin, and leaf. Their quality was further evaluated by chemometrics methods such as principal component analysis and neural network analysis. The results showed amino acid content was high in immature stem and skinless asparagus and low in leaf, whereas the mineral elements were in four parts. Quality evaluation results showed four parts were divided into three grades. Immature stem and skinless asparagus were grouped into cluster 1 with the best quality as high-quality raw materials in food and health-care products. Meanwhile, three AA (Cys, His, Arg) and two mineral elements (Na, Cr) were identified as quality evaluation iconic substances.
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Affiliation(s)
- Wei Liu
- College of Agriculture, Henan University of Science and Technology, Luoyang, 471000, P. R. China
| | - Mengyu Sun
- College of Life Sciences, Zhengzhou University, Zhengzhou, 450001, P. R. China
| | - Yinglong He
- College of Agriculture, Henan University of Science and Technology, Luoyang, 471000, P. R. China
| | - Qi Guo
- College of Agriculture, Henan University of Science and Technology, Luoyang, 471000, P. R. China
| | - Lixia Zhang
- College of Agriculture, Henan University of Science and Technology, Luoyang, 471000, P. R. China
| | - Zhihao Wang
- Henan Fengcai Agricultural Development Co. LTD., Sui County, 476900, P. R. China
| | - Guofeng Zhang
- Henan Huamiao Agricultural Development Co. LTD., Hebi, 458030, P. R. China
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11
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Hoy ZX, Phuang ZX, Farooque AA, Fan YV, Woon KS. Municipal solid waste management for low-carbon transition: A systematic review of artificial neural network applications for trend prediction. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 344:123386. [PMID: 38242306 DOI: 10.1016/j.envpol.2024.123386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 11/16/2023] [Accepted: 01/16/2024] [Indexed: 01/21/2024]
Abstract
Improper municipal solid waste (MSW) management contributes to greenhouse gas emissions, necessitating emissions reduction strategies such as waste reduction, recycling, and composting to move towards a more sustainable, low-carbon future. Machine learning models are applied for MSW-related trend prediction to provide insights on future waste generation or carbon emissions trends and assist the formulation of effective low-carbon policies. Yet, the existing machine learning models are diverse and scattered. This inconsistency poses challenges for researchers in the MSW domain who seek to identify and optimize the machine learning techniques and configurations for their applications. This systematic review focuses on MSW-related trend prediction using the most frequently applied machine learning model, artificial neural network (ANN), while addressing potential methodological improvements for reducing prediction uncertainty. Thirty-two papers published from 2013 to 2023 are included in this review, all applying ANN for MSW-related trend prediction. Observing a decrease in the size of data samples used in studies from daily to annual timescales, the summarized statistics suggest that well-performing ANN models can still be developed with approximately 33 annual data samples. This indicates promising opportunities for modeling macroscale greenhouse gas emissions in future works. Existing literature commonly used the grid search (manual) technique for hyperparameter (e.g., learning rate, number of neurons) optimization and should explore more time-efficient automated optimization techniques. Since there are no one-size-fits-all performance indicators, it is crucial to report the model's predictive performance based on more than one performance indicator and examine its uncertainty. The predictive performance of newly-developed integrated models should also be benchmarked to show performance improvement clearly and promote similar applications in future works. The review analyzed the shortcomings, best practices, and prospects of ANNs for MSW-related trend predictions, supporting the realization of practical applications of ANNs to enhance waste management practices and reduce carbon emissions.
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Affiliation(s)
- Zheng Xuan Hoy
- School of Energy and Chemical Engineering, Xiamen University Malaysia, Jalan Sunsuria, Bandar Sunsuria, 43900, Sepang, Selangor, Malaysia
| | - Zhen Xin Phuang
- School of Energy and Chemical Engineering, Xiamen University Malaysia, Jalan Sunsuria, Bandar Sunsuria, 43900, Sepang, Selangor, Malaysia
| | - Aitazaz Ahsan Farooque
- Canadian Center for Climate Change and Adaptation, University of Prince Edward Island, St Peter's Bay, PE, Canada; Faculty of Sustainable Design Engineering, University of Prince Edward Island, Charlottetown, PE, Canada
| | - Yee Van Fan
- Sustainable Process Integration Laboratory - SPIL, NETME Centre, Faculty of Mechanical Engineering, Brno University of Technology - VUT Brno, Technická 2896/2, 61669, Brno, Czech Republic
| | - Kok Sin Woon
- School of Energy and Chemical Engineering, Xiamen University Malaysia, Jalan Sunsuria, Bandar Sunsuria, 43900, Sepang, Selangor, Malaysia.
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12
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Yang Y, Zhang W, Chen S, Wang X, Xia Y, Liu J, Hu B, Lu Q, Zhang B. Structure-Energy Relationship Prediction of the HZSM-5 Zeolite with Different Acid Site Distributions by the Neural Network Model. ACS OMEGA 2024; 9:3392-3400. [PMID: 38284028 PMCID: PMC10809367 DOI: 10.1021/acsomega.3c06689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 11/30/2023] [Accepted: 12/13/2023] [Indexed: 01/30/2024]
Abstract
Zeolites are a very important family of catalysts. The catalytic activity of zeolites depends on the distribution of acid sites, which has been extensively studied. However, the relationship between the acid site distribution and catalytic efficiency remains unestablished. An onerous computational burden can be imposed when static calculations are applied to analyze the relationship between a catalyst structure and its energy. To resolve this issue, the current work uses neural network (NN) models to evaluate the relationship. By taking the typical HZSM-5 zeolite as an example, we applied the provided atomic coordinates to predict the energy. The network performances of the artificial neural network (ANN) and high-dimensional neural network (HDNN) are compared using the trained results from a dataset containing the identical number of acid sites. Furthermore, the importance of the feature is examined with the aid of a random forest model to identify the pivotal variables influencing the energy. In addition, the HDNN is employed to forecast the energy of an HZSM-5 system with varying numbers of acid sites. This study emphasizes that the energy of zeolites can be rapidly and accurately predicted through the NN, which can assist our understanding of the relationship between the structure and properties, thereby providing more accurate and efficient methods for the application of zeolite materials.
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Affiliation(s)
- Yang Yang
- School
of New Energy, North China Electric Power
University, Beijing 102206, People’s Republic
of China
| | - Wenming Zhang
- School
of New Energy, North China Electric Power
University, Beijing 102206, People’s Republic
of China
| | - Shengbin Chen
- School
of New Energy, North China Electric Power
University, Beijing 102206, People’s Republic
of China
| | - Xiaogang Wang
- School
of New Energy, North China Electric Power
University, Beijing 102206, People’s Republic
of China
| | - Yuangu Xia
- School
of New Energy, North China Electric Power
University, Beijing 102206, People’s Republic
of China
| | - Ji Liu
- National
Engineering Research Center of New Energy Power Generation, North China Electric Power University, Beijing 102206, People’s Republic of China
- State
Key Laboratory of Alternate Electrical Power System With Renewable
Energy Sources, North China Electric Power
University, Beijing 102206, People’s Republic
of China
- School
of New Energy, North China Electric Power
University, Beijing 102206, People’s Republic
of China
| | - Bin Hu
- National
Engineering Research Center of New Energy Power Generation, North China Electric Power University, Beijing 102206, People’s Republic of China
- State
Key Laboratory of Alternate Electrical Power System With Renewable
Energy Sources, North China Electric Power
University, Beijing 102206, People’s Republic
of China
- School
of New Energy, North China Electric Power
University, Beijing 102206, People’s Republic
of China
| | - Qiang Lu
- National
Engineering Research Center of New Energy Power Generation, North China Electric Power University, Beijing 102206, People’s Republic of China
- State
Key Laboratory of Alternate Electrical Power System With Renewable
Energy Sources, North China Electric Power
University, Beijing 102206, People’s Republic
of China
- School
of New Energy, North China Electric Power
University, Beijing 102206, People’s Republic
of China
| | - Bing Zhang
- National
Engineering Research Center of New Energy Power Generation, North China Electric Power University, Beijing 102206, People’s Republic of China
- State
Key Laboratory of Alternate Electrical Power System With Renewable
Energy Sources, North China Electric Power
University, Beijing 102206, People’s Republic
of China
- School
of New Energy, North China Electric Power
University, Beijing 102206, People’s Republic
of China
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13
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Ji T, Li J, Fang H, Zhang R, Yang J, Fan L. Rapid dataset generation methods for stacked construction solid waste based on machine vision and deep learning. PLoS One 2024; 19:e0296666. [PMID: 38227593 PMCID: PMC10790992 DOI: 10.1371/journal.pone.0296666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2023] [Accepted: 12/13/2023] [Indexed: 01/18/2024] Open
Abstract
The development of urbanization has brought convenience to people, but it has also brought a lot of harmful construction solid waste. The machine vision detection algorithm is the crucial technology for finely sorting solid waste, which is faster and more stable than traditional methods. However, accurate identification relies on large datasets, while the datasets from the field working conditions are scarce, and the manual annotation cost of datasets is high. To rapidly and automatically generate datasets for stacked construction waste, an acquisition and detection platform was built to automatically collect different groups of RGB-D images for instances labeling. Then, based on the distribution points generation theory and data augmentation algorithm, a rapid-generation method for synthetic construction solid waste datasets was proposed. Additionally, two automatic annotation methods for real stacked construction solid waste datasets based on semi-supervised self-training and RGB-D fusion edge detection were proposed, and datasets under real-world conditions yield better models training results. Finally, two different working conditions were designed to validate these methods. Under the simple working condition, the generated dataset achieved an F1-score of 95.98, higher than 94.81 for the manually labeled dataset. In the complicated working condition, the F1-score obtained by the rapid generation method reached 97.74. In contrast, the F1-score of the dataset obtained manually labeled was only 85.97, which demonstrates the effectiveness of proposed approaches.
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Affiliation(s)
- Tianchen Ji
- College of Mechanical Engineering and Automation, Huaqiao University, Xiamen, Fujian, China
| | - Jiantao Li
- College of Mechanical Engineering and Automation, Huaqiao University, Xiamen, Fujian, China
| | - Huaiying Fang
- College of Mechanical Engineering and Automation, Huaqiao University, Xiamen, Fujian, China
| | - RenCheng Zhang
- College of Mechanical Engineering and Automation, Huaqiao University, Xiamen, Fujian, China
| | - Jianhong Yang
- College of Mechanical Engineering and Automation, Huaqiao University, Xiamen, Fujian, China
| | - Lulu Fan
- Shenzhen Municipal Engineering Corporation, Shenzhen, Guangdong, China
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14
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Hoy ZX, Woon KS, Chin WC, Van Fan Y, Yoo SJ. Curbing global solid waste emissions toward net-zero warming futures. Science 2023; 382:797-800. [PMID: 37972189 DOI: 10.1126/science.adg3177] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Accepted: 09/22/2023] [Indexed: 11/19/2023]
Abstract
No global analysis has considered the warming that could be averted through improved solid waste management and how much that could contribute to meeting the Paris Agreement's 1.5° and 2°C pathway goals or the terms of the Global Methane Pledge. With our estimated global solid waste generation of 2.56 to 3.33 billion tonnes by 2050, implementing abrupt technical and behavioral changes could result in a net-zero warming solid waste system relative to 2020, leading to 11 to 27 billion tonnes of carbon dioxide warming-equivalent emissions under the temperature limits. These changes, however, require accelerated adoption within 9 to 17 years (by 2033 to 2041) to align with the Global Methane Pledge. Rapidly reducing methane, carbon dioxide, and nitrous oxide emissions is necessary to maximize the short-term climate benefits and stop the ongoing temperature rise.
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Affiliation(s)
- Zheng Xuan Hoy
- New Energy Science and Engineering Department, School of Energy and Chemical Engineering, Xiamen University Malaysia, Bandar Sunsuria 43900, Malaysia
| | - Kok Sin Woon
- New Energy Science and Engineering Department, School of Energy and Chemical Engineering, Xiamen University Malaysia, Bandar Sunsuria 43900, Malaysia
| | - Wen Cheong Chin
- Department of Mathematics, Xiamen University Malaysia, Bandar Sunsuria 43900, Malaysia
| | - Yee Van Fan
- Sustainable Process Integration Laboratory (SPIL), NETME Centre, Faculty of Mechanical Engineering, Brno University of Technology, Brno 61669, Czech Republic
| | - Seung Jick Yoo
- Department of Climate and Environmental Studies, Sookmyung Women's University, Seoul 04310, Korea
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15
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Soultanidis V, Voudrias EA. Modelling of demolition waste generation: Application to Greek residential buildings. WASTE MANAGEMENT & RESEARCH : THE JOURNAL OF THE INTERNATIONAL SOLID WASTES AND PUBLIC CLEANSING ASSOCIATION, ISWA 2023; 41:1469-1479. [PMID: 36912503 DOI: 10.1177/0734242x231155818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
The construction sector in Europe is among the biggest waste generators, producing 370 million tonnes of construction and demolition waste (CDW) every year, which contain important secondary materials. Quantification of CDW is important from their circular management and environmental impact point of view. Thus, the overall objective of this study was to develop a modelling methodology for estimating demolition waste (DW) generation. The volumes (m3) of individual construction materials contained in 45 residential buildings in Greece were accurately estimated using computer-aided design (CAD) software and the materials were classified according to European List of Waste. These materials will become waste upon demolition, with a total estimated generation rate of 1590 kg m-2 of top view area and with concrete and bricks representing 74.5% of total. Linear regression models were developed to predict the total and individual amounts of 12 different building materials based on structural building characteristics. To test the accuracy of the models, the materials of two residential buildings were quantified and classified and the results were compared with the model predictions. Depending on the model used, the % differences between models' predictions and CAD estimates for total DW averaged 11.1% ± 7.4% for the first case study and 2.5% ± 1.5% for the second. The models can be used for accurate quantification of total and individual DW and their management within the framework of circular economy.
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Affiliation(s)
- Vangelis Soultanidis
- Department of Environmental Engineering, Democritus University of Thrace, Xanthi, Greece
| | - Evangelos A Voudrias
- Department of Environmental Engineering, Democritus University of Thrace, Xanthi, Greece
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16
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Mahmud TS, Ng KTW, Hasan MM, An C, Wan S. A cross-jurisdictional comparison on residential waste collection rates during earlier waves of COVID-19. SUSTAINABLE CITIES AND SOCIETY 2023; 96:104685. [PMID: 37274541 PMCID: PMC10225168 DOI: 10.1016/j.scs.2023.104685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 05/18/2023] [Accepted: 05/27/2023] [Indexed: 06/06/2023]
Abstract
There is currently a lack of studies on residential waste collection during COVID-19 in North America. SARIMA models were developed to predict residential waste collection rates (RWCR) across four North American jurisdictions before and during the pandemic. Unlike waste disposal rates, RWCR is relatively less sensitive to the changes in COVID-19 regulatory policies and administrative measures, making RWCR more appropriate for cross-jurisdictional comparisons. It is hypothesized that the use of RWCR in forecasting models will help us to better understand the residential waste generation behaviors in North America. Both SARIMA models performed satisfactorily in predicting Regina's RWCR. The SARIMA DCV model's performance is noticeably better during COVID-19, with a 15.7% lower RMSE than that of the benchmark model (SARIMA BCV). The skewness of overprediction ratios was noticeably different between jurisdictions, and modeling errors were generally lower in less populated cities. Conflicting behavioral changes might have altered the residential waste generation characteristics and recycling behaviors differently across the jurisdictions. Overall, SARIMA DCV performed better in the Canadian jurisdiction than in U.S. jurisdictions, likely due to the model's bias on a less variable input dataset. The use of RWCR in forecasting models helps us to better understand the residential waste generation behaviors in North America and better prepare us for a future global pandemic.
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Affiliation(s)
- Tanvir Shahrier Mahmud
- Environmental Systems Engineering, Faculty of Engineering and Applied Science, University of Regina, 3737 Wascana Parkway, Regina, Saskatchewan S4S 0A2, Canada
| | - Kelvin Tsun Wai Ng
- Environmental Systems Engineering, Faculty of Engineering and Applied Science, University of Regina, 3737 Wascana Parkway, Regina, Saskatchewan S4S 0A2, Canada
| | - Mohammad Mehedi Hasan
- Environmental Systems Engineering, Faculty of Engineering and Applied Science, University of Regina, 3737 Wascana Parkway, Regina, Saskatchewan S4S 0A2, Canada
| | - Chunjiang An
- Department of Building, Civil, and Environmental Engineering, Concordia University, 1455 De Maisonneuve Blvd. W., Montreal, Quebec H3G 1M8, Canada
| | - Shuyan Wan
- Department of Building, Civil, and Environmental Engineering, Concordia University, 1455 De Maisonneuve Blvd. W., Montreal, Quebec H3G 1M8, Canada
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17
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Liu Y, Liu Y, Wang S, Chen T, Gao B, Gao H, Wang H. Independent parallel pyrolysis kinetics of model components in sewage sludge analyzed by BPM neural network. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:97486-97497. [PMID: 37594705 DOI: 10.1007/s11356-023-29184-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 08/01/2023] [Indexed: 08/19/2023]
Abstract
Analyzing the kinetic behavior of sewage sludge pyrolysis is essential for the design of efficient reactors to produce biofuel and syngas. To understand the complex pyrolysis process of sewage sludge, we pyrolyzed six model components (i.e., cellulose, hemicellulose, lignin, protein, soluble sugars, and lipid) using a thermogravimetric analyzer. The effects of the heating rate on the pyrolysis process were examined at four different heating rates (5, 15, 25, and 50 °C/min). As temperature increased, the derivative thermogravimetric peaks shifted to higher temperature zones. The temperature ranges of the maximum mass loss rate for cellulose, hemicellulose, lignin, protein, soluble sugars, and lipid were within 326.1-368.0 °C, 288.7-315.5 °C, 375.1-429.4 °C, 291.9-308.0 °C, 251.0-314.1 °C, and 410.8-454.1 °C, respectively. The apparent activation energies of the model components were obtained using non-isothermal kinetic analysis methods (Flynn-Wall-Ozawa and Kissinger-Akahira-Sunose). In addition, a back-propagation artificial neural network with a momentum algorithm (BPM) was developed to predict the relationship between the pyrolysis experiment and the activation value. The best BPM model (BPM5) for predicting the cellulose pyrolysis was identified.
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Affiliation(s)
- Yanting Liu
- Zhongye Changtian International Engineering Co., Ltd., Changsha, 410205, China
| | - Yanjun Liu
- School of Chemical and Environmental Engineering, China University of Mining and Technology (Beijing), Beijing, 100083, China.
| | - Sheng Wang
- School of Chemical and Environmental Engineering, China University of Mining and Technology (Beijing), Beijing, 100083, China
| | - Tan Chen
- College of Life and Environmental Sciences, Minzu University of China, Beijing, 100081, China
| | - Bingli Gao
- Planning and Construction Bureau of Xiong'an New Area, Xiong'an, 071700, Hebei, China
| | - Hang Gao
- School of Emergency Science and Engineering, Jilin Jianzhu University, Changchun, Jilin, 130018, China
| | - Hongtao Wang
- School of Environment, Tsinghua University, Beijing, 100084, China
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18
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Zhao W, Zhang P, Chen D, Wang H, Gu B, Zhang J. Data mining from process monitoring of typical polluting enterprise. ENVIRONMENTAL MONITORING AND ASSESSMENT 2023; 195:1109. [PMID: 37644145 DOI: 10.1007/s10661-023-11733-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 08/17/2023] [Indexed: 08/31/2023]
Abstract
With the increasing volume of environmental monitoring data, extracting valuable insights from multivariate time series sensor data can facilitate comprehensive information utilization and support informed decision-making in environmental management. However, there is a dearth of comprehensive research on multivariate data analysis for process monitoring in typical polluting enterprises. In this study, an artificial neural network model based on back-propagation algorithm (BP-ANN) was developed to predict the wastewater and exhaust gas emissions using IoT data obtained from process monitoring of a typical polluting enterprise located in Taizhou, Zhejiang Province, China. The results indicate that the model constructed has a high predictive coefficient of determination (R2) with values of 0.8510, 0.9565, 0.9561, 0.9677, and 0.9061 for chemical oxygen demand (COD), potential of hydrogen (pH), electrical conductivity (EC), flue gas emission (FGE), and non-methane hydrocarbon concentration (NMHC) respectively. For the first time, the variable importance measure (VIM)-assisted BP-ANN was employed to investigate the internal and external correlations between wastewater and exhaust gas treatment, thereby enhancing the interpretability of mapping features in the BP-ANN model. The predicted errors for pH and FGE have been demonstrated to fall within the range of - 0.62 ~ 0.30 and - 0.21 ~ 0.15 m3/s, respectively, with average relative errors of 1.05% and 9.60%, which is advantageous in detecting anomalous data and forecasting pollution indicator values. Our approach successfully addresses the challenge of segregating data analysis for wastewater disposal and exhaust gas disposal in the process monitoring of polluting enterprises, while also unearthing potential variables that significantly contribute to the BP-ANN model, thereby facilitating the selection and extraction of characteristic variables.
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Affiliation(s)
- Wenya Zhao
- Taizhou Pollution Control Technology Center Co. LTD, Taizhou , Zhejiang, 318000, China
- Key Laboratory of the Eco-Environmental Big Data of Taizhou, Taizhou , Zhejiang, 318000, China
| | - Peili Zhang
- Taizhou Pollution Control Technology Center Co. LTD, Taizhou , Zhejiang, 318000, China.
- Key Laboratory of the Eco-Environmental Big Data of Taizhou, Taizhou , Zhejiang, 318000, China.
| | - Da Chen
- Taizhou Pollution Control Technology Center Co. LTD, Taizhou , Zhejiang, 318000, China
- Key Laboratory of the Eco-Environmental Big Data of Taizhou, Taizhou , Zhejiang, 318000, China
| | - Hao Wang
- Taizhou Pollution Control Technology Center Co. LTD, Taizhou , Zhejiang, 318000, China
- Key Laboratory of the Eco-Environmental Big Data of Taizhou, Taizhou , Zhejiang, 318000, China
| | - Binghua Gu
- Taizhou Pollution Control Technology Center Co. LTD, Taizhou , Zhejiang, 318000, China
- Key Laboratory of the Eco-Environmental Big Data of Taizhou, Taizhou , Zhejiang, 318000, China
| | - Jue Zhang
- Taizhou Pollution Control Technology Center Co. LTD, Taizhou , Zhejiang, 318000, China
- Key Laboratory of the Eco-Environmental Big Data of Taizhou, Taizhou , Zhejiang, 318000, China
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19
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Huang L, Li M, Xu T, Dong SQ. A waste classification method based on a capsule network. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:86454-86462. [PMID: 37405600 DOI: 10.1007/s11356-023-27970-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Accepted: 05/24/2023] [Indexed: 07/06/2023]
Abstract
Garbage recycling and automatic sorting are efficient ways to address the paradox of rising municipal waste. Although traditional image classification methods can solve the rubbish image classification problem, they ignore the spatial relationship between features, which can easily lead to misclassification of the same object. In this paper, we propose the ResMsCapsule network, which is a trash picture categorization model based on the capsule network. By combining the residual network and multi-scale module, the ResMsCapsule network can improve the performance of the basic capsule network greatly. Extensive experiments using the publicly available dataset TrashNet show that the ResMsCapsule method has a simpler network structure and higher garbage classification accuracy. The classification accuracy of the ResMsCapsule network is 91.41%, and the number of parameters is only 40% of that of ResNet18, which is better than other image classification algorithms.
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Affiliation(s)
- Lei Huang
- School of Mathematics and Physics, North China Electric Power University, Beijing, 102206, China
| | - Min Li
- School of Mathematics and Physics, North China Electric Power University, Beijing, 102206, China.
| | - Tao Xu
- College of Science, China University of Petroleum, Beijing, 102249, China
| | - Shao-Qun Dong
- College of Science, China University of Petroleum, Beijing, 102249, China
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20
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Shekoohiyan S, Hadadian M, Heidari M, Hosseinzadeh-Bandbafha H. Life cycle assessment of Tehran Municipal solid waste during the COVID-19 pandemic and environmental impacts prediction using machine learning. CASE STUDIES IN CHEMICAL AND ENVIRONMENTAL ENGINEERING 2023; 7:100331. [PMID: 37521456 PMCID: PMC9998284 DOI: 10.1016/j.cscee.2023.100331] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Revised: 03/08/2023] [Accepted: 03/09/2023] [Indexed: 08/01/2023]
Abstract
Life cycle assessment and machine learning were combined to find the best option for Tehran's waste management for future pandemics. The ReCipe results showed the waste's destructive effects after COVID-19 were greater than before due to waste composition changes. Plastic waste has changed from 7.5 to 11%. Environmental burdens of scenarios were Sc-1 (increase composting to 50%) > Sc-3 > Sc-4 > Sc-b2 > Sc-5 > Sc-2 (increase recycling from 9 to 20%). The artificial neural network and gradient-boosted regression tree could predict environmental impacts with high R2. Based on the results, the environmental burdens of solid waste after COVID-19 should be investigated.
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Affiliation(s)
- Sakine Shekoohiyan
- Department of Environmental Health Engineering, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Mobina Hadadian
- Department of Environmental Health Engineering, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Mohsen Heidari
- Department of Environmental Health Engineering, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Homa Hosseinzadeh-Bandbafha
- Department of Mechanical Engineering of Agricultural Machinery, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
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21
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Li C, Zhang C, Yu T, Ma X, Yang Y, Liu X, Hou Q, Li B, Lin K, Yang Z, Wang L. Identification of soil parent materials in naturally high background areas based on machine learning. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 875:162684. [PMID: 36894078 DOI: 10.1016/j.scitotenv.2023.162684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Revised: 02/28/2023] [Accepted: 03/02/2023] [Indexed: 06/18/2023]
Abstract
Recently, farmlands with high geological background of Cd derived from carbonate rock (CA) and black shale areas (BA) have received wide attention. However, although both CA and BA belong to high geological background areas, the mobility of soil Cd differs significantly between them. In addition to the difficulty in reaching the parent material in deep soil, it is challenging to perform land use planning in high geological background areas. This study attempts to determine the key soil geochemical parameters related to the spatial patterns of lithology and the main factors influencing the geochemical behavior of soil Cd, and ultimately uses them and machine-learning methods to identify CA and BA. In total, 10,814 and 4323 surface soil samples were collected from CA and BA, respectively. Hot spot analysis revealed that soil properties and soil Cd were significantly correlated with the underlying bedrock, except for TOC and S. Further research confirmed that the concentration and mobility of Cd in high geological background areas were mainly affected by pH and Mn. The soil parent materials were then predicted using artificial neural network (ANN), random forest (RF) and support vector machine (SVM) models. The ANN and RF models showed higher Kappa coefficients and overall accuracies than those of the SVM model, suggesting that ANNs and RF have the potential to predict soil parent materials from soil data, which might help in ensuring safe land use and coordinating activities in high geological background areas.
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Affiliation(s)
- Cheng Li
- School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, PR China
| | - Chaosheng Zhang
- School of Geography, Archaeology & Irish Studies, National University of Ireland, University Road, Galway H91 CF50, Ireland
| | - Tao Yu
- School of Science, China University of Geosciences, Beijing 100083, PR China
| | - Xudong Ma
- School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, PR China
| | - Yeyu Yang
- Key Laboratory of Karst Dynamics, MNR&GZAR, Institute of Krast Geology, CAGS, Guilin 541004, China
| | - Xu Liu
- School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, PR China
| | - Qingye Hou
- School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, PR China
| | - Bo Li
- School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, PR China
| | - Kun Lin
- School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, PR China
| | - Zhongfang Yang
- School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, PR China.
| | - Lei Wang
- Guangxi Bureau of Geology & Mineral Prospecting & Exploitation, Nanning 530023, PR China
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22
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Cao Z, Sun B, Zhou G, Mao S, Zhu S, Zhang J, Ke C, Zhao Y, Shao J. Memristor-based neural networks: a bridge from device to artificial intelligence. NANOSCALE HORIZONS 2023; 8:716-745. [PMID: 36946082 DOI: 10.1039/d2nh00536k] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
Since the beginning of the 21st century, there is no doubt that the importance of artificial intelligence has been highlighted in many fields, among which the memristor-based artificial neural network technology is expected to break through the limitation of von Neumann so as to realize the replication of the human brain by enabling strong parallel computing ability and efficient data processing and become an important way towards the next generation of artificial intelligence. A new type of nanodevice, namely memristor, which is based on the variability of its resistance value, not only has very important applications in nonvolatile information storage, but also presents obsessive progressiveness in highly integrated circuits, making it one of the most promising circuit components in the post-Moore era. In particular, memristors can effectively simulate neural synapses and build neural networks; thus, they can be applied for the preparation of various artificial intelligence systems. This study reviews the research progress of memristors in artificial neural networks in detail and highlights the structural advantages and frontier applications of neural networks based on memristors. Finally, some urgent problems and challenges in current research are summarized and corresponding solutions and future development trends are put forward.
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Affiliation(s)
- Zelin Cao
- Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China.
- Shaanxi International Joint Research Center for Applied Technology of Controllable Neutron Source, School of Science, Xijing University, Xi'an 710123, China
| | - Bai Sun
- Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China.
| | - Guangdong Zhou
- College of Artificial Intelligence, Brain-inspired Computing & Intelligent Control of Chongqing Key Lab, Southwest University, Chongqing 400715, China
| | - Shuangsuo Mao
- Fujian Provincial Collaborative Innovation Center for Advanced High-Field Superconducting Materials and Engineering, Fujian Normal University, Fuzhou, Fujian 350117, China
| | - Shouhui Zhu
- School of Physical Science and Technology, Key Laboratory of Advanced Technology of Materials, Southwest Jiaotong University, Chengdu, Sichuan 610031, China
| | - Jie Zhang
- School of Electrical Engineering, Southwest Jiaotong University, Chengdu, Sichuan 610031, China
| | - Chuan Ke
- School of Electrical Engineering, Southwest Jiaotong University, Chengdu, Sichuan 610031, China
| | - Yong Zhao
- Fujian Provincial Collaborative Innovation Center for Advanced High-Field Superconducting Materials and Engineering, Fujian Normal University, Fuzhou, Fujian 350117, China
- School of Physical Science and Technology, Key Laboratory of Advanced Technology of Materials, Southwest Jiaotong University, Chengdu, Sichuan 610031, China
- School of Electrical Engineering, Southwest Jiaotong University, Chengdu, Sichuan 610031, China
| | - Jinyou Shao
- Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China.
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23
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Huang C, Fan Z, Fan M, Xu Z, Gao J. A Predictive Model of Capillary Forces and Contact Diameters between Two Plates Based on Artificial Neural Network. MICROMACHINES 2023; 14:754. [PMID: 37420987 DOI: 10.3390/mi14040754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 03/23/2023] [Accepted: 03/27/2023] [Indexed: 07/09/2023]
Abstract
Many efforts have been devoted to the forecasting of the capillary force generated by capillary adsorption between solids, which is fundamental and essential in the fields of micro-object manipulation and particle wetting. In this paper, an artificial neural network (ANN) model optimized by a genetic algorithm (GA-ANN) was proposed to predict the capillary force and contact diameter of the liquid bridge between two plates. The mean square error (MSE) and correlation coefficient (R2) were employed to evaluate the prediction accuracy of the GA-ANN model, theoretical solution method of the Young-Laplace equation and simulation approach based on the minimum energy method. The results showed that the values of MSE of capillary force and contact diameter using GA-ANN were 10.3 and 0.0001, respectively. The values of R2 were 0.9989 and 0.9977 for capillary force and contact diameter in regression analysis, respectively, demonstrating the accuracy of the proposed predictive model. The sensitivity analysis was conducted to investigate the influence of input parameters, including liquid volume and separation distance, on the capillary force and contact diameter. The liquid volume and separation distance played dominant roles in affecting the capillary force and contact diameter.
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Affiliation(s)
- Congcong Huang
- School of Mechanical Engineering, Shandong University of Technology, Zibo 255049, China
| | - Zenghua Fan
- School of Mechanical Engineering, Shandong University of Technology, Zibo 255049, China
- Shandong Provincial Key Laboratory of Precision Manufacturing and Non-Traditional Machining, Shandong University of Technology, Zibo 255049, China
| | - Ming Fan
- School of Mechanical Engineering, Shandong University of Technology, Zibo 255049, China
| | - Zhi Xu
- School of Mechanical Engineering, Shandong University of Technology, Zibo 255049, China
| | - Jun Gao
- School of Mechanical Engineering, Shandong University of Technology, Zibo 255049, China
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24
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Jose J, Sasipraba T. An optimal model for municipal solid waste management using hybrid dual faster R-CNN. ENVIRONMENTAL MONITORING AND ASSESSMENT 2023; 195:462. [PMID: 36907939 DOI: 10.1007/s10661-023-10984-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 01/28/2023] [Indexed: 06/18/2023]
Abstract
Economic development, population growth, and rapid urbanization are the reasons for an increasing generation of waste all over the world. Recently, the statistics showed that 2.1 million of tons municipal solid waste (MSW) was produced in 2016, which is estimated to enhance by 3.4 million tons in 2050. In recent times, municipal solid waste generation is dramatically increasing due to factors such as rapid urbanization, altered living standard, and increased population. These factors make the municipal solid waste management system complex and break the pollution-controlling strategies. So it necessitates the system to accurately predict the waste composition. Based on the waste classification, a suitable decomposition technique is preferred. Therefore, this paper proposed CMSOA optimized dual faster R-CNN based waste management system to accurately classify the waste composition. The proposed system is formed by hybridizing dual faster RCNN along with complex-valued encoding multi-chain seeker optimization algorithm (CMSOA). Various evaluation measures, namely accuracy, precision, recall, F-measure, RMSE, MAE, and MAPE metrics, are computed, and the case study analysis is conducted on the major five cities of Maharashtra. The comparative analysis is carried out for various approaches, and from the analysis, the results revealed that the proposed method provides better classification results than other methods.
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Affiliation(s)
- Jithina Jose
- Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, India.
| | - T Sasipraba
- Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, India
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25
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Lan DY, He PJ, Qi YP, Wu TW, Xian HY, Wang RH, Lü F, Zhang H. Optimizing the Quality of Machine Learning for Identifying the Share of Biogenic and Fossil Carbon in Solid Waste. Anal Chem 2023; 95:4412-4420. [PMID: 36820858 DOI: 10.1021/acs.analchem.2c04940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2023]
Abstract
Insights into carbon sources (biogenic and fossil carbon) and contents in solid waste are vital for estimating the carbon emissions from incineration plants. However, the traditional methods are time-, labor-, and cost-intensive. Herein, high-quality data sets were established after analyzing the carbon contents and infrared spectra of substantial samples using elemental analysis and attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR), respectively. Then, five classification and eight regression machine learning (ML) models were evaluated to recognize the proportion of biogenic and fossil carbon in solid waste. Using the optimized data preprocessing approach, the random forest (RF) classifier with hyperparameter tuning ranked first in classifying the carbon group with a test accuracy of 0.969, and the carbon contents were successfully predicted by the RF regressor with R2 = 0.926 considering performance-interpretability-computation time competition. The above proposed algorithms were further validated with real environmental samples, which exhibited robust performance with an accuracy of 0.898 for carbon group classification and an R2 value of 0.851 for carbon content prediction. The reliable results indicate that ATR-FTIR coupled with ML algorithms is feasible for rapidly identifying both carbon groups and content, facilitating the calculation and assessment of carbon emissions from solid waste incineration.
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Affiliation(s)
- Dong-Ying Lan
- Institute of Waste Treatment & Reclamation, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
| | - Pin-Jing He
- Institute of Waste Treatment & Reclamation, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China.,Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, China
| | - Ya-Ping Qi
- Institute of Waste Treatment & Reclamation, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
| | - Ting-Wei Wu
- Institute of Waste Treatment & Reclamation, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
| | - Hao-Yang Xian
- Institute of Waste Treatment & Reclamation, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
| | - Rui-Heng Wang
- Institute of Waste Treatment & Reclamation, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
| | - Fan Lü
- Institute of Waste Treatment & Reclamation, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China.,Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, China
| | - Hua Zhang
- Institute of Waste Treatment & Reclamation, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China.,Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, China
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26
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Modeling industrial hydrocyclone operational variables by SHAP-CatBoost - A “conscious lab” approach. POWDER TECHNOL 2023. [DOI: 10.1016/j.powtec.2023.118416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2023]
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27
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Solano Meza JK, Orjuela Yepes D, Rodrigo-Ilarri J, Rodrigo-Clavero ME. Comparative Analysis of the Implementation of Support Vector Machines and Long Short-Term Memory Artificial Neural Networks in Municipal Solid Waste Management Models in Megacities. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:4256. [PMID: 36901265 PMCID: PMC10002305 DOI: 10.3390/ijerph20054256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 02/21/2023] [Accepted: 02/24/2023] [Indexed: 06/18/2023]
Abstract
The development of methodologies to support decision-making in municipal solid waste (MSW) management processes is of great interest for municipal administrations. Artificial intelligence (AI) techniques provide multiple tools for designing algorithms to objectively analyze data while creating highly precise models. Support vector machines and neuronal networks are formed by AI applications offering optimization solutions at different managing stages. In this paper, an implementation and comparison of the results obtained by two AI methods on a solid waste management problem is shown. Support vector machine (SVM) and long short-term memory (LSTM) network techniques have been used. The implementation of LSTM took into account different configurations, temporal filtering and annual calculations of solid waste collection periods. Results show that the SVM method properly fits selected data and yields consistent regression curves, even with very limited training data, leading to more accurate results than those obtained by the LSTM method.
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Affiliation(s)
- Johanna Karina Solano Meza
- Department of Environmental Engineering, Santo Tomás University, Road 9 Street 51-11, Bogotá 110231, Colombia
| | - David Orjuela Yepes
- Department of Environmental Engineering, Santo Tomás University, Road 9 Street 51-11, Bogotá 110231, Colombia
| | - Javier Rodrigo-Ilarri
- Instituto de Ingeniería del Agua y Medio Ambiente (IIAMA), Universitat Politècnica de València, 46022 Valencia, Spain
| | - María-Elena Rodrigo-Clavero
- Instituto de Ingeniería del Agua y Medio Ambiente (IIAMA), Universitat Politècnica de València, 46022 Valencia, Spain
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28
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Dogan H, Aydın Temel F, Cagcag Yolcu O, Turan NG. Modelling and optimization of sewage sludge composting using biomass ash via deep neural network and genetic algorithm. BIORESOURCE TECHNOLOGY 2023; 370:128541. [PMID: 36581236 DOI: 10.1016/j.biortech.2022.128541] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 12/22/2022] [Accepted: 12/24/2022] [Indexed: 06/17/2023]
Abstract
In this study, the use of Deep Cascade Forward Neural Network (DCFNN) was investigated to model both linear and non-linear chaotic relationships in co-composting of dewatered sewage sludge and biomass fly ash (BFA). Model results were evaluated in comparison with RSM, Feed Forward Neural Network (FFNN) and Feed Back Neural Network (FBNN), and Cascade Forward Neural Network (CFNN). DCFNN produced predictive results with MAPE values less than 1% for all datasets in all experimental designs except one with 1.99%. Furthermore, the decision variables were optimized by Genetic Algorithm (GA). The desirability level obtained from the optimization results was found to be 100% in a few designs and above 95% in all other designs. The results showed that DCFNN is a reliable and consistent tool for modeling composting process parameters, also GA is a satisfactory tool for determining which outputs the input parameters will produce in an experimental setup.
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Affiliation(s)
- Hale Dogan
- Department of Environmental Engineering, Faculty of Engineering, Ondokuz Mayıs University, Samsun 55200, Turkey
| | - Fulya Aydın Temel
- Department of Environmental Engineering, Faculty of Engineering, Giresun University, Giresun 28200, Turkey
| | - Ozge Cagcag Yolcu
- Department of Statistics, Faculty of Sciences and Arts, Marmara University, İstanbul 34722, Turkey
| | - Nurdan Gamze Turan
- Department of Environmental Engineering, Faculty of Engineering, Ondokuz Mayıs University, Samsun 55200, Turkey
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29
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A novel deep learning simulation to predict radon activity concentration in soil layers. J Radioanal Nucl Chem 2023. [DOI: 10.1007/s10967-022-08735-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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30
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Cha GW, Choi SH, Hong WH, Park CW. Development of Machine Learning Model for Prediction of Demolition Waste Generation Rate of Buildings in Redevelopment Areas. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 20:107. [PMID: 36612429 PMCID: PMC9819715 DOI: 10.3390/ijerph20010107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 12/14/2022] [Accepted: 12/17/2022] [Indexed: 06/17/2023]
Abstract
Owing to a rapid increase in waste, waste management has become essential, for which waste generation (WG) information has been effectively utilized. Various studies have recently focused on the development of reliable predictive models by applying artificial intelligence to the construction and prediction of WG information. In this study, research was conducted on the development of machine learning (ML) models for predicting the demolition waste generation rate (DWGR) of buildings in redevelopment areas in South Korea. Various ML algorithms (i.e., artificial neural network (ANN), K-nearest neighbors (KNN), linear regression (LR), random forest (RF), and support vector machine (SVM)) were applied to the development of an optimal predictive model, and the main hyper parameters (HPs) for each algorithm were optimized. The results suggest that ANN-ReLu (coefficient of determination (R2) 0.900, the ratio of percent deviation (RPD) 3.16), SVM-polynomial (R2 0.889, RPD 3.00), and ANN-logistic (R2 0.883, RPD 2.92) are the best ML models for predicting the DWGR. They showed average errors of 7.3%, 7.4%, and 7.5%, respectively, compared to the average observed values, confirming the accurate predictive performance, and in the uncertainty analysis, the d-factor of the models appeared less than 1, showing that the presented models are reliable. Through a comparison with ML algorithms and HPs applied in previous related studies, the results herein also showed that the selection of various ML algorithms and HPs is important in developing optimal ML models for WG management.
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Affiliation(s)
- Gi-Wook Cha
- School of Science and Technology Acceleration Engineering, Kyungpook National University, Daegu 41566, Republic of Korea
| | - Se-Hyu Choi
- School of Architectural, Civil, Environmental and Energy Engineering, Kyungpook National University, Daegu 41566, Republic of Korea
| | - Won-Hwa Hong
- School of Architectural, Civil, Environmental and Energy Engineering, Kyungpook National University, Daegu 41566, Republic of Korea
| | - Choon-Wook Park
- Industry Academic Cooperation Foundation, Kyungpook National University, Daegu 41566, Republic of Korea
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31
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Impacts of nested forward validation techniques on machine learning and regression waste disposal time series models. ECOL INFORM 2022. [DOI: 10.1016/j.ecoinf.2022.101897] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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32
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Adusei KK, Ng KTW, Karimi N, Mahmud TS, Doolittle E. Modeling of municipal waste disposal behaviors related to meteorological seasons using recurrent neural network LSTM models. ECOL INFORM 2022. [DOI: 10.1016/j.ecoinf.2022.101925] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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33
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Liu Z, Lu M, Zhang Y, Zhou J, Wang J. Identification of heavy metal leaching patterns in municipal solid waste incineration fly ash based on an explainable machine learning approach. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2022; 317:115387. [PMID: 35653843 DOI: 10.1016/j.jenvman.2022.115387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 05/09/2022] [Accepted: 05/21/2022] [Indexed: 06/15/2023]
Abstract
The leaching risk of heavy metal (HM) in municipal solid waste incineration fly ash (MSWI-FA) leads to a big challenge for FA landfilling. In this work, the HM leaching patterns were identified via 6 highly available indices of FA, in which 160 stabilized FA samples were collected from 4 incineration plants in eastern China and an explainable machine learning approach based on boosting and game analysis was conducted to assess the leaching potentials of 6 HMs (Cr, Cd, Cu, Ni, Pb and Zn). We found that, there remained high exceeding risks of Cd and Pb in stabilized FA. In addition, the S-Cl (soluble chlorine) content was the most influential factor of the leaching behaviors of Cd, Cu, Pb and Zn, more important than pH in regard to Cu, Pb and Zn. We quantified the influence of S-Cl on the HM leaching of Cd, Cu, Pb and Zn, whereby their leaching concentrations would increase by 223.5%, 215.2%, 216.5% and 222.6%, respectively, for every 0.5 mol/L order increase in S-Cl concentration. Finally, a fast determination criterion for the FA landfill was proposed, that is, FA of which S-Cl was less than 0.412 mol/L was acceptable.
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Affiliation(s)
- Zeyuan Liu
- College of Environmental & Resource Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Min Lu
- School of Environmental and Chemical Engineering, Shanghai University, Shanghai, 200444, China
| | - Yufeng Zhang
- School of Environmental Sci & Engineering, Nanjing Tech University, Nanjing, 211816, China
| | - Jizhi Zhou
- School of Environmental Sci & Engineering, Nanjing Tech University, Nanjing, 211816, China.
| | - Jinnan Wang
- College of Environmental & Resource Sciences, Zhejiang University, Hangzhou, 310058, China; Chinese Academy of Environmental Planning, Beijing, 100012, China.
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34
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Ouyang S, Xiong Z, Zhao J, Li Z. Separator performance modeling and analysis using artificial neural network and response surface method. ANN NUCL ENERGY 2022. [DOI: 10.1016/j.anucene.2022.109139] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
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35
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Mohammadi K, Immonen J, Blackburn LD, Tuttle JF, Andersson K, Powell KM. A review on the application of machine learning for combustion in power generation applications. REV CHEM ENG 2022. [DOI: 10.1515/revce-2021-0107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Abstract
Although the world is shifting toward using more renewable energy resources, combustion systems will still play an important role in the immediate future of global energy. To follow a sustainable path to the future and reduce global warming impacts, it is important to improve the efficiency and performance of combustion processes and minimize their emissions. Machine learning techniques are a cost-effective solution for improving the sustainability of combustion systems through modeling, prediction, forecasting, optimization, fault detection, and control of processes. The objective of this study is to provide a review and discussion regarding the current state of research on the applications of machine learning techniques in different combustion processes related to power generation. Depending on the type of combustion process, the applications of machine learning techniques are categorized into three main groups: (1) coal and natural gas power plants, (2) biomass combustion, and (3) carbon capture systems. This study discusses the potential benefits and challenges of machine learning in the combustion area and provides some research directions for future studies. Overall, the conducted review demonstrates that machine learning techniques can play a substantial role to shift combustion systems towards lower emission processes with improved operational flexibility and reduced operating cost.
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Affiliation(s)
- Kasra Mohammadi
- Department of Chemical Engineering , University of Utah , 50 S. Central Campus Dr., Room 3290 MEB , Salt Lake City , UT 84112-9203 , USA
| | - Jake Immonen
- Department of Chemical Engineering , University of Utah , 50 S. Central Campus Dr., Room 3290 MEB , Salt Lake City , UT 84112-9203 , USA
| | - Landen D. Blackburn
- Department of Chemical Engineering , University of Utah , 50 S. Central Campus Dr., Room 3290 MEB , Salt Lake City , UT 84112-9203 , USA
| | - Jacob F. Tuttle
- Department of Chemical Engineering , University of Utah , 50 S. Central Campus Dr., Room 3290 MEB , Salt Lake City , UT 84112-9203 , USA
| | - Klas Andersson
- Department of Space, Earth and Environment, Energy Technology , Chalmers University of Technology , SE – 412 96 Göteborg , Sweden
| | - Kody M. Powell
- Department of Chemical Engineering , University of Utah , 50 S. Central Campus Dr., Room 3290 MEB , Salt Lake City , UT 84112-9203 , USA
- Department of Mechanical Engineering , University of Utah , 1495 E 100 S., Room 1550 MEK , Salt Lake City , UT 84112 , USA
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36
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Forecasting Heterogeneous Municipal Solid Waste Generation via Bayesian-Optimised Neural Network with Ensemble Learning for Improved Generalisation. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2022.107946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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37
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Horned Melon Pulp, Peel, and Seed: New Insight into Phytochemical and Biological Properties. Antioxidants (Basel) 2022; 11:antiox11050825. [PMID: 35624689 PMCID: PMC9137901 DOI: 10.3390/antiox11050825] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 04/18/2022] [Accepted: 04/21/2022] [Indexed: 12/16/2022] Open
Abstract
Artificial neural intelligence was established for the estimation, prediction, and optimization of many agricultural and food processes to enable enhanced and balanced utilization of fresh and processed fruits. The predictive capabilities of artificial neural networks (ANNs) are evaluated to estimate the phytochemical composition and the antioxidant and antimicrobial activity of horned melon (Cucumis metuliferus) pulp, peel, and seed. Using multiobjective optimization, the main goals were successively achieved through analysis of antimicrobial potential against sensitive microorganisms for peel (Bacillus cereus, Pseudomonas aeruginosa, Aspergillus brasiliensis, and Penicillium aurantiogriseum), pulp (Salmonella enterica subsp. enterica serotype Typhimurium), and seed samples (Saccharomyces cerevisiae and Candida albicans), and its connection with phytochemical and nutritional composition and antioxidant activity. The highly potent extracts were obtained from peels which represent a waste part with strong antioxidant and antifungal capacity. Briefly, the calculated inhibition zone minimums for sensitive microorganisms were 25.3−30.7 mm, while the optimal results achieved with carotenoids, phenolics, vitamin C, proteins, lipids, DPPH, ABTS, and RP were: 332.01 mg β-car/100 g, 1923.52 mg GAE/100 g, 928.15 mg/100 g, 5.73 g/100 g, 2.3 g/100 g, 226.56 μmol TE/100 g, 8042.55 μmol TE/100 g, and 7526.36 μmol TE/100 g, respectively. These results imply the possibility of using horned melon peel extract as an antioxidant and antifungal agent for food safety and quality.
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38
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Prediction of China’s Industrial Solid Waste Generation Based on the PCA-NARBP Model. SUSTAINABILITY 2022. [DOI: 10.3390/su14074294] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Industrial solid waste (ISW) accounts for the most significant proportion of solid waste in China. Improper treatment of ISW will cause significant environmental pollution. As the basis of decision-making and the management of solid waste resource utilization, the accurate prediction of industrial solid waste generation (ISWG) is crucial. Therefore, combined with China’s national conditions, this paper selects 14 influential factors in four aspects: society, economy, environment and technology, and then proposes a new prediction model called the principal component analysis nonlinear autoregressive back propagation (PCA-NARBP) neural network model. Compared with the back propagation (BP) neural network model and nonlinear autoregressive back propagation (NARBP) neural network model, the mean absolute percentage error (MAPE) of this model reaches 1.25%, which shows that it is more accurate, includes fewer errors and is more generalizable. An example is given to verify the effectiveness, feasibility and stability of the model. The forecast results show that the output of ISW in China will still show an upward trend in the next decade, and limit the total amount to about 4.6 billion tons. This can not only provide data support for decision-makers, but also put forward targeted suggestions on the current management situation in China.
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39
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ANN Hybrid Model for Forecasting Landfill Waste Potential in Lithuania. SUSTAINABILITY 2022. [DOI: 10.3390/su14074122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Abstract
Waste management is currently a fast-growing environmental business and one of solutions to manage the huge amount of waste being generated on landfills is to use the disposed waste as an energy source. There is a major focus on energy forecasting, highlighting the importance of having reliable data on the volume and composition of municipal solid waste in landfills. However, the lack of historical data is forcing the development of machine-learning based models. This study contributes to this field by proposing a hybrid ANN-based model to forecast the total amount of landfill waste, different waste fraction and the potential for energy recovery. The proposed model includes an adaptive number of inputs adjusted to the relevant waste fraction and to the specific landfill. The obtained results substantiated that the proposed model allows for stable and accurate forecasting of recovered energy potential in cases where there is insufficient historical data. The experiments showed that the model with 12 inputs (meaning the forecast of the future value takes into account the last 12 months of data) was the most accurate in the energy forecasting task, with the lowest forecasting error in terms of mean absolute error −8.9878 gigawatt hours per year.
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40
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Natural Melanin: Current Trends, and Future Approaches, with Especial Reference to Microbial Source. Polymers (Basel) 2022; 14:polym14071339. [PMID: 35406213 PMCID: PMC9002885 DOI: 10.3390/polym14071339] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 02/09/2022] [Accepted: 02/24/2022] [Indexed: 02/04/2023] Open
Abstract
Melanin is a universal natural dark polymeric pigment, arising in microorganisms, animals, and plants. There is a couple of pieces of literature on melanin, each focusing on a different issue, the goal of the present review is to focus on microbial melanin. It has numerous benefits with very few drawbacks. The current situation and expected trends are discussed. Intriguing, numerous studies have provoked a serious necessity for a comprehensive assessment of microbial melanin pigments. So that, such review would help scholars from diverse backgrounds to realize the importance of melanin pigments isolated from microorganisms, with this aim in mind, information, and hypothesis from this review could be the paradigm for studies on melanin in the next era.
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Vu HL, Ng KTW, Richter A, An C. Analysis of input set characteristics and variances on k-fold cross validation for a Recurrent Neural Network model on waste disposal rate estimation. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2022; 311:114869. [PMID: 35287077 DOI: 10.1016/j.jenvman.2022.114869] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 03/01/2022] [Accepted: 03/06/2022] [Indexed: 06/14/2023]
Abstract
The use of machine learning techniques in waste management studies is increasingly popular. Recent literature suggests k-fold cross validation may reduce input dataset partition uncertainties and minimize overfitting issues. The objectives are to quantify the benefits of k-fold cross validation for municipal waste disposal prediction and to identify the relationship of testing dataset variance on predictive neural network model performance. It is hypothesized that the dataset characteristics and variances may dictate the necessity of k-fold cross validation on neural network waste model construction. Seven RNN-LSTM predictive models were developed using historical landfill waste records and climatic and socio-economic data. The performance of all trials was acceptable in the training and validation stages, with MAPE all less than 10%. In this study, the 7-fold cross validation reduced the bias in selection of testing sets as it helps to reduce MAPE by up to 44.57%, MSE by up to 54.15%, and increased R value by up to 8.33%. Correlation analysis suggests that fewer outliers and less variance of the testing dataset correlated well with lower modeling error. The length of the continuous high waste season and length of total high waste period appear not important to the model performance. The result suggests that k-fold cross validation should be applied to testing datasets with higher variances. The use of MSE as an evaluation index is recommended.
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Affiliation(s)
- Hoang Lan Vu
- Environmental Systems Engineering, Faculty of Engineering and Applied Science, University of Regina, Regina, Saskatchewan, S4S 0A2, Canada
| | - Kelvin Tsun Wai Ng
- Environmental Systems Engineering, Faculty of Engineering and Applied Science, University of Regina, Regina, Saskatchewan, S4S 0A2, Canada.
| | - Amy Richter
- Environmental Systems Engineering, Faculty of Engineering and Applied Science, University of Regina, Regina, Saskatchewan, S4S 0A2, Canada
| | - Chunjiang An
- Department of Building, Civil, and Environmental Engineering, Concordia University, 1455 Boulevard de Maisonneuve O, Montréal, Quebec, H3G 1M8, Canada
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Li R, Xu A, Zhao Y, Chang H, Li X, Lin G. Genetic algorithm (GA) - Artificial neural network (ANN) modeling for the emission rates of toxic volatile organic compounds (VOCs) emitted from landfill working surface. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2022; 305:114433. [PMID: 34995942 DOI: 10.1016/j.jenvman.2022.114433] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Revised: 11/11/2021] [Accepted: 01/02/2022] [Indexed: 06/14/2023]
Abstract
Volatile organic compounds (VOCs) emitted from the working surface of landfills have received increasing attention due to the potential risks to human health. Quantifying the emission rates of risky VOCs is important to their health risk assessment but is also challenging because of their high variation and complicated relationship between the emission rates and various influencing factors. In this study, a continuous nine-month sampling of VOCs was conducted on a landfill working surface to identify dominant VOCs that are risky to human health and to construct artificial neural network (ANN) models for their emission rates by involving 105 datasets. Among the 63 detected VOCs, ethanol presented the highest emission rate (885.28 ± 1398.10 μg·m-2·s-1), and the dominant compounds with high emission rates and detection frequencies were characterized in each category. According to the human toxicity impact scores calculated with USEtox method, carbon tetrachloride, ethanol, tetrachloroethylene, 1, 2-dichloroethane, benzene, ethylbenzene, and chloroform were identified as the dominant carcinogenic VOCs, and acrolein, carbon tetrachloride, and 1, 2-dichloropropane were the dominant noncarcinogenic VOCs. ANN models were established for the emission rates of six typical risky VOCs, with meteorological conditions and waste compositions as input parameters and emission rates as output parameters. With the structure optimization and genetic algorithm, all the ANN models achieved good performance and excellent prediction capability with high R2 and low root mean square error (RMSE) values. The emission rates under a 95% probability were predicted for each risky VOCs via the established ANN models, by randomly sampling the input parameters under their data distribution. The approach proposed and results obtained can provide scientific methodology and important information for the monitoring, prediction, and health risk assessment of the VOCs emitted from MSW landfills.
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Affiliation(s)
- Rong Li
- School of Environment, Beijing Normal University, Beijing, 100875, PR China; State Environmental Protection Key Laboratory of Odor Pollution Control, Tianjin Academy of Eco-environmental Sciences, Tianjin, 300191, PR China
| | - Ankun Xu
- School of Environment, Beijing Normal University, Beijing, 100875, PR China; State Environmental Protection Key Laboratory of Odor Pollution Control, Tianjin Academy of Eco-environmental Sciences, Tianjin, 300191, PR China
| | - Yan Zhao
- School of Environment, Beijing Normal University, Beijing, 100875, PR China; State Environmental Protection Key Laboratory of Odor Pollution Control, Tianjin Academy of Eco-environmental Sciences, Tianjin, 300191, PR China.
| | - Huimin Chang
- School of Environment, Beijing Normal University, Beijing, 100875, PR China
| | - Xiang Li
- School of Environment, Beijing Normal University, Beijing, 100875, PR China
| | - Guannv Lin
- School of Environment, Beijing Normal University, Beijing, 100875, PR China
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43
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Xi H, Li Z, Han J, Shen D, Li N, Long Y, Chen Z, Xu L, Zhang X, Niu D, Liu H. Evaluating the capability of municipal solid waste separation in China based on AHP-EWM and BP neural network. WASTE MANAGEMENT (NEW YORK, N.Y.) 2022; 139:208-216. [PMID: 34974315 DOI: 10.1016/j.wasman.2021.12.015] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/08/2021] [Revised: 12/04/2021] [Accepted: 12/07/2021] [Indexed: 05/17/2023]
Abstract
With the increase in municipal solid waste (MSW), most cities face solid waste management issues. In this study, the analytic hierarchy process (AHP) and artificial neural network (ANN) models were improved to assess the MSW separation capability based on 18 selected indicators of solid waste separation in 15 cities in China. The entropy weight method (EWM) was used in AHP to optimize and determine the indicators and then evaluate their weights, which showed that the general public budget expenditure had the highest weight (0.5239). This implied that the MSW separation capability could be mainly influenced by government financial support. ANN based on scan optimization and machine learning methods were established (R2 = 0.9992) to predict the missing indicators. The mapping relationship between MSW separation indicators and capabilities was also significantly improved from R2 = 0.5317 to R2 = 0.9993, thereby increasing the prediction accuracy of MSW separation capabilities to 95.15%. Thus, this research provides a new avenue for MSW separation and establishes a combined model to predict the separation capability in practical applications.
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Affiliation(s)
- Hao Xi
- School of Environmental Science and Engineering, Zhejiang Gongshang University, Hangzhou, Zhejiang 310018, China
| | - Zhiheng Li
- School of Environmental Science and Engineering, Zhejiang Gongshang University, Hangzhou, Zhejiang 310018, China
| | - Jingyi Han
- School of Environmental Science and Engineering, Zhejiang Gongshang University, Hangzhou, Zhejiang 310018, China
| | - Dongsheng Shen
- School of Environmental Science and Engineering, Zhejiang Gongshang University, Hangzhou, Zhejiang 310018, China
| | - Na Li
- School of Environmental Science and Engineering, Zhejiang Gongshang University, Hangzhou, Zhejiang 310018, China
| | - Yuyang Long
- School of Environmental Science and Engineering, Zhejiang Gongshang University, Hangzhou, Zhejiang 310018, China
| | - Zhenlong Chen
- School of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou, Zhejiang 310018, China
| | - Linglin Xu
- School of Environmental Science and Engineering, Zhejiang Gongshang University, Hangzhou, Zhejiang 310018, China
| | - Xianghong Zhang
- School of Environmental Science and Engineering, Zhejiang Gongshang University, Hangzhou, Zhejiang 310018, China
| | - Dongjie Niu
- College of Environmental Science and Engineering, Tongji University, Shanghai 200086, China
| | - Huijun Liu
- School of Environmental Science and Engineering, Zhejiang Gongshang University, Hangzhou, Zhejiang 310018, China.
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44
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Xu A, Li R, Chang H, Xu Y, Li X, Lin G, Zhao Y. Artificial neural network (ANN) modeling for the prediction of odor emission rates from landfill working surface. WASTE MANAGEMENT (NEW YORK, N.Y.) 2022; 138:158-171. [PMID: 34896736 DOI: 10.1016/j.wasman.2021.11.045] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 11/22/2021] [Accepted: 11/27/2021] [Indexed: 06/14/2023]
Abstract
Landfills release significant odorous compounds from the working surface, and their emission rates are crucial for odor and health risk assessment. A total of 99 valid datasets of odor emissions from a landfill working surface were obtained from in situ monitoring for 9 months. Meteorological parameters (temperature, humidity, atmospheric pressure) and waste properties (contents of protein, lipid, carbohydrate, ash, and moisture) were used to construct artificial neural network (ANN) models for the emission rate prediction of typical compounds. The optimal structures and performance of the ANN models were determined by comparing and training with different structural configurations. The ANN models with genetic algorithm (GA) optimization show better performance than those without GA. With the data distribution of input parameters, the ranges of the emission rates of typical compounds were predicted by combining the established ANN models and the Monte Carlo approach. The sensitivity and uncertainty analyses revealed that temperature, atmospheric pressure, protein and lipid contents are parameters sensitive to emission rates, and meteorological parameters have significant impacts on the uncertainty. The established ANN models for the prediction of emission rates can provide scientific evidence and an approach to assess and control the odor and health risk in waste sectors.
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Affiliation(s)
- Ankun Xu
- School of Environment, Beijing Normal University, Beijing 100875, PR China; State Ecology and Environment Key Laboratory of Odor Pollution Control, Tianjin Academy of Eco-environmental Sciences, Tianjin 300191, PR China
| | - Rong Li
- School of Environment, Beijing Normal University, Beijing 100875, PR China
| | - Huimin Chang
- School of Environment, Beijing Normal University, Beijing 100875, PR China
| | - Yingjie Xu
- School of Environment, Beijing Normal University, Beijing 100875, PR China
| | - Xiang Li
- School of Environment, Beijing Normal University, Beijing 100875, PR China
| | - Guannv Lin
- School of Environment, Beijing Normal University, Beijing 100875, PR China
| | - Yan Zhao
- School of Environment, Beijing Normal University, Beijing 100875, PR China; State Ecology and Environment Key Laboratory of Odor Pollution Control, Tianjin Academy of Eco-environmental Sciences, Tianjin 300191, PR China.
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45
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Evolutionary-based neuro-fuzzy modelling of combustion enthalpy of municipal solid waste. Neural Comput Appl 2022. [DOI: 10.1007/s00521-021-06870-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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46
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Wang L, Qi C, Lu Y, Arowo M, Shao L. Degradation of Bisphenol A by ozonation in a rotating packed bed: Modeling by response surface methodology and artificial neural network. CHEMOSPHERE 2022; 286:131702. [PMID: 34343916 DOI: 10.1016/j.chemosphere.2021.131702] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 06/30/2021] [Accepted: 07/26/2021] [Indexed: 06/13/2023]
Abstract
The ozonation process of Bisphenol A (BPA) in a rotating packed bed (RPB) was modeled by response surface methodology (RSM) and artificial neural network (ANN). Experiments were performed according to the Box-Behnken design, and the interactive effects of various parameters including ozone concentration, pH, rotation speed of RPB and liquid flow rate on BPA degradation efficiency were investigated. Ozone concentration and pH had the most significant interactive effects on BPA degradation efficiency while rotation speed of RPB had no significant interactive effects with other variables. A second order polynomial equation was obtained to predict BPA degradation efficiency. Also, a multi-layered feed-forward ANN model was constructed based on the data of RSM experiments. Six neurons in hidden layer had the highest correlation coefficient (RANN = 0.99158). A comparison between RSM and ANN models suggested that both can accurately predict BPA degradation efficiency (RRSM = 0.99559). The highest BPA degradation efficiency (99.52 %) was achieved under the conditions of ozone concentration of 20 mg L-1, pH of 11, liquid flow rate of 10 L h-1 and rotation speed of RPB of 800 rpm, which was well predicted by the RSM model (99.54 %) and the ANN model (99.82 %). However, the RSM model was slightly better than the ANN model owing to its higher determination coefficient (R2RSM = 0.9912, R2ANN = 0.9827) and lower mean square error (MSERSM = 0.0001684, MSEANN = 0.0003305).
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Affiliation(s)
- Lei Wang
- Research Center of the Ministry of Education for High Gravity Engineering and Technology, Beijing University of Chemical Technology, Beijing, 100029, China
| | - Chu Qi
- College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, 100029, China
| | - Yuan Lu
- CenerTech Oilfield Chemical Co., Ltd., Tianjin, 300450, China
| | - Moses Arowo
- Department of Chemical & Process Engineering, Moi University, Eldoret, 3900, Kenya
| | - Lei Shao
- Research Center of the Ministry of Education for High Gravity Engineering and Technology, Beijing University of Chemical Technology, Beijing, 100029, China.
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Vu HL, Ng KTW, Richter A, Kabir G. The use of a recurrent neural network model with separated time-series and lagged daily inputs for waste disposal rates modeling during COVID-19. SUSTAINABLE CITIES AND SOCIETY 2021; 75:103339. [PMID: 34513573 PMCID: PMC8423673 DOI: 10.1016/j.scs.2021.103339] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 09/04/2021] [Accepted: 09/04/2021] [Indexed: 05/18/2023]
Abstract
A new modeling framework is proposed to estimate mixed waste disposal rates in a Canadian capital city during the pandemic. Different Recurrent Neural Network models were developed using climatic, socioeconomic, and COVID-19 related daily variables with different input lag times and study periods. It is hypothesized that the use of distinct time series and lagged inputs may improve modeling accuracy. Considering the entire 7.5-year period from Jan 2013 to Sept 2020, multi-variate weekday models were sensitive with lag times in the testing stage. It appears that the selection of input variables is more important than waste model complexity. Models applying COVID-19 related inputs generally had better performance, with average MAPE of 10.1%. The optimized lag times are however similar between the periods, with slightly longer average lag for the COVID-19 at 5.3 days. Simpler models with least input variables appear to better simulate waste disposal rates, and both 'Temp-Hum' (Temperature-Humidity) and 'Temp-New Test' (Temperature-COVID new test case) models capture the general disposal trend well, with MAPE of 10.3% and 9.4%, respectively. The benefits of the use of separated time series inputs are more apparent during the COVID-19 period, with noticeable decrease in modeling error.
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Affiliation(s)
- Hoang Lan Vu
- Environmental Systems Engineering, Faculty of Engineering and Applied Science, University of Regina, 3737 Wascana Parkway, SK S4S 0A2, Canada
| | - Kelvin Tsun Wai Ng
- Environmental Systems Engineering, Faculty of Engineering and Applied Science, University of Regina, 3737 Wascana Parkway, SK S4S 0A2, Canada
| | - Amy Richter
- Environmental Systems Engineering, Faculty of Engineering and Applied Science, University of Regina, 3737 Wascana Parkway, SK S4S 0A2, Canada
| | - Golam Kabir
- Industrial Systems Engineering, Faculty of Engineering and Applied Science, University of Regina, Saskatchewan S4S 0A2, Canada
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Vu HL, Ng KTW, Richter A, Karimi N, Kabir G. Modeling of municipal waste disposal rates during COVID-19 using separated waste fraction models. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 789:148024. [PMID: 34082208 PMCID: PMC9632937 DOI: 10.1016/j.scitotenv.2021.148024] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2021] [Revised: 05/02/2021] [Accepted: 05/22/2021] [Indexed: 05/04/2023]
Abstract
Municipal waste disposal behaviors in Regina, the capital city of Saskatchewan, Canada have significantly changed during the COVID-19 pandemic. About 7.5 year of waste disposal data at the Regina landfill was collected, verified, and consolidated. Four modeling approaches were examined to predict total waste disposal at the Regina landfill during the COVID-19 period, including (i) continuous total (Baseline), (ii) continuous fraction, (iii) truncated total, and (iv) truncated fraction. A single feature input recurrent neural network model was adopted for each approach. It is hypothesized that waste quantity modeling using different waste fractions and separate time series can better capture disposal behaviors of residents during the lockdown. Compared to the baseline approach, the use of waste fractions in modeling improves both result accuracy and precision. In general, the use of continuous time series over-predicted total waste disposal, especially when actual disposal rates were less than 50 t/day. Compared to the baseline approach, mean absolute error (MAE), mean absolute percentage error (MAPE), and mean square error (MSE) were reduced. The R value increased from 0.63 to 0.79. Comparing to the baseline, the truncated total and the truncated fraction approaches better captured the total waste disposal behaviors during the COVID-19 period, probably due to the periodicity of the weeklong data set. For both approaches, MAE and MAPE were lower than 70 and 22%, respectively. The model performance of the truncated fraction appears the best, with an MAPE of 19.8% and R value of 0.92. Results suggest the uses of waste fractions and separated time series are beneficial, especially if the input set is heavily skewed.
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Affiliation(s)
- Hoang Lan Vu
- Environmental Systems Engineering, Faculty of Engineering and Applied Science, University of Regina, Saskatchewan S4S 0A2, Canada
| | - Kelvin Tsun Wai Ng
- Environmental Systems Engineering, Faculty of Engineering and Applied Science, University of Regina, Saskatchewan S4S 0A2, Canada.
| | - Amy Richter
- Environmental Systems Engineering, Faculty of Engineering and Applied Science, University of Regina, Saskatchewan S4S 0A2, Canada
| | - Nima Karimi
- Environmental Systems Engineering, Faculty of Engineering and Applied Science, University of Regina, Saskatchewan S4S 0A2, Canada
| | - Golam Kabir
- Industrial Systems Engineering, Faculty of Engineering and Applied Science, University of Regina, Saskatchewan S4S 0A2, Canada
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Intelligent Computing with Levenberg-Marquardt Backpropagation Neural Networks for Third-Grade Nanofluid Over a Stretched Sheet with Convective Conditions. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2021; 47:8211-8229. [PMID: 34603929 PMCID: PMC8479501 DOI: 10.1007/s13369-021-06202-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 09/09/2021] [Indexed: 01/04/2023]
Abstract
This article discussed the influence of activation energy on MHD flow of third-grade nanofluid model (MHD-TGNFM) along with the convective conditions and used the technique of backpropagation in artificial neural network using Levenberg–Marquardt technique (BANN-LMT). The PDEs representing (MHD-TGNFM) transformed into the system of ODEs. The dataset for BANN-LMT is computed for the six scenarios by using the Adam numerical method by varying the local Hartman number (Ha), Prandtl number (Pr), local chemical reaction parameter (σ), Schmidt number (Sc), concentration Biot number (γ2) and thermal Biot number (γ1). By testing, validation and training process of (BANN-LMT), the estimated solutions are interpreted for (MHD-TGNFM). The validation of the performance of (BANN-LMT) is done through the MSE, error histogram and regression analysis. The concentration profile increases when there is an increase in Biot number and the local Hartmann number; meanwhile, it decreases for the higher values of Schmidt number and the local chemical reaction parameter.
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Muravyev NV, Luciano G, Ornaghi HL, Svoboda R, Vyazovkin S. Artificial Neural Networks for Pyrolysis, Thermal Analysis, and Thermokinetic Studies: The Status Quo. Molecules 2021; 26:3727. [PMID: 34207246 PMCID: PMC8235697 DOI: 10.3390/molecules26123727] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Revised: 06/15/2021] [Accepted: 06/15/2021] [Indexed: 11/29/2022] Open
Abstract
Artificial neural networks (ANNs) are a method of machine learning (ML) that is now widely used in physics, chemistry, and material science. ANN can learn from data to identify nonlinear trends and give accurate predictions. ML methods, and ANNs in particular, have already demonstrated their worth in solving various chemical engineering problems, but applications in pyrolysis, thermal analysis, and, especially, thermokinetic studies are still in an initiatory stage. The present article gives a critical overview and summary of the available literature on applying ANNs in the field of pyrolysis, thermal analysis, and thermokinetic studies. More than 100 papers from these research areas are surveyed. Some approaches from the broad field of chemical engineering are discussed as the venues for possible transfer to the field of pyrolysis and thermal analysis studies in general. It is stressed that the current thermokinetic applications of ANNs are yet to evolve significantly to reach the capabilities of the existing isoconversional and model-fitting methods.
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Affiliation(s)
- Nikita V. Muravyev
- N.N. Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences, 4 Kosygina Str., 119991 Moscow, Russia
| | - Giorgio Luciano
- CNR, Istituto di Scienze e Tecnologie Chimiche “Giulio Natta”, Via De Marini 6, 16149 Genova, Italy;
| | - Heitor Luiz Ornaghi
- Department of Materials, Federal University for Latin American Integration (UNILA), Silvio Américo Sasdelli Avenue, 1842, Foz do Iguaçu-Paraná 85866-000, Brazil;
| | - Roman Svoboda
- Department of Physical Chemistry, Faculty of Chemical Technology, University of Pardubice, Studentská 95, CZ-53210 Pardubice, Czech Republic;
| | - Sergey Vyazovkin
- Department of Chemistry, University of Alabama at Birmingham, 901 S. 14th Street, Birmingham, AL 35294, USA
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