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Al-Zamzami M, Al-Gheethi A, Alzaeemi SA, Al-Sahari M, Al-Maqtari Q, Noman E. Validity of zinc oxide nanoparticles biosynthesized in food wastes extract in treating real samples of printing ink wastewater; prediction models using feed-forward neural network (FFNN). CHEMOSPHERE 2024; 362:142793. [PMID: 38972458 DOI: 10.1016/j.chemosphere.2024.142793] [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: 12/20/2023] [Revised: 05/12/2024] [Accepted: 07/04/2024] [Indexed: 07/09/2024]
Abstract
In the present study, biosynthesized ZnO nanoparticles in food wastewater extract (FWEZnO NPs) was used in the photocatalytic degradation of real samples of printing ink wastewater. FWEZnO NPs were prepared using green synthesis methods using a composite food waste sample (2 kg) consisted of rice 30%, bread 20 %, fruits 10 %, chicken 10 %, lamb 10%, and vegetable 20%. The photocatalysis process was optimized using response surface methodology (RSM) as a function of time (15-180 min), pH 2-10 and FWEZnO NP (20-120 mg/100 mL), while the print ink effluent after each treatment process was evaluated using UV-Vis-spectrophotometer. The behaviour of printing ink wastewater samples for photocatalytic degradation and responses for independent factors were simulated using feed-forward neural network (FFNN). FWEZnO NPs having 62.48 % of the purity with size between 18 and 25 nm semicrystalline nature. The main functional groups were -CH, CH2, and -OH, while lipid, carbon-hydrogen stretching, and amino acids were the main component in FWEZnO NP, which contributed to the adsorption of ink in the initial stage of photocatalysis. The optimal conditions for printing ink wastewater were recorded after 17 min, at pH 9 and with 20 mg/100 mL of FWEZnO NPs, at which the decolorization was 85.62 vs. 82.13% of the predicted and actual results, respectively, with R2 of 0.7777. The most significant factor in the photocatalytic degradation was time and FWEZnO NPs. The FFNN models revealed that FWEZnO NPs exhibit consistency in the next generation of data (large-scale application) with an low errors (R2 0.8693 with accuracy of 82.89%). The findings showing a small amount of catalyst is needed for effective breakdown of dyes in real samples of printing ink wastewater.
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Affiliation(s)
- Mohammed Al-Zamzami
- Micropollutant Research Centre (MPRC), Institute of Integrated Engineering, Universiti Tun Hussein Onn Malaysia, 86400, Parit Raja, Batu Pahat, Johor, Malaysia
| | - Adel Al-Gheethi
- Global Centre for Environmental Remediation (GCER), University of Newcastle and CRC for Contamination Assessment and Remediation of the Environment (crcCARE), Newcastle, Australia.
| | - Shehab Abdulhabib Alzaeemi
- Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, 86400, Parit Raja, Batu Pahat, Johor, Malaysia
| | - Mohamed Al-Sahari
- Micropollutant Research Centre (MPRC), Institute of Integrated Engineering, Universiti Tun Hussein Onn Malaysia, 86400, Parit Raja, Batu Pahat, Johor, Malaysia
| | - Qais Al-Maqtari
- Micropollutant Research Centre (MPRC), Institute of Integrated Engineering, Universiti Tun Hussein Onn Malaysia, 86400, Parit Raja, Batu Pahat, Johor, Malaysia
| | - Efaq Noman
- Department of Microbiology, Faculty of Applied Sciences, Taiz University, Yemen
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Huang LT, Hou JY, Liu HT. Machine-learning intervention progress in the field of organic waste composting: Simulation, prediction, optimization, and challenges. WASTE MANAGEMENT (NEW YORK, N.Y.) 2024; 178:155-167. [PMID: 38401429 DOI: 10.1016/j.wasman.2024.02.022] [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: 12/01/2023] [Revised: 01/24/2024] [Accepted: 02/14/2024] [Indexed: 02/26/2024]
Abstract
Aerobic composting stands as a widely-adopted method for treating organic solid waste (OSW), simultaneously producing organic fertilizers and soil amendments. This biologically-driven biochemical reaction process, however, presents challenges due to its complex non-linear metabolism and the heterogeneous nature of the solid medium. These characteristics inherently limit the simulation accuracy and efficiency optimization in aerobic composting. Recently, significant efforts have been made to simulate and control composting process parameters, as well as predicting and optimizing composting product quality. Notably, the integration of machine learning (ML) in aerobic composting of organic waste has garnered considerable attention for its applicability and predictive capability in exploring the complex non-linear relationships of organic waste composting parameters. Despite numerous studies on ML applications in OSW composting, a systematic review of research findings in this field is lacking. This study offers a systematic overview of the application level, current status, and versatility of ML in OSW composting. It spans various aspects, such as compost maturity, environmental pollutants, nutrients, moisture, heat loss, and microbial metabolism. The survey reveals that ML-intervention predominantly focuses on compost maturity and environmental pollutants, followed by nutrients, moisture, heat loss, and microbial activity. The most commonly employed predictive models and optimization algorithms are artificial neural networks (47%) and genetic algorithms (10%). These demonstrate high prediction accuracy and maximize composting efficiency in the simulation and prediction of organic waste composting, alongside regulation of key parameters. Deep neural networks and ensemble learning models prove effective in achieving superior predictive performance by selecting feature variables in compost maturity and pollutant residue prediction of organic waste composting in a simpler and more objective manner.
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Affiliation(s)
- Li-Ting Huang
- Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China
| | - Jia-Yi Hou
- Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
| | - Hong-Tao Liu
- Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.
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3
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Sokač Cvetnić T, Krog K, Valinger D, Gajdoš Kljusurić J, Benković M, Jurina T, Jakovljević T, Radojčić Redovniković I, Jurinjak Tušek A. Application of Multivariate Regression and Artificial Neural Network Modelling for Prediction of Physicochemical Properties of Grape-Skin Compost. Bioengineering (Basel) 2024; 11:285. [PMID: 38534559 DOI: 10.3390/bioengineering11030285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Revised: 03/07/2024] [Accepted: 03/13/2024] [Indexed: 03/28/2024] Open
Abstract
The reusability of by-products in the food industry is consistent with sustainable and greener production; therefore, the aim of this paper was to evaluate the applicability of multiple linear regression (MLR), piecewise linear regression (PLR) and artificial neural network models (ANN) to the prediction of grape-skin compost's physicochemical properties (moisture, dry matter, organic matter, ash content, carbon content, nitrogen content, C/N ratio, total colour change of compost samples, pH, conductivity, total dissolved solids and total colour change of compost extract samples) during in-vessel composting based on the initial composting conditions (air-flow rate, moisture content and day of sampling). Based on the coefficient of determination for prediction, the adjusted coefficient of determination for calibration, the root-mean-square error of prediction (RMSEP), the standard error of prediction (SEP), the ratio of prediction to deviation (RPD) and the ratio of the error range (RER), it can be concluded that all developed MLR and PLR models are acceptable for process screening. Furthermore, the ANN model developed for predicting moisture and dry-matter content can be used for quality control (RER >11). The obtained results show the great potential of multivariate modelling for analysis of the physicochemical properties of compost during composting, confirming the high applicability of modelling in greener production processes.
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Affiliation(s)
- Tea Sokač Cvetnić
- Faculty of Food Technology and Biotechnology, University of Zagreb, Pierottijeva 6, 10 000 Zagreb, Croatia
| | - Korina Krog
- Faculty of Food Technology and Biotechnology, University of Zagreb, Pierottijeva 6, 10 000 Zagreb, Croatia
| | - Davor Valinger
- Faculty of Food Technology and Biotechnology, University of Zagreb, Pierottijeva 6, 10 000 Zagreb, Croatia
| | - Jasenka Gajdoš Kljusurić
- Faculty of Food Technology and Biotechnology, University of Zagreb, Pierottijeva 6, 10 000 Zagreb, Croatia
| | - Maja Benković
- Faculty of Food Technology and Biotechnology, University of Zagreb, Pierottijeva 6, 10 000 Zagreb, Croatia
| | - Tamara Jurina
- Faculty of Food Technology and Biotechnology, University of Zagreb, Pierottijeva 6, 10 000 Zagreb, Croatia
| | - Tamara Jakovljević
- Croatian Forest Research Institute, Cvjetno naselje 41, 10 450 Jastrebarsko, Croatia
| | | | - Ana Jurinjak Tušek
- Faculty of Food Technology and Biotechnology, University of Zagreb, Pierottijeva 6, 10 000 Zagreb, Croatia
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Etemadi S, Khashei M. Etemadi regression in chemometrics: Reliability-based procedures for modeling and forecasting. Heliyon 2024; 10:e26399. [PMID: 38434293 PMCID: PMC10907519 DOI: 10.1016/j.heliyon.2024.e26399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 11/18/2023] [Accepted: 02/12/2024] [Indexed: 03/05/2024] Open
Abstract
The creation of predictive models with a high degree of generalizability in chemical analysis and process optimization is of paramount importance. Nonetheless, formulating a prediction model based on collected data from chemical measurements that maximize quantitative generalizability remains a challenging task for chemometrics experts. To tackle this challenge, a range of forecasting models with varying characteristics, structures, and capabilities has been developed, utilizing either accuracy-based or reliability-based modeling strategies. While the majority of models follow the accuracy-based approach, a recently proposed reliability-based approach, known as the Etemadi approach, has shown impressive performance across various scientific fields. The Etemadi models were constructed through a reliability-based parameter estimation process in such a manner that maximizes the models' reliability. However, the foundation of modeling procedures for chemometrics purposes is built upon the assumption that high generalizability in inaccessible/test data is achieved through the accuracy-based training procedure in which errors in available historical/training data are minimized. After conducting a thorough review of the current literature, we have found that none of the forecasting models for chemometrics purposes incorporate reliability into their modeling procedures. Given the dynamic and highly sensitive nature of chemistry experiments and processes, implementing a reliable model that controls performance criteria variation is a promising strategy for achieving stable and robust forecasts. To address this research gap, this paper introduces several key innovations, which can be highlighted as follows: (1) Proposing a general design structure based on a new optimal reliability-based parameter estimation process. (2) Introducing a novel risk-based modeling strategy that minimizes the performance variation of models implemented under different conditions in chemical laboratory experiments, to generate a more generalizable model for diverse applications in chemometrics. (3) Specifying the degree of influence that each reliability and accuracy factor has in enhancing the generalizability and uncertainty modeling of chemometric models. Empirical evidence confirms the effectiveness and superior performance of reliability-based models compared to accuracy-based models in 78.95% of the cases across various fields, including Pharmacology, Biochemistry, Agrochemical, Geochemical, Biological, Pollutants, Physicochemical Properties, and Gases Experiment. Furthermore, the study's findings demonstrate that the reliability-based modeling approach outperforms the accuracy-based strategy in terms of MAE, MSE, ARV, and RMSE by an average of 4.697%, 5.646%, 5.646%, and 4.342%, respectively. It is also statistically proven that reliability has a more significant impact on improving the generalizability of chemometric models than accuracy. This emphasizes the importance of including reliability as a crucial factor in chemometrics modeling, a consideration that has been overlooked in traditional modeling processes. Consequently, reliability-based modeling approaches can be regarded as a viable alternative to conventional accuracy-based modeling methods for chemical modeling purposes.
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Affiliation(s)
- Sepideh Etemadi
- Department of Industrial and Systems Engineering, Isfahan University of Technology (IUT), Isfahan, 84156-83111, Iran
| | - Mehdi Khashei
- Department of Industrial and Systems Engineering, Isfahan University of Technology (IUT), Isfahan, 84156-83111, Iran
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Yousefi M, Akbari H, Adibzadeh A, Mohammadi AA, Baziar M, Abbasi Farajzadeh M, Akbari H. Adsorption of diazinon from aqueous solution using metal organic framework and functionalized graphene: Comparison of BBD, ANN models. CHEMOSPHERE 2024; 351:141222. [PMID: 38224747 DOI: 10.1016/j.chemosphere.2024.141222] [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: 09/25/2023] [Revised: 12/28/2023] [Accepted: 01/12/2024] [Indexed: 01/17/2024]
Abstract
In the present study, metal organic frameworks (MOFs) and aminated graphitic carbonaceous structure (ACS-RGO) through chemical synthesis prepared by a simple precipitation method and used for diazinon removal. Several techniques such as XRD , FESEM and FTIR were applied for identification of MOF-5 and ACS-RGO. Also, response surface methodology (RSM) was employed in this work to look at the effectiveness of diazinon adsorption. To forecast pesticide removal, we applied artificial neural network (ANN) and Box-Behnken Design (BBD) models. For the ANN model, a sensitivity analysis was also performed. The effect of independent variables like solution pH, various concentrations of diazinon, MOFs and ACS-RGO adsorbent dose and contact time were assessed to find out the optimum conditions. Based on the model prediction, the optimal condition for adsorption ACS-RGO and MOF-5 were determined to be pH 6.6 and 6.6, adsorbent dose of 0.59 and 0.906 g/L, and mixing time of 52.15 and 36.96 min respectively. These conditions resulted in 96.69% and 80.62% diazinon removal using ACS-RGO and MOF-5, respectively. Isotherm studies proved the adsorption of ACS-RGO and MOF-5 following the Langmuir isotherm model for diazinon removal. Diazinon removal followed by the pseudo-second and Pseudo-first order kinetics model provides a better fit for analyzing the kinetic data associated with pesticide adsorption for ACS-RGO and MOF-5, respectively. Based on the obtained results, the predicted values for the efficiency of diazinon removal with the ANN and BBD were similar (R2=0.98). Therefore, two models were able to predict diazinon removal by ACS-RGO and MOF-5.
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Affiliation(s)
- Mahmood Yousefi
- Health Research Center, Lifestyle Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | - Hesam Akbari
- Health Research Center, Lifestyle Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | - Amir Adibzadeh
- Health Research Center, Lifestyle Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran; Department of Environmental Health Engineering, Faculty of Health, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | - Ali Akbar Mohammadi
- Department of Environmental Health Engineering, Neyshabur University of Medical Sciences, Neyshabur, Iran
| | - Mansour Baziar
- Department of Environmental Health Engineering, Ferdows Faculty of Medical Sciences, Birjand University of Medical Sciences, Birjand, Iran
| | - Mohsen Abbasi Farajzadeh
- Health Management Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran; Student Research Committee, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | - Hamed Akbari
- Health Research Center, Lifestyle Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran.
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Munir MT, Li B, Naqvi M, Nizami AS. Green loops and clean skies: Optimizing municipal solid waste management using data science for a circular economy. ENVIRONMENTAL RESEARCH 2024; 243:117786. [PMID: 38036215 DOI: 10.1016/j.envres.2023.117786] [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: 10/16/2023] [Revised: 11/20/2023] [Accepted: 11/23/2023] [Indexed: 12/02/2023]
Abstract
The interplay between Municipal Solid Waste (MSW) Management and data science unveils a panorama of opportunities and challenges, set against the backdrop of rising global waste and evolving technological landscapes. This article threads through the multifaceted aspects of incorporating data science into MSW management, unearthing key findings, novel knowledge, and instigating a call to action for stakeholders (e.g. policymakers, local authorities, waste management professionals, technology developers, and the general public) across the spectrum. Predominant challenges like the enigmatic nature of "black-box" models and tangible knowledge gaps in the sector are scrutinized, ushering in a narrative that emphasizes transparent, stakeholder-inclusive, and policy-adaptive approaches. Notably, a conscious shift towards "white-box" and "grey-box" data science models has been spotlighted as a pivotal response to transparency issues. Furthermore, the discourse highlights the necessity of crafting data science solutions that are specifically moulded to the nuanced challenges of MSW management, and it underscores the importance of recalibrating existing policies to be reflexive to technological advancements. A resolute call echoes for stakeholders to not just adapt but immerse themselves in a continuous learning trajectory, championing transparency, and fostering collaborations that hinge on innovative, data-driven methodologies. Thus, as the realms of data science and MSW management entwine, the article sheds light on the potential transformation awaiting waste management paradigms, contingent on the nurtured amalgamation of technological advances, policy alignment, and collaborative synergy.
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Affiliation(s)
| | - Bing Li
- Water Research Center, Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
| | - Muhammad Naqvi
- College of Engineering and Technology, American University of the Middle East, Kuwait.
| | - Abdul-Sattar Nizami
- Sustainable Development Study Center, Government College University, Lahore, 54000, Pakistan
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7
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Xu F, Zhang W, Wang Y, Tian X, Chu J. Enhancing and monitoring spore production in Clostridium butyricum using pH-based regulation strategy and a robust soft sensor based on back-propagation neural networks. Biotechnol Bioeng 2024; 121:551-565. [PMID: 37921467 DOI: 10.1002/bit.28597] [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: 05/17/2023] [Revised: 09/11/2023] [Accepted: 10/30/2023] [Indexed: 11/04/2023]
Abstract
Clostridium butyricum is a probiotic that forms anaerobic spores and plays a crucial role in regulating gut microbiota. However, the total viable cell count and spore yield of C. butyricum in industrial production are comparatively low. To this end, we investigated the metabolic characteristics of the strain and proposed three distinct pH regulation strategies for enhancing spore production. In addition, precise measurement of fermentation parameters such as substrate concentration, total viable cell count, and spore concentration is crucial for successful industrial probiotics production. Nevertheless, online measurement of these intricate parameters in the fermentation of C. butyricum poses a considerable challenge owing to the complex, nonlinear, multivariate, and strongly coupled characteristics of the production process. Therefore, we analyzed the capacitance and conductivity acquired from a viable cell sensor as the core parameters for the fermentation process. Subsequently, a robust soft sensor was developed using a seven-input back-propagation neural network model with input variables of fermentation time, capacitance, conductivity, pH, initial total sugar concentration, ammonium ion concentration, and calcium ion concentration. The model enables the online monitoring of total viable biomass count, substrate concentrations, and spore yield, and can be extended to similar fermentation processes with pH changes as a characteristic feature.
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Affiliation(s)
- Feng Xu
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, People's Republic of China
- School of Biotechnology, East China University of Science and Technology, Shanghai, People's Republic of China
| | - Wenxiao Zhang
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, People's Republic of China
- School of Biotechnology, East China University of Science and Technology, Shanghai, People's Republic of China
| | - Yonghong Wang
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, People's Republic of China
- School of Biotechnology, East China University of Science and Technology, Shanghai, People's Republic of China
| | - Xiwei Tian
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, People's Republic of China
- School of Biotechnology, East China University of Science and Technology, Shanghai, People's Republic of China
| | - Ju Chu
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, People's Republic of China
- School of Biotechnology, East China University of Science and Technology, Shanghai, People's Republic of China
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Sharma M, Agarwal S, Agarwal Malik R, Kumar G, Pal DB, Mandal M, Sarkar A, Bantun F, Haque S, Singh P, Srivastava N, Gupta VK. Recent advances in microbial engineering approaches for wastewater treatment: a review. Bioengineered 2023; 14:2184518. [PMID: 37498651 PMCID: PMC10376923 DOI: 10.1080/21655979.2023.2184518] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 09/09/2022] [Accepted: 09/14/2022] [Indexed: 07/28/2023] Open
Abstract
In the present era of global climate change, the scarcity of potable water is increasing both due to natural and anthropogenic causes. Water is the elixir of life, and its usage has risen significantly due to escalating economic activities, widespread urbanization, and industrialization. The increasing water scarcity and rising contamination have compelled, scientists and researchers, to adopt feasible and sustainable wastewater treatment methods in meeting the growing demand for freshwater. Presently, various waste treatment technologies are adopted across the globe, such as physical, chemical, and biological treatment processes. There is a need to replace these technologies with sustainable and green technology that encourages the use of microorganisms since they have proven to be more effective in water treatment processes. The present review article is focused on demonstrating how effectively various microbes can be used in wastewater treatment to achieve environmental sustainability and economic feasibility. The microbial consortium used for water treatment offers many advantages over pure culture. There is an urgent need to develop hybrid treatment technology for the effective remediation of various organic and inorganic pollutants from wastewater.
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Affiliation(s)
- Monika Sharma
- Department of Zoology, University of Jammu, Jammu and Kashmir, India
| | - Sangita Agarwal
- Department of Applied Science, RCC Institute of Information Technology Kolkata, West Bengal, India
| | - Richa Agarwal Malik
- Department of Environmental Studies, PGDAV College, University of Delhi, New Delhi, India
| | - Gaurav Kumar
- Department of Environmental Studies, PGDAV College, University of Delhi, New Delhi, India
| | - Dan Bahadur Pal
- Department of Chemical Engineering, Harcourt Butler Technical University, Kanpur, Uttar Pradesh, India
| | - Mamun Mandal
- Laboratory of Applied Stress Biology, Department of Botany, University of Gour Banga, Malda, West Bengal, India
| | - Abhijit Sarkar
- Laboratory of Applied Stress Biology, Department of Botany, University of Gour Banga, Malda, West Bengal, India
| | - Farkad Bantun
- Department of Microbiology, Faculty of Medicine, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Shafiul Haque
- Research and Scientific Studies Unit, College of Nursing and Allied Health Sciences, Jazan University, Jazan, Saudi Arabia
- Gilbert and Rose-Marie Chagoury School of Medicine, Lebanese American University, Beirut, Lebanon
- Centre of Medical and Bio-Allied Health Sciences Research, Ajman University, Ajman, United Arab Emirates
| | - Pardeep Singh
- Department of Environmental Studies, PGDAV College, University of Delhi, New Delhi, India
| | - Neha Srivastava
- Department of Chemical Engineering and Technology, Indian Institute of Technology (BHU), Varanasi, India
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Zaki M, Rowles LS, Adjeroh DA, Orner KD. A Critical Review of Data Science Applications in Resource Recovery and Carbon Capture from Organic Waste. ACS ES&T ENGINEERING 2023; 3:1424-1467. [PMID: 37854077 PMCID: PMC10580293 DOI: 10.1021/acsestengg.3c00043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 09/11/2023] [Accepted: 09/11/2023] [Indexed: 10/20/2023]
Abstract
Municipal and agricultural organic waste can be treated to recover energy, nutrients, and carbon through resource recovery and carbon capture (RRCC) technologies such as anaerobic digestion, struvite precipitation, and pyrolysis. Data science could benefit such technologies by improving their efficiency through data-driven process modeling along with reducing environmental and economic burdens via life cycle assessment (LCA) and techno-economic analysis (TEA), respectively. We critically reviewed 616 peer-reviewed articles on the use of data science in RRCC published during 2002-2022. Although applications of machine learning (ML) methods have drastically increased over time for modeling RRCC technologies, the reviewed studies exhibited significant knowledge gaps at various model development stages. In terms of sustainability, an increasing number of studies included LCA with TEA to quantify both environmental and economic impacts of RRCC. Integration of ML methods with LCA and TEA has the potential to cost-effectively investigate the trade-off between efficiency and sustainability of RRCC, although the literature lacked such integration of techniques. Therefore, we propose an integrated data science framework to inform efficient and sustainable RRCC from organic waste based on the review. Overall, the findings from this review can inform practitioners about the effective utilization of various data science methods for real-world implementation of RRCC technologies.
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Affiliation(s)
- Mohammed
T. Zaki
- Wadsworth
Department of Civil and Environmental Engineering, West Virginia University, Morgantown, West Virginia 26505, United States
| | - Lewis S. Rowles
- Department
of Civil Engineering and Construction, Georgia
Southern University, Statesboro, Georgia 30458, United States
| | - Donald A. Adjeroh
- Lane
Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, West Virginia 26505, United States
| | - Kevin D. Orner
- Wadsworth
Department of Civil and Environmental Engineering, West Virginia University, Morgantown, West Virginia 26505, United States
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10
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Rezaeinia S, Ebrahimi AA, Dalvand A, Ehrampoush MH, Fallahzadeh H, Mokhtari M. Application of artificial neural network and dynamic adsorption models to predict humic substances extraction from municipal solid waste leachate. Sci Rep 2023; 13:12421. [PMID: 37528123 PMCID: PMC10393967 DOI: 10.1038/s41598-023-39373-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 07/25/2023] [Indexed: 08/03/2023] Open
Abstract
Sustainable municipal solid waste leachate (MSWL) management requires a paradigm shift from removing contaminants to effectively recovering resources and decreasing contaminants simultaneously. In this study, two types of humic substances, fulvic acid (FA) and humic acid (HA) were extracted from MSWL. HA was extracted using HCl and NaOH solution, followed by FA using a column bed under diversified operations such as flow rate, input concentration, and bed height. Also, this work aims to evaluate efficiency of Artificial Neural Network (ANN) and Dynamic adsorption models in predicting FA. With the flow rate of 0.3 mL/min, bed height of 15.5 cm, and input concentration of 4.27 g/mL, the maximum capacity of FA was obtained at 23.03 mg/g. FTIR analysis in HA and FA revealed several oxygen-containing functional groups including carboxylic, phenolic, aliphatic, and ketone. The high correlation coefficient value (R2) and a lower mean squared error value (MSE) were obtained using the ANN, indicating the superior ability of ANN to predict adsorption capacity compared to traditional modeling.
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Affiliation(s)
- Salimeh Rezaeinia
- Environmental Science and Technology Research Center, Department of Environmental Health Engineering, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
| | - Ali Asghar Ebrahimi
- Environmental Science and Technology Research Center, Department of Environmental Health Engineering, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
| | - Arash Dalvand
- Environmental Science and Technology Research Center, Department of Environmental Health Engineering, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
| | - Mohammad Hassan Ehrampoush
- Environmental Science and Technology Research Center, Department of Environmental Health Engineering, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
| | - Hossien Fallahzadeh
- Department of Biostatistics and Epidemiology, Research Center of Prevention and Epidemiology of Non‑Communicable Disease, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
| | - Mehdi Mokhtari
- Environmental Science and Technology Research Center, Department of Environmental Health Engineering, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran.
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Naveenkumar R, Iyyappan J, Pravin R, Kadry S, Han J, Sindhu R, Awasthi MK, Rokhum SL, Baskar G. A strategic review on sustainable approaches in municipal solid waste management andenergy recovery: Role of artificial intelligence,economic stability andlife cycle assessment. BIORESOURCE TECHNOLOGY 2023; 379:129044. [PMID: 37044151 DOI: 10.1016/j.biortech.2023.129044] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 04/06/2023] [Accepted: 04/09/2023] [Indexed: 05/03/2023]
Abstract
The consumption of energy levels has increased in association with economic growth and concurrently increased the energy demand from renewable sources. The need under Sustainable Development Goals (SDG) intends to explore various technological advancements for the utilization of waste to energy. Municipal Solid Waste (MSW) has been reported as constructive feedstock to produce biofuels, biofuel carriers and biochemicals using energy-efficient technologies in risk freeways. The present review contemplates risk assessment and challenges in sorting and transportation of MSW and different aspects of conversion of MSW into energy are critically analysed. The circular bioeconomy of energy production strategies and management of waste are also analysed. The current scenario on MSW and its impacts on the environment are elucidated in conjunction with various policies and amendments equipped for the competent management of MSW in order to fabricate a sustained environment.
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Affiliation(s)
- Rajendiran Naveenkumar
- Biological Systems Engineering, University of Wisconsin-Madison, Madison, WI 53706, United States; Forest Products Laboratory, USDA Forest Service, Madison, WI 53726, United States
| | - Jayaraj Iyyappan
- Department of Biotechnology, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Thandalam, Chennai 602107, India
| | - Ravichandran Pravin
- Department of Biotechnology, St. Joseph's College of Engineering, Chennai 600119. India
| | - Seifedine Kadry
- Department of Applied Data Science, Noroff University College, Kristiansand, Norway; Artificial Intelligence Research Center (AIRC), Ajman University, Ajman 346, United Arab Emirates; Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon
| | - Jeehoon Han
- Department of Chemical Engineering, Pohang University of Science and Technology, Pohang 37673, Republic of Korea
| | - Raveendran Sindhu
- Department of Food Technology, TKM Institute of Technology, Kollam, Kerala, India
| | - Mukesh Kumar Awasthi
- College of Natural Resources and Environment, Northwest A&F University, Yangling, Shaanxi Province 712100, China
| | | | - Gurunathan Baskar
- Department of Biotechnology, St. Joseph's College of Engineering, Chennai 600119. India; Department of Applied Data Science, Noroff University College, Kristiansand, Norway.
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12
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Guo C, Wan D, Li Y, Zhu Q, Luo Y, Luo W, Cui Y. Quantitative prediction of the hydraulic performance of free water surface constructed wetlands by integrating numerical simulation and machine learning. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2023; 337:117745. [PMID: 36965370 DOI: 10.1016/j.jenvman.2023.117745] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 02/24/2023] [Accepted: 03/13/2023] [Indexed: 06/18/2023]
Abstract
Quantitative prediction of the design parameter-influenced hydraulic performance is significant for optimizing free water surface constructed wetlands (FWS CWs) to reduce point and non-point source pollution and improve land utilization. However, owing to limitations of the test conditions and data scale, a quantitative prediction model of the hydraulic performance under multiple design parameters has not yet been established. In this study, we integrated field test data, mechanism model, statistical regression, and machine learning (ML) to construct such quantitative prediction models. A FWS CW numerical model was established by integrating 13 groups of trace data from field tests. Subsequently, training, test and extension datasets comprising 125 (5^3), 25 (L25(56)) and 16 (L16(44)) data points, respectively, were generated via numerical simulation of multi-level value combination of three quantitative design parameters, namely, water depth, hydraulic loading rate (HLR), and aspect ratio. The short circuit index (φ10), Morrill dispersion index (MDI), hydraulic efficiency (λ) and moment index (MI) were used as representative hydraulic performance indicators. Training set with large samples were analyzed to determine the variation rules of different hydraulic indicators. Based on the control variable method, φ10, λ, and MI grew exponentially with increasing aspect ratio whereas MDI showed a decreasing trend; with increasing water depth, φ10, λ, and MI showed polynomial decreases whereas MDI increased; with increasing HLR, φ10, λ, and MI slowly increased linearly whereas MDI showed the opposite trend. Finally, we constructed models based on multivariate nonlinear regression (MNLR) and ML (random forest (RF), multilayer perceptron (MLP), and support vector regression. The coefficients of determination (R2) of the MNLR and ML models fitting the training and test sets were all greater than 0.9; however, the generalization abilities of different models in the extension set were different. The most robust MLP, MNLR without interaction term, and RF models were recommended as the preferred models to hydraulic performance prediction. The extreme importance of aspect ratio in hydraulic performance was revealed. Thus, gaps in the current understanding of multivariate quantitative prediction of the hydraulic performance of FWS CWs are addressed while providing an avenue for researching FWS CWs in different regions according to local conditions.
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Affiliation(s)
- Changqiang Guo
- Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 210008, China; Key Laboratory of Basin Water Resources and Eco-Environmental Science in Hubei Province, Changjiang River Scientific Research Institute of Changjiang Water Resources Commission, Wuhan, 430010, China
| | - Di Wan
- Key Laboratory of Basin Water Resources and Eco-Environmental Science in Hubei Province, Changjiang River Scientific Research Institute of Changjiang Water Resources Commission, Wuhan, 430010, China; State Key Laboratory of Water Resource and Hydropower Engineering Science, Wuhan University, Wuhan, 430072, China
| | - Yalong Li
- Key Laboratory of Basin Water Resources and Eco-Environmental Science in Hubei Province, Changjiang River Scientific Research Institute of Changjiang Water Resources Commission, Wuhan, 430010, China
| | - Qing Zhu
- Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 210008, China
| | - Yufeng Luo
- State Key Laboratory of Water Resource and Hydropower Engineering Science, Wuhan University, Wuhan, 430072, China
| | - Wenbing Luo
- Key Laboratory of Basin Water Resources and Eco-Environmental Science in Hubei Province, Changjiang River Scientific Research Institute of Changjiang Water Resources Commission, Wuhan, 430010, China
| | - Yuanlai Cui
- State Key Laboratory of Water Resource and Hydropower Engineering Science, Wuhan University, Wuhan, 430072, China.
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13
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Singh S, Soti A, Kulshreshtha NM, Kumar N, Brighu U, Gupta AB, Bezbaruah AN. Optimization of depth of filler media in horizontal flow constructed wetlands for maximizing removal rate coefficients of targeted pollutant(s). BIORESOURCE TECHNOLOGY 2023; 376:128898. [PMID: 36931442 DOI: 10.1016/j.biortech.2023.128898] [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: 01/31/2023] [Revised: 03/09/2023] [Accepted: 03/11/2023] [Indexed: 06/18/2023]
Abstract
Varying the depth of HFCW media causes differences in the redox status within the system, and hence the community structure and diversity of bacteria, affecting removal rates of different pollutants. The key functional microorganisms of CWs that remove contaminants belong to the phyla Proteobacteria, Bacteroidetes, Actinobacteria, and Firmicutes. Secondary data of 111 HFCWs (1232 datasets) were analyzed to deduce the relationship between volumetric removal rate coefficients (KBOD, KTN, KTKN, and KTP) and depth. Equations of depth were derived in terms of rate coefficients using machine learning approach (MLR and SVR) (R2 = 0.85, 0.87 respectively). These equations were then used to find the optimum depth for pollutant(s) removal using Grey wolf optimization (GWO). The computed optimum depths were 1.48, 1.71, 1.91, 2.09, and 2.14 m for the removal of BOD, TKN, TN, TP, and combined nutrients, respectively, which were validated through primary data. This study would be helpful for optimal design of HFCWs.
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Affiliation(s)
- Saurabh Singh
- Department of Civil Engineering, Malaviya National Institute of Technology, JLN Marg, Jaipur 302017, India
| | - Abhishek Soti
- Department of Civil Engineering, Malaviya National Institute of Technology, JLN Marg, Jaipur 302017, India
| | - Niha Mohan Kulshreshtha
- Department of Civil Engineering, Malaviya National Institute of Technology, JLN Marg, Jaipur 302017, India
| | - Nikhil Kumar
- Department of Electrical Engineering, Malaviya National Institute of Technology, JLN Marg, Jaipur 302017, India
| | - Urmila Brighu
- Department of Civil Engineering, Malaviya National Institute of Technology, JLN Marg, Jaipur 302017, India
| | - Akhilendra Bhushan Gupta
- Department of Civil Engineering, Malaviya National Institute of Technology, JLN Marg, Jaipur 302017, India.
| | - Achintya N Bezbaruah
- Nanoenvirology Research Group, Department of Civil and Environmental Engineering, North Dakota State University, Fargo, ND 58105, USA
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14
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Wang K, Zhao W, Lin L, Wang T, Wei P, Ledesma-Amaro R, Zhang AH, Ji XJ. A robust soft sensor based on artificial neural network for monitoring microbial lipid fermentation processes using Yarrowia lipolytica. Biotechnol Bioeng 2023; 120:1015-1025. [PMID: 36522163 DOI: 10.1002/bit.28310] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 12/07/2022] [Accepted: 12/13/2022] [Indexed: 12/23/2022]
Abstract
Microbial oils produced by Yarrowia lipolytica offer an environmentally friendly and sustainable alternative to petroleum as well as traditional lipids from animals and plants. The accurate measurement of fermentation parameters, including the substrate concentration, dry cell weight, and lipid accumulation, is the foundation of process control, which is indispensable for industrial lipid production. However, it remains a great challenge to measure the complex parameters online during the lipid fermentation process, which is nonlinear, multivariate, and characterized by strong coupling. As a type of AI technology, the artificial neural network model is a powerful tool for handling extremely complex problems, and it can be employed to develop a soft sensor to monitor the microbial lipid fermentation process of Y. lipolytica. In this study, we first analyzed and emphasized the volume of sodium hydroxide and dissolved oxygen concentration as central parameters of the fermentation process. Then, a soft sensor based on a four-input artificial neural network model was developed, in which the input variables were fermentation time, dissolved oxygen concentration, initial glucose concentration, and additional volume of sodium hydroxide. This provides the possibility of online monitoring of dry cell weight, glucose concentration, and lipid production with high accuracy, which can be extended to similar fermentation processes characterized by the addition of bases or acids, as well as changes of the dissolved oxygen concentration.
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Affiliation(s)
- Kaifeng Wang
- State Key Laboratory of Materials-Oriented Chemical Engineering, College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, Nanjing, People's Republic of China
| | - Wenyang Zhao
- Institute of Network and Cloud Computing Technology, College of Computer Science and Technology, Nanjing Tech University, Nanjing, People's Republic of China
| | - Lu Lin
- State Key Laboratory of Materials-Oriented Chemical Engineering, College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, Nanjing, People's Republic of China
| | - Tianjing Wang
- Institute of Network and Cloud Computing Technology, College of Computer Science and Technology, Nanjing Tech University, Nanjing, People's Republic of China
| | - Ping Wei
- State Key Laboratory of Materials-Oriented Chemical Engineering, College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, Nanjing, People's Republic of China
| | - Rodrigo Ledesma-Amaro
- Department of Bioengineering and Imperial College Centre for Synthetic Biology, Imperial College London, London, UK
| | - Ai-Hui Zhang
- Department of Chemical and Biochemical Engineering, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, People's Republic of China
| | - Xiao-Jun Ji
- State Key Laboratory of Materials-Oriented Chemical Engineering, College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, Nanjing, People's Republic of China
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15
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Tsui TH, van Loosdrecht MCM, Dai Y, Tong YW. Machine learning and circular bioeconomy: Building new resource efficiency from diverse waste streams. BIORESOURCE TECHNOLOGY 2023; 369:128445. [PMID: 36473583 DOI: 10.1016/j.biortech.2022.128445] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 11/29/2022] [Accepted: 12/02/2022] [Indexed: 06/17/2023]
Abstract
Biorefinery systems are playing pivotal roles in the technological support of resource efficiency for circular bioeconomy. Meanwhile, artificial intelligence presents great potential in handling scientific tasks of high-dimensional complexity. This review article scrutinizes the status of machine learning (ML) applications in four critical biorefinery systems (i.e. composting, fermentation, anaerobic digestion, and thermochemical conversions) as well as their advancements against traditional modeling techniques of mechanistic approach. The contents cover their algorithm selections, modeling challenges, and prospective improvements. Perspectives are sketched to further inform collective efforts on crucial aspects. The multidisciplinary interchange of modeling knowledge will enable a more progressive digital transformation of sustainability efforts in supporting sustainable development goals.
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Affiliation(s)
- To-Hung Tsui
- Environmental Research Institute, National University of Singapore, 1 Create Way, 138602, Singapore; Energy and Environmental Sustainability for Megacities (E2S2) Phase II, Campus for Research Excellence and Technological Enterprise (CREATE), 1 Create Way, Singapore, 138602, Singapore
| | | | - Yanjun Dai
- School of Mechanical Engineering, Shanghai Jiaotong University, 800 Dongchuan Road, Shanghai, 200240, China
| | - Yen Wah Tong
- Environmental Research Institute, National University of Singapore, 1 Create Way, 138602, Singapore; Energy and Environmental Sustainability for Megacities (E2S2) Phase II, Campus for Research Excellence and Technological Enterprise (CREATE), 1 Create Way, Singapore, 138602, Singapore; Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, 117585, Singapore.
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16
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Singh NK, Yadav M, Singh V, Padhiyar H, Kumar V, Bhatia SK, Show PL. Artificial intelligence and machine learning-based monitoring and design of biological wastewater treatment systems. BIORESOURCE TECHNOLOGY 2023; 369:128486. [PMID: 36528177 DOI: 10.1016/j.biortech.2022.128486] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 12/09/2022] [Accepted: 12/11/2022] [Indexed: 06/17/2023]
Abstract
Artificial intelligence (AI) and machine learning (ML) are currently used in several areas. The applications of AI and ML based models are also reported for monitoring and design of biological wastewater treatment systems (WWTS). The available information is reviewed and presented in terms of bibliometric analysis, model's description, specific applications, and major findings for investigated WWTS. Among the applied models, artificial neural network (ANN), fuzzy logic (FL) algorithms, random forest (RF), and long short-term memory (LSTM) were predominantly used in the biological wastewater treatment. These models are tested by predictive control of effluent parameters such as biological oxygen demand (BOD), chemical oxygen demand (COD), nutrient parameters, solids, and metallic substances. Following model performance indicators were mainly used for the accuracy analysis in most of the studies: root mean squared error (RMSE), mean square error (MSE), and determination coefficient (DC). Besides, outcomes of various models are also summarized in this study.
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Affiliation(s)
- Nitin Kumar Singh
- Department of Environmental Science & Engineering, Marwadi University, Rajkot 360003, Gujarat, India.
| | - Manish Yadav
- Central Mine Planning Design Institute Limited, Coal India Limited, India
| | - Vijai Singh
- Department of Biosciences, School of Science, Indrashil University, Rajpur, Mehsana 382715, Gujarat, India
| | | | - Vinod Kumar
- Centre for Climate and Environmental Protection, School of Water, Energy and Environment, Cranfield University, Cranfield MK43 0AL, United Kingdom
| | - Shashi Kant Bhatia
- Department of Biological Engineering, College of Engineering, Konkuk University, Seoul 05029, South Korea
| | - Pau-Loke Show
- Zhejiang Provincial Key Laboratory for Subtropical Water Environment and Marine Biological Resources Protection, Wenzhou University, Wenzhou 325035, China; Department of Sustainable Engineering, Saveetha School of Engineering, SIMATS, Chennai 602105, India; Department of Chemical and Environmental Engineering, University of Nottingham, 43500 Semenyih, Selangor Darul Ehsan, Malaysia
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17
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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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18
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Yang B, Xiao Z, Meng Q, Yuan Y, Wang W, Wang H, Wang Y, Feng X. Deep learning-based prediction of effluent quality of a constructed wetland. ENVIRONMENTAL SCIENCE AND ECOTECHNOLOGY 2023; 13:100207. [PMID: 36203649 PMCID: PMC9529666 DOI: 10.1016/j.ese.2022.100207] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 09/16/2022] [Accepted: 09/16/2022] [Indexed: 06/16/2023]
Abstract
Data-driven approaches that make timely predictions about pollutant concentrations in the effluent of constructed wetlands are essential for improving the treatment performance of constructed wetlands. However, the effect of the meteorological condition and flow changes in a real scenario are generally neglected in water quality prediction. To address this problem, in this study, we propose an approach based on multi-source data fusion that considers the following indicators: water quality indicators, water quantity indicators, and meteorological indicators. In this study, we establish four representative methods to simultaneously predict the concentrations of three representative pollutants in the effluent of a practical large-scale constructed wetland: (1) multiple linear regression; (2) backpropagation neural network (BPNN); (3) genetic algorithm combined with the BPNN to solve the local minima problem; and (4) long short-term memory (LSTM) neural network to consider the influence of past results on the present. The results suggest that the LSTM-predicting model performed considerably better than the other deep neural network-based model or linear method, with a satisfactory R2. Additionally, given the huge fluctuation of different pollutant concentrations in the effluent, we used a moving average method to smooth the original data, which successfully improved the accuracy of traditional neural networks and hybrid neural networks. The results of this study indicate that the hybrid modeling concept that combines intelligent and scientific data preprocessing methods with deep learning algorithms is a feasible approach for forecasting water quality in the effluent of actual engineering.
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Affiliation(s)
- Bowen Yang
- State Key Laboratory of Urban Water Resource and Environment, School of Civil and Environmental Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong, 518055, PR China
| | - Zijie Xiao
- State Key Laboratory of Urban Water Resource and Environment, School of Civil and Environmental Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong, 518055, PR China
| | - Qingjie Meng
- Shenzhen Shenshui Water Resources Consulting CO, LTD, Shenzhen, Guangdong, 518022, PR China
| | - Yuan Yuan
- College of Biological Engineering, Beijing Polytechnic, Beijing, 10076, PR China
| | - Wenqian Wang
- State Key Laboratory of Urban Water Resource and Environment, School of Civil and Environmental Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong, 518055, PR China
| | - Haoyu Wang
- State Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater Pollution Control, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Yongmei Wang
- State Key Laboratory of Urban Water Resource and Environment, School of Civil and Environmental Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong, 518055, PR China
| | - Xiaochi Feng
- State Key Laboratory of Urban Water Resource and Environment, School of Civil and Environmental Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong, 518055, PR China
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19
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Tippawan P, Jienkulsawad P, Limleamthong P, Arpornwichanop A. Composting time minimization of mature vermicompost using desirability and response surface methodology approach. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2022.108037] [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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20
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Bayındır Y, Cagcag Yolcu O, Aydın Temel F, Turan NG. Evaluation of a cascade artificial neural network for modeling and optimization of process parameters in co-composting of cattle manure and municipal solid waste. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2022; 318:115496. [PMID: 35724572 DOI: 10.1016/j.jenvman.2022.115496] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 06/02/2022] [Accepted: 06/05/2022] [Indexed: 06/15/2023]
Abstract
The present study was carried out to improve, test, and validate the Cascade Forward Neural Network (CFNN) for co-composting of municipal solid waste (MSW) and cattle manure (CM). Composting was performed in vessel pilot-scale reactors with different CM rates for 105 days. The CFNN used 5 input variables containing CM and MSW mixture combinations, and 1 output for each of the compost quality parameters. The CFNN results were compared with Response Surface Methodology (RSM) and Feed Forward Neural Network (FFNN) results. Multi-objective optimization process using Genetic Algorithm (GA), the total desirability, which has a much better value than the RSM, was obtained as 0.4455 and the CM ratio and processing time were determined as approximately 23.39% and 104.86 days, respectively. It is concluded that CFNN is a unique modeling tool, exhibiting superior modeling and prediction performance in MSW and compost modeling for CM.
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Affiliation(s)
- Yasemin Bayındır
- Department of Environmental Engineering, Faculty of Engineering, Ondokuz Mayıs University, Samsun, 55200, Turkey
| | - Ozge Cagcag Yolcu
- Department of Statistics, Faculty of Sciences and Arts, Marmara University, İstanbul, 34722, Turkey
| | - Fulya Aydın Temel
- Department of Environmental Engineering, Faculty of Engineering, Giresun University, Giresun, Turkey.
| | - Nurdan Gamze Turan
- Department of Environmental Engineering, Faculty of Engineering, Ondokuz Mayıs University, Samsun, 55200, Turkey
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21
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Zhou Y, Xiao R, Klammsteiner T, Kong X, Yan B, Mihai FC, Liu T, Zhang Z, Kumar Awasthi M. Recent trends and advances in composting and vermicomposting technologies: A review. BIORESOURCE TECHNOLOGY 2022; 360:127591. [PMID: 35809873 DOI: 10.1016/j.biortech.2022.127591] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 07/01/2022] [Accepted: 07/02/2022] [Indexed: 06/15/2023]
Abstract
Composting technologies have come a long way, developing from static heaps and windrow composting to smart, artificial intelligence-assisted reactor composting. While in previous years, much attention has been paid to identifying ideal organic waste streams and suitable co-composting candidates, more recent efforts tried to determine novel process-enhancing supplements. These include various single and mixed microbial cultures, additives, bulking agents, or combinations thereof. However, there is still ample need to fine-tune the composting process in order to reduce its impact on the environment and streamline it with circular economy goals. In this review, we highlight recent advances in integrating mathematical modelling, novel supplements, and reactor designs with (vermi-) composting practices and provide an outlook for future developments. These results should serve as reference point to target adjusting screws for process improvement and provide a guideline for waste management officials and stakeholders.
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Affiliation(s)
- Yuwen Zhou
- College of Natural Resources and Environment, Northwest A&F University, Yangling, Shaanxi Province 712100, China
| | - Ran Xiao
- Interdisciplinary Research Center for Agriculture Green Development in Yangtze River Basin, College of Resources and Environment, Southwest University, Chongqing 400715, China
| | - Thomas Klammsteiner
- Department of Microbiology, University of Innsbruck, Technikerstrasse 25d, 6020 Innsbruck, Austria
| | - Xiaoliang Kong
- College of Resources and Environment, Hunan Agricultural University, Changsha 410128, China
| | - Binghua Yan
- College of Resources and Environment, Hunan Agricultural University, Changsha 410128, China
| | - Florin-Constantin Mihai
- CERNESIM Center, Department of Exact Sciences and Natural Sciences, Institute of Interdisciplinary Research, "Alexandru Ioan Cuza" University of Iasi, 700506 Iasi, Romania
| | - Tao Liu
- College of Natural Resources and Environment, Northwest A&F University, Yangling, Shaanxi Province 712100, China
| | - Zengqiang Zhang
- College of Natural Resources and Environment, Northwest A&F University, Yangling, Shaanxi Province 712100, China
| | - Mukesh Kumar Awasthi
- College of Natural Resources and Environment, Northwest A&F University, Yangling, Shaanxi Province 712100, China.
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22
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Andeobu L, Wibowo S, Grandhi S. Artificial intelligence applications for sustainable solid waste management practices in Australia: A systematic review. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 834:155389. [PMID: 35460765 DOI: 10.1016/j.scitotenv.2022.155389] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 04/14/2022] [Accepted: 04/15/2022] [Indexed: 05/17/2023]
Abstract
Solid waste generation and its impact on human health and the environment have long been a matter of concern for governments across the world. In recent years, there has been increasing emphasis on resource recovery (reusing, recycling and extracting energy from waste) using more advanced approaches such as artificial intelligence (AI) in Australia. AI is a powerful technology that is increasingly gaining popularity and application in various fields. The adoption of AI techniques offers alternative innovative approaches to solid waste management (SWM). Although there are previous studies on AI technologies and SWM, no study has assessed the adoption of AI applications in solving the diverse SWM problems for achieving sustainable waste management in Australia. Moreover, there are inconsistencies and a lack of awareness on how AI technologies function in relation to their application to SWM. This study examines the application of AI technologies in various areas of SWM (generation, sorting, collection, vehicle routing, treatment, disposal and waste management planning) to enhance sustainable waste management practices in Australia. To achieve the aims of this study, prior studies from 2005 to 2021 from various databases are collected and analyzed. The study focuses on the adoption of AI applications on SWM, compares the performance of AI applications, explores the benefits and challenges, and provides best practice recommendations on how resource efficiency can be optimized to improve economic, environmental and social outcomes. This study found that AI-based models have better prediction abilities when compared to other models used in forecasting solid waste generation and recycling. Findings show that waste generation in Australia has been steadily increasing and requires upgraded and improved recovery infrastructure and the appropriate adoption of AI technologies to enhance sustainable SWM. Australia's adoption of AI recycling technologies would benefit from a national approach that seeks consistency across jurisdictions, while catering for regional differences. This study will benefit researchers, governments, policy-makers, municipalities and other waste management organizations to increase current recycling rates, eliminate the need for manual labor, reduce costs, maximize efficiency, and transform the way we approach the management of solid waste.
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Affiliation(s)
- Lynda Andeobu
- Central Queensland University, 120 Spencer Street, Melbourne 3000, Australia.
| | - Santoso Wibowo
- Central Queensland University, 120 Spencer Street, Melbourne 3000, Australia.
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Determination of Cetane Number from Fatty Acid Compositions and Structures of Biodiesel. Processes (Basel) 2022. [DOI: 10.3390/pr10081502] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Biodiesel, which possesses the dominant advantages of low emissions and environmental friendliness, is a competitive alternative fuel to petroleum-derived diesel. The cetane number, which indicates ignition delay characteristics, is considered the most significant fuel property of biodiesel. Determining the cetane number for biodiesel by general testing equipment is time-consuming and costly; hence, a simple and convenient predictive formula for the cetane number of biodiesel is a significant task to be carried out. A reliable and convenient predictive method for determining the cetane number is proposed in this study. The key parameters for the cetane number of biodiesel were first screened out. The analysis of multiple linear regressions using the available software SPSS for statistical analysis was carried out to obtain the regression coefficients of those key parameters and intercepts to establish the predictive model. Other available experimental data verified the validity of the proposed predictive equation. The determination coefficient of the formula reaches as high as 94.7%, and the standard error is 3.486. The key parameters, including the number of carbon atoms (NC), allylic position equivalent (APE), and double-bond equivalent (DBE), were more significant for influencing the cetane number of biodiesel. In addition, the increase of NC or the decrease of either APE or DBE results in the increase of the cetane number. Moreover, the present formula is found to obtain closer cetane numbers to those experimental data and features superior prediction capability.
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Navidpour AH, Hosseinzadeh A, Huang Z, Li D, Zhou JL. Application of machine learning algorithms in predicting the photocatalytic degradation of perfluorooctanoic acid. CATALYSIS REVIEWS 2022. [DOI: 10.1080/01614940.2022.2082650] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Affiliation(s)
- Amir H. Navidpour
- Centre for Green Technology, School of Civil and Environmental Engineering, University of Technology Sydney, Ultimo, NSW, Australia
| | - Ahmad Hosseinzadeh
- Centre for Green Technology, School of Civil and Environmental Engineering, University of Technology Sydney, Ultimo, NSW, Australia
| | - Zhenguo Huang
- Centre for Green Technology, School of Civil and Environmental Engineering, University of Technology Sydney, Ultimo, NSW, Australia
| | - Donghao Li
- Department of Chemistry, Yanbian University, Yanji, Jilin Province, China
| | - John L. Zhou
- Centre for Green Technology, School of Civil and Environmental Engineering, University of Technology Sydney, Ultimo, NSW, Australia
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25
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Xia W, Jiang Y, Chen X, Zhao R. Application of machine learning algorithms in municipal solid waste management: A mini review. WASTE MANAGEMENT & RESEARCH : THE JOURNAL OF THE INTERNATIONAL SOLID WASTES AND PUBLIC CLEANSING ASSOCIATION, ISWA 2022; 40:609-624. [PMID: 34269157 PMCID: PMC9016669 DOI: 10.1177/0734242x211033716] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Population growth and the acceleration of urbanization have led to a sharp increase in municipal solid waste production, and researchers have sought to use advanced technology to solve this problem. Machine learning (ML) algorithms are good at modeling complex nonlinear processes and have been gradually adopted to promote municipal solid waste management (MSWM) and help the sustainable development of the environment in the past few years. In this study, more than 200 publications published over the last two decades (2000-2020) were reviewed and analyzed. This paper summarizes the application of ML algorithms in the whole process of MSWM, from waste generation to collection and transportation, to final disposal. Through this comprehensive review, the gaps and future directions of ML application in MSWM are discussed, providing theoretical and practical guidance for follow-up related research.
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Affiliation(s)
- Wanjun Xia
- School of Computing and
Artificial Intelligence, Southwest Jiaotong University, Chengdu, Sichuan,
China
- Library, Southwest Jiaotong
University, Chengdu, Sichuan, China
- Wanjun Xia, School of Computing and
Artificial Intelligence, Southwest Jiaotong University, West Park of
Hi-Tech Zone, Chengdu, Sichuan 611756, China.
| | - Yanping Jiang
- Library, Southwest Jiaotong
University, Chengdu, Sichuan, China
| | - Xiaohong Chen
- Library, Southwest Jiaotong
University, Chengdu, Sichuan, China
| | - Rui Zhao
- Faculty of Geosciences and
Environmental Engineering, Southwest Jiaotong University, Chengdu, Sichuan,
China
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26
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Index Evaluation of Different Hospital Management Modes Based on Deep Learning Model. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:8507288. [PMID: 35528337 PMCID: PMC9068311 DOI: 10.1155/2022/8507288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 04/16/2022] [Indexed: 11/18/2022]
Abstract
In order to effectively improve the efficiency of hospital public management, we designed a hospital management index system based on deep learning model and analysed the application effect of reverse broadcast neural network model in hospital. The results show that in the performance analysis of the model, compared with other classical algorithms, the constructed model has the highest accuracy and the shortest delay. The weight analysis of each index in the model shows that the weight of rational utilization rate of beds in tertiary public hospitals is the highest, and the weight of rational utilization rate of beds in secondary public hospitals is the highest. The further analysis of the model training effect shows that the actual value of most output indexes is consistent with the predicted value, and the residual error of the predicted value is close to 0.
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Guo C, Cui Y. Machine learning exhibited excellent advantages in the performance simulation and prediction of free water surface constructed wetlands. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2022; 309:114694. [PMID: 35182978 DOI: 10.1016/j.jenvman.2022.114694] [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: 10/10/2021] [Revised: 01/19/2022] [Accepted: 02/06/2022] [Indexed: 06/14/2023]
Abstract
Optimizing the design and operation parameters of free water surface constructed wetlands (FWS CWs) in runoff regulation and wastewater treatment is necessary to improve the comprehensive performance. In this study, nine machine learning (ML) algorithms were successfully developed to optimize the parameter combinations for FWS CWs. The scale effect of surface area on wetland performance was determined based on consistently smaller predictions (-6.2% to -28.9%) of the nine well-established ML algorithms. The models most suitable for FWS CW performance simulation and prediction were random forest and extra trees algorithms because of their high R2 values (0.818 in both) with the training set and low mean absolute relative errors (4.7% and 3.8%, respectively) with the test set. Results from feature analysis of the six tree-based algorithms emphasized the importance of water depth and layout of inlet and outlet, and revealed the negligible effect of the aspect ratio. Feature importance and partial dependence analysis enhanced the interpretability of the tree-based algorithms. The proposed ML algorithms enabled the implementation of an extended scenario at a low cost in real time. Therefore, ML algorithms are suitable for expressing the complex and uncertain effects of the design and operation parameters on the performance of FWS CWs. Acquiring datasets consisting of more extensive, uniform, and unbiased parameter combinations is crucial for developing more robust and practical ML algorithms for the optimal design of FWS CWs.
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Affiliation(s)
- Changqiang Guo
- Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 210008, China; Key Laboratory of Basin Water Resources and Eco-Environmental Science in Hubei Province, Changjiang River Scientific Research Institute of Changjiang Water Resources Commission, Wuhan, 430010, China
| | - Yuanlai Cui
- State Key Laboratory of Water Resource and Hydropower Engineering Science, Wuhan University, Wuhan, 430072, China.
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28
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Machine learning-based modeling and analysis of PFOS removal from contaminated water by nanofiltration process. Sep Purif Technol 2022. [DOI: 10.1016/j.seppur.2022.120775] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
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Guo C, Cui Y. Utilizing artificial neural network to simulate and predict the hydraulic performance of free water surface constructed wetlands. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2022; 305:114334. [PMID: 34953224 DOI: 10.1016/j.jenvman.2021.114334] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 10/30/2021] [Accepted: 12/16/2021] [Indexed: 06/14/2023]
Abstract
Optimizing the hydraulic performance of free water surface constructed wetlands (FWS CWs) is of great economic and ecological value. However, there is a complex nonlinear relationship between the hydraulic performance and design parameters of FWS CWs. In this study, an artificial neural network (ANN) was applied to simulate and predict the hydraulic performance corresponding to different combinations of design parameters, and orthogonal design L9 (34) was used to determine the optimal combination of the important hyperparameters of the ANN. Based on the convenient scenario prediction ability of ANN, sensitivity analysis of different design parameters was carried out by the control variate method and full factor experiment. The results showed that the combination of 3 hidden layers, 15 neural nodes in each hidden layer, 0.001 learning rate, and 8 batch sizes was optimal for the established ANN model, achieving a coefficient of determination of 0.828 in the validation set and a satisfactory prediction effect in the test set. The narrow feature distribution interval in the training set restricted the generalization ability of the ANN model to some extent. Of the four continuous design parameters, the water depth and aspect ratio had an important influence on the effective volume ratio. The layout of inlet and outlet was the most influential design parameter, as confirmed by the full factor experiment of five factors and four levels. The established ANN allowed real-time implementation in an extended scenario at a low cost. This study suggests that the ANN can simultaneously project complex and uncertain effects of several design parameters on wetland performance. In future research, acquiring further comprehensive, impartial, and unbiased experimental datasets is necessary to establish a more robust and generalizing ANN model that can guide the optimal design of FWS CWs.
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Affiliation(s)
- Changqiang Guo
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058, China; Key Laboratory of Basin Water Resources and Eco-Environmental Science in Hubei Province, Yangtze River Scientific Research Institute, Wuhan, 430010, China
| | - Yuanlai Cui
- State Key Laboratory of Water Resource and Hydropower Engineering Science, Wuhan University, Wuhan, 430072, China.
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30
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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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31
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Hosseinzadeh A, Zhou JL, Altaee A, Li D. Machine learning modeling and analysis of biohydrogen production from wastewater by dark fermentation process. BIORESOURCE TECHNOLOGY 2022; 343:126111. [PMID: 34648964 DOI: 10.1016/j.biortech.2021.126111] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Revised: 10/05/2021] [Accepted: 10/07/2021] [Indexed: 06/13/2023]
Abstract
Dark fermentation process for simultaneous wastewater treatment and H2 production is gaining attention. This study aimed to use machine learning (ML) procedures to model and analyze H2 production from wastewater during dark fermentation. Different ML procedures were assessed based on the mean squared error (MSE) and determination coefficient (R2) to select the most robust models for modeling the process. The research showed that gradient boosting machine (GBM), support vector machine (SVM), random forest (RF) and AdaBoost were the most appropriate models, which were optimized by grid search and deeply analyzed by permutation variable importance (PVI) to identify the relative importance of process variables. All four models demonstrated promising performances in predicting H2 production with high R2 values (0.893, 0.885, 0.902 and 0.889) and small MSE values (0.015, 0.015, 0.016 and 0.015). Moreover, RF-PVI demonstrated that acetate, butyrate, acetate/butyrate, ethanol, Fe and Ni were of high importance in decreasing order.
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Affiliation(s)
- Ahmad Hosseinzadeh
- Centre for Green Technology, School of Civil and Environmental Engineering, University of Technology Sydney, NSW 2007, Australia
| | - John L Zhou
- Centre for Green Technology, School of Civil and Environmental Engineering, University of Technology Sydney, NSW 2007, Australia.
| | - Ali Altaee
- Centre for Green Technology, School of Civil and Environmental Engineering, University of Technology Sydney, NSW 2007, Australia
| | - Donghao Li
- Department of Chemistry, Yanbian University, Park Road 977, Yanji 133002, Jilin Province, China
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Predictive analysis of the value of information flow on the shop floor of developing countries using artificial neural network based deep learning. Heliyon 2021; 7:e08315. [PMID: 34816031 PMCID: PMC8593436 DOI: 10.1016/j.heliyon.2021.e08315] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 07/29/2021] [Accepted: 10/29/2021] [Indexed: 02/06/2023] Open
Abstract
To facilitate the continuous improvement of performance and the management of information flow (MIF) for production and manufacturing purposes on the shop floor of developing countries, there is a need to characterize information flow that will be shared during the process. MIF provides a key performance shop floor metric called the value of information flow (VIF). Previous methods have been used to analyze VIF in developed countries. However, these methods are sometimes limited when applied to developing countries where the shop floor is disorganized. It then renders the MIF with the imported software inefficient because of the gap between the user environments. Taking Cameroon as a case study, this study proposes a new method of modeling and analyzing the information flow and its value based on the characteristics of information flow (CIF) for developing countries. In addition, a predictive analysis of the VIF based on CIF using an artificial neural network (ANN) on one hand and optimized ANN with particle swarm optimizer (PSO) and genetic algorithms (GA) on the other is performed. The ANN model of regression developed has the following performance: coefficient of determination: 0.99 and mean squared error (MSE): 0.00043. For the PSO-ANN, the MSE decreased to 0.00011, and this model result was similar to that of the deep learning model used for regression. The GA-ANN model results were not as satisfactory as those of the PSO-ANN model. A predictive system to analyze VIF is proposed for managers of companies in developing countries.
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Meena M, Shubham S, Paritosh K, Pareek N, Vivekanand V. Production of biofuels from biomass: Predicting the energy employing artificial intelligence modelling. BIORESOURCE TECHNOLOGY 2021; 340:125642. [PMID: 34315128 DOI: 10.1016/j.biortech.2021.125642] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 07/18/2021] [Accepted: 07/19/2021] [Indexed: 06/13/2023]
Abstract
Bioenergy may be a major replacement of fossil fuels which can make the path easier for sustainable development and decrease the dependency on conventional sources of energy. The main concern with the bioenergy is the availability of feedstock, dealing with its economics as well as its demand and supply chain management. This review deals with the finding of distinct potential of different Artificial Intelligence technologies focusing the challenges in bioenergy production system and its overall improvement in application. The study also highlights the contribution of Artificial Intelligence techniques for the prediction of energy from biomass and evaluates the computing-reasoning techniques for managing bioenergy production, biomass supply chain and optimization of process parameters for efficient bioconversion technologies.
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Affiliation(s)
- Manish Meena
- Centre for Energy and Environment, Malviya National Institute of Technology, JLN Marg, Jaipur, Rajasthan 302017 India
| | - Shubham Shubham
- Centre for Energy and Environment, Malviya National Institute of Technology, JLN Marg, Jaipur, Rajasthan 302017 India
| | - Kunwar Paritosh
- Centre for Energy and Environment, Malviya National Institute of Technology, JLN Marg, Jaipur, Rajasthan 302017 India
| | - Nidhi Pareek
- Department of Microbiology, School of Life Sciences, Central University of Rajasthan, Bandarsindri, Kishangarh, Ajmer, Rajasthan 305801, India
| | - Vivekanand Vivekanand
- Centre for Energy and Environment, Malviya National Institute of Technology, JLN Marg, Jaipur, Rajasthan 302017 India.
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34
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Almuntashiri A, Hosseinzadeh A, Volpin F, Ali SM, Dorji U, Shon H, Phuntsho S. Removal of pharmaceuticals from nitrified urine. CHEMOSPHERE 2021; 280:130870. [PMID: 34162101 DOI: 10.1016/j.chemosphere.2021.130870] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 05/05/2021] [Accepted: 05/11/2021] [Indexed: 05/17/2023]
Abstract
In this study, granular activated carbon (GAC) was examined for the removal of five of the most commonly detected pharmaceuticals (naproxen, carbamazepine, acetaminophen, ibuprofen and metronidazole) from a nitrified urine to make the urine-derived fertiliser nutrient safe for food crops. Batch experiments were conducted to investigate the adsorption kinetics that described the removal of micropollutants (equal concentrations of 0.2 mM) from the synthetic nitrified urine at different GAC dosages (10-3000 mg/L). Artificial neural network modelling was also used to predict and simulate the removal of pharmaceuticals from nitrified urine. Langmuir and Freundlich isotherm models described the equilibrium data, with the Langmuir model providing slightly higher correlations. At the highest dose of 3000 mg/L GAC, all the pharmaceuticals showed a removal rates of over 90% after 1 h of adsorption time and 99% removal rates after 6 h of adsorption time. This study concludes that GAC is able to remove the targeted xenobiotics without affecting the concentration of N and P in the urine, suggesting that nitrified urine could be safely used as a nutrient product in future.
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Affiliation(s)
- Abdulaziz Almuntashiri
- School of Civil and Environmental Engineering, University of Technology Sydney (UTS), City Campus, 15 Broadway, Ultimo, NSW, 2007, Australia.
| | - Ahmad Hosseinzadeh
- School of Civil and Environmental Engineering, University of Technology Sydney (UTS), City Campus, 15 Broadway, Ultimo, NSW, 2007, Australia.
| | - Federico Volpin
- School of Civil and Environmental Engineering, University of Technology Sydney (UTS), City Campus, 15 Broadway, Ultimo, NSW, 2007, Australia.
| | - Syed Muztuza Ali
- School of Civil and Environmental Engineering, University of Technology Sydney (UTS), City Campus, 15 Broadway, Ultimo, NSW, 2007, Australia.
| | - Ugyen Dorji
- School of Civil and Environmental Engineering, University of Technology Sydney (UTS), City Campus, 15 Broadway, Ultimo, NSW, 2007, Australia.
| | - Hokyong Shon
- School of Civil and Environmental Engineering, University of Technology Sydney (UTS), City Campus, 15 Broadway, Ultimo, NSW, 2007, Australia.
| | - Sherub Phuntsho
- School of Civil and Environmental Engineering, University of Technology Sydney (UTS), City Campus, 15 Broadway, Ultimo, NSW, 2007, Australia.
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Singh D, Chavan D, Pandey AK, Periyaswami L, Kumar S. Determination of landfill gas generation potential from lignocellulose biomass contents of municipal solid waste. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 785:147243. [PMID: 33930808 DOI: 10.1016/j.scitotenv.2021.147243] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Revised: 03/31/2021] [Accepted: 04/15/2021] [Indexed: 06/12/2023]
Abstract
The presence of heat, methane (CH4) and oxygen in landfill sub-surface causes initiation of spontaneous waste ignition posing severe environmental impacts. A municipal solid waste (MSW) reactor (trough) was designed to monitor landfill gases (LFGs) i.e., CH4 and CO2 and its potential from different waste categories (synthetic waste, fresh waste, 3-month, 6-month, 3-year and 5-year-old waste) collected from open MSW dumpsite. The quantity of cellulose (C), hemicellulose (H) and lignin (L) contents (C + H: L) present in organic waste fraction of each waste category was determined. Results showed that fresh waste which has higher ratio of C + H: L is responsible for maximum CH4 and CO2 generation i.e., 31,660 and 46,078 ml/g of volatile solid, respectively. The ratio of C + H: L observed in fresh waste, 3-month, 6-month, 3-year and 5-year-old waste was 2.62, 1.70, 1.32, 1.21 and 1, respectively. The study also showed that LFG generation is directly proportional to lignocellulose biomass contents present in MSW. Artificial neural network (ANN) modelling was used for the cross validation of CH4 yield (valuable product) which showed ±4% error between experimental and predicted data.
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Affiliation(s)
- Deval Singh
- CSIR-National Environmental Engineering Research Institute (CSIR-NEERI), Nehru Marg, Nagpur 440 020, India
| | - Digambar Chavan
- CSIR-National Environmental Engineering Research Institute (CSIR-NEERI), Nehru Marg, Nagpur 440 020, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201 002, Uttar Pradesh, India
| | - Ashutosh Kumar Pandey
- CSIR-National Environmental Engineering Research Institute (CSIR-NEERI), Nehru Marg, Nagpur 440 020, India
| | - Lakshmikanthan Periyaswami
- CSIR-National Environmental Engineering Research Institute (CSIR-NEERI), Nehru Marg, Nagpur 440 020, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201 002, Uttar Pradesh, India
| | - Sunil Kumar
- CSIR-National Environmental Engineering Research Institute (CSIR-NEERI), Nehru Marg, Nagpur 440 020, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201 002, Uttar Pradesh, India.
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36
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Ahmed SF, Mofijur M, Nuzhat S, Chowdhury AT, Rafa N, Uddin MA, Inayat A, Mahlia TMI, Ong HC, Chia WY, Show PL. Recent developments in physical, biological, chemical, and hybrid treatment techniques for removing emerging contaminants from wastewater. JOURNAL OF HAZARDOUS MATERIALS 2021; 416:125912. [PMID: 34492846 DOI: 10.1016/j.jhazmat.2021.125912] [Citation(s) in RCA: 161] [Impact Index Per Article: 53.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2021] [Revised: 04/07/2021] [Accepted: 04/14/2021] [Indexed: 05/25/2023]
Abstract
Emerging contaminants (ECs) in wastewater have recently attracted the attention of researchers as they pose significant risks to human health and wildlife. This paper presents the state-of-art technologies used to remove ECs from wastewater through a comprehensive review. It also highlights the challenges faced by existing EC removal technologies in wastewater treatment plants and provides future research directions. Many treatment technologies like biological, chemical, and physical approaches have been advanced for removing various ECs. However, currently, no individual technology can effectively remove ECs, whereas hybrid systems have often been found to be more efficient. A hybrid technique of ozonation accompanied by activated carbon was found significantly effective in removing some ECs, particularly pharmaceuticals and pesticides. Despite the lack of extensive research, nanotechnology may be a promising approach as nanomaterial incorporated technologies have shown potential in removing different contaminants from wastewater. Nevertheless, most existing technologies are highly energy and resource-intensive as well as costly to maintain and operate. Besides, most proposed advanced treatment technologies are yet to be evaluated for large-scale practicality. Complemented with techno-economic feasibility studies of the treatment techniques, comprehensive research and development are therefore necessary to achieve a full and effective removal of ECs by wastewater treatment plants.
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Affiliation(s)
- S F Ahmed
- Science and Math Program, Asian University for Women, Chattogram 4000, Bangladesh
| | - M Mofijur
- School of Information Systems and Modelling, Faculty of Engineering and Information Technology, University of Technology, Sydney, NSW 2007, Australia; Mechanical Engineering Department, Prince Mohammad Bin Fahd University, Al Khobar 31952, Saudi Arabia
| | - Samiha Nuzhat
- Environmental Sciences Program, Asian University for Women, Chattogram 4000, Bangladesh; Water and Life Bangladesh, Dhaka, Bangladesh
| | | | - Nazifa Rafa
- Environmental Sciences Program, Asian University for Women, Chattogram 4000, Bangladesh
| | - Md Alhaz Uddin
- Department of Civil Engineering, College of Engineering, Jouf University, Sakaka, Saudi Arabia
| | - Abrar Inayat
- Department of Sustainable and Renewable Energy Engineering, University of Sharjah, Sharjah 27272, United Arab Emirates; Biomass & Bioenergy Research Group, Center for Sustainable Energy and Power Systems Research, Research Institute of Sciences and Engineering, University of Sharjah, 27272 Sharjah, United Arab Emirates
| | - T M I Mahlia
- School of Information Systems and Modelling, Faculty of Engineering and Information Technology, University of Technology, Sydney, NSW 2007, Australia
| | - Hwai Chyuan Ong
- School of Information Systems and Modelling, Faculty of Engineering and Information Technology, University of Technology, Sydney, NSW 2007, Australia
| | - Wen Yi Chia
- Department of Chemical and Environmental Engineering, Faculty of Science and Engineering, University of Nottingham Malaysia, Jalan Broga, 43500 Semenyih, Selangor Darul Ehsan, Malaysia
| | - Pau Loke Show
- Department of Chemical and Environmental Engineering, Faculty of Science and Engineering, University of Nottingham Malaysia, Jalan Broga, 43500 Semenyih, Selangor Darul Ehsan, Malaysia.
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Vinoth Arul Raj J, Praveen Kumar R, Vijayakumar B, Gnansounou E, Bharathiraja B. Modelling and process optimization for biodiesel production from Nannochloropsis salina using artificial neural network. BIORESOURCE TECHNOLOGY 2021; 329:124872. [PMID: 33640695 DOI: 10.1016/j.biortech.2021.124872] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/02/2021] [Revised: 02/11/2021] [Accepted: 02/12/2021] [Indexed: 06/12/2023]
Abstract
In the present investigation, calcium oxide solid nanocatalyst derived from the egg shell and Nannochloropsis salina were used for the production of biodiesel. The morphological characteristics and functional groups of synthesized nanocatalyst was characterized by SEM and FTIR analysis. Process variables optimization for biodiesel production was studied using RSM and ANN. The R2 values for RSM and ANN was found to be 0.8751 and 0.957 which showed that the model was significantly fit with the experimental data. The maximum FAME conversion for the synthesized nanocatalyst CaO was found to be 86.1% under optimum process conditions (nanocatalyst amount: 3% (w/v); oil to methanol ratio 1:6 (v/v); reaction temperature: 60 °C; reaction time 55 min). Concentration of FAME present in biodiesel was identified by GC-MS analysis.
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Affiliation(s)
- J Vinoth Arul Raj
- Department of Biotechnology, Arunai Engineering College, Thiruvannaamalai 606603, India
| | - R Praveen Kumar
- Department of Biotechnology, Arunai Engineering College, Thiruvannaamalai 606603, India
| | - B Vijayakumar
- Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Chennai 600062, India
| | - Edgard Gnansounou
- Bioenergy and Energy Planning Research Group, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
| | - B Bharathiraja
- Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Chennai 600062, India.
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Recent Advances in the Prediction of Fouling in Membrane Bioreactors. MEMBRANES 2021; 11:membranes11060381. [PMID: 34073707 PMCID: PMC8225185 DOI: 10.3390/membranes11060381] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 05/14/2021] [Accepted: 05/15/2021] [Indexed: 11/17/2022]
Abstract
Compared to the traditional activated sludge process, the membrane bioreactor (MBR) has several advantages such as the production of high-quality effluent, generation of low excess sludge, smaller footprint requirements, and ease of automatic control of processes. The MBR has a broader prospect of its applications in wastewater treatment and reuse. However, membrane fouling is the biggest obstacle for its wider application. This paper reviews the techniques available to predict fouling in MBR, discusses the problems associated with predicting fouling status using artificial neural networks and mathematical models, summarizes the current state of fouling prediction techniques, and looks into the trends in their development.
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Wang J, Chen Z, Lang X, Wang S, Yang L, Wu X, Zhou X, Chen Z. Quantitative evaluation of infectious health care wastes from numbers of confirmed, suspected and out-patients during COVID-19 pandemic: A case study of Wuhan. WASTE MANAGEMENT (NEW YORK, N.Y.) 2021; 126:323-330. [PMID: 33794444 PMCID: PMC7983547 DOI: 10.1016/j.wasman.2021.03.026] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Revised: 02/07/2021] [Accepted: 03/16/2021] [Indexed: 05/16/2023]
Abstract
The fight against coronavirus disease 2019 (COVID-19) is still running its courses. Proper management and disposal of health care wastes (HCWs) are critical to win the fight. To achieve aforementioned tasks, prediction of their production is highly desired. In this study, primary data of production of three kinds of HCWs collected from Wuhan, the first epidemic epicenter worldwide and a mega city with more than 10 million population who has went through a lockdown period of 78 days, were reported for their first time. HCWs were classified into routine HCWs, infectious HCWs (IHCWs) and infectious municipal solid wastes. Among them, infectious HCWs from designated hospitals for COVID-19 were recognized as the most dangerous one. A multiple linear regression (MLR) model was built to predict the production of IHCWs with high significance. Numbers of patients were demonstrated high correlations with the production of IHCWs in an order of confirmed patients > out-patients > suspected patients. By the MLR model, production rates of IHCWs by confirmed, suspected and out patients were determined as 3.2, 1.8 and 0.1 kg/patient, respectively. In addition, constant production of IHCWs during the pandemic period was determined as 13 tons/d. This is the first study on quantitative evaluation of infectious HCWs during COVID-19 pandemic. The achievements in this study have potentials to shed light on global efforts to the prediction, management and disposal of vast HCWs generated in the war against COVID-19.
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Affiliation(s)
- Jia Wang
- School of Environmental Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, PR China
| | - Zhuqi Chen
- School of Environmental Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, PR China; Key Laboratory of Material Chemistry for Energy Conversion and Storage, Ministry of Education; Hubei Key Laboratory of Material Chemistry and Service Failure, School of Chemistry and Chemical Engineering, Huazhong University of Science and Technology, Wuhan 430074, PR China.
| | - Xiaojuan Lang
- School of Business Administration, Zhongnan University of Economics and Law, Wuhan 430074, PR China
| | - Songlin Wang
- School of Environmental Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, PR China
| | - Lie Yang
- Hubei Key Laboratory of Mineral Resources Processing and Environment, School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, PR China
| | - Xiaolong Wu
- Hubei Solid Waste and Chemical Pollution Prevention Center, Wuhan 430000, PR China
| | - Xinquan Zhou
- School of Environmental Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, PR China
| | - Zhulei Chen
- School of Environmental Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, PR China
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40
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A Review of Composting Process Models of Organic Solid Waste with a Focus on the Fates of C, N, P, and K. Processes (Basel) 2021. [DOI: 10.3390/pr9030473] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
To foster a circular economy in line with compost quality assessment, a deep understanding of the fates of nutrients and carbon in the composting process is essential to achieve the co-benefits of value-added and environmentally friendly objectives. This paper is a review aiming to fill in the knowledge gap about the composting process. Firstly, a systematic screening search and a descriptive analysis were conducted on composting models involving the fates of Carbon (C), Nitrogen (N), Phosphorus (P) and Potassium (K) over the past decade, followed by the development of a checklist to define the gap between the existing models and target models. A review of 22 models in total led to the results that the mainstream models involved the fates of C and N, while only a few models involved P and K as target variables. Most of the models described the laboratory-scale composting process. Mechanism-derived models were relatively complex; however, the application of the fractionation of substrates could contribute to reducing the complexity. Alternatively, data-driven models can help us obtain more accurate predictions and involve the fates of more nutrients, depending on the data volume. Finally, the perspective of developing composting models for the fates of C, N, P, and K was proposed.
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41
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Sharma D, Pandey AK, Yadav KD, Kumar S. Response surface methodology and artificial neural network modelling for enhancing maturity parameters during vermicomposting of floral waste. BIORESOURCE TECHNOLOGY 2021; 324:124672. [PMID: 33445008 DOI: 10.1016/j.biortech.2021.124672] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 01/01/2021] [Accepted: 01/02/2021] [Indexed: 06/12/2023]
Abstract
In this study, the mixture of floral waste and cattle dung in different proportions was utilised to convert into vermicompost using earthworm Eisenia fetida. In the design of the experiment, the optimum amount of floral waste (1325 g) and cattle dung (500 g) was obtained for vermicompost using central composite design (CCD) and compared with the output of artificial neural network (ANN). The optimum proportions of vermicompost showed pH of 7.10, electrical conductivity of 3.39 mS/cm, total organic carbon of 34.01%, C: N ratio of 13, phosphorous of 5.31 g/kg and potassium of 14.45 g/kg. This vermicompost was enriched with sufficient concentration of nutrients like potassium, sodium, phosphorous, and calcium, which are beneficial for the growth of the plants. The current study was based on comparing response surface methodology (RSM) and ANN for maturity parameters and the value of R2 in both the cases was near 1.
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Affiliation(s)
- Dayanand Sharma
- Civil Engineering Department, National Institute of Technology Patna, Ashok Rajpath, Mahendru, Patna, Bihar 800005, India
| | - Ashutosh Kumar Pandey
- CSIR-National Environmental Engineering Research Institute (NEERI), Nehru Marg, Nagpur 440 020, India
| | - Kunwar Durg Yadav
- Civil Engineering Department, S. V. National Institute of Technology, Surat, Gujarat 395 007, India
| | - Sunil Kumar
- CSIR-National Environmental Engineering Research Institute (NEERI), Nehru Marg, Nagpur 440 020, India.
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42
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Guo HN, Wu SB, Tian YJ, Zhang J, Liu HT. Application of machine learning methods for the prediction of organic solid waste treatment and recycling processes: A review. BIORESOURCE TECHNOLOGY 2021; 319:124114. [PMID: 32942236 DOI: 10.1016/j.biortech.2020.124114] [Citation(s) in RCA: 81] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 09/04/2020] [Accepted: 09/07/2020] [Indexed: 05/23/2023]
Abstract
Conventional treatment and recycling methods of organic solid waste contain inherent flaws, such as low efficiency, low accuracy, high cost, and potential environmental risks. In the past decade, machine learning has gradually attracted increasing attention in solving the complex problems of organic solid waste treatment. Although significant research has been carried out, there is a lack of a systematic review of the research findings in this field. This study sorts the research studies published between 2003 and 2020, summarizes the specific application fields, characteristics, and suitability of different machine learning models, and discusses the relevant application limitations and future prospects. It can be concluded that studies mostly focused on municipal solid waste management, followed by anaerobic digestion, thermal treatment, composting, and landfill. The most widely used model is the artificial neural network, which has been successfully applied to various complicated non-linear organic solid waste related problems.
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Affiliation(s)
- Hao-Nan Guo
- Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Shu-Biao Wu
- Aarhus Institute of Advanced Studies, Aarhus University, DK-8000 Aarhus C, Denmark
| | - Ying-Jie Tian
- CAS Research Center on Fictitious Economy & Data Science, Beijing 100190, China
| | - Jun Zhang
- Guangxi Key Laboratory of Environmental Pollution Control Theory and Technology, Guilin University of Technology, Guilin 541004, China
| | - Hong-Tao Liu
- Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; Engineering Laboratory for Yellow River Delta Modern Agriculture, Chinese Academy of Sciences, Beijing 100101, China.
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43
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Hosseinzadeh A, Zhou JL, Altaee A, Baziar M, Li D. Effective modelling of hydrogen and energy recovery in microbial electrolysis cell by artificial neural network and adaptive network-based fuzzy inference system. BIORESOURCE TECHNOLOGY 2020; 316:123967. [PMID: 32777721 DOI: 10.1016/j.biortech.2020.123967] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Revised: 07/29/2020] [Accepted: 08/02/2020] [Indexed: 06/11/2023]
Abstract
This study aims to analyze and model cathodic H2 recovery (rcat), coulombic efficiency (CE) with inputs of voltage, electrical conductivity (EC) and anode potential, and H2 production rate and total energy recovery with inputs of rcat and CE in a microbial electrolysis cell using artificial neural network (ANN) and adaptive network-based fuzzy inference system (ANFIS) procedures. Both ANN and ANFIS models demonstrated great goodness of fit for rcat, CE, H2 production rate and total energy recovery prediction with high R2 values. The sum square error values for rcat (0.0017), CE (0.0163), H2 production rate (0.1062) and total energy recovery (0.0136) in ANN models were slightly higher than those in ANFIS models at 0.0005, 0.0091, 0.1247 and 0.0148 respectively. Sensitivity analysis by ANN models demonstrated that voltage, EC, rcat and rcat were the most effective factors for rcat, CE, H2 production rate and total energy recovery, respectively.
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Affiliation(s)
- Ahmad Hosseinzadeh
- Centre for Green Technology, School of Civil and Environmental Engineering, University of Technology Sydney, NSW 2007, Australia
| | - John L Zhou
- Centre for Green Technology, School of Civil and Environmental Engineering, University of Technology Sydney, NSW 2007, Australia.
| | - Ali Altaee
- Centre for Green Technology, School of Civil and Environmental Engineering, University of Technology Sydney, NSW 2007, Australia
| | - Mansour Baziar
- Ferdows School of Paramedical and Health, Birjand University of Medical Sciences, Birjand, Iran
| | - Donghao Li
- Department of Chemistry, MOE Key Laboratory of Biological Resources of Changbai Mountain & Functional Molecules, Yanbian University, Yanji 133002, Jilin Province, PR China
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44
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Sun FS, Yu GH, Ning JY, Zhu XD, Goodman BA, Wu J. Biological removal of cadmium from biogas residues during vermicomposting, and the effect of earthworm hydrolysates on Trichoderma guizhouense sporulation. BIORESOURCE TECHNOLOGY 2020; 312:123635. [PMID: 32531739 DOI: 10.1016/j.biortech.2020.123635] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Revised: 06/01/2020] [Accepted: 06/02/2020] [Indexed: 06/11/2023]
Abstract
The removal efficiency of Cd from biogas residues (BR) by earthworms (Eisenia fetida) during vermicomposting and the optimum addition of earthworm hydrolysates for production of Trichoderma guizhouense NJAU 4742 spores were determined. The results showed that vermicomposting could effectively remove Cd (up to 18.9%) from the BR. Synchrotron radiation based FTIR spectromicroscopy demonstrated a weakened correlation between functional groups after vermicomposting, suggesting that the activity of earthworms affects the binding sites and bioavailability of heavy metals. Under optimum conditions, the hydrolysis rate of earthworms was ~97% and the removal efficiency of Cd was up to 93%. Furthermore, addition of 20% of earthworm hydrolysate promoted the largest production of Trichoderma sporulation (~2.95 × 108 cfu/g straw), indicating the possibility of earthworm hydrolysates promoting the growth of Trichoderma guizhouense is a suitable way to recycle earthworms after vermicomposting.
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Affiliation(s)
- Fu-Sheng Sun
- Institute of Surface-Earth System Science, School of Earth System Science, Tianjin University, Tianjin 300072, China
| | - Guang-Hui Yu
- Institute of Surface-Earth System Science, School of Earth System Science, Tianjin University, Tianjin 300072, China; Jiangsu Provincial Key Lab for Organic Solid Waste Utilization, College of Resource & Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, China; Key Laboratory of Microbial Technology for Industrial Pollution Control of Zhejiang Province, College of Environment, Zhejiang University of Technology, Hangzhou 310014, China.
| | - Jing-Yuan Ning
- Jiangsu Provincial Key Lab for Organic Solid Waste Utilization, College of Resource & Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, China
| | - Xiao-Dong Zhu
- Jiangsu Provincial Key Lab for Organic Solid Waste Utilization, College of Resource & Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, China
| | - Bernard A Goodman
- College of Physical Science and Technology, Guangxi University, Nanning 530004, China
| | - Jun Wu
- Key Laboratory of Microbial Technology for Industrial Pollution Control of Zhejiang Province, College of Environment, Zhejiang University of Technology, Hangzhou 310014, China.
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45
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Rath S, Tripathy A, Tripathy AR. Prediction of new active cases of coronavirus disease (COVID-19) pandemic using multiple linear regression model. Diabetes Metab Syndr 2020; 14:1467-1474. [PMID: 32771920 PMCID: PMC7395225 DOI: 10.1016/j.dsx.2020.07.045] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Revised: 07/26/2020] [Accepted: 07/28/2020] [Indexed: 11/18/2022]
Abstract
INTRODUCTION AND AIMS The COVID-19 pandemic originated from the city of Wuhan of China has highly affected the health, socio-economic and financial matters of the different countries of the world. India is one of the countries which is affected by the disease and thousands of people on daily basis are getting infected. In this paper, an analysis of daily statistics of people affected by the disease are taken into account to predict the next days trend in the active cases in Odisha as well as India. MATERIAL AND METHODS A valid global data set is collected from the WHO daily statistics and correlation among the total confirmed, active, deceased, positive cases are stated in this paper. Regression model such as Linear and Multiple Linear Regression techniques are applied to the data set to visualize the trend of the affected cases. RESULTS Here a comparison of Linear Regression and Multiple Linear Regression model is performed where the score of the model R2tends to be 0.99 and 1.0 which indicates a strong prediction model to forecast the next coming days active cases. Using the Multiple Linear Regression model as on July month, the forecast value of 52,290 active cases are predicted towards the next month of 15th August in India and 9,358 active cases in Odisha if situation continues like this way. CONCLUSION: These models acquired remarkable accuracy in COVID-19 recognition. A strong correlation factor determines the relationship among the dependent (active) with the independent variables (positive, deceased, recovered).
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Affiliation(s)
- Smita Rath
- Department of Computer Science and Engineering, Siksha 'O' Anusandhan Deemed to be University, Odisha, India.
| | - Alakananda Tripathy
- Department of Computer Science and Engineering, Siksha 'O' Anusandhan Deemed to be University, Odisha, India.
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46
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Zhang AH, Zhu KY, Zhuang XY, Liao LX, Huang SY, Yao CY, Fang BS. A robust soft sensor to monitor 1,3-propanediol fermentation process by Clostridium butyricum based on artificial neural network. Biotechnol Bioeng 2020; 117:3345-3355. [PMID: 32678455 DOI: 10.1002/bit.27507] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Accepted: 07/15/2020] [Indexed: 02/02/2023]
Abstract
With the aggravation of environmental pollution and energy crisis, the sustainable microbial fermentation process of converting glycerol to 1,3-propanediol (1,3-PDO) has become an attractive alternative. However, the difficulty in the online measurement of glycerol and 1,3-PDO creates a barrier to the fermentation process and then leads to the residual glycerol and therefore, its wastage. Thus, in the present study, the four-input artificial neural network (ANN) model was developed successfully to predict the concentration of glycerol, 1,3-PDO, and biomass with high accuracy. Moreover, an ANN model combined with a kinetic model was also successfully developed to simulate the fed-batch fermentation process accurately. Hence, a soft sensor from the ANN model based on NaOH-related parameters has been successfully developed which cannot only be applied in software to solve the difficulty of glycerol and 1,3-PDO online measurement during the industrialization process, but also offer insight and reference for similar fermentation processes.
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Affiliation(s)
- Ai-Hui Zhang
- Department of Chemical and Biochemical Engineering, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, Fujian, China
| | - Kai-Yi Zhu
- Department of Chemical and Biochemical Engineering, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, Fujian, China
| | - Xiao-Yan Zhuang
- Department of Chemical and Biochemical Engineering, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, Fujian, China
| | - Lang-Xing Liao
- Department of Chemical and Biochemical Engineering, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, Fujian, China
| | - Shi-Yang Huang
- Department of Chemical and Biochemical Engineering, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, Fujian, China
| | - Chuan-Yi Yao
- Department of Chemical and Biochemical Engineering, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, Fujian, China.,The Key Lab for Synthetic Biotechnology of Xiamen City, Xiamen University, Xiamen, Fujian, China
| | - Bai-Shan Fang
- Department of Chemical and Biochemical Engineering, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, Fujian, China.,The Key Lab for Synthetic Biotechnology of Xiamen City, Xiamen University, Xiamen, Fujian, China.,The Key Laboratory for Chemical Biology of Fujian Province, Xiamen University, Xiamen, Fujian, China.,The National Engineering Laboratory for Green Chemical Productions of Alcohols-Ethers-Esters, Xiamen University, Xiamen, Fujian, China
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47
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Hosseinzadeh A, Zhou JL, Altaee A, Baziar M, Li X. Modeling water flux in osmotic membrane bioreactor by adaptive network-based fuzzy inference system and artificial neural network. BIORESOURCE TECHNOLOGY 2020; 310:123391. [PMID: 32344239 DOI: 10.1016/j.biortech.2020.123391] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Revised: 04/11/2020] [Accepted: 04/13/2020] [Indexed: 06/11/2023]
Abstract
Osmotic Membrane Bioreactor (OMBR) is an emerging technology for wastewater treatment with membrane fouling as a major challenge. This study aims to develop Adaptive Network-based Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) models in simulating and predicting water flux in OMBR. Mixed liquor suspended solid (MLSS), electrical conductivity (EC) and dissolved oxygen (DO) were used as model inputs. Good prediction was demonstrated by both ANFIS models with R2 of 0.9755 and 0.9861, and ANN models with R2 of 0.9404 and 0.9817, for thin film composite (TFC) and cellulose triacetate (CTA) membranes, respectively. The root mean square error for TFC (0.2527) and CTA (0.1230) in ANFIS models was lower than in ANN models at 0.4049 and 0.1449. Sensitivity analysis showed that EC was the most important factor for both TFC and CTA membranes in ANN models, while EC (TFC) and MLSS (CTA) are key parameters in ANFIS models.
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Affiliation(s)
- Ahmad Hosseinzadeh
- Centre for Green Technology, School of Civil and Environmental Engineering, University of Technology Sydney, NSW 2007, Australia
| | - John L Zhou
- Centre for Green Technology, School of Civil and Environmental Engineering, University of Technology Sydney, NSW 2007, Australia.
| | - Ali Altaee
- Centre for Green Technology, School of Civil and Environmental Engineering, University of Technology Sydney, NSW 2007, Australia
| | - Mansour Baziar
- Ferdows School of Paramedical and Health, Birjand University of Medical Sciences, Birjand, Iran
| | - Xiaowei Li
- School of Environmental and Chemical Engineering, Organic Compound Pollution Control Engineering, Ministry of Education, Institute for the Conservation of Cultural Heritage, Shanghai University, Shanghai 200444, PR China
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