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Na I, Kim T, Qiu P, Son Y. Machine learning model to predict rate constants for sonochemical degradation of organic pollutants. ULTRASONICS SONOCHEMISTRY 2024; 110:107032. [PMID: 39178555 PMCID: PMC11386492 DOI: 10.1016/j.ultsonch.2024.107032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2024] [Revised: 08/02/2024] [Accepted: 08/15/2024] [Indexed: 08/26/2024]
Abstract
In this study, machine learning (ML) algorithms were employed to predict the pseudo-1st-order reaction rate constants for the sonochemical degradation of aqueous organic pollutants under various conditions. A total of 618 sets of data, including ultrasonic, solution, and pollutant characteristics, were collected from 89 previous studies. Considering the difference between the electrical power (Pele) and calorimetric power (Pcal), the collected data were divided into two groups: data with Pele and data with Pcal. Eight input variables, including frequency, power density, pH, temperature, initial concentration, solubility, vapor pressure, and octanol-water partition coefficient (Kow), and one target variable of the degradation rate constant, were selected for ML. Statistical analysis was conducted, and outliers were determined separately for the two groups. ML models, including random forest (RF), extreme gradient boosting (XGB), and light gradient boosting machine (LGB), were used to predict the pseudo-1st-order reaction rate constants for the removal of aqueous pollutants. The prediction performance of the ML models was evaluated using different metrics, including the root mean squared error (RMSE), mean absolute error (MAE), and R squared (R2). A significantly higher prediction performance was obtained using data without outliers and augmented data. Consequently, all the applied ML models could be used to predict the sonochemical degradation of aqueous pollutants, and the XGB model showed the highest accuracy in predicting the rate constants. In addition, the power density and frequency were the most influential factors among the eight input variables in prediction with the Shapley additive explanation (SHAP) values method. The degradation rate constants of the two pollutants over a wide frequency range (20-1,000 kHz) were predicted using the trained ML model (XGB) and the prediction results were analyzed.
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Affiliation(s)
- Iseul Na
- Department of Environmental Engineering, Kumoh National Institute of Technology, Gumi 39177, Republic of Korea; Department of Energy Engineering Convergence, Kumoh National Institute of Technology, Gumi 39177, Republic of Korea
| | - Taeho Kim
- AI Lab, ROSIS IT, Seoul 07547, Republic of Korea
| | - Pengpeng Qiu
- State Key Laboratory for Modification of Chemical Fibers and Polymer Materials, Institute of Functional Materials, College of Materials Science and Engineering, Donghua University, Shanghai 201620, PR China
| | - Younggyu Son
- Department of Environmental Engineering, Kumoh National Institute of Technology, Gumi 39177, Republic of Korea; Department of Energy Engineering Convergence, Kumoh National Institute of Technology, Gumi 39177, Republic of Korea.
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2
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Lyu H, Xu Z, Zhong J, Gao W, Liu J, Duan M. Machine learning-driven prediction of phosphorus adsorption capacity of biochar: Insights for adsorbent design and process optimization. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 369:122405. [PMID: 39236616 DOI: 10.1016/j.jenvman.2024.122405] [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: 06/03/2024] [Revised: 08/14/2024] [Accepted: 08/31/2024] [Indexed: 09/07/2024]
Abstract
Phosphorus (P) pollution in aquatic environments poses significant environmental challenges, necessitating the development of effective remediation strategies, and biochar has emerged as a promising adsorbent for P removal at the cost of extensive research resources worldwide. In this study, a machine learning approach was proposed to simulate and predict the performance of biochar in removing P from water. A dataset consisting of 190 types of biochar was compiled from literature, encompassing various variables including biochar characteristics, water quality parameters, and operating conditions. Subsequently, the random forest and CatBoost algorithms were fine-tuned to establish a predictive model for P adsorption capacity. The results demonstrated that the optimized CatBoost model exhibited high prediction accuracy with an R2 value of 0.9573, and biochar dosage, initial P concentration in water, and C content in biochar were identified as the predominant factors. Furthermore, partial dependence analysis was employed to examine the impact of individual variables and interactions between two features, providing valuable insights for adsorbent design and operating condition optimization. This work presented a comprehensive framework for applying a machine learning approach to address environmental issues and provided a valuable tool for advancing the design and implementation of biochar-based water treatment systems.
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Affiliation(s)
- Huafei Lyu
- Key Laboratory of Breeding Biotechnology and Sustainable Aquaculture, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, 430072, China
| | - Ziming Xu
- School of Environmental Engineering, Wuhan Textile University, Wuhan, 430073, China
| | - Jian Zhong
- School of Environmental Engineering, Wuhan Textile University, Wuhan, 430073, China
| | - Wenhao Gao
- School of Environmental Engineering, Wuhan Textile University, Wuhan, 430073, China
| | - Jingxin Liu
- School of Environmental Engineering, Wuhan Textile University, Wuhan, 430073, China; Engineering Research Centre for Clean Production of Textile Dyeing and Printing, Ministry of Education, Wuhan Textile University, Wuhan, 430073, China.
| | - Ming Duan
- Key Laboratory of Breeding Biotechnology and Sustainable Aquaculture, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, 430072, China.
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Behera SK, Karthika S, Mahanty B, Meher SK, Zafar M, Baskaran D, Rajamanickam R, Das R, Pakshirajan K, Bilyaminu AM, Rene ER. Application of artificial intelligence tools in wastewater and waste gas treatment systems: Recent advances and prospects. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 370:122386. [PMID: 39260284 DOI: 10.1016/j.jenvman.2024.122386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/13/2024] [Revised: 08/17/2024] [Accepted: 08/31/2024] [Indexed: 09/13/2024]
Abstract
The non-linear complex relationships among the process variables in wastewater and waste gas treatment systems possess a significant challenge for real-time systems modelling. Data driven artificial intelligence (AI) tools are increasingly being adopted to predict the process performance, cost-effective process monitoring, and the control of different waste treatment systems, including those involving resource recovery. This review presents an in-depth analysis of the applications of emerging AI tools in physico-chemical and biological processes for the treatment of air pollutants, water and wastewater, and resource recovery processes. Additionally, the successful implementation of AI-controlled wastewater and waste gas treatment systems, along with real-time monitoring at the industrial scale are discussed.
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Affiliation(s)
- Shishir Kumar Behera
- Process Simulation Research Group, School of Chemical Engineering, Vellore Institute of Technology, Vellore, 632 014, Tamil Nadu, India.
| | - S Karthika
- Department of Chemical Engineering, Alagappa College of Technology, Anna University, Chennai, 600 025, Tamil Nadu, India
| | - Biswanath Mahanty
- Division of Biotechnology, Karunya Institute of Technology & Sciences, Coimbatore, 641 114, Tamil Nadu, India
| | - Saroj K Meher
- Systems Science and Informatics Unit, Indian Statistical Institute, Bangalore, 560059, India
| | - Mohd Zafar
- Department of Applied Biotechnology, College of Applied Sciences & Pharmacy, University of Technology and Applied Sciences - Sur, P.O. Box: 484, Zip Code: 411, Sur, Oman
| | - Divya Baskaran
- Department of Chemical and Biomolecular Engineering, Chonnam National University, Yeosu, Jeonnam, 59626, South Korea; Department of Biomaterials, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Chennai, 600 077, Tamil Nadu, India
| | - Ravi Rajamanickam
- Department of Chemical Engineering, Annamalai University, Chidambaram, 608002, Tamil Nadu, India
| | - Raja Das
- Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Vellore, 632 014, Tamil Nadu, India
| | - Kannan Pakshirajan
- Department of Biosciences and Bioengineering, Indian Institute of Technology Guwahati, Guwahati, 781 039, Assam, India
| | - Abubakar M Bilyaminu
- Department of Water Supply, Sanitation and Environmental Engineering, IHE Delft Institute for Water Education, P. O. Box 3015, 2601, DA Delft, the Netherlands
| | - Eldon R Rene
- Department of Water Supply, Sanitation and Environmental Engineering, IHE Delft Institute for Water Education, P. O. Box 3015, 2601, DA Delft, the Netherlands
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Wang K, Liu L, Ben X, Jin D, Zhu Y, Wang F. Hybrid deep learning based prediction for water quality of plain watershed. ENVIRONMENTAL RESEARCH 2024; 262:119911. [PMID: 39233036 DOI: 10.1016/j.envres.2024.119911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2024] [Revised: 08/30/2024] [Accepted: 08/31/2024] [Indexed: 09/06/2024]
Abstract
Establishing a highly reliable and accurate water quality prediction model is critical for effective water environment management. However, enhancing the performance of these predictive models continues to pose challenges, especially in the plain watershed with complex hydraulic conditions. This study aims to evaluate the efficacy of three traditional machine learning models versus three deep learning models in predicting the water quality of plain river networks and to develop a novel hybrid deep learning model to further improve prediction accuracy. The performance of the proposed model was assessed under various input feature sets and data temporal frequencies. The findings indicated that deep learning models outperformed traditional machine learning models in handling complex time series data. Long Short-Term Memory (LSTM) models improved the R2 by approximately 29% and lowered the Root Mean Square Error (RMSE) by about 48.6% on average. The hybrid Bayes-LSTM-GRU (Gated Recurrent Unit) model significantly enhanced prediction accuracy, reducing the average RMSE by 18.1% compared to the single LSTM model. Models trained on feature-selected datasets exhibited superior performance compared to those trained on original datasets. Higher temporal frequencies of input data generally provide more useful information. However, in datasets with numerous abrupt changes, increasing the temporal interval proves beneficial. Overall, the proposed hybrid deep learning model demonstrates an efficient and cost-effective method for improving water quality prediction performance, showing significant potential for application in managing water quality in plain watershed.
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Affiliation(s)
- Kefan Wang
- College of Environmental & Resource Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Lei Liu
- College of Environmental & Resource Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Xuechen Ben
- Zhejiang Zone-King Environmental Sci&Tech Co. Ltd., Hangzhou, 310064, China
| | - Danjun Jin
- Zhejiang Zone-King Environmental Sci&Tech Co. Ltd., Hangzhou, 310064, China
| | - Yao Zhu
- Taizhou Ecology and Environment Bureau Wenling Branch, Wenling, Zhejiang, 317599, China
| | - Feier Wang
- College of Environmental & Resource Sciences, Zhejiang University, Hangzhou, 310058, China; Zhejiang Ecological Civilization Academy, Anji, Zhejiang, 313300, China.
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Sun S, Ren Y, Zhou Y, Guo F, Choi J, Cui M, Khim J. Prediction of micropollutant degradation kinetic constant by ultrasonic using machine learning. CHEMOSPHERE 2024; 363:142701. [PMID: 38925516 DOI: 10.1016/j.chemosphere.2024.142701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/24/2024] [Revised: 06/20/2024] [Accepted: 06/23/2024] [Indexed: 06/28/2024]
Abstract
A prediction model based on XGBoost is proposed for ultrasonic degradation of micropollutants' kinetic constants. After parameter optimization through iteration, the model achieves Evaluation metrics with R2 and SMAPE reaching 0.99 and 2.06%, respectively. The impact of design parameters on predicting kinetic constants for ultrasound degradation of trace pollutants was assessed using Shapley additive explanations (SHAP). Results indicate that power density and frequency significantly impact the predictive performance. The database was sorted based on power density and frequency values. Subsequently, 800 raw data were split into small databases of 200 each. After confirming that reducing the database size doesn't affect prediction accuracy, ultrasound degradation experiments were conducted for five pollutants, yielding experimental data. A small database with experimental conditions within the numerical range was selected. Data meeting both feature conditions were filtered, resulting in an optimized 60-data group. After incorporating experimental data, a model was trained for prediction. Degradation kinetic constants for experiments (kE) were compared with predicted constants (for 800 data-based model: kP-800 and for 60 data-based model: kP-60). Results showed ibuprofen, bisphenol A, carbamazepine, and 17β-Estradiol performed better on the 60-data group (kP-60/kE: 1.00, 0.99, 1.00, 1.00), while caffeine suited the model trained on the 800-data group (kP-800/kE: 1.02).
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Affiliation(s)
- Shiyu Sun
- School of Civil, Environmental, and Architectural Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Yangmin Ren
- School of Civil, Environmental, and Architectural Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Yongyue Zhou
- School of Civil, Environmental, and Architectural Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Fengshi Guo
- School of Civil, Environmental, and Architectural Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Jongbok Choi
- Department of Environmental Engineering, Kumoh National Institute of Technology, Gumi, 39177, Republic of Korea
| | - Mingcan Cui
- School of Civil, Environmental, and Architectural Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea.
| | - Jeehyeong Khim
- School of Civil, Environmental, and Architectural Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea.
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Baskar G, Nashath Omer S, Saravanan P, Rajeshkannan R, Saravanan V, Rajasimman M, Shanmugam V. Status and future trends in wastewater management strategies using artificial intelligence and machine learning techniques. CHEMOSPHERE 2024; 362:142477. [PMID: 38844107 DOI: 10.1016/j.chemosphere.2024.142477] [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: 01/23/2024] [Revised: 04/24/2024] [Accepted: 05/27/2024] [Indexed: 06/27/2024]
Abstract
The two main things needed to fulfill the world's impending need for water in the face of the widespread water crisis are collecting water and recycling. To do this, the present study has placed a greater focus on water management strategies used in a variety of contexts areas. To distribute water effectively, save it, and satisfy water quality requirements for a variety of uses, it is imperative to apply intelligent water management mechanisms while keeping in mind the population density index. The present review unveiled the latest trends in water and wastewater recycling, utilizing several Artificial Intelligence (AI) and machine learning (ML) techniques for distribution, rainfall collection, and control of irrigation models. The data collected for these purposes are unique and comes in different forms. An efficient water management system could be developed with the use of AI, Deep Learning (DL), and the Internet of Things (IoT) structure. This study has investigated several water management methodologies using AI, DL and IoT with case studies and sample statistical assessment, to provide an efficient framework for water management.
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Affiliation(s)
- Gurunathan Baskar
- Department of Biotechnology, St. Joseph's College of Engineering, Chennai, 600119. India; School of Engineering, Lebanese American University, Byblos, 1102 2801, Lebanon.
| | - Soghra Nashath Omer
- School of Bio-Sciences and Technology, Vellore Institute of Technology, Vellore, 632014, Tamil Nadu, India
| | - Panchamoorthy Saravanan
- Department of Petrochemical Technology, UCE - BIT Campus, Anna University, Tiruchirappalli, Tamil Nadu, 620024, India
| | - R Rajeshkannan
- Department of Chemical Engineering, Annamalai University, Chidambaram, Tamil Nadu, 608002, India
| | - V Saravanan
- Department of Chemical Engineering, Annamalai University, Chidambaram, Tamil Nadu, 608002, India
| | - M Rajasimman
- Department of Chemical Engineering, Annamalai University, Chidambaram, Tamil Nadu, 608002, India
| | - Venkatkumar Shanmugam
- School of Bio-Sciences and Technology, Vellore Institute of Technology, Vellore, 632014, Tamil Nadu, India.
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Hou J, Wang L, Wang J, Chen L, Han B, Li Y, Yu L, Liu W. A comprehensive evaluation of influencing factors of neonicotinoid insecticides (NEOs) in farmland soils across China: First focus on film mulching. JOURNAL OF HAZARDOUS MATERIALS 2024; 470:134284. [PMID: 38615648 DOI: 10.1016/j.jhazmat.2024.134284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 04/04/2024] [Accepted: 04/10/2024] [Indexed: 04/16/2024]
Abstract
Neonicotinoid insecticide (NEO) residues in agricultural soils have concerning and adverse effects on agroecosystems. Previous studies on the effects of farmland type on NEOs are limited to comparing greenhouses with open fields. On the other hand, both NEOs and microplastics (MPs) are commonly found in agricultural fields, but their co-occurrence characteristics under realistic fields have not been reported. This study grouped farmlands into three types according to the covering degree of the film, collected 391 soil samples in mainland China, and found significant differences in NEO residues in the soils of the three different farmlands, with greenhouse having the highest NEO residue, followed by farmland with film mulching and farmland without film mulching (both open fields). Furthermore, this study found that MPs were significantly and positively correlated with NEOs. As far as we know this is the first report to disclose the association of film mulching and MPs with NEOs under realistic fields. Moreover, multiple linear regression and random forest models were used to comprehensively evaluate the factors influencing NEOs (including climatic, soil, and agricultural indicators). The results indicated that the random forest model was more reliable, with MPs, farmland type, and total nitrogen having higher relative contributions.
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Affiliation(s)
- Jie Hou
- Key Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - LiXi Wang
- Key Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - JinZe Wang
- Key Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - LiYuan Chen
- Key Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China; Co-Innovation Center for Sustainable Forestry in Southern China, College of Ecology and Environment, Nanjing Forestry University, Nanjing 210037, China.
| | - BingJun Han
- Key Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - YuJun Li
- Key Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Lu Yu
- Key Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - WenXin Liu
- Key Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China.
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Anandhi G, Iyapparaja M. Photocatalytic degradation of drugs and dyes using a maching learning approach. RSC Adv 2024; 14:9003-9019. [PMID: 38500628 PMCID: PMC10945304 DOI: 10.1039/d4ra00711e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2024] [Accepted: 03/02/2024] [Indexed: 03/20/2024] Open
Abstract
The waste management industry uses an increasing number of mathematical prediction models to accurately forecast the behavior of organic pollutants during catalytic degradation. With the increasing quantity of waste generated, these models are critical for reinforcing the efficiency of wastewater treatment strategies. The application of machine-learning techniques in recent years has notably improved predictive models for waste management, which are essential for mitigating the impact of toxic commercial waste on global water supply. Organic contaminants, dyes, pesticides, surfactants, petroleum by-products, and prescription drugs pose risks to human health. Because traditional techniques face challenges in ensuring water quality, modern strategies are vital. Machine learning has emerged as a valuable tool for predicting the photocatalytic degradation of medicinal drugs and dyes, providing a promising avenue for addressing urgent demands in removing organic pollutants from wastewater. This research investigates the synergistic application of photocatalysis and machine learning for pollutant degradation, showcasing a sustainable solution with promising effects on environmental remediation and computational efficiency. This study contributes to green chemistry by providing a clever framework for addressing present-day water pollution challenges and achieving era-driven answers.
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Affiliation(s)
- Ganesan Anandhi
- Department of Smart Computing, School of Computer Science Engineering and Information Systems, Vellore Institute of Technology Vellore 632014 Tamil Nadu India
| | - M Iyapparaja
- Department of Smart Computing, School of Computer Science Engineering and Information Systems, Vellore Institute of Technology Vellore 632014 Tamil Nadu India
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Alfarra F, Ozcan HK, Cihan P, Ongen A, Guvenc SY, Ciner MN. Artificial intelligence methods for modeling gasification of waste biomass: a review. ENVIRONMENTAL MONITORING AND ASSESSMENT 2024; 196:309. [PMID: 38407668 DOI: 10.1007/s10661-024-12443-2] [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: 04/06/2023] [Accepted: 02/12/2024] [Indexed: 02/27/2024]
Abstract
Gasification is a highly promising thermochemical process that shows considerable potential for the efficient conversion of waste biomass into syngas. The assessment of the feasibility and comparative advantages of different biomass and waste gasification schemes is contingent upon a multifaceted combination of interrelated criteria. Conventional analytical approaches employed to facilitate decision-making rely on a multitude of inadequately defined parameters. Consequently, substantial efforts have been directed toward enhancing the efficiency and productivity of thermochemical conversion processes. In recent times, artificial intelligence (AI)-based models and algorithms have gained prominence, serving as indispensable tools for expediting these processes and formulating strategies to address the growing demand for energy. Notably, machine learning (ML) and deep learning (DL) have emerged as cutting-edge AI models, demonstrating exceptional effectiveness and profound relevance in the realm of thermochemical conversion systems. This study provides an overview of the machine learning (ML) and deep learning (DL) approaches utilized during gasification and evaluates their benefits and drawbacks. Many industries and applications related to energy conversion systems use AI algorithms. Predicting the output of conversion systems and subjects linked to optimization are two of this science's critical applications. This review sheds light on the burgeoning utility of AI, particularly ML and DL, which have garnered significant attention due to their applications in productivity prediction, process optimization, real-time process monitoring, and control. Furthermore, the integration of hybrid models has become commonplace, primarily owing to their demonstrated success in modeling and optimization tasks. Importantly, the adoption of these algorithms significantly enhances the model's capability to tackle intricate challenges, as DL methodologies have evolved to offer heightened accuracy and reduced susceptibility to errors. Within the scope of this study, an exhaustive exploration of ML and DL techniques and their applications has been conducted, uncovering existing research knowledge gaps. Based on a comprehensive critical analysis, this review offers recommendations for future research directions, accentuating the pivotal findings and conclusions derived from the study.
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Affiliation(s)
- Fatma Alfarra
- Engineering Faculty, Department of Environmental Engineering, Istanbul University-Cerrahpasa, 34320, Avcilar, Istanbul, Turkey.
| | - H Kurtulus Ozcan
- Engineering Faculty, Department of Environmental Engineering, Istanbul University-Cerrahpasa, 34320, Avcilar, Istanbul, Turkey
| | - Pınar Cihan
- Corlu Engineering Faculty, Department of Computer Engineering, Tekirdag Namık Kemal Universtiy, 59860, Çorlu, Tekirdag, Turkey
| | - Atakan Ongen
- Engineering Faculty, Department of Environmental Engineering, Istanbul University-Cerrahpasa, 34320, Avcilar, Istanbul, Turkey
| | - Senem Yazici Guvenc
- Department of Environmental Engineering, Faculty of Civil Engineering, Yildiz Technical University, Davutpasa Campus, 34220, Istanbul, Turkey
| | - Mirac Nur Ciner
- Engineering Faculty, Department of Environmental Engineering, Istanbul University-Cerrahpasa, 34320, Avcilar, Istanbul, Turkey
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Mathaba M, Banza J. A comprehensive review on artificial intelligence in water treatment for optimization. Clean water now and the future. JOURNAL OF ENVIRONMENTAL SCIENCE AND HEALTH. PART A, TOXIC/HAZARDOUS SUBSTANCES & ENVIRONMENTAL ENGINEERING 2024; 58:1047-1060. [PMID: 38293764 DOI: 10.1080/10934529.2024.2309102] [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: 12/06/2023] [Accepted: 01/13/2024] [Indexed: 02/01/2024]
Abstract
Given the severe effects that toxic compounds present in wastewater streams have on humans, it is imperative that water and wastewater streams pollution be addressed globally. This review comprehensively examines various water and wastewater treatment methods and water quality management methods based on artificial intelligence (AI). Machine learning (ML) and AI have become a powerful tool for addressing problems in the real world and has gained a lot of interest since it can be used for a wide range of activities. The foundation of ML techniques involves training of a network with collected data, followed by application of learned network to the process simulation and prediction. The creation of ML models for process simulations requires measured data. In order to forecast and simulate chemical and physical processes such chemical reactions, heat transfer, mass transfer, energy, pharmaceutics and separation, a variety of machine-learning algorithms have recently been developed. These models have shown to be more adept at simulating and modeling processes than traditional models. Although AI offers many advantages, a number of disadvantages have kept these methods from being extensively applied in actual water treatment systems. Lack of evidence of application in actual water treatment scenarios, poor repeatability and data availability and selection are a few of the main problems that need to be resolved.
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Affiliation(s)
- Machodi Mathaba
- Department of Chemical Engineering, Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg, South Africa
| | - JeanClaude Banza
- Department of Chemical Engineering, Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg, South Africa
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Zhang Y, Wang Y. Recent trends of machine learning applied to multi-source data of medicinal plants. J Pharm Anal 2023; 13:1388-1407. [PMID: 38223450 PMCID: PMC10785154 DOI: 10.1016/j.jpha.2023.07.012] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 07/17/2023] [Accepted: 07/19/2023] [Indexed: 01/16/2024] Open
Abstract
In traditional medicine and ethnomedicine, medicinal plants have long been recognized as the basis for materials in therapeutic applications worldwide. In particular, the remarkable curative effect of traditional Chinese medicine during corona virus disease 2019 (COVID-19) pandemic has attracted extensive attention globally. Medicinal plants have, therefore, become increasingly popular among the public. However, with increasing demand for and profit with medicinal plants, commercial fraudulent events such as adulteration or counterfeits sometimes occur, which poses a serious threat to the clinical outcomes and interests of consumers. With rapid advances in artificial intelligence, machine learning can be used to mine information on various medicinal plants to establish an ideal resource database. We herein present a review that mainly introduces common machine learning algorithms and discusses their application in multi-source data analysis of medicinal plants. The combination of machine learning algorithms and multi-source data analysis facilitates a comprehensive analysis and aids in the effective evaluation of the quality of medicinal plants. The findings of this review provide new possibilities for promoting the development and utilization of medicinal plants.
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Affiliation(s)
- Yanying Zhang
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, 650200, China
- College of Traditional Chinese Medicine, Yunnan University of Chinese Medicine, Kunming, 650500, China
| | - Yuanzhong Wang
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, 650200, China
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Meng F, Wang J, Chen Z, Qiao F, Yang D. Shaping the concentration of petroleum hydrocarbon pollution in soil: A machine learning and resistivity-based prediction method. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2023; 345:118817. [PMID: 37597372 DOI: 10.1016/j.jenvman.2023.118817] [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: 06/13/2023] [Revised: 08/03/2023] [Accepted: 08/12/2023] [Indexed: 08/21/2023]
Abstract
A new method relying on machine learning and resistivity to predict concentrations of petroleum hydrocarbon pollution in soil was proposed as a means of investigation and monitoring. Currently, determining pollutant concentrations in soil is primarily achieved through costly sampling and testing of numerous borehole samples, which carries the risk of further contamination by penetrating the aquifer. Additionally, conventional petroleum hydrocarbon geophysical surveys struggle to establish a correlation between survey results and pollutant concentration. To overcome these limitations, three machine learning models (KNN, RF, and XGBOOST) were combined with the geoelectrical method to predict petroleum hydrocarbon concentrations in the source area. The results demonstrate that the resistivity-based prediction method utilizing machine learning is effective, as validated by R-squared values of 0.91 and 0.94 for the test and validation sets, respectively, and a root mean squared error of 0.19. Furthermore, this study confirmed the feasibility of the approach using actual site data, along with a discussion of its advantages and limitations, establishing it as an inexpensive option to investigate and monitor changes in petroleum hydrocarbon concentration in soil.
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Affiliation(s)
- Fansong Meng
- School of Earth Science and Engineering, Hohai University, Nanjing, 210098, China
| | - Jinguo Wang
- School of Earth Science and Engineering, Hohai University, Nanjing, 210098, China.
| | - Zhou Chen
- School of Earth Science and Engineering, Hohai University, Nanjing, 210098, China
| | - Fei Qiao
- School of Earth Science and Engineering, Hohai University, Nanjing, 210098, China
| | - Dong Yang
- School of Earth Science and Engineering, Hohai University, Nanjing, 210098, China
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13
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Zhang P, Liu C, Lao D, Nguyen XC, Paramasivan B, Qian X, Inyinbor AA, Hu X, You Y, Li F. Unveiling the drives behind tetracycline adsorption capacity with biochar through machine learning. Sci Rep 2023; 13:11512. [PMID: 37460544 DOI: 10.1038/s41598-023-38579-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 07/11/2023] [Indexed: 07/20/2023] Open
Abstract
This study aimed to develop a robust predictive model for tetracycline (TC) adsorption onto biochar (BC) by employing machine learning techniques to investigate the underlying driving factors. Four machine learning algorithms, namely Random Forest (RF), Gradient Boosting Decision Tree (GBDT), eXtreme Gradient Boosting (XGBoost) and Artificial Neural Networks (ANN), were used to model the adsorption of TC on BC using the data from 295 adsorption experiments. The analysis revealed that the RF model had the highest predictive accuracy (R2 = 0.9625) compared to ANN (R2 = 0.9410), GBDT (R2 = 0.9152), and XGBoost (R2 = 0.9592) models. This study revealed that BC with a specific surface area (S (BET)) exceeding 380 cm3·g-1 and particle sizes ranging between 2.5 and 14.0 nm displayed the greatest efficiency in TC adsorption. The TC-to-BC ratio was identified as the most influential factor affecting adsorption efficiency, with a weight of 0.595. The concentration gradient between the adsorbate and adsorbent was demonstrated to be the principal driving force behind TC adsorption by BC. A predictive model was successfully developed to estimate the sorption performance of various types of BC for TC based on their properties, thereby facilitating the selection of appropriate BC for TC wastewater treatment.
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Affiliation(s)
- Pengyan Zhang
- Key Laboratory of Tarim Oasis Agriculture (Tarim University), Ministry of Education, Xinjiang, 843300, China
- College of Water Resources and Architectural Engineering, Tarim University, Xinjiang, 843300, China
| | - Chong Liu
- Key Laboratory of Tarim Oasis Agriculture (Tarim University), Ministry of Education, Xinjiang, 843300, China
- College of Water Resources and Architectural Engineering, Tarim University, Xinjiang, 843300, China
| | - Dongqing Lao
- Key Laboratory of Tarim Oasis Agriculture (Tarim University), Ministry of Education, Xinjiang, 843300, China.
- College of Information Engineering, Tarim University, Xinjiang, 843300, China.
| | - Xuan Cuong Nguyen
- Institution of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam
| | - Balasubramanian Paramasivan
- Department of Biotechnology and Medical Engineering, National Institute of Technology Rourkela, Odisha, 769008, India
| | - Xiaoyan Qian
- Key Laboratory of Tarim Oasis Agriculture (Tarim University), Ministry of Education, Xinjiang, 843300, China
- College of Water Resources and Architectural Engineering, Tarim University, Xinjiang, 843300, China
| | - Adejumoke Abosede Inyinbor
- Department of Physical Sciences, Industrial Chemistry Programme, Landmark University, Omu-Aran, Kwara State, Nigeria
| | - Xuefei Hu
- Key Laboratory of Tarim Oasis Agriculture (Tarim University), Ministry of Education, Xinjiang, 843300, China
- College of Water Resources and Architectural Engineering, Tarim University, Xinjiang, 843300, China
| | - Yongjun You
- Key Laboratory of Tarim Oasis Agriculture (Tarim University), Ministry of Education, Xinjiang, 843300, China
- College of Water Resources and Architectural Engineering, Tarim University, Xinjiang, 843300, China
| | - Fayong Li
- Key Laboratory of Tarim Oasis Agriculture (Tarim University), Ministry of Education, Xinjiang, 843300, China
- College of Water Resources and Architectural Engineering, Tarim University, Xinjiang, 843300, China
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Astray G, Soria-Lopez A, Barreiro E, Mejuto JC, Cid-Samamed A. Machine Learning to Predict the Adsorption Capacity of Microplastics. NANOMATERIALS (BASEL, SWITZERLAND) 2023; 13:1061. [PMID: 36985954 PMCID: PMC10051191 DOI: 10.3390/nano13061061] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 03/10/2023] [Accepted: 03/11/2023] [Indexed: 06/18/2023]
Abstract
Nowadays, there is an extensive production and use of plastic materials for different industrial activities. These plastics, either from their primary production sources or through their own degradation processes, can contaminate ecosystems with micro- and nanoplastics. Once in the aquatic environment, these microplastics can be the basis for the adsorption of chemical pollutants, favoring that these chemical pollutants disperse more quickly in the environment and can affect living beings. Due to the lack of information on adsorption, three machine learning models (random forest, support vector machine, and artificial neural network) were developed to predict different microplastic/water partition coefficients (log Kd) using two different approximations (based on the number of input variables). The best-selected machine learning models present, in general, correlation coefficients above 0.92 in the query phase, which indicates that these types of models could be used for the rapid estimation of the absorption of organic contaminants on microplastics.
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Affiliation(s)
- Gonzalo Astray
- Universidade de Vigo, Departamento de Química Física, Facultade de Ciencias, 32004 Ourense, Spain
| | - Anton Soria-Lopez
- Universidade de Vigo, Departamento de Química Física, Facultade de Ciencias, 32004 Ourense, Spain
| | - Enrique Barreiro
- Universidade de Vigo, Departamento de Informática, Escola Superior de Enxeñaría Informática, 32004 Ourense, Spain
| | - Juan Carlos Mejuto
- Universidade de Vigo, Departamento de Química Física, Facultade de Ciencias, 32004 Ourense, Spain
| | - Antonio Cid-Samamed
- Universidade de Vigo, Departamento de Química Física, Facultade de Ciencias, 32004 Ourense, Spain
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15
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Zhu X, Liu B, Sun L, Li R, Deng H, Zhu X, Tsang DCW. Machine learning-assisted exploration for carbon neutrality potential of municipal sludge recycling via hydrothermal carbonization. BIORESOURCE TECHNOLOGY 2023; 369:128454. [PMID: 36503096 DOI: 10.1016/j.biortech.2022.128454] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 12/02/2022] [Accepted: 12/04/2022] [Indexed: 06/17/2023]
Abstract
In the context of advocating carbon neutrality, there are new requirements for sustainable management of municipal sludge (MS). Hydrothermal carbonization (HTC) is a promising technology to deal with high-moisture MS considering its low energy consumption (without drying pretreatment) and value-added products (i.e., hydrochar). This study applied machine learning (ML) methods to conduct a holistic assessment with higher heating value (HHV) of hydrochar, carbon recovery (CR), and energy recovery (ER) as model targets, yielding accurate prediction models with R2 of 0.983, 0.844 and 0.858, respectively. Furthermore, MS properties showed positive (e.g., carbon content, HHV) and negative (e.g., ash content, O/C, and N/C) influences on the hydrochar HHV. By comparison, HTC parameters play a critical role for CR (51.7%) and ER (52.5%) prediction. The primary sludge was an optimal HTC feedstock while anaerobic digestion sludge had the lowest potential. This study provided a comprehensive reference for sustainable MS treatment and industrial application.
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Affiliation(s)
- Xinzhe Zhu
- School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China; Guangdong Provincial Key Laboratory of Environmental Pollution Control and Remediation Technology, Sun Yat-sen University, Guangzhou 510275, China
| | - Bingyou Liu
- School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China
| | - Lianpeng Sun
- School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China; Guangdong Provincial Key Laboratory of Environmental Pollution Control and Remediation Technology, Sun Yat-sen University, Guangzhou 510275, China.
| | - Ruohong Li
- School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China; Guangdong Provincial Key Laboratory of Environmental Pollution Control and Remediation Technology, Sun Yat-sen University, Guangzhou 510275, China
| | - Huanzhong Deng
- School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China
| | - Xiefei Zhu
- Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China; Department of Thermal Science and Energy Engineering, University of Science and Technology of China, 96 Jinzhai Road, Hefei, Anhui 230026, China
| | - Daniel C W Tsang
- Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China
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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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Liu J, Xu Z, Zhang W. Unraveling the role of Fe in As(III & V) removal by biochar via machine learning exploration. Sep Purif Technol 2023. [DOI: 10.1016/j.seppur.2023.123245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
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18
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Zhang Z, Zhang X, Zhang D, Zhang X, Qiu F, Li W, Liu Z, Shu J, Tang C. Application of Machine Learning in a Mineral Leaching Process-Taking Pyrolusite Leaching as an Example. ACS OMEGA 2022; 7:48130-48138. [PMID: 36591162 PMCID: PMC9798733 DOI: 10.1021/acsomega.2c06129] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 12/06/2022] [Indexed: 06/17/2023]
Abstract
In this study, several machine learning models were used to analyze the process variables of electric-field-enhanced pyrolusite leaching and predict the leaching rate of manganese, and the applicability of those models in the leaching process of hydrometallurgy was compared. It showed that there was no correlation between the six leaching conditions; in addition to the leaching time, the concentrations of sulfuric acid and ferrous sulfate had great influences on the leaching of pyrolusite. The results of the prediction models showed that the support vector regression model has the best prediction performance, with regression index (R 2) = 0.92 and mean square error = 25.04, followed by the gradient boosting regression model (R 2 > 0.85). In this research, machine learning models were applied to the optimization of the manganese leaching process, and the research process and methods were also applicable to other hydrometallurgical processes for majorization and result prediction.
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Affiliation(s)
- Zheng Zhang
- School
of Chemistry and Chemical Engineering, Chongqing
University of Technology, Chongqing400054, China
| | - Xianming Zhang
- Engineering
Research Center for Waste Oil Recovery Technology and Equipment, Ministry
of Education, Chongqing Technology and Business
University, Chongqing400067, China
| | - Dan Zhang
- School
of Chemistry and Chemical Engineering, Chongqing
University of Technology, Chongqing400054, China
| | - Xingran Zhang
- School
of Chemistry and Chemical Engineering, Chongqing
University of Technology, Chongqing400054, China
- Engineering
Research Center for Waste Oil Recovery Technology and Equipment, Ministry
of Education, Chongqing Technology and Business
University, Chongqing400067, China
- School
of Chemistry and Chemical Engineering, Chongqing
University, Chongqing400044, China
| | - Facheng Qiu
- School
of Chemistry and Chemical Engineering, Chongqing
University of Technology, Chongqing400054, China
| | - Wensheng Li
- School
of Chemistry and Chemical Engineering, Chongqing
University of Technology, Chongqing400054, China
| | - Zuohua Liu
- School
of Chemistry and Chemical Engineering, Chongqing
University, Chongqing400044, China
| | - Jiancheng Shu
- Key
Laboratory of Solid Waste Treatment and Resource Recycle (SWUST),
Ministry of Education, Southwest University
of Science and Technology, 59 Qinglong Road, Mianyang621010, China
| | - Chengli Tang
- Chongqing
Chemical Industry Vocational College, Chongqing401228, China
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19
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Guo Z, Ahmad HA, Tian Y, Zhao Q, Zeng M, Wu N, Hao L, Liang J, Ni SQ. Extensive data analysis and kinetic modelling of dosage and temperature dependent role of graphene oxides on anammox. CHEMOSPHERE 2022; 308:136307. [PMID: 36067812 DOI: 10.1016/j.chemosphere.2022.136307] [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: 06/01/2022] [Revised: 08/28/2022] [Accepted: 08/29/2022] [Indexed: 06/15/2023]
Abstract
Anaerobic ammonium oxidation (anammox) is carbon friendly biological nitrogen removal process, and recently more focus is given to improving the anammox activity. Because of its high adsorption and modifiability, graphene and its derivative in wastewater treatment have received much attention. However, the specific effects and mechanisms of graphene oxide (GO) and reduced graphene oxide (RGO) on anammox are still controversial. Extensive data analysis was performed to explore the effects of GO and RGO on anammox. Statistical analysis revealed that 100 mg/L GO significantly promoted the anammox process, while 200 mg/L of GO inhibited the anammox process. The promotion of anammox performance under the influence of RGO was dependent on the temperature. The Logistic model was utilized for depicting the variation of nitrogen removal efficiency under promoting dosage of graphene oxides. A neural network model-based analysis was performed to reach anammox's potential mechanisms under the influence of two graphene oxides. Spearman correlation analysis showed that GO and RGO had significant positive correlations with nitrogen removal efficiency and specific anammox activity (p < 0.01), especially for RGO. In addition, the abundance of Planctomycetes and Nitrospirae was positively correlated with the addition of graphene oxides. This work comprehensively unraveled the role of graphene oxide materials on the anammox process and provided practical directions for the enhancement of anammox.
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Affiliation(s)
- Zheng Guo
- College of Marine and Environmental Sciences, Tianjin University of Science & Technology, 300457, Tianjin, China; Shandong Provincial Key Laboratory of Water Pollution Control and Resource Reuse, Shandong Key Laboratory of Environmental Processes and Health, School of Environmental Science and Engineering, Shandong University, Qingdao, Shandong, 266237, China
| | - Hafiz Adeel Ahmad
- Shandong Provincial Key Laboratory of Water Pollution Control and Resource Reuse, Shandong Key Laboratory of Environmental Processes and Health, School of Environmental Science and Engineering, Shandong University, Qingdao, Shandong, 266237, China
| | - Yuhe Tian
- College of Marine and Environmental Sciences, Tianjin University of Science & Technology, 300457, Tianjin, China
| | - Qingyu Zhao
- College of Marine and Environmental Sciences, Tianjin University of Science & Technology, 300457, Tianjin, China
| | - Ming Zeng
- College of Marine and Environmental Sciences, Tianjin University of Science & Technology, 300457, Tianjin, China.
| | - Nan Wu
- College of Engineering and Technology, Tianjin Agricultural University, Tianjin, 300384, China
| | - Linlin Hao
- College of Marine and Environmental Sciences, Tianjin University of Science & Technology, 300457, Tianjin, China
| | - Jiaqi Liang
- College of Engineering and Technology, Tianjin Agricultural University, Tianjin, 300384, China
| | - Shou-Qing Ni
- Shandong Provincial Key Laboratory of Water Pollution Control and Resource Reuse, Shandong Key Laboratory of Environmental Processes and Health, School of Environmental Science and Engineering, Shandong University, Qingdao, Shandong, 266237, China.
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20
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Zhang W, Huang W, Tan J, Guo Q, Wu B. Heterogeneous catalysis mediated by light, electricity and enzyme via machine learning: Paradigms, applications and prospects. CHEMOSPHERE 2022; 308:136447. [PMID: 36116627 DOI: 10.1016/j.chemosphere.2022.136447] [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: 08/09/2022] [Revised: 09/08/2022] [Accepted: 09/11/2022] [Indexed: 06/15/2023]
Abstract
Energy crisis and environmental pollution have become the bottleneck of human sustainable development. Therefore, there is an urgent need to develop new catalysts for energy production and environmental remediation. Due to the high cost caused by blind screening and limited valuable computing resources, the traditional experimental methods and theoretical calculations are difficult to meet with the requirements. In the past decades, computer science has made great progress, especially in the field of machine learning (ML). As a new research paradigm, ML greatly accelerates the theoretical calculation methods represented by first principal calculation and molecular dynamics, and establish the physical picture of heterogeneous catalytic processes for energy and environment. This review firstly summarized the general research paradigms of ML in the discovery of catalysts. Then, the latest progresses of ML in light-, electricity- and enzyme-mediated heterogeneous catalysis were reviewed from the perspective of catalytic performance, operating conditions and reaction mechanism. The general guidelines of ML for heterogeneous catalysis were proposed. Finally, the existing problems and future development trend of ML in heterogeneous catalysis mediated by light, electricity and enzyme were summarized. We highly expect that this review will facilitate the interaction between ML and heterogeneous catalysis, and illuminate the development prospect of heterogeneous catalysis.
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Affiliation(s)
- Wentao Zhang
- Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, People's Republic of China
| | - Wenguang Huang
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment of PRC, Guangzhou, 510655, People's Republic of China.
| | - Jie Tan
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment of PRC, Guangzhou, 510655, People's Republic of China
| | - Qingwei Guo
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment of PRC, Guangzhou, 510655, People's Republic of China
| | - Bingdang Wu
- School of Environmental Science and Engineering, Suzhou University of Science and Technology, Suzhou, 215009, People's Republic of China; Key Laboratory of Suzhou Sponge City Technology, Suzhou, 215002, People's Republic of China.
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21
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Wang Z, Chu Y, Chang H, Xie P, Zhang C, Li F, Ho SH. Advanced insights on removal of antibiotics by microalgae-bacteria consortia: A state-of-the-art review and emerging prospects. CHEMOSPHERE 2022; 307:136117. [PMID: 35998727 DOI: 10.1016/j.chemosphere.2022.136117] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Revised: 08/02/2022] [Accepted: 08/16/2022] [Indexed: 06/15/2023]
Abstract
Antibiotics abuse has triggered a growing environmental problem, posing a major threat to both ecosystem and human health. Unfortunately, there are still several shortcomings to current antibiotics removal technologies. Microalgae-bacteria consortia have been shown to be a promising antibiotics treatment technology owing to advantages of high antibiotics removal efficiency, low operational cost, and carbon emission reduction. This review aims to introduce the removal mechanisms, influencing factors, and future research perspectives for using microalgae-bacteria consortia to remove antibiotics. The interaction mechanisms between microalgae and bacteria are comprehensively revealed, and their exclusive advantages have been summarized in a "Trilogy" strategy, including "reinforced physical contact", "upgraded substance utilization along with antibiotics degradation", and "robust biological regulation". What's more, the relationship between different interaction mechanisms is emphatically analyzed. The important influencing factors, including concentration and classes of antibiotics, environmental conditions, and operational parameters, of antibiotics removal were also assessed. Three innovative treatment systems (microalgae-bacteria fuel cells (MBFCs), microalgae-bacteria membrane photobioreactors (MB-MPBRs), and microalgae-bacteria granular sludge (MBGS)) along with three advanced techniques (metabolic engineering, machine learning, and molecular docking and dynamics) are then introduced. In addition, concrete implementing schemes of the above advanced techniques are also provided. Finally, the current challenges and future research directions in using microalgae-bacteria consortia to remove antibiotics have been summarized. Overall, this review addresses the current state of microalgae-bacteria consortia for antibiotics treatment and provides corresponding recommendations for enhancing antibiotics removal efficiency.
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Affiliation(s)
- Zeyuan Wang
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin, Heilongjiang Province 150090, China
| | - Yuhao Chu
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin, Heilongjiang Province 150090, China
| | - Haixing Chang
- School of Chemistry and Chemical Engineering, Chongqing University of Technology, Chongqing, 400054, PR China
| | - Peng Xie
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin, Heilongjiang Province 150090, China
| | - Chaofan Zhang
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin, Heilongjiang Province 150090, China.
| | - Fanghua Li
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin, Heilongjiang Province 150090, China
| | - Shih-Hsin Ho
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin, Heilongjiang Province 150090, China.
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22
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Almalawi A, Khan AI, Alqurashi F, Abushark YB, Alam MM, Qaiyum S. Modeling of Remora Optimization with Deep Learning Enabled Heavy Metal Sorption Efficiency Prediction onto Biochar. CHEMOSPHERE 2022; 303:135065. [PMID: 35618070 DOI: 10.1016/j.chemosphere.2022.135065] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 05/11/2022] [Accepted: 05/20/2022] [Indexed: 06/15/2023]
Abstract
Environmental distresses linked to heavy metal (HM) impurity in the water received significant attention among research communities. Recently, advancements in industrial sectors like paper industries, mining, non-ferrous metallurgy, electroplating, mineral paint production, etc. have resulted in massive heavy metals in wastewater. In contrast to organic pollutants, HMs are not recyclable and can be simply engrossed by living organisms. Recently, different solutions have been employed for removing HMs from water and wastewater, like membrane filtration, chemical precipitation, adsorption, ion-exchange, flotation, flocculation, etc. Sorption can be considered one of the efficient solutions for eradicating HMs from waste water. With this motivation, this article concentrates on the design of Remora Optimization with Deep Learning Enabled Heavy Metal Sorption Efficiency Prediction (RODL-HMSEP) model onto Biochar. The proposed RODL-HMSEP technique intends to determine the sorption performance of HMs of various biochar features. Initially, the density based clustering (DBSCAN) technique is applied to simulating the features of metal adsorption data and splitting them into clusters of identical features. Besides, deep belief network (DBN) model was employed for prediction and the efficiency of the DBN model is optimally adjusted with utilize of RO technique. The experimental validation of the RODL-HMSEP technique ensured the promising performance of the RODL-HMSEP technique on the prediction of sorption efficiency onto biochar over other methods The experimental validation of the RODL-HMSEP technique ensured the promising performance of the RODL-HMSEP technique on the prediction of sorption efficiency onto biochar over other methods.
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Affiliation(s)
- Abdulmohsen Almalawi
- Computer Science Department, King Abdulaziz University, Jeddah, 21589, Saudi Arabia.
| | - Asif Irshad Khan
- Computer Science Department, King Abdulaziz University, Jeddah, 21589, Saudi Arabia.
| | - Fahad Alqurashi
- Computer Science Department, King Abdulaziz University, Jeddah, 21589, Saudi Arabia.
| | - Yoosef B Abushark
- Computer Science Department, King Abdulaziz University, Jeddah, 21589, Saudi Arabia.
| | - Md Mottahir Alam
- Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, 21589, Saudi Arabia.
| | - Sana Qaiyum
- Center for Research in Data Sciences, Universiti Teknologi PETRONAS, Seri Iskandar 21 32610, 22 Perak, Malaysia.
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Aoudjit L, Salazar H, Zioui D, Sebti A, Martins PM, Lanceros-Méndez S. Solar Photocatalytic Membranes: An Experimental and Artificial Neural Network Modeling Approach for Niflumic Acid Degradation. MEMBRANES 2022; 12:membranes12090849. [PMID: 36135867 PMCID: PMC9504027 DOI: 10.3390/membranes12090849] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 08/18/2022] [Accepted: 08/22/2022] [Indexed: 05/26/2023]
Abstract
The presence of contaminants of emerging concern (CEC), such as pharmaceuticals, in water sources is one of the main concerns nowadays due to their hazardous properties causing severe effects on human health and ecosystem biodiversity. Niflumic acid (NFA) is a widely used anti-inflammatory drug, and it is known for its non-biodegradability and resistance to chemical and biological degradation processes. In this work, a 10 wt.% TiO2/PVDF-TrFE nanocomposite membrane (NCM) was prepared by the solvent casting technique, fully characterized, and implemented on an up-scaled photocatalytic membrane reactor (PMR). The photocatalytic activity of the NCM was evaluated on NFA degradation under different experimental conditions, including NFA concentration, pH of the media, irradiation time and intensity. The NCM demonstrated a remarkable photocatalytic efficiency on NFA degradation, as efficiency of 91% was achieved after 6 h under solar irradiation at neutral pH. The NCM proved effective in long-term use, with maximum efficiency losses of 7%. An artificial neural network (ANN) model was designed to model NFA's photocatalytic degradation behavior, demonstrating a good agreement between experimental and predicted data, with an R2 of 0.98. The relative significance of each experimental condition was evaluated, and the irradiation time proved to be the most significant parameter affecting the NFA degradation efficiency. The designed ANN model provides a reliable framework l for modeling the photocatalytic activity of TiO2/PVDF-TrFE and related NCM.
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Affiliation(s)
- Lamine Aoudjit
- Unité de Développement des Équipements Solaires, UDES/Centre de Développement des Energies Renouvelables, CDER, Bou Ismail 42415, Algeria
| | - Hugo Salazar
- Physics Centre of Minho and Porto Universities (CF-UM-UP), University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal
- LaPMET—Laboratory of Physics for Materials and Emergent Technologies, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal
- Centre/Department of Chemistry, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal
| | - Djamila Zioui
- Unité de Développement des Équipements Solaires, UDES/Centre de Développement des Energies Renouvelables, CDER, Bou Ismail 42415, Algeria
| | - Aicha Sebti
- Unité de Développement des Équipements Solaires, UDES/Centre de Développement des Energies Renouvelables, CDER, Bou Ismail 42415, Algeria
| | - Pedro Manuel Martins
- Centre of Molecular and Environmental Biology, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal
- Institute of Science and Innovation on Bio-Sustainability (IB-S), University of Minho, 4710-057 Braga, Portugal
| | - Senentxu Lanceros-Méndez
- BCMaterials, Basque Centre for Materials, Applications and Nanostructures, UPV/EHU Science Park, 48940 Leioa, Spain
- IKERBASQUE, Basque Foundation for Science, 48013 Bilbao, Spain
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Abstract
Nowadays, biochar is being studied to a great degree because of its potential for carbon sequestration, soil improvement, climate change mitigation, catalysis, wastewater treatment, energy storage, and waste management. The present review emphasizes on the utilization of biochar and biochar-based nanocomposites to play a key role in decontaminating dyes from wastewater. Numerous trials are underway to synthesize functionalized, surface engineered biochar-based nanocomposites that can sufficiently remove dye-contaminated wastewater. The removal of dyes from wastewater via natural and modified biochar follows numerous mechanisms such as precipitation, surface complexation, ion exchange, cation–π interactions, and electrostatic attraction. Further, biochar production and modification promote good adsorption capacity for dye removal owing to the properties tailored from the production stage and linked with specific adsorption mechanisms such as hydrophobic and electrostatic interactions. Meanwhile, a framework for artificial neural networking and machine learning to model the dye removal efficiency of biochar from wastewater is proposed even though such studies are still in their infancy stage. The present review article recommends that smart technologies for modelling and forecasting the potential of such modification of biochar should be included for their proper applications.
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25
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Bagheri M, Ibrahim ZZ, Wolf ID, Akhir MF, Talaat WIAW, Oryani B. Sea-level projections using a NARX-NN model of tide gauge data for the coastal city of Kuala Terengganu in Malaysia. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022:10.1007/s11356-022-21662-4. [PMID: 35789462 DOI: 10.1007/s11356-022-21662-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2021] [Accepted: 06/21/2022] [Indexed: 06/15/2023]
Abstract
The impact of global warming presents an increased risk to the world's shorelines. The Intergovernmental Panel on Climate Change (IPCC) reported that the twenty-first century experienced a severe global mean sea-level rise due to human-induced climate change. Therefore, coastal planners require reasonably accurate estimates of the rate of sea-level rise and the potential impacts, including extreme sea-level changes, floods, and shoreline erosion. Also, land loss as a result of disturbance of shoreline is of interest as it damages properties and infrastructure. Using a nonlinear autoregressive network with an exogenous input (NARX) model, this study attempted to simulate (1991 to 2012) and predict (2013-2020) sea-level change along Merang kechil to Kuala Marang in Terengganu state shoreline areas. The simulation results show a rising trend with a maximum rate of 28.73 mm/year and an average of about 8.81 mm/year. In comparison, the prediction results show a rising sea level with a maximum rate of 79.26 mm/year and an average of about 25.34 mm/year. The database generated from this study can be used to inform shoreline defense strategies adapting to sea-level rise, flood, and erosion. Scientists can forecast sea-level increases beyond 2020 using simulated sea-level data up to 2020 and apply it for future research. The data also helps decision-makers choose measures for vulnerable shoreline settlements to adapt to sea-level rise. Notably, the data will provide essential information for policy development and implementation to facilitate operational decision-making processes for coastal cities.
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Affiliation(s)
- Milad Bagheri
- Institute of Oceanography and Environment, Universiti Malaysia Terengganu, 21030, Kuala Nerus, Malaysia.
| | - Zelina Z Ibrahim
- Department of Environment, Faculty of Environmental and Forestry, Universiti Putra Malaysia, 43400, Seri Kembangan, Malaysia
| | - Isabelle D Wolf
- School of Geography and Sustainable Communities, University of Wollongong, Northfields Avenue, Wollongong, NSW, 2522, Australia
- Centre for Ecosystem Science, University of New South Wales, Sydney, NSW, 2052, Australia
| | - Mohd Fadzil Akhir
- Institute of Oceanography and Environment, Universiti Malaysia Terengganu, 21030, Kuala Nerus, Malaysia
| | | | - Bahareh Oryani
- Technology Management, Economics and Policy Program, College of Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, South Korea
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26
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Zhu M, Wang J, Yang X, Zhang Y, Zhang L, Ren H, Wu B, Ye L. A review of the application of machine learning in water quality evaluation. ECO-ENVIRONMENT & HEALTH (ONLINE) 2022; 1:107-116. [PMID: 38075524 PMCID: PMC10702893 DOI: 10.1016/j.eehl.2022.06.001] [Citation(s) in RCA: 45] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 05/19/2022] [Accepted: 06/01/2022] [Indexed: 12/31/2023]
Abstract
With the rapid increase in the volume of data on the aquatic environment, machine learning has become an important tool for data analysis, classification, and prediction. Unlike traditional models used in water-related research, data-driven models based on machine learning can efficiently solve more complex nonlinear problems. In water environment research, models and conclusions derived from machine learning have been applied to the construction, monitoring, simulation, evaluation, and optimization of various water treatment and management systems. Additionally, machine learning can provide solutions for water pollution control, water quality improvement, and watershed ecosystem security management. In this review, we describe the cases in which machine learning algorithms have been applied to evaluate the water quality in different water environments, such as surface water, groundwater, drinking water, sewage, and seawater. Furthermore, we propose possible future applications of machine learning approaches to water environments.
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Affiliation(s)
- Mengyuan Zhu
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, China
| | - Jiawei Wang
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, China
| | - Xiao Yang
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, China
| | - Yu Zhang
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, China
| | - Linyu Zhang
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, China
| | - Hongqiang Ren
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, China
| | - Bing Wu
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, China
| | - Lin Ye
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, China
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27
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Prediction of Arsenic Removal from Contaminated Water Using Artificial Neural Network Model. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12030999] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Arsenic is a deleterious heavy metal that is usually removed from polluted water based on adsorption processes. The latest mode of modeling such a process is to implement artificial intelligence (AI). In the current work, a new artificial neural network (ANN) model was developed to predict the adsorption efficiency of arsenate (As(III)) from contaminated water by analyzing different architectures of an adaptive network-based fuzzy inference system (ANFIS). The database for the current study consisted of the experimental data of the adsorption of As(III) by different adsorbents/biosorbents. The data were randomly divided into two sets: 70% for the training phase and 30% for the testing phase. Four statistical evaluation metrics, namely, mean square error (MSE), root-mean-square error (RMSE), Pearson’s correlation coefficient (R%), and the determination coefficient (R2) were used for the analysis. The best performing ANFIS model was characterized with the average values of 97.72%, 0.9333, 0.137, and 0.274 of R%, R2, MSE, and RMSE, respectively. In addition, a parametric investigation revealed that the most dominating parameters on the adsorption process efficiency were in the following order: pH, As initial concentration, contact time, adsorbent dosage, inoculum size, and temperature. The results of the current study would be useful in the adsorption process scale-up and optimization.
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Yuan J, Zhang G, Yu SS, Chen Z, Li Z, Zhang Y. A multi-timescale smart grid energy management system based on adaptive dynamic programming and Multi-NN Fusion prediction method. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.108284] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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