Development of artificial intelligence for modeling wastewater heavy metal removal: State of the art, application assessment and possible future research

被引:174
作者
Bhagat, Suraj Kumar [1 ]
Tran Minh Tung [1 ]
Yaseen, Zaher Mundher [1 ]
机构
[1] Ton Duc Thang Univ, Fac Civil Engn, Ho Chi Minh City, Vietnam
关键词
Adsorption capacity; Artificial intelligence models; Heavy metal; State of the art; Treatment technique; Future research; NEURAL-NETWORK ANN; RESPONSE-SURFACE METHODOLOGY; FUZZY INFERENCE SYSTEM; FIXED-BED COLUMN; ULTRASOUND-ENHANCED REMOVAL; BOX-BEHNKEN DESIGN; AQUEOUS-SOLUTION; PROCESS OPTIMIZATION; CR(VI) ADSORPTION; COPPER REMOVAL;
D O I
10.1016/j.jclepro.2019.119473
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The presence of various forms of heavy metals (HMs) (e.g., Cu, Cd, Pb, Zn, Cr, Ni, As, Co, Hg, Fe, Mn, Sb, and Ce) in water bodies and sediment has been increasing due to industrial and agricultural runoff. HM removal in nature is highly stochastic, nonlinear, nonstationary, and redundant. Over the last two decades, the implementation of artificial intelligence (AI) models for HM removal has been massively conducted. The divergence in the selection of predictors, target variables, the optimization, normalization of the algorithm, function, and architecture of AI models are time-consuming processes, which limit the optimal use of such models for HM removal simulation. The selection of sustainable, cost-efficient, and user-friendly treatment techniques that have minimal reverse impact on the ecosystem is immensely challenging. The focus of the established researches is to find an optimal AI models for specific removal techniques. Predictors and target variables can be sorted using several techniques, and the selection of algorithm, function, and architecture based on individual treatment techniques have been coherently ordered and argued. In this review, each element of the predictive models and their corresponding treatment processes, including its pros and cons, are discussed thoroughly. The performance matrices are also discussed in accordance with the behavior of each model. Moreover, multiple perspectives that can enlighten interested multi-domain scientists and scholars, such as AI model developers, data scientists, wastewater treatment researchers, and environmental policymakers, on the actual status of the models' progression are summarized. A comprehensive gap and assessments are also conducted to provide an insightful vision on this topic. Finally, several research directions, which could bridge the gap in the same domain are proposed and recommended on the basis of the identified research limitations. (C) 2019 Elsevier Ltd. All rights reserved.
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页数:38
相关论文
共 229 条
[1]  
Abdi Herve, 2013, Methods Mol Biol, V930, P549, DOI 10.1007/978-1-62703-059-5_23
[2]   Prediction and optimization studies for bioleaching of molybdenite concentrate using artificial neural networks and genetic algorithm [J].
Abdollahi, Hadi ;
Noaparast, Mohammad ;
Shafaei, Sied Ziaedin ;
Akcil, Ata ;
Panda, Sandeep ;
Kashi, Mohammad Hazrati ;
Karimi, Pouya .
MINERALS ENGINEERING, 2019, 130 :24-35
[3]   The use of Artificial Neural Network (ANN) for modeling of Cu (II) ion removal from aqueous solution by flotation and sorptive flotation process [J].
Abdulhussein, Shahad A. ;
Alwared, Abeer, I .
ENVIRONMENTAL TECHNOLOGY & INNOVATION, 2019, 13 :353-363
[4]   Removal of Cr(VI) from polluted solutions by electrocoagulation: Modeling of experimental results using artificial neural network [J].
Aber, S. ;
Amani-Ghadim, A. R. ;
Mirzajani, V. .
JOURNAL OF HAZARDOUS MATERIALS, 2009, 171 (1-3) :484-490
[5]   Prediction of sulphur removal with Acidithiobacillus sp using artificial neural networks [J].
Acharya, C ;
Mohanty, S ;
Sukla, LB ;
Misra, VN .
ECOLOGICAL MODELLING, 2006, 190 (1-2) :223-230
[6]   Past, present and prospect of an Artificial Intelligence (AI) based model for sediment transport prediction [J].
Afan, Haitham Abdulmohsin ;
El-shafie, Ahmed ;
Mohtar, Wan Hanna Melini Wan ;
Yaseen, Zaher Mundher .
JOURNAL OF HYDROLOGY, 2016, 541 :902-913
[7]   Evaluation of a newly developed biosorbent using packed bed column for possible application in the treatment of industrial effluents for removal of cadmium ions [J].
Ahmad, Muhammad Fayyaz ;
Haydar, Sajjad .
JOURNAL OF THE TAIWAN INSTITUTE OF CHEMICAL ENGINEERS, 2016, 62 :122-131
[8]   Application of artificial neural network for the prediction of biosorption capacity of immobilized Bacillus subtilis for the removal of cadmium ions from aqueous solution [J].
Ahmad, Muhammad Fayyaz ;
Haydar, Sajjad ;
Bhatti, Amanat Ali ;
Bari, Abdul Jabbar .
BIOCHEMICAL ENGINEERING JOURNAL, 2014, 84 :83-90
[9]  
AK S.J, 2002, LEAST SQUARES SUPPOR
[10]  
Al Sudani Z. A., 2019, J HYDROL