The use of artificial intelligence models in the prediction of optimum operational conditions for the treatment of dye wastewaters with similar structural characteristics

Picos-Benítez, Alain R.; Martínez‐Vargas, Blanca L.; Durón-Torres, Sergio M.; Brillas, Enric; Peralta‐Hernández, Juan M.

doi:10.1016/j.psep.2020.06.020

Cited by 52 publications

(9 citation statements)

References 44 publications

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“…80 Some of these predictive models were also implemented in processes for treating industrial wastewaters. For example, Picos-Beni ́tez et al 81 assessed the effectiveness of an ANN-GA model for the evaluation and optimization of wastewaters treatment containing sulfate withbromophenol blue dye using an electro-oxidation (EO) process. In a detergent industrial WWTP, FFNN (MLP), a cascade forward neural network and SVR approaches were tested to predict the performance of the WWTP of the industry by using data collected over a period of 6 months of parameters such as of COD, BOD, TDS, TSS, and oil and grease content.…”

Section: Wastewater Treatment Modeling Using Machine Learningmentioning

confidence: 99%

A Review of Computational Modeling in Wastewater Treatment Processes

Duarte,

Martins,

Oliveira

et al. 2023

ACS EST Water

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Wastewater treatment companies are facing several challenges related to the optimization of energy efficiency, meeting more restricted water quality standards, and resource recovery potential. Over the past decades, computational models have gained recognition as effective tools for addressing some of these challenges, contributing to the economic and operational efficiencies of wastewater treatment plants (WWTPs). To predict the performance of WWTPs, numerous deterministic, stochastic, and time series-based models have been developed. Mechanistic models, incorporating physical and empirical knowledge, are dominant as predictive models. However, these models represent a simplification of reality, resulting in model structure uncertainty and a constant need for calibration. With the increasing amount of available data, data-driven models are becoming more attractive. The implementation of predictive models can revolutionize the way companies manage WWTPs by permitting the development of digital twins for process simulation in (near) real-time. In data-driven models, the structure is not explicitly specified but is instead determined by searching for relationships in the available data. Thus, the main objective of the present review is to discuss the implementation of machine learning models for the prediction of WWTP effluent characteristics and wastewater inflows as well as anomaly detection studies and energy consumption optimization in WWTPs. Furthermore, an overview considering the merging of both mechanistic and machine learning models resulting in hybrid models is presented as a promising approach. A critical assessment of the main gaps and future directions on the implementation of mathematical modeling in wastewater treatment processes is also presented, focusing on topics such as the explainability of data-driven models and the use of Transfer Learning processes.

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Section: Wastewater Treatment Modeling Using Machine Learningmentioning

confidence: 99%

A Review of Computational Modeling in Wastewater Treatment Processes

Duarte,

Martins,

Oliveira

et al. 2023

ACS EST Water

View full text Add to dashboard Cite

show abstract

“…For process parameter optimization, Picos-Benítez et al [101] utilized an ANN-GA model to predict the treatment performance of sulfate wastewaters with Bromophenol blue dye using an electro-oxidation (EO) process and obtained the optimum operational conditions. They found that the AI model is a powerful tool in designing and controlling the WWT processes.…”

Section: Process Parameters Optimizationmentioning

confidence: 99%

A Review on Applications of Artificial Intelligence in Wastewater Treatment

Wang,

Cheng,

Liu

et al. 2023

Sustainability

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In recent years, artificial intelligence (AI), as a rapidly developing and powerful tool to solve practical problems, has attracted much attention and has been widely used in various areas. Owing to their strong learning and accurate prediction abilities, all sorts of AI models have also been applied in wastewater treatment (WWT) to optimize the process, predict the efficiency and evaluate the performance, so as to explore more cost-effective solutions to WWT. In this review, we summarize and analyze various AI models and their applications in WWT. Specifically, we briefly introduce the commonly used AI models and their purposes, advantages and disadvantages, and comprehensively review the inputs, outputs, objectives and major findings of particular AI applications in water quality monitoring, laboratory-scale research and process design. Although AI models have gained great success in WWT-related fields, there are some challenges and limitations that hinder the widespread applications of AI models in real WWT, such as low interpretability, poor model reproducibility and big data demand, as well as a lack of physical significance, mechanism explanation, academic transparency and fair comparison. To overcome these hurdles and successfully apply AI models in WWT, we make recommendations and discuss the future directions of AI applications.

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“…Picoz Benitez et al [90] reported the optimization of the EO process with the help of artificial neural network (ANN) and genetic algorithms (GA) for treatment of bromophenol blue dye. The root mean square error (RMSE) and mean absolute percentage error (MAPE) values were measured for the performance of ANN and were found to be 10.73 % and 8.81 %, respectively, determined from the real and predicted values.…”

Section: Dissociative Adsorption Of Watermentioning

confidence: 99%

Mechanism and Techno‐Economic Analysis of the Electrochemical Process

Ranga

Sinha

2023

ChemBioEng Reviews

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The textile industry utilizes numerous chemicals ranging from solvents to resins and caustic soda to bleach, having a harsh environmental impact. A large amount of colored dye in wastewater is released. Non-biodegradable heavy metals and chlorine accumulate in organs of marine and terrestrial life, leading to various diseases. So, there is an urgent need to treat textile wastewater. Up to date, techno-economic analyses of all electrochemical processes have not been reviewed for textile wastewater treatment. In this review, the focus is on the tertiary treatment methods, mainly on electrochemical treatments, i.e., electrocoagulation, electro-Fenton, electrooxidation, photoelectrochemical (PEC) process, and solar electrophoto-Fenton process (SPEF) with their mechanisms and techno-economic analyses. SPEF treatment was found to be advantageous in terms of efficiency and economic perspective as it utilizes solar energy instead of electrical energy and the cost of the PEC process can also be recovered by the generation of hydrogen.

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The use of artificial intelligence models in the prediction of optimum operational conditions for the treatment of dye wastewaters with similar structural characteristics

Cited by 52 publications

References 44 publications

A Review of Computational Modeling in Wastewater Treatment Processes

A Review of Computational Modeling in Wastewater Treatment Processes

A Review on Applications of Artificial Intelligence in Wastewater Treatment

Mechanism and Techno‐Economic Analysis of the Electrochemical Process

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