A review of artificial intelligence in water purification and wastewater treatment: Recent advancements

Safeer, Soma; Pandey, Ravi P.; Rehman, Bushra; Safdar, Tuba; Ahmad, Iftikhar; Hasan, Shadi W.; Ullah, Asmat

doi:10.1016/j.jwpe.2022.102974

Cited by 73 publications

(27 citation statements)

References 150 publications

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“…In this kind of model, the structure is not explicitly specified, but it is instead determined by searching for relationships in the available data. 12 Over the last several years, some reviews on the application of AI models to water/wastewater treatment have become available, providing a systematic overview of the application of AI mainly in technology, both physical/chemical 13 and biological 14,15 treatments, and management. 16 For example, Safeer et al 13 reviewed the recent advancements and applications of AI in water purification and wastewater treatment processes.…”

Section: Introductionmentioning

confidence: 99%

“…12 Over the last several years, some reviews on the application of AI models to water/wastewater treatment have become available, providing a systematic overview of the application of AI mainly in technology, both physical/chemical 13 and biological 14,15 treatments, and management. 16 For example, Safeer et al 13 reviewed the recent advancements and applications of AI in water purification and wastewater treatment processes. Regarding water purification, this review emphasizes specific processes such as coagulation/flocculation, disinfection, membrane filtration, and desalination.…”

Section: Introductionmentioning

confidence: 99%

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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: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Review of Computational Modeling in Wastewater Treatment Processes

Duarte,

Martins,

Oliveira

et al. 2023

ACS EST Water

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“…However, the true response function can only be approximated or estimated by different data-driven modeling approaches such as response surface methodology (RSM), artificial neural networking (ANN), support vector machine (SVM), and others. Recent advancements in data-driven modeling of separation processes in water purification and wastewater treatment have highlighted the importance of modern modeling tools like RSM, ANN, and SVM [ 20 ]. The last two (i.e., ANN and SVM) are part of machine learning (ML), which is a type of artificial intelligence (AI) that uses historical data as input to predict new output values for a studied system or process.…”

Section: Introductionmentioning

confidence: 99%

Application of Surface-Modified Nanoclay in a Hybrid Adsorption-Ultrafiltration Process for Enhanced Nitrite Ions Removal: Chemometric Approach vs. Machine Learning

Cojocaru¹,

Pascariu²,

Enache³

et al. 2023

Nanomaterials

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Herein, we report the results of a study on combining adsorption and ultrafiltration in a single-stage process to remove nitrite ions from contaminated water. As adsorbent, a surface-modified nanoclay was employed (i.e., Nanomer® I.28E, containing 25–30 wt. % trimethyl stearyl ammonium). Ultrafiltration experiments were conducted using porous polymeric membranes (Ultracel® 10 kDa). The hybrid process of adsorption-ultrafiltration was modeled and optimized using three computational tools: (1) response surface methodology (RSM), (2) artificial neural network (ANN), and (3) support vector machine (SVM). The optimal conditions provided by machine learning (SVM) were found to be the best, revealing a rejection efficiency of 86.3% and an initial flux of permeate of 185 LMH for a moderate dose of the nanoclay (0.674% w/v). Likewise, a new and more retentive membrane (based on PVDF-HFP copolymer and halloysite (HS) inorganic nanotubes) was produced by the phase-inversion method, characterized by SEM, EDX, AFM, and FTIR techniques, and then tested under optimal conditions. This new composite membrane (PVDF-HFP/HS) with a thickness of 112 μm and a porosity of 75.32% unveiled an enhanced rejection efficiency (95.0%) and a lower initial flux of permeate (28 LMH). Moreover, molecular docking simulations disclosed the intermolecular interactions between nitrite ions and the functional moiety of the organonanoclay.

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“…From the modelling and prediction viewpoint, the data-driven AI-based approaches attempt to acquire complex non-linear relationships among the input and output variables without the need for any formal mechanistic relationship. 20,21 In recent years, ANNbased predictive models are increasingly being adopted to simulate contaminant removal during wastewater treatment (Table 2). Data-driven black-box model, e.g., ANN requires no formal mathematical relationship and can be of great use in the absence of detailed mechanistic insight into complex biological processes.…”

Section: Introductionmentioning

confidence: 99%

“…From the modelling and prediction viewpoint, the data‐driven AI‐based approaches attempt to acquire complex non‐linear relationships among the input and output variables without the need for any formal mechanistic relationship 20,21 . In recent years, ANN‐based predictive models are increasingly being adopted to simulate contaminant removal during wastewater treatment (Table 2).…”

Section: Introductionmentioning

confidence: 99%

Modelling selenite and chemical oxygen demand removal in an inverse fluidized bed bioreactor using genetic algorithm optimized artificial neural network

Sinharoy,

Mahanty,

Behera

et al. 2023

J of Chemical Tech & Biotech

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BACKGROUNDA genetic algorithm (GA) optimized artificial neural network (ANN) model was developed to simulate selenite removal efficiency (SRe) and chemical oxygen demand (COD) removal efficiency (CRe) in a single‐stage inverse fluidized bed reactor (IFBR) operated under different hydraulic retention time (HRT) (12–48 h), and inlet selenite concentration (12.7–635 mg L−1). Multi‐objective optimization for SRe and CRe with the ANN model was adopted to estimate the pareto optimal solutions in the bioreactor system.RESULTSGA optimized ANN topology suggested five neurons in the hidden layer, and the optimal data partitioning between training, test, and validation subsets (in 70:15:15 ratio) using cross‐validation. The average fitness for SRe and CRe of the resulting ANN model over training (R2 = 0.885), test (R2 = 0.967) and validation (R2 = 0.972) subsets were reasonably good. Sensitivity analysis suggested an increase in SRe and CRe with HRT, while an increase in inlet selenite concentration had a negative effect on SRe. Local interpretable model‐agnostic explanations and SHapley Additive exPlanations for the developed model suggested a positive coefficient of the predictors on CRe. Pareto optimal solution for SRe (92.87%–95.26%), and CRe (91.33%–99.35%) were suggested for HRT and inlet selenite concentration varying between 35.67 h and 36 h, and 0.1 g L−1 and 0.884 g L−1, respectively.CONCLUSIONSThe GA optimized ANN model could be exploited for the prediction of contaminant removal efficiencies in the bioreactor system with a fair degree of complexity (i.e., IFBR). Multiple objective pareto optimal solutions can identify the best operating conditions relevant for wastewater treatment. © 2023 Society of Chemical Industry (SCI).

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A review of artificial intelligence in water purification and wastewater treatment: Recent advancements

Cited by 73 publications

References 150 publications

A Review of Computational Modeling in Wastewater Treatment Processes

A Review of Computational Modeling in Wastewater Treatment Processes

Application of Surface-Modified Nanoclay in a Hybrid Adsorption-Ultrafiltration Process for Enhanced Nitrite Ions Removal: Chemometric Approach vs. Machine Learning

Modelling selenite and chemical oxygen demand removal in an inverse fluidized bed bioreactor using genetic algorithm optimized artificial neural network

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