Monte Carlo Simulation, Artificial Intelligence and Machine Learning-based Modelling and Optimization of Three-dimensional Electrochemical Treatment of Xenobiotic Dye Wastewater

Aug 3, 2026

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The present study investigates the synergistic performance of a three-dimensional electrochemical process for decolourising methyl orange from xenobiotic textile wastewater, complemented by adsorption technology. Using 15 mA/cm2 current density, 3.62 kWh/kg energy consumption and 79.53% current efficiency, approximately 98% dye removal was achieved, and the 50 mg/L pollutant was rapidly mineralized with a half-life of 4.66 min. A graphite intercalation compound was polarized to enhance direct electrooxidation and •OH generation. Process parameter optimization employed ANN, SVM and random forest, with ANN and RF providing the best operating conditions; Monte Carlo simulations and sensitivity analysis quantified prediction uncertainty.

LanguageEnglish

CategoryResearch & Report

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Pages31

Reading Time1 h 18 min

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Abstract

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Types of Textile Wastewater Treatment

Environmental Processes (2024) 11:41 [https://doi.org/10.1007/s40710-024-00719-1](https://doi.org/10.1007/s40710-024-00719-1) RESEARCH Monte Carlo Simulation, Artificial Intelligence and Machine Learning-based Modelling and Optimization of Three-dimensional Electrochemical Treatment of Xenobiotic Dye Wastewater Voravich Ganthavee1 · Merenghege M. R. Fernando1 · Antoine P. Trzcinski1 Received: 16 January 2024 / Accepted: 10 July 2024 / Published online: 1 August 2024 © The Author(s) 2024, corrected publication 2024 Abstract The present study investigates the synergistic performance of the three-dimensional electrochemical process to decolourise methyl orange (MO) dye pollutant from xenobiotic textile wastewater. The textile dye was treated using electrochemical technique with strong oxidizing potential, and additional adsorption technology was employed to effectively remove dye pollutants from wastewater. Approximately 98% of MO removal efficiency was achieved using 15 mA/cm2 of current density, 3.62 kWh/kg of energy consumption and 79.53% of current efficiency. The 50 mg/L MO pollutant was rapidly mineralized with a half-life of 4.66 min at a current density of 15 mA/cm2. Additionally, graphite intercalation compound (GIC) was electrically polarized in the three-dimensional electrochemical reactor to enhance the direct electrooxidation and.OH generation, thereby improving synergistic treatment efficiency. Decolourisation of MO-polluted wastewater was optimized by artificial intelligence (AI) and machine learning (ML) techniques such as Artificial Neural Networks (ANN), Support Vector Machine (SVM), and random forest (RF) algorithms. Statistical metrics indicated the superiority of the model followed this order: ANN>RF>SVM>Multiple regression. The optimization results of the process parameters by artificial neural network (ANN) and random forest (RF) approaches showed that a current density of 15 mA/cm2, electrolysis time of 30 min and initial MO concentration of 50 mg/L were the best operating parameters to maintain current and energy efficiencies of the electrochemical reactor. Finally, Monte Carlo simulations and sensitivity analysis showed that ANN yielded the best prediction efficiency with the lowest uncertainty and variability level, whereas the predictive outcome of random forest was slightly better. Highlights • In-depth analysis of various artificial intelligence optimization techniques. • Prediction efficiency of artificial intelligence and machine learning algorithms. • 98% dye removal and 100% regeneration of graphite intercalation compound. • Advanced statistical analysis of targeted responses and data fitting techniques. • Analysis of uncertainties and variability using Monte Carlo simulation. Keywords Dye removal · Adsorption and electrochemical treatment · Artificial neural network · Support vector machine · Random forest · Monte Carlo simulation Extended author information available on the last page of the article 1 Introduction The textile, printing and dyeing industries are some of the largest producers of dye wastewater, contributing up to about 0.7 million metric tons of chemical dyes produced annually, accounting for 17 to 20% of water pollution worldwide (Pavlović et al. 2014) . In Bangladesh, the textile sector currently exports nearly 28 billion USD annually, up to 82% of the country’s total export earnings (Hossain et al. 2018) . In 2021, the textile industries in Bangladesh produced approximately 2.91 million metric tons of fabrics and around 349 million metric tons of wastewater generated from conventional dyeing practices (Hossain et al. 2018) . Figure 1 represents the water and chemical consumption of the textile processing industry in Bangladesh. 1.1 Types of Textile Wastewater Treatment Globally, about 60% of the annual output of synthetic dyes consists of azo compounds (Liu et al. 2022) . These azo dyes possess stable azo function groups (N = N) and aromati

FAQs

What treatment approach was used to decolourise methyl orange wastewater?

A three-dimensional electrochemical process was applied with strong oxidizing capability, and adsorption technology was used additionally to remove dye pollutants from wastewater.

Which machine learning models were used to optimize and predict the decolourisation process?

Artificial Neural Networks (ANN), Support Vector Machine (SVM), and random forest (RF) were used to model and optimize process parameters and to compare prediction performance.

How did Monte Carlo simulation and sensitivity analysis contribute to the study?

Monte Carlo simulation and sensitivity analysis were used to evaluate prediction efficiency and quantify uncertainty and variability, showing ANN achieved the best prediction with the lowest uncertainty.

Aug 3, 2026

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