Scholarlink

<p>This study explores the application of machine learning (ML) models to optimize the adsorption-based treatment of distillery wastewater using sugarcane bagasse fly ash as an adsorbent. Four ML models—random forest, extreme gradient boosting (XGBoost), artificial neural network, and k-nearest neighbors were developed to predict the removal rates of chemical oxygen demand, biochemical oxygen demand, total suspended solids, and color. The models were trained and validated using data from batch adsorption experiments conducted under varying conditions of temperature, contact time, agitation speed, adsorbent dosage, and particle size. Descriptive statistics indicated significant variation in both the parameters and treatment efficiency, reflecting the experimental conditions. The XGBoost model consistently outperformed other models, achieving the highest coefficient of determination values (0.9910–0.9991) and lowest root mean squared error and mean absolute error values across all target variables. Feature importance analysis and sensitivity analysis revealed temperature as the most significant factor influencing pollutant removal, followed by contact time and agitation speed. Validation of unseen data further confirmed the XGBoost model’s superior predictive accuracy. The study demonstrates the potential of ML, particularly the XGBoost algorithm, in optimizing adsorption-based processes for treating distillery wastewater. These models can predict treatment outcomes under various operational conditions, potentially leading to more efficient treatment strategies. The research contributes to the growing application of ML in environmental remediation and wastewater management, offering a promising approach to enhance the efficiency and sustainability of distillery wastewater treatment.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Adsorption-based distillery effluent treatment: comparative analysis of machine learning models for predicting treatment efficiency

  • 期刊论文

  • 发表日期：

    2025年8月5日

查看全文
  • Chaitali K. Nikhar,
  • Gayatri S. Vyas,
  • Rupa S. Dalvi,
  • Dipak Y. Bhoye

摘要

This study explores the application of machine learning (ML) models to optimize the adsorption-based treatment of distillery wastewater using sugarcane bagasse fly ash as an adsorbent. Four ML models—random forest, extreme gradient boosting (XGBoost), artificial neural network, and k-nearest neighbors were developed to predict the removal rates of chemical oxygen demand, biochemical oxygen demand, total suspended solids, and color. The models were trained and validated using data from batch adsorption experiments conducted under varying conditions of temperature, contact time, agitation speed, adsorbent dosage, and particle size. Descriptive statistics indicated significant variation in both the parameters and treatment efficiency, reflecting the experimental conditions. The XGBoost model consistently outperformed other models, achieving the highest coefficient of determination values (0.9910–0.9991) and lowest root mean squared error and mean absolute error values across all target variables. Feature importance analysis and sensitivity analysis revealed temperature as the most significant factor influencing pollutant removal, followed by contact time and agitation speed. Validation of unseen data further confirmed the XGBoost model’s superior predictive accuracy. The study demonstrates the potential of ML, particularly the XGBoost algorithm, in optimizing adsorption-based processes for treating distillery wastewater. These models can predict treatment outcomes under various operational conditions, potentially leading to more efficient treatment strategies. The research contributes to the growing application of ML in environmental remediation and wastewater management, offering a promising approach to enhance the efficiency and sustainability of distillery wastewater treatment.