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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM65589
Volume:
10 |
6 |
Jun 2026
Pages:
1-10
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Chemistry
Suresh Gyan Vihar University, Jaipur, India
Department Mechanical Engineering
Suresh Gyan Vihar University, Jaipur, India
Department Electrical Engineering
Suresh Gyan Vihar University, Jaipur, India
Chemistry
Suresh Gyan Vihar University, Jaipur, India
Chemistry
Suresh Gyan Vihar University, Jaipur, India
Rapid industrialization and urban expansion have intensified the discharge of complex wastewater streams, necessitating efficient, sustainable, and cost-effective treatment solutions. Biological and agro-waste-derived treatment processes have emerged as promising alternatives due to their eco-friendly nature and resource recovery potential; however, their performance varies significantly under diverse operating conditions. In this study, a machine learning (ML)–based framework is proposed to systematically evaluate and predict the purification efficiency of biological and agro-waste-based wastewater treatment processes. Key physicochemical parameters, including biochemical oxygen demand (BOD), chemical oxygen demand (COD), total dissolved solids (TDS), nutrients, and pH, are used as input features to model treatment performance. Multiple ML algorithms namely linear regression, support vector machines, random forest, and extreme gradient boosting (XGBoost) are implemented and comparatively analyzed. Experimental results demonstrate that agro-waste-derived treatments, particularly biochar and lignocellulosic-based adsorbents, achieve superior removal efficiencies for organic and dissolved pollutants compared to conventional biological processes. Among the evaluated models, XGBoost exhibits the highest predictive accuracy and lowest error, indicating its robustness in capturing nonlinear relationships between wastewater characteristics and treatment outcomes. Feature importance analysis further reveals that organic load indicators, such as COD and BOD, are the dominant factors influencing treatment efficiency. The proposed ML-driven evaluation framework provides a reliable decision-support tool for selecting and optimizing sustainable wastewater treatment strategies, thereby supporting data-driven management of industrial and municipal wastewater systems.
KEYWORDS
This study presents a machine learning–based framework for the evaluation and comparative analysis of biological and agro-waste-derived wastewater treatment processes. Experimental results demonstrate that agro-waste-based treatments, particularly biochar- and lignocellulosic-based adsorbents, achieve superior removal efficiencies for organic and dissolved pollutants compared to conventional biological methods. Among the implemented ML models, XGBoost exhibited the highest predictive accuracy and lowest error, highlighting its robustness in modeling nonlinear relationships between influent water quality parameters and treatment performance. Feature importance analysis further revealed that COD and BOD are the dominant factors influencing overall treatment efficiency, providing actionable insights for process optimization. The proposed framework offers a reliable, data-driven decision-support tool for selecting and optimizing sustainable wastewater treatment strategies. Future work may focus on scaling the approach to industrial-level operations, integrating hybrid treatment processes, and exploring real-time predictive modeling for continuous wastewater management.
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Publication Date
6/28/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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