Machine Learning-Driven Optimization of Textile Industry Effluent Treatment: A Case Study of Environmental Risk Assessment, Predictive Modeling, and Multi-Objective Optimization for Sustainable Wastewater Management


Keywords:
Textile Wastewater, Environmental Risk Assessment, Deep Learning, Machine Learning, Optimization, Multi Objective Genetic Algorithm, Sustainable Wastewater TreatmentAbstract
Industrial wastewater, particularly from the textile sector, contains complex pollutants that pose
significant environmental risks. This study focuses on characterizing textile effluent and assessing its
environmental impact using machine learning and optimization techniques. The analysis revealed that the
primary pollutant parameters exceed regulatory discharge limits, leading to a high environmental risk
classification. Advanced data-driven methodologies, including deep learning and machine learning models,
were applied to classify risk levels and predict pollution trends. Time-series models and classification
algorithms were utilized to analyze pollutant variations over time, while Random Forest regression and
classification models enabled accurate pollutant trend predictions. To mitigate the environmental risks
associated with textile wastewater, multiple optimization strategies were evaluated, considering cost
effectiveness and treatment efficiency. This approach successfully optimized pollutant removal efficiency,
minimized treatment costs, and reduced energy consumption while ensuring compliance with
environmental regulations.
Furthermore, scenario-based modeling included process optimization, implementation of advanced
treatment technologies, and integration of sustainable practices such as water and energy conservation, as
well as carbon and water footprint reduction. The study highlights the transformative potential of deep
learning in wastewater management, offering predictive capabilities that enable proactive environmental
risk mitigation. This research serves as a valuable reference for both academia and industry by providing a
systematic, data-driven framework for optimizing wastewater treatment processes.
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