Application of Machine Learning for Optimization and Comparative Analysis of Heavy Metal Recovery from Printed Circuit Board (PCB) Industrial Wastewater: A Focus on Oxalate Precipitation and Competing Technologies

The rapid proliferation of the electronics manufacturing industry has resulted in the generation of large volumes of wastewater contaminated with toxic heavy metals, including copper (Cu), lead (Pb), nickel (Ni), tin (Sn), gold (Au), and silver (Ag), leached from printed circuit board (PCB) etching and plating processes. Conventional treatment methods such as chemical precipitation, ion exchange, membrane separation, and electrochemical recovery are widely employed; however, these processes suffer from poor process optimization, high operational costs, and limited adaptability to variable influent conditions. This study presents a comprehensive framework for applying machine learning (ML) algorithms including Artificial Neural Networks (ANN), Random Forest (RF), Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost) to optimize and comparatively analyze these treatment technologies with a focus on oxalate precipitation as a novel metal recovery route. Experimental data encompassing key parameters such as pH, temperature, oxalate-to-metal molar ratio, reaction time, and initial metal concentration were used to train and validate predictive models. Results demonstrate that ANN and XGBoost models achieved prediction accuracy exceeding R² = 0.97 for removal efficiency, enabling identification of optimal operating conditions. A comparative ML-driven analysis revealed that oxalate precipitation yielded superior selectivity for Cu and Pb recovery (>95%), converting recovered metals to stable metal oxides with direct industrial reuse potential. This study pioneers the integration of ML with oxalate-based metal recovery from PCB wastewater, providing a scalable and data-driven tool for sustainable industrial water treatment.