Metal bioleaching from printed circuit boards by bio-Fenton process: Optimization and prediction by response surface...

Area of Science:

• Environmental Science
• Chemical Engineering
• Materials Science

Background:

• Printed circuit boards (PCBs) in e-waste pose environmental challenges and represent a valuable resource for metal recycling.
• Efficient recovery of metals from PCBs is crucial for a circular economy and requires optimized recycling processes.

Purpose of the Study:

• To optimize and predict the enzymatic metal bioleaching from discarded cellphone PCBs using the bio-Fenton process.
• To evaluate the effectiveness of Response Surface Methodology (RSM) and Artificial Intelligence (AI) models for process optimization and prediction.

Main Methods:

• Utilized Box-Behnken design (BBD) of RSM for experimental design.
• Employed artificial intelligence models, specifically Support Vector Machine (SVM) and Artificial Neural Network (ANN), for predictive modeling.
• Investigated the impact of glucose oxidase (GOx) content, Fe2+ concentration, PCB pulp density, and shaking speed on metal bioleaching.

Main Results:

• Achieved maximum simultaneous enzymatic metal extraction: 100% Cu, 70% Ni, 40% Pb, and 100% Zn at optimized conditions (GOx: 300 U/L, Fe2+: 10 mM, pulp density: 1 g/L, shaking speed: 335 rpm).
• The Artificial Neural Network (ANN) model demonstrated superior prediction accuracy (R² > 0.99) compared to the Support Vector Machine (SVM) model.
• FTIR analysis confirmed the disintegration of the PCB polymeric base by hydroxyl radicals, facilitating metal liberation.

Conclusions:

• Optimized bio-Fenton process enables efficient and simultaneous enzymatic metal bioleaching from cellphone PCBs.
• AI models, particularly ANN, are effective tools for predicting and optimizing complex bioleaching processes.
• High selective metal recovery (>99%) is achievable through chemical precipitation of the bioleachate, supporting circular economy principles.