Using bone char for contaminated wastewater treatment and soil remediation is an intriguing approach to environmental management and an environmentally friendly way of recycling waste. The bone char remediation strategy for heavy metal-polluted wastewater was primarily affected by bone char characteristics, factors of solution, and heavy metal (HM) chemistry. Therefore, the optimal parameters of HM sorption by bone char depend on the research being performed. Regarding enhancing HM immobilization by bone char, a generic strategy for determining optimal parameters and predicting outcomes is crucial. The primary objective of this research was to employ artificial neural network (ANN) technology to determine the optimal parameters via sensitivity analysis and to predict objective function through simulation. Sensitivity analysis found that for multi-metals sorption (Cd, Ni, and Zn), the order of significance for pyrolysis parameters was reaction temperature > heating rate > residence time. The primary variables for single metal sorption were solution pH, HM concentration, and pyrolysis temperature. Regarding binary sorption, the incubation parameters were evaluated in the following order: HM concentrations > solution pH > bone char mass > incubation duration. This approach can be used for further experiment design and improve the immobilization of HM by bone char for water remediation.
Research on plastic pollution is crucial, particularly with the recent emphasis on converting waste plastics into oil for sustainable energy. Very few studies have utilized artificial neural network (ANN) modeling for plastic thermal conversion, such as predicting fuel yield from mixed plastics and performing sensitivity analyses to identify which plastics produce more oil. Meanwhile, no study has conducted a comparative analysis of different models for catalytic and non-catalytic thermal conversion of various plastics, nor has a sensitivity analysis of process parameters using ANN for oil production. This study aims to (1) validate and predict oil yield across different catalytic and non-catalytic thermal conversion processes for plastics using MATLAB-based ANN training; (2) perform sensitivity analysis on process parameters affecting oil production; and (3) forecast oil yield using virtual input parameters not included in real experiments. The models demonstrate R2 values near 1 and mean squared error (MSE) values close to zero, indicating strong validation. For catalytic polyethylene (PE) pyrolysis, the impact ranking is reaction temperature (36.9 %) > pressure (32.1 %) > Zn loading in ZSM5 (30.9 %). In non-catalytic PE and biomass co-torrefaction, the impact ranking is reaction temperature (47.2 %) > feedstock-to-solvent ratio (23.9 %) > biomass-to-PE ratio (16.6 %) > experimental duration (12.1 %). For catalytic mixed plastic (MP) torrefaction, the ranking is reaction temperature (54.8 %) > duration (18.4 %) > solid-to-liquid ratio (15.9 %) > NaOH amount (10.8 %). In non-catalytic MP pyrolysis, the significance ranking is particle size (44.51 %) > pyrolysis temperature (34.4 %) > pyrolysis duration (21.06 %). Accordingly, temperature, catalyst loading, and duration are critical for catalytic processes, while particle size and temperatures affect non-catalytic pyrolysis. The predicted and experimental outcomes differ by only 1 to 3, demonstrating that the models accurately simulate the predicted values. This study uses ANN sensitivity analysis to compare catalytic and non-catalytic methods, offering insights into scale-up applications and sustainability.