Abstract
This Research has focused on optimizing metallurgical processes by integrating Artificial Neural Networks with Shapley additive explanation modeling. This approach helps understand the intricate relationships and mechanisms underlying chemical processes. Neural networks provide accurate predictions based on input parameter interactions, while Shapley values identify the relative importance of each input variable and offer detailed explanations for model predictions, enhancing transparency and interpretability. In this study on roasting and leaching processes for sodium dichromate formation, the neural network - Shapley modeling framework was employed. The goal was to uncover the intricate interplay between input variables and sodium dichromate formation, providing valuable insights for process optimization and prediction. Key factors such as temperature, roasting time, reaction time, and sulfuric acid concentration were optimized in relation to the efficacy of sodium dichromate formation under different settings. The suggested neural networks model predicted optimal yields for combined roasting and leaching settings. The optimum conditions included a roasting temperature of 1046.26 °C, roasting time of 2.7 h, Cr: NaCl ratio of 1.5, leaching time of 41 min at a temperature of 40 °C, and sulfuric acid concentration of 12M. Global sensitivity analysis revealed that the yields of different metals were directly influenced by the temperature during roasting, concentration of sulfuric acid, Cr:NaCl ratio, roasting time, leaching temperature, and leaching time. These parameters were ranked in terms of sensitivity coefficients, indicating their relative importance.