Exploring the adsorption desulfurization efficiency using RSM and ANN methodologies

Zeolites, known for their extensive surface area and customizable adsorption characteristics, demonstrate significant efficiency in adsorptive desulfurization. This research investigates the application of Response Surface Methodology (RSM) and Artificial Neural Networks (ANN) for modeling and optimizing the sulfur adsorption performance of modified zeolites. Key structural and operational parameters were investigated, including surface area, micropore volume, temperature, time, and sulfur compound molecular weight. Using the central composite design (CCD), RSM modeled the adsorption process by fitting experimental data through least-squares regression, providing valuable insights into parameter effects. The quadratic model achieved an adjusted correlation coefficient (R²) value of 0.9502 and a predicted R² value of 0.9475, indicating excellent predictive accuracy. While RSM highlighted significant trends, its limitations in capturing complex nonlinear interactions led to the adoption of ANN for more accurate predictions. The Radial Basis Function (RBF) and Multilayer Perceptron (MLP) models were developed among various ANN architectures. The RBF network achieved superior precision with an R² of 0.9951 and a mean square error (MSE) of 0.0015, outperforming the MLP. Furthermore, a global sensitivity analysis (GSA) was performed to identify the most influential input parameters, highlighting micropore volume as the dominant factor. An uncertainty analysis based on Monte Carlo simulations also confirmed the robustness and predictive stability of the optimized MLP model. Validation with new datasets confirmed ANN's reliability, making it a robust alternative to traditional modeling techniques. This study demonstrates ANN's potential as a powerful tool for optimizing adsorptive desulfurization processes. The findings pave the way for achieving ultra-low sulfur fuels through efficient and scalable approaches, reducing experimental efforts while enhancing process insights.