Issue Highlights

Steady-state simulation tools guide process design, optimization, and equipment selection in the chemical industries.Accurate validation requires reliable process data, but errors and dynamic conditions complicate fidelity.This article outlines strategies for validating simulations through expert engagement (informal validation) and through comparisons with process data (formal validation), aiming for accuracy within the bounds of measurement variability.Despite the critical importance of simulation validation, it remains underrepresented in the literature.A case study on Kraft mill washing illustrates how employing effective data treatment and practical validation techniques can enhance confidence in simulations, thereby rendering them invaluable for troubleshooting and day-to-day decision making. [1]tassium sulphate production from an aqueous sodium sulphate from lead-acid battery recycling: Impact of feedstock impurities on products yields Barialo Zorzor, Michael Fabrik, Hussameldin IbrahimThe demand for renewable energy requires efficient energy storage solutions.Lead-acid batteries are preferred for many industrial uses but need proper waste management and recycling.Lead-acid battery recycling produces Na 2 SO 4 , which cannot be sold due to quality concerns.Na 2 SO 4 is a precursor for producing K 2 SO 4 , a valuable fertilizer.Previous research assumed pure Na 2 SO 4 , but recycled Na 2 SO 4 contains contaminants.HSC Chemistry software modelled K 2 SO 4 and NaCl production from impure Na 2 SO 4 and KCl.Under ideal conditions, over 90% yield of K 2 SO 4 and NaCl was achieved without impurities.Impurities reduced yields, but levels from 1% to 4% still achieved over 90% yields. [2]-driven models for predicting CO 2 uptake in metal-organic frameworks (MOFs) Sofiene Achour, Zied HosniThis study explores the use of machine learning (ML) to predict CO 2 uptake in metal-organic frameworks (MOFs), with a focus on the CATBoost model.The model demonstrated superior predictive accuracy, reducing RMSE by 15% and enhancing R 2 .SHapley Additive exPlanations (SHAP) analysis identified key factors like surface area, pressure, and pore volume as crucial for CO 2 adsorption.Our investigation underscores ML's role in optimizing MOF design for carbon capture.Despite its success, the study suggests expanding the dataset and integrating advanced ML techniques for improved predictions, reinforcing computational methods as essential tools for accelerating MOF discovery.