The development of machine learning-based ensemble models for the prediction of complex
processes with non-linear nature (such as adsorption) has been remarkably advanced over …

End product quality prediction is one of the key issues in smart manufacturing. Reliable
evaluation and parameter optimization is needed to ensure their high-quality production …

Simulation of adsorption capacity of a nanocomposite material was performed in this study
in order to save time and cost in performing adsorption experiments. By develo** the …

Abstract Machine learning (ML) and artificial intelligence (AI) have enabled transformative
impact on materials science by accelerating cutting-edge insights from computational …

This study employed some machine learning (ML) techniques with Python programming to
forecast the adsorption capacity of MOF adsorbents for thiophenic compounds namely …

Green processing based on supercritical solvents has attracted much attention recently in
different fields such as pharmaceutical industry due to its superior characteristics …

Practical chemical process is usually a dynamic process including uncertainty. Stochastic
constraints can be used to chemical process modeling, where constraints cannot be strictly …

Accurate prediction of ethane and ethylene compositions in binary distillation columns is
critical for optimizing industrial separation processes. Traditional modeling approaches often …

The high-speed railway subgrade compaction quality is controlled by the compaction
degree (K), with the maximum dry density (ρdmax) serving as a crucial indicator for its …

The effect of anti-skid masterbatch (ASM) filled acrylonitrile butadiene styrene (ABS)
composite on friction coefficient (COF) and specific wear rate (SWR) characteristics are …