Abstract
Effective modeling of chemical kinetics is critical for industrial plant analysis and design. In this study, we explore the use of artificial intelligence technologies to model chemical kinetics and obtain accurate results. Specifically, we investigate the supercritical upgrading of hexadecanoic acid as a model compound for coffee ground pyrolysis crude bio-oil over a 2-h holding time. The solvent and catalyst used are ethanol and MgNiMo/AC, respectively. Gas chromatography and gas chromatography-mass spectrometry are used to analyze and characterize the product obtained. Based on experimental data, we determine the reaction pathway and develop a genetic algorithm (GA) based model using power law kinetics and the Runge-Kutta method to estimate kinetic parameters such as reaction order, frequency factor, and activation energy. According to the findings, the most favorable liquid product yield for supercritical ethanol upgrading occurs at a temperature of 350 °C. The provided process requires less labor than previous methods, reduces a significant portion of calculation, and is a potent resource for addressing a broad range of other issues in physics and engineering.
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