Hybrid modelling incorporating reaction and mechanistic data for accelerating the development of isooctanol oxidation

Abstract

Alcohol oxidation is a widely used green chemical reaction. The reaction process produces flammable and explosive hydrogen, so the design of the reactor must meet stringent safety requirements. Based on the limited experimental data, utilizing the traditional numerical method of computational fluid dynamics (CFD) to simulate the gas-liquid two-phase flow reactor can mitigate the risk of danger under varying working conditions. However, the calculation process is highly time-consuming. Therefore, by integrating process simulation, computational fluid dynamics, and deep learning technologies, an intelligent hybrid chemical model based on machine learning was proposed to expedite CFD calculations, enhance the prediction of flow fields, conversion rates, and concentrations inside the reactor, and offer insights for designing and optimizing the reactor for the alcohol oxidation system. The results show that the hybrid model based on the long and short-term memory neural network achieves 99.8% accuracy in conversion rate prediction and 99.9% accuracy in product concentration prediction. Through validation, the hybrid model is accelerated by about 360 times compared with instrumental analysis in conversion rate prediction and about 45 times compared with CFD calculation in concentration distribution prediction. This hybrid model can quickly predict the conversion rate and product concentration distribution in the gas-liquid two-phase flow reactor and provide a model reference for fast prediction and accurate control in the actual chemical production process.