An Intelligent System for Predicting the Methanol Conversion Rate from the Direct Hydrogenation of CO₂ under Uncertainty

Abstract

In this study, an intelligent system was developed for the real-time monitoring of methanol conversion from the direct hydrogenation of CO2 under the effect of uncertainty in process conditions. The modeling and simulation of methanol synthesis were conducted using Aspen Hysys, the first-principal modeling software. The Aspen model was then shifted into dynamic mode by introducing a ±5% uncertainty in key process conditions, i.e., temperature, pressure, and mass flow rate, to produce a dataset comprising 370 samples. The data samples were then employed to build a Gaussian Process Regression (GPR) model to predict the methanol conversion rate. The GPR model has a root-mean-square error (RMSE) of 0.83127 and a coefficient of determination (R2) of 0.98078.

Keywords

• machine learning
• artificial intelligence
• soft sensors
• renewable methanol