Artificial Neural Network Surrogate Modelling for Predicting and Optimising CO₂ Conversion to Methanol Under Uncertainty

This study introduces a data-driven surrogate modelling framework that combines an artificial neural network (ANN) with particle swarm optimisation (PSO) and a genetic algorithm (GA) to optimise methanol production under uncertain conditions. A steady-state Aspen Plus model was developed and converted into dynamic mode by applying ± 5% uncertainty across 12 key process variables, generating 3880 data points that reflect realistic operational variability. The ANN model was trained and validated on the samples, achieving predictive accuracy (R² = 0.988, RMSE = 28.59) on unseen test data. Key features of the work include the use of the ANN as a surrogate model, its integration within PSO and GA optimisation frameworks and its application alongside Sobol and Fourier amplitude sensitivity test (FAST) methods to identify the most influential process variables affecting the methanol production rate. The proposed framework resulted in performance improvements, with PSO achieving an increase of 38.63% and GA 33.14% in methanol production. Cross-validation with the Aspen Plus model confirmed the reliability of the optimised operating conditions, with relative errors ranging from 0.07% to 2.15%. Overall, the study demonstrates the effectiveness of integrating surrogate modelling with intelligent optimisation techniques to improve the efficiency and robustness of methanol production processes under uncertainty.