Abstract:
Artificial Neural Networks (ANN) utilization as surrogates within particle swarm
optimization (PSO) and genetic algorithm (GA) frameworks for methanol flow rate
optimization under uncertainty is explored in this work. First, Aspen Plus model with
steady-state conditions of the CO2 hydrogenation to methanol process was developed. The
process model was then transformed to a dynamic mode by inserting ±5% uncertainty in
the process and 3880 data samples were generated. An ANN model, developed using
MATLAB 2022a, was trained using these data samples. ANN achieved an impressive
accuracy having a root mean square error (RMSE) of 26.83 and correlation coefficient (R)
of 0.995 while testing for unseen data during cross-validation of the model. Then the ML
model ANN was used as a surrogate in the PSO and GA for optimization methods to
identify optimal conditions that maximize the methanol flow rate amidst uncertainty.
Results show consistent improvements over the standalone Aspen model, with PSO
slightly outperforming GA. Validation in Aspen Plus confirms the efficacy of the proposed
methodology. This study highlights the potential of ANN-based surrogate modeling and
its application in intelligent data-driven optimization for complex chemical processes
under uncertainty, ultimately contributing to more efficient production systems.
