Surrogate model-based optimization of methanol synthesis process for multiple objectives: A pathway towards achieving sustainable development goals

This work combines a generalized regression neural network (GRNN) with a non -dominated sorting genetic algorithm (NSGA-II) to optimize a methanol synthesis plant for multiple objectives. The proposed method begins by developing and validating the model for the methanol synthesis involving the recycling of unreacted gas stream into the methanol reactor feed. The data generated using this model is used to develop a GRNN-based surrogate model to expedite the evaluation of fitness values for the optimization algorithm. The objective functions considered for simultaneous optimization are minimizing the total energy cost per unit of energy input (TEC), maximizing overall resource utilization (R), and minimizing CO2-gram equivalent emissions per unit of energy input (EPEI). Pareto optimal solutions are obtained for three bi-and one tri-objective optimization case studies using the GRNN-assisted multi -objective optimization (GRNNAMOO) method. The developed GRNNAMOO approach reduces the computational time from multiple days to a few seconds for all case study scenarios. For the tri-objective optimization case, the operating point selected using multi -criteria decision -making achieves 84.37% of R, 10.45 $/GJ of TEC, and an EPEI value of 81.34 gCO2eq/MJ. These results represent 7.05% higher R, 0.34 $/GJ lower TEC, and 0.82 gCO2eq/MJ lower EPEI than the Aspen plus simulation results. Further, the carbon footprint values obtained are significantly lower than 110 gCO2eq/MJ reported for modern methanol plants for nearly the same R and TEC values. The proposed approach aligns with the United Nations' sustainability development goals by lowering energy costs and promoting sustainable energy through improved environmental sustainability in methanol production plants.