ANN assisted optimization of phosgene free hexamethylene diisocyanate production for economic and environmental benefits.

The production of hexamethylene-1,6-diisocyanate (HDI) via carbamate cracking avoids the use of phosgene; however, the environmental impact of this non-phosgene synthesis route has not been thoroughly studied. Conventional process simulations typically focus solely on economic indicators, which may overlook important environmental considerations. We have developed a multi-objective optimization framework that collectively accounts for total annual cost, green degree and total energy consumption for such a non-phosgene synthesis process of HDI. We used data from rigorous simulations to construct a surrogate model based on artificial neural networks. This model simplifies the numerical correlation among optimization objectives and process/operational parameters. The bat algorithm was employed for multi-objective optimization on such surrogate models with significantly reduced computational burden, resulting in a series of optimal solutions as the Pareto front. The optimal solution exhibits a 28.79% reduction in total energy consumption and a 19.72% reduction in the total annual cost compared to the real-world production data; the green degree of the optimized process is increased by 21.60%.