Internal and external HIDiCs (heat-integrated distillation columns) optimization by genetic algorithm

HIDiC (Heat-Integrated Distillation Column) is an effective energy-saving configuration especially for the separation of close boiling point mixtures. In this work, the stochastic methodology has been applied for optimization of both internal and external HIDiCs. The use of GA (Genetic Algorithm) to find the optimal HIDiC configuration is presented while the fitness function is set to be the TAC (Total Annual Cost). HIDiC simulation has been performed based on the modified MESH equations using a rigorous thermodynamic model. Introducing a novel integer variable (the Layout number) leading to a more effective solution for the examined case study. This variable can generate systematically more energy efficient candidates for both internal and external HIDiCs. Benzene-toluene separation has been investigated by the proposed optimization procedure. The multivariable problem can be successfully optimized by GA while a good initial estimation is not essential. Based on the final results, up to 6.60% and 9.75% TAC reduction have been accomplished in external and internal HIDiCs optimization using the proposed method compared to the reported solutions in a previous work for the examined case study. However, VRC (Vapor Recompression Column) optimization at the end of presented work results 7.89% TAC reduction rather than optimal HIDiC. - Highlights: • HIDiC (Heat-Integrated Distillation Columns) and VRC (Vapor Recompression Column) Optimization. • Using Genetic Algorithm as a stochastic optimization procedure for the first time for this problem. • Introducing a new integer variable, Layout number, to produce more heat integration options. • Achieving economic improvements for both External/Internal HIDiC compared to previous work