Abstract The use of optimization tools in distillation processes can be computationally intensive, due to the scale of the simulation problems associated with the optimization structure. In many situations, it is desirable to obtain a distillation column configuration that is capable of achieving pre-established degrees of purity for the products of interest while simultaneously minimizing operating costs and annualized investments. In this paper, we present the development of an optimization–simulation tool for distillation problems using a low-parameter metaheuristic (the Symbiotic Organisms Search algorithm, SOS) coupled with a simulation tool with sparse Jacobian matrix calculations (using a coloring graph algorithm) and code vectorization. The applicability of the developed framework was demonstrated in a separation train for a non-ideal ternary mixture. The numerical results obtained indicate that the computational tool proved to be viable from a computational time standpoint—with significant gains over the calculation of the Jacobian matrix using finite differences—approaching the vicinity of the optimum region in a small number of iterations. In addition, the possibility of integrating simulation and optimization tools for distillation processes in a single environment was demonstrated, avoiding complex communication structures between the simulator and the optimizer. The results obtained also indicated substantial improvements—in terms of minimizing a metric that involves investments and energy expenditures—in comparison with non-optimized designs for the same separation train.
Platt et al. (Fri,) studied this question.