An optimization-simulation strategy has been applied by coupling a commercial process simulator (Aspen HYSYS®) with a programming tool (MATLAB®) to produce a precise steady state simulation-based optimization of a whole green-field saturated gas plant as a real case study. The plant has more than 100-components and comprises interacting three-phase fractionation towers, pumps, compressors and exchangers. The literature predominantly uses this coupling to optimize individual units at small scales, while paying more attention to optimizing discrete design decisions. However, bridging the gap to scalable continuous design variables is indispensable for industry. The strategy adopted is a merge between sensitivity analysis and constrained bounding of the variables along with stochastic optimization algorithms from MATLAB® such as genetic algorithm (GA) and particle swarm optimization (PSO) techniques. The benefits and shortcomings of each optimization technique have been investigated in terms of defined inputs, performance, and finally the elapsed time for such highly complex case study. Although, both GA and PSO were satisfactory for the optimization, the GA provided greater confidence in optimization with wider ranges of constrained bounds. The implemented strategy precisely reached the best operating conditions, within the range covered, by minimizing the total annual cost while maintaining at least 92% butane recovery as a process guarantee for the whole plant. The optimization-simulation strategy applied in the current work is recommended to be used in brownfields to optimize the operating conditions since they are susceptible to continuous changes in feedstock conditions.
Keywords: HYSYS automation; MATLAB; Saturated gas plant; Sensitivity analysis; Simulation; Stochastic optimization.
© 2020 THE AUTHORS. Published by Elsevier BV on behalf of Cairo University.