Bayoumy SH, El-Marsafy SM, Ahmed TS. Optimization of a saturated gas plant: Meticulous simulation-based optimization - A case study.
J Adv Res 2020;
22:21-33. [PMID:
31956439 PMCID:
PMC6961219 DOI:
10.1016/j.jare.2019.11.011]
[Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Revised: 11/25/2019] [Accepted: 11/27/2019] [Indexed: 11/24/2022] Open
Abstract
A viable optimization-simulation strategy by coupling Aspen HYSYS with MATLAB.
The optimization strategy has been applied to a complex complete saturated-gas plant.
Different stochastic algorithms have been applied.
The benefits and shortcoming of each method have been investigated.
The implemented strategy precisely reached the optimum operating conditions.
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.
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