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Optimization of a saturated gas plant: Meticulous simulation-based optimization - A case study.

ABSTRACT: 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.

SUBMITTER: Bayoumy SH

PROVIDER: S-EPMC6961219 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Optimization of a saturated gas plant: Meticulous simulation-based optimization - A case study.

Bayoumy Salah H SH El-Marsafy Sahar M SM Ahmed Tamer S TS

Journal of advanced research 20191130

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 ...[more]

PMID: 31956439

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Project description:Spatial capture-recapture (SCR) models have improved the ability to estimate densities of rare and elusive animals. However, SCR models have seldom been validated even as model formulations diversify and expand to incorporate new sampling methods and/or additional sources of information on model parameters. Information on the relationship between encounter probabilities, sources of additional information, and the reliability of density estimates, is rare but crucial to assessing reliability of SCR-based estimates. We used a simulation-based approach that incorporated prior empirical work to assess the accuracy and precision of density estimates from SCR models using spatially unstructured sampling. To assess the consequences of sparse data and potential sources of bias, we simulated data under six scenarios corresponding to three different levels of search effort and two levels of correlation between search effort and animal density. We then estimated density for each scenario using four models that included increasing amounts of information from harvested individuals and telemetry to evaluate the impact of additional sources of information. Model results were sensitive to the quantity of available information: density estimates based on low search effort were biased high and imprecise, whereas estimates based on high search effort were unbiased and precise. A correlation between search effort and animal density resulted in a positive bias in density estimates, though the bias decreased with increasingly informative datasets. Adding information from harvested individuals and telemetered individuals improved density estimates based on low and moderate effort but had negligible impact for datasets resulting from high effort. We demonstrated that density estimates from SCR models using spatially unstructured sampling are reliable when sufficient information is provided. Accurate density estimates can result if empirical-based simulations such as those presented here are used to develop study designs with appropriate amounts of effort and information sources.

Dataset Information

Optimization of a saturated gas plant: Meticulous simulation-based optimization - A case study.

Publications

Optimization of a saturated gas plant: Meticulous simulation-based optimization - A case study.

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