Integrating stochastic programming and reliability in the optimal synthesis of chemical processes
•Novel two-stage stochastic programming GDP (Generalized Disjunctive Programming) model with reliability constraints to deal with both the exogenous and endogenous uncertainties in process synthesis.•The reliability model is incorporated into the flowsheet superstructure optimization, considering th...
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| Vydáno v: | Computers & chemical engineering Ročník 157; s. 107616 |
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| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier Ltd
01.01.2022
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| Témata: | |
| ISSN: | 0098-1354 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | •Novel two-stage stochastic programming GDP (Generalized Disjunctive Programming) model with reliability constraints to deal with both the exogenous and endogenous uncertainties in process synthesis.•The reliability model is incorporated into the flowsheet superstructure optimization, considering the impact of selecting parallel units for improving plant availability.•Improved LOA algorithm to solve the hybrid GDP model with implicit nested disjunctions, obtaining optimal solutions by avoiding zero-flow numerical difficulties.•Quantification of the value of stochastic solution (VSS) and value of reliable solution (VRS) are used as the key measures for assessing the benefits of stochastic programming and reliability-based design optimization.•Simultaneous optimization of reliability and exogenous uncertainty in process design provides potential improvement for operational flexibility and economic performance.
Plant availability and operating uncertainties are critical considerations for the design and operation of chemical processes as they directly impact service level and economic performance. This paper proposes a two-stage stochastic programming GDP (Generalized Disjunctive Programming) model with reliability constraints to deal with both the exogenous and endogenous uncertainties in process synthesis, where the reliability model is incorporated into the flowsheet superstructure optimization. The proposed stochastic programming model anticipates the market uncertainties through scenarios for selecting the optimal flowsheet topology, equipment sizes and operating conditions, while considering the impact of selecting parallel units for improving plant availability. An improved logic-based outer approximation algorithm is applied to solve the resulting hybrid GDP model, which effectively avoids numerical difficulties with zero flows and provides high quality design solutions. The applicability of the proposed modeling framework and the efficiency of solution strategy are illustrated with two well-known conceptual design case studies: methanol synthesis process and toluene hydrodealkylation process. The model, which integrates reliability (endogenous uncertainty) and exogenous uncertainty, shows the best economic performance with the increasing operational flexibility and plant availability. |
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| ISSN: | 0098-1354 |
| DOI: | 10.1016/j.compchemeng.2021.107616 |