Combinatorial foundation for logical formulation in process network synthesis

Brendel, Marc; Friedler, Ferenc; Fan, L.T.

doi:10.1016/s0098-1354(00)00569-x

Cited by 21 publications

(15 citation statements)

References 10 publications

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“…The foundation of the graph-theoretic approach to flowsheet generation comprises (a) a novel graph representation in terms of process graphs (P-graphs), (b) a set of rigorous axioms portraying the unique structural features of process networks, and (c) a group of algorithms (34)(35)(36)(37)(38)(40)(41)(42)(43); also see Appendix 2, Supporting Information).…”

Section: Methodsmentioning

confidence: 99%

Downstream Process Synthesis for Biochemical Production of Butanol, Ethanol, and Acetone from Grains: Generation of Optimal and Near-Optimal Flowsheets with Conventional Operating Units

et al. 2004

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Manufacturing butanol, ethanol, and acetone through grain fermentation has been attracting increasing research interest. In the production of these chemicals from fermentation, the cost of product recovery constitutes the major portion of the total production cost. Developing cost-effective flowsheets for the downstream processing is, therefore, crucial to enhancing the economic viability of this manufacturing method. The present work is concerned with the synthesis of such a process that minimizes the cost of the downstream processing. At the outset, a wide variety of processing equipment and unit operations, i.e., operating units, is selected for possible inclusion in the process. Subsequently, the exactly defined superstructure with minimal complexity, termed maximal structure, is constructed from these operating units with the rigorous and highly efficient graph-theoretic method for process synthesis based on process graphs (P-graphs). Finally, the optimal and near-optimal flowsheets in terms of cost are identified.

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Section: Methodsmentioning

confidence: 99%

Downstream Process Synthesis for Biochemical Production of Butanol, Ethanol, and Acetone from Grains: Generation of Optimal and Near-Optimal Flowsheets with Conventional Operating Units

et al. 2004

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“…[20][21][22][23] Also, alternative methodologies to create and represent general process superstructures have been proposed. [24][25][26][27][28][29] In theory, the superstructure-based synthesis approach can be very effective because it considers a large number of process alternatives and it involves the simultaneous optimization of the structure and the operational conditions of the process. But this power comes at a price: the mathematical complexity of the resulting optimization model; generally, a large-scale nonconvex mixed-integer nonlinear program (MINLP).…”

Section: Introductionmentioning

confidence: 99%

Surrogate‐based superstructure optimization framework

2011

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in Wiley Online Library (wileyonlinelibrary.com).In principle, optimization-based ''superstructure'' methods for process synthesis can be more powerful than sequential-conceptual methods as they account for all complex interactions between design decisions. However, these methods have not been widely adopted because they lead to mixed-integer nonlinear programs that are hard to solve, especially when realistic unit operation models are used. To address this challenge, we develop a superstructure-based strategy where complex unit models are replaced with surrogate models built from data generated via commercial process simulators. In developing this strategy, we study aspects such as the systematic design of process unit surrogate models, the generation of simulation data, the selection of the surrogate's structure, and the required model fitting. We also present how these models can be reformulated and incorporated into mathematical programming superstructure formulations. Finally, we discuss the application of the proposed strategy to a number of applications.

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“…The MCDM science proposed a variety of methods to perform this task, which will not be discussed in this paper. Process synthesis and integration can also provide a convenient framework for generating alternatives (see for instance [43][44][45][46][47]). Finally, LCA can be used to generate process alternatives by identifying the ''hot spots'' of an existing process.…”

Section: Step 1b: Generation Of Alternativesmentioning

confidence: 99%