High-Level Optimization Model for the Retrofit Planning of Process Networks

Jackson, Jennifer R.; Grossmann, Ignacio E.

doi:10.1021/ie010699x

Cited by 25 publications

(7 citation statements)

References 22 publications

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“…One of the possible representations of discrete‐continuous problems is Generalized Disjunctive Programming (GDP), developed by Raman and Grossmann4 as an extension of the disjunctive programming paradigm developed by Balas 5. GDP was first applied to problems formulated in equation oriented environment, e.g., in the optimal design of reactive distillation columns6 and retrofit design 7. It was also applied to batch processes, e.g., in the synthesis of biotechnological processes,8 and finally to problems formulated by implicit models in modular process simulators, e.g., the rigorous design of distillation columns,9 and flowsheet optimization with complex cost and design functions 10…”

Section: Introductionmentioning

confidence: 99%

Proliferative activity in primary breast carcinomas is a salient prognostic factor

Michels

Marescaux

Delozier

et al. 2004

Cancer

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This article describes alternative GDP formulation and convex hull representations for process synthesis problems and their implementation in a unique MINLP process synthesizer MIPSYN. A special translation of variables in mixed‐integer, relaxed, and logic‐based variations has been proposed, which enables modeling and solving process alternatives in a narrowed lifted space of variables, defined by nonzero lower and upper bounds. Based on these translation variations, alternative formulations have been developed for convex hulls, multiple‐term generalized disjunctive programming problems, and logic‐based outer‐approximation algorithm, all of them being specialized for the synthesis of process flowsheets. Several studies were performed and three different large‐scale synthesis problems were solved to test the performance and efficiency of different formulations. This initial research indicates that the proposed alternative convex hull representation usually outperforms the conventional one when solving both MILP and NLP steps in highly combinatorial MINLP process networks problems. © 2009 American Institute of Chemical Engineers AIChE J, 2009

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Section: Introductionmentioning

confidence: 99%

Proliferative activity in primary breast carcinomas is a salient prognostic factor

Michels

Marescaux

Delozier

et al. 2004

Cancer

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“…We assume that for process 3, 83% of converted C makes E and the remaining 17% makes B, and that reactants A and B are fed to process 2 in a 10:1 ratio 16 . Existing capacities of each process are 27, 30, and 25 tons, respectively.…”

Section: Descriptionmentioning

confidence: 99%

Modeling of Purchase and Sales Contracts in Supply Chain Optimization

Park¹,

Park²,

Grossmann³

2006

Ind. Eng. Chem. Res.

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This work presents a novel approach for modeling of different types of contracts that a company may sign with its suppliers and customers. The main objective is to expand the scope of current planning and supply chain optimization models by including the selection of the types of contracts as an additional decision. The solution approach relies on representing the decision of choosing different contracts using disjunctive programming for both short-term and long-term production planning models. The resulting formulation is converted into a mixed-integer linear programming (MILP) problem. The advantages of the proposed models are highlighted in two case studies of increasing complexity.

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“…Given limited capital investments to make process improvements and cost estimations over a given time horizon, the problem consists of identifying those modifications that yield the highest economic improvement in terms of economic potential, which is defined as the income from product sales minus the cost of raw materials, energy and process modifications. Sawaya and Grossmann (2004) have developed a GDP model for this problem, which is a modification of work by Jackson and Grossmann (2002).…”

Section: Retrofit Planning Problemmentioning

confidence: 99%

Advances in Logic‐Based Optimization Approaches to Process Integration and Supply Chain Management

Grossmann¹

2005

Chemical Engineering

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Optimization as an enabling technology has been one of the big success stories in process systems engineering. In this paper we present a review on recent research work in the area of logic-based discrete/continuous optimization. In particular, recent advances are presented in the modeling and solution of nonlinear mixed-integer and generalized disjunctive programming, global optimization and constraint programming. The impact of these techniques is illustrated with several examples in the areas of process integration and supply chain management.. where f(x, y) is the objective function (e.g. cost), h(x, y) = 0 are the equations that describe the performance of the system (material balances, production rates), and g(x,y) ≤ 0 are inequalities that define the specifications or constraints for feasible plans and schedules. The variables x are continuous and generally correspond to state variables, while y are the discrete variables, which generally are restricted to take 0-1 values to define for instance the assignments of equipment and sequencing of tasks. Problem (MIP) corresponds to a mixed-integer nonlinear program (MINLP) when any of the functions involved are nonlinear. If all functions are linear it corresponds to a mixed-integer linear program (MILP). If there are no 0-1 variables, the problem (MIP) reduces to a nonlinear program (NLP) or linear program (LP) depending on whether or not the functions are linear.It should be noted that (MIP) problems, and their special cases, may be regarded as steady-state models. Hence, one important extension is the case of dynamic models, which in the case of discrete time models gives rise to multiperiod optimization problems, while for the case of continuous time it gives rise to optimal control problems that contain differential-algebraic equation (DAE) models.Mathematical programming, and optimization in general, have found extensive use in process systems engineering. A major reason for this is that in these problems there are often many alternative solutions, and hence, it is often not easy to find the optimal solution. Furthermore, in many cases the economics is such that finding the optimum solution translates into large savings. Therefore, there might be a large economic penalty to just sticking to suboptimal solutions. In summary, optimization has become a major technology that helps companies to remain competitive.Applications in Process Integration (Process Design and Synthesis) have been dominated by NLP and MINLP models due to the need for the explicit handling of performance equations, although simpler targeting models in process synthesis can give rise to LP and MILP problems. An extensive review of optimization models for process integration can be found in Grossmann et al. (1999). In contrast, Supply Chain Management problems tend to be dominated by linear models, LP and MILP, for planning and scheduling (see Grossmann et al. 2002 for a review). Finally, global optimization has concentrated more on

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High-Level Optimization Model for the Retrofit Planning of Process Networks

Cited by 25 publications

References 22 publications

Proliferative activity in primary breast carcinomas is a salient prognostic factor

Proliferative activity in primary breast carcinomas is a salient prognostic factor

Modeling of Purchase and Sales Contracts in Supply Chain Optimization

Advances in Logic‐Based Optimization Approaches to Process Integration and Supply Chain Management

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