Pyomo.GDP: Disjunctive Models in Python

Chen, Qi; Johnson, Emma S.; Siirola, John Daniel; Grossmann, Ignacio E.

doi:10.1016/b978-0-444-64241-7.50143-9

Cited by 19 publications

(17 citation statements)

References 11 publications

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“…In this work, we propose a superstructure optimization approach for the optimal design of an ethylene-propylene coproduction plant embedding models for the units described in Section 2 . We represent the superstructure with a Generalized Disjunctive Programming (GDP) model ( Chen et al, 2018 ;Trespalacios and Grossmann, 2014 ; Vecchietti and Grossmann, 0 0 ), in which the presence of process units is associated to Boolean variables. Its general formulation is as follows,…”

Section: Generalized Disjunctive Programmingmentioning

confidence: 99%

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Optimal design of ethylene and propylene coproduction plants with generalized disjunctive programming and state equipment network models

Pedrozo

Reartes

Vecchietti

et al. 2021

Computers & Chemical Engineering

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In this work, we propose a superstructure optimization approach for the optimal design of an ethylene and propylene coproduction plant. We formulate a superstructure that embeds ethane and propane steam cracking technologies, propane dehydrogenation and olefin metathesis processes. We represent the superstructure with a Generalized Disjunctive Programming model, and solve the problem through a custom implementation of the Logic-based Outer Approximation algorithm in GAMS. We propose a stateequipment-network representation to model potential distillation trains, as well as alternative acetylene reactor configurations. Rigorous models are formulated for distillation columns, compressors, turboexpanders, vessels and several process equipment units. The objective function is to maximize the net present value. We analyze four international price scenarios for raw material and utility costs, while considering global ethylene and propylene prices. We obtain the optimal scheme for each case. Numerical results show that the combination of ethane steam cracking, olefin metathesis and ethylene dimerization is the most profitable configuration under low ethane price scenarios, whereas the combination of ethane and propane steam cracking together with propane dehydrogenation is the optimal solution when the propane price is on the order of ethane price.

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Section: Generalized Disjunctive Programmingmentioning

confidence: 99%

“…To solve the complex GDP problem, we have implemented the Logic-based Outer Approximation algorithm ( Chen et al, 2018 ;Türkay and Grossmann, 1996 ; Vecchietti and Grossmann, 20 0 0 ) within the modeling environment GAMS ( Rosenthal, 2014 ), as described in Section 3.3 .…”

Section: Generalized Disjunctive Programmingmentioning

confidence: 99%

Optimal design of ethylene and propylene coproduction plants with generalized disjunctive programming and state equipment network models

Pedrozo

Reartes

Vecchietti

et al. 2021

Computers & Chemical Engineering

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show abstract

“…Once the reformulation to MINLP is made, the model can be sent to the user's solver of choice. Direct logic-based decomposition approaches [22,39] are also possible for solving GDP models, with implementations available in Pyomo.GDP via the GDPopt solver [46].…”

Section: Solution Strategiesmentioning

confidence: 99%

“…However, as a closed-source commercial platform, academic interest has been limited. More recently, Pyomo.GDP [46] has emerged as an open-source ecosystem for GDP modeling and development, built on top of the Pyomo algebraic modeling language [47] in Python. As an open-source platform, it has been able to incorporate recent innovations in reformulation strategies [48] and logic-based solution algorithms [22].…”

Section: Introductionmentioning

confidence: 99%

Modern Modeling Paradigms Using Generalized Disjunctive Programming

Chen

Grossmann

2019

Processes

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Models involving decision variables in both discrete and continuous domain spaces are prevalent in process design. Generalized Disjunctive Programming (GDP) has emerged as a modeling framework to explicitly represent the relationship between algebraic descriptions and the logical structure of a design problem. However, fewer formulation examples exist for GDP compared to the traditional Mixed-Integer Nonlinear Programming (MINLP) modeling approach. In this paper, we propose the use of GDP as a modeling tool to organize model variants that arise due to characterization of different sections of an end-to-end process at different detail levels. We present an illustrative case study to demonstrate GDP usage for the generation of model variants catered to process synthesis integrated with purchasing and sales decisions in a techno-economic analysis. We also show how this GDP model can be used as part of a hierarchical decomposition scheme. These examples demonstrate how GDP can serve as a useful model abstraction layer for simplifying model development and upkeep, in addition to its traditional usage as a platform for advanced solution strategies.

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“…IPOPT (Wachter and Biegler, 2006) was used for the solution of the NLP optimization problem, while Bonmin solver (Grossmann et al, 2005) was used to solve the MINLP problem using the algorithm B-Hyb (Hybrid outer-approximation based branch-and-cut algorithm). For the solution of the GDP problem, we used the new logic-based solver GDPopt (Chen et al, 2018).…”

Section: Case Studymentioning

confidence: 99%

Kaibel column: Modeling, optimization, and conceptual design of multi-product dividing wall columns

Rawlings

Chen

Grossmann

2019

Computers & Chemical Engineering

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In this work, we present the modeling, optimization, and conceptual design of a dividing wall column for the separation of four products, commonly referred to in the literature as a Kaibel column. For its solution, we propose and validate a rigorous tray-by-tray model, followed by its reformulation to include a mixed-integer nonlinear programming and a general disjunctive programming formulation to respond to the conceptual design problem attached to these complex configurations. Considering the validated model and the two formulations, the Kaibel column is solved, obtaining four high-purity products and new optimal tray locations for the feed and two side product streams, when the mixed-integer nonlinear programming formulation is applied. The use of these optimally located side streams showed reductions in the energy consumption when compared to cases were non-optimal fixed tray locations are used. When the general disjunctive programming problem was solved, the minimum number of trays needed in the main column and dividing wall are obtained, showing a great reduction of the remixing effects in the Kaibel column, and with that, a more energy efficient configuration. The models were coded in Pyomo using the solver IPOPT for the solution of the nonlinear programming problem, the solver Bonmin for the solution of the mixedinteger nonlinear programming problem, and GDPopt for the solution of the general disjunctive programming optimization problem.

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Pyomo.GDP: Disjunctive Models in Python

Cited by 19 publications

References 11 publications

Optimal design of ethylene and propylene coproduction plants with generalized disjunctive programming and state equipment network models

Optimal design of ethylene and propylene coproduction plants with generalized disjunctive programming and state equipment network models

Modern Modeling Paradigms Using Generalized Disjunctive Programming

Kaibel column: Modeling, optimization, and conceptual design of multi-product dividing wall columns

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