Globally Optimal Design of Double Pipe Heat Exchangers using Local Properties and Discretized Models

Peccini, Alice; Costa, André L.H.; Bagajewicz, Miguel J.

doi:10.1016/b978-0-12-818634-3.50032-1

Cited by 1 publication

(3 citation statements)

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“…This scenario suggests one of the challenges ahead: how to move away from simplified models based on analytical solutions and address the optimization design problem using rigorous modeling. We have already found instances where better modeling avoids undersizing, 57 as presented later.…”

Section: Minlm Using Geometrically Distributedmentioning

confidence: 69%

“…The expression of the overall heat transfer coefficient using adequate correlations for evaluation of the heat transfer coefficients can be inserted into eq , but it is important to observe that all the variables present in the correlations are related to geometrical design variables that can only assume discrete values. Therefore, the application of the techniques described in section to transform the resultant mathematical expression in a linear model yields the following linear set of constraints: where is a parameter that depends on the physical properties and the discrete values of the design variables.…”

Section: Minlm Using Geometrically Distributed Propertiesmentioning

confidence: 99%

“…This optimization formulation was applied to the design problem described in Table 10. 57 The search space considers 5 options of tube length, 9 options of inner and outer tube diameters, and 20 options of number of units (aligned in series).…”

Section: Minlm Using Geometrically Distributedmentioning

confidence: 99%

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110th Anniversary: On the Departure from Heuristics and Simplified Models toward Globally Optimal Design of Process Equipment

Costa

Bagajewicz

2019

Ind. Eng. Chem. Res.

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Despite the large advances in computational tools attained by the Process Systems Engineering community, industry for the most part still performs basic design of process equipment using trial-and-verification procedures guided by heuristic rules. In this opinion article, we present a discussion on how to depart from the use of these heuristics-based procedures, most step-by-step, sometimes computeraided. We believe that there are direction changes, some incipient and some in full development already, toward the use of optimization tools for the task. The academic literature is dominated by mixed-integer nonlinear models, solved using different techniques (mostly stochastic or mathematical programming-based). These procedures have practical limitations that have hindered the migration of practitioners away from current heuristics and simplified model-based tools. We discuss these drawbacks and propose solutions. We first show how the use of commercially available discrete values of the design geometrical variables followed by reformulation can render linear models, solved using mixed-integer linear programming, sometimes integer linear programming. We also show how reformulation or judicious discretization of continuous variables can lead to a large reduction in the number of nonlinearities, such that they can be solved using commercial and noncommercial global solvers with larger efficiency and robustness. Next, we present the use of set trimming to aid global optimization of equipment designs, as means of reducing the search space. Then, we present the technique of smart enumeration, which can be used instead of MINLP models and eventually after set-trimming. We also discuss changes in modeling and propose to move away from simple models, where we argue for abandoning physical properties as well as transport-like coefficients that are based on averages, to use properties and transport coefficients calculated locally inside and along the equipment. Then, we discuss the impact of the above proposals on flowsheet synthesis and retrofit problems. As these advances show higher memory and speed needs, we discuss how parallel computing (using the cloud, small computer clusters, or supercomputers) can address the aforementioned challenges.

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Section: Minlm Using Geometrically Distributedmentioning

confidence: 69%