In this paper a mixedintegernon-linearprogramming (MINLP) model is proposed for the design of shell and tube heat exchangers. The model rigorously follows the TEMA (Tubular Exchanger Manufacturers Association) Stan...
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In this paper a mixedintegernon-linearprogramming (MINLP) model is proposed for the design of shell and tube heat exchangers. The model rigorously follows the TEMA (Tubular Exchanger Manufacturers Association) Standards and Bell-Delaware Method is used to the shell side calculations. Mechanical design features (shell and tube bundle diameters, internal and external tube diameters, tubes length, pitch and tube arrangement, number of tubes and tube passes) and thermal-hydraulic variables (heat, area, individual and overall heat transfer coefficients, shell and tube pressure drops and fouling) are variables to be optimized. The equipment is designed under pressure drop and fouling limits. Three cases from the literature are studied, with two different objective functions, considering just the heat transfer area minimization or the annual cost minimization, including area and pumping expenses. More realistic values are obtained when compared with the literature, considering fouling and pressure drop effects according to TEMA Standards.
Plate Fin Heat Exchanger design is a very complex task. In most cases, heuristic-based procedures are used. In order to improve the company profits, the PFHE design problem is stated according to mathematical programm...
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Plate Fin Heat Exchanger design is a very complex task. In most cases, heuristic-based procedures are used. In order to improve the company profits, the PFHE design problem is stated according to mathematical programming techniques. First of all, objective functions such as manufacturing cost, physical volume are detailed as well as operating and manufacturing constraints. Finally, optimization variables including the geometrical fin parameters are described. Since most of the geometrical parameters of the exchanger (core number, geometrical fin parameters, etc...) have discrete values, this formulation results in a mixed integer non linear programming (MINLP) problem. Different solution strategies are discussed. For example, the solution of the relaxed problem using a Successive Quadratic programming (SQP) algorithm. Another example is the solution of the original MINLP problem using Simulated Annealing (SA) or Branch and Bound (BB) algorithms. The efficiency of the developed tool is illustrated by two industrial case studies : the manufacturing cost reduction is greater than 10%.
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