A Novel Sequential Approach for the Design of Heat Exchanger Networks

Abstract

This paper presents a new algorithm for the design of heat exchanger networks (HEN) that tries to take advantage of the strengths of the sequential and simultaneous approaches. It is divided into two sequential parts. The first one is an adaptation of the transportation model (TransHEN). It maintains the concept of temperature intervals and considers the possibility of heat transfer between all the hot and cold streams inside those intervals, and at the same time it allows the a priori calculation of the logarithmic mean temperature difference between all possible heat exchanges, and therefore it maintains the area estimation linear in the model. The second step (HENDesign model), uses a superstructure that contains all the possible alternatives in which the matches predicted by the first stage model can exchange heat to design the final heat exchanger network. Unlike the sequential approach, in this model, all heat flows, temperatures, areas, etc. are reoptimized maintaining the set of matches predicted in the first stage. The model is highly nonlinear and nonconvex, however, it is relatively easy to get good results, because the model starts with the values predicted by the TransHEN model. The algorithm has been tested using fifteen benchmark problems commonly used in literature to compare the performance of heat exchanger network algorithms. In eleven out of the fifteen cases present better or equal results than the best ones reported in the open literature. In three the results presented only marginal differences in total annualized cost (lower than 0.5%) and only a difference of 2.4% in the largest one.

Heat exchanger networks with different shell and tube configurations

“…The minimum energy cost can be calculated by minimizing the cost of utilities in previous equations. If we assume pure countercurrent heat exchangers, the calculation of the area is straightforward, because it can be calculated as the summation of the areas of heat exchanged by two streams in the different temperature intervals (Caballero et al, 2021;Nemet et al, 2018). However, the efficiency factor Ft and the Number of shells in series for a 1-2 S&T heat exchanger, depend on the inlet and outlet temperatures of the hot and cold streams.…”

“…are commonly not included, but they can have an important impact of the HEN performance and total cost. Some models that simultaneously design the HEN and the equipment details are, for example, (Ravagnani and Caballero, 2007) or the excellent work by Kazi et al, (2021) In this work, we present a two-stage sequential model inspired by the works by Nemet et al, (2018) and Caballero et al, (2021) that tries to capture the most important parameters of each heat exchanger into a HEN without performing a rigorous design of all the mechanical details to get a cost-optimal HEN. The first stage relies on the concept of temperature intervals and takes de form of an extended transportation model.…”

Synthesis of Heat-Integrated Distillation Sequences with Nonsharp Splits Using a Sequential Metaheuristic Method

“…Solution approaches also vary from deterministic to stochastic. Recent efforts that use deterministic methods are the stepwise method of Nemet et al.’s (TransHEN/HENSyn), later improved by Caballero et al On the side of stochastic approaches, hybrid methods such as SA/Rocket Fireworks Optimization and Random Walk methods have been applied to simultaneous models. The reader is also referred to a recent review work by Bogataj et al on heat integration covering works published in the last 50 years.…”

Modified Mixed-Integer Linear Programming Formulation Implemented in Microsoft Excel to Synthesize a Heat Exchanger Network with Multiple Utilities to Compare Process Flowsheets

“…Also, Rathjens and Fieg [7] used a genetic algorithm and deterministic local optimization techniques for solving the SST. In addition, the HEN obtention by solving a MINLP using results from the resolution of mixed-integer linear programming (MILP) problems of the same process-stream data has been claimed by Nemet et al [8], with optimization of the temperatures, flowrates, and areas associated with each match by Caballero et al [9]. The GAMS program has been handled for model optimization [4][5][6]8,9].…”