No abstract

Heat Exchanger Network (HEN) synthesis is an important field of study in process engineering. However, obtaining optimal HEN design is a complex task. When mathematically formulated, it may require sophisticated methods to achieve good solutions. The complexity increases even more for large-scale HEN. In this work, a hybrid meta-heuristic method is presented. A rather simple Simulated Annealing approach is used for the combinatorial level, while a strategy named rocket fireworks optimization is developed and applied to the continuous domain. An advantage over other approaches is that the algorithm was written in C11, which is free and faster when compared to many other languages. The developed method was able to provide the lowest costs solutions reported so far to six cases well studied in the literature. An important feature of the approach here proposed is that, differently from other approaches, it does not split HEN into smaller problems during the optimization.

No abstract

Heat exchanger network (HEN) synthesis has been a well-studied subject over the past decades. Many studies and methodologies were proposed to make possible the energy recovery, minimizing the utilities consumption and the number of heat transfer equipment.Most of papers published in this subject are based on Pinch Analysis and mathematical programming. Some recent papers use meta-heuristic techniques like Genetic Algorithms or Simulated Annealing to solve the HEN synthesis problem and good results are found but with large computational effort.In this paper an optimization model for the synthesis of HEN is proposed. The approach is based on the use of Particle Swarm Optimization to determine the HEN that minimizes the total annualized cost, accounting for capital costs of heat exchangers and the energy costs for utilities and pumping duties. The algorithm is based on a superstructure simultaneous optimization model for the HEN synthesis considering stream splitting. Some examples from the literature were used to show the application of the proposed algorithm, and the results confirm the achievement of the optimum HEN configuration with little computational effort.

In this paper, the shell-and-tube heat exchangers design is formulated as an optimization problem and solved with particle swarm optimization (PSO). The objective is to minimize the global cost including area cost and pumping cost or just area minimization, depending on data availability, rigorously following the standards of the Tubular Exchanger Manufacturers Association and respecting pressure drops and fouling limits. Given fluids temperatures, flow rates, physical properties (density, heat capacity, viscosity, and thermal conductivity), pressure drop and fouling limits, and area cost data, the proposed methodology calculates the optimal mechanical and thermal-hydraulic variables. The Bell−Delaware method is used for the shell-side calculations. Some literature cases are studied and results show that in this type of problem, with a very large number of nonlinear equations, the PSO algorithm presents better results, avoiding local minima.

No abstract

No abstract

No abstract