The Synthesis Of Heat Exchanger Networks

The typical process industries, such as Petroleum Refining, Chemistry, fertilizer, need plenty of energy. For the energy supply shortages and the energy efficiency lags far behind the developed countries, it seriously hindered the sustainable development of national economy. So, energy conservation and decrease the energy consumption has become a trend in the development of industrial sectors. Heat exchanger network (HEN) is an important subsystem of energy recovery process. HEN synthesis and optimization play an important role in economizing energy in process industry and reducing energy consumption. So, optimizing the heat exchanger network structure and maximizing the heat recovery have great significant.Focus on this theme, this paper would carry out research work as follows:We synthesize the heat exchanger networks using mathematical methods. First, simplify the heat exchanger networks. Then, based on the stage-wise superstructure model, the mathematical model of the HEN is expressed in a Mixed Integer Non-Linear mathematical Programming model (MINLP). Using this algorithm, each exchanger of the network is designed in order to minimize its total annual cost,which consists of operational cost and the cost of hot utility and cold utility. Finally, solve the model.For the MINLP is non-convex, nonlinear and complex, it can hardly be solved by traditional methods such as Steepest Descent Method, Conjugate Gradient Method. So, a hybrid genetic algorithm for optimal design of HEN is developed. Genetic algorithms can solve the function which cannot be differentiable and continuous, and is very suitable for solving MINLP model. Because heat exchanger networks synthesis need to take into account the energy balance of the streams and heat exchangers, the temperature range, the constraints of the split streams, logic variables of heat exchangers, et al. A new method of penalty function is used to deal with the constraints. In order to avoid a large number of infeasible solutions, the genetic algorithm is combined with simulated annealing algorithm and renovation strategy, which can prevent precocious convergence effectively and accelerate the rate of convergence. A program is used to solve the model by Matlab. Some simple examples are optimized with different methods,which shows the advantages of the hybrid genetic algorithm. At last,calculate some examples of heat exchanger networks and obtained the optimal heat exchanger networks which is the minimum total cost of heat transfer.This research work demonstrated the feasibility of solving heat exchanger networks models by hybrid genetic algorithm and the good results. Further research could be taken to enhance the energy saving of the heat exchanger network.