Heat exchanger network optimum design method for considering stream heat capacity change

Abstract

The invention discloses a heat exchanger network optimum design method for considering stream heat capacity change. The method comprises the following steps: step S1, initializing a network, and building a feasible initial network; step S2, converting a multi-section stream into a single-section stream; step S3, initializing parameters; step S4, changing a current network to generate a new network; step S5, building a linear programming model to optimize a new network; step S6, judging whether the new network is accepted: if so, replacing the current network by the new network; step S7, judging whether to cool: if so, updating a simulated annealing temperature; step S8, judging whether a terminal condition is reached; if the terminal condition is not reached, returning to the step S4; step S9, converting to the multi-section stream, and judging whether the network is needed to adjust, if the network is not needed to adjust, completing; step S10, adjusting the network; and S11, completing. According to the heat exchanger network optimum design method, the automatic optimal design of the heat exchange network can be realized, an optimal heat exchange network can be found under a set restriction condition, and the problem that the stream heat capacity is changed along with the temperature can be processed, so that an optimal result accords with actual production.

Description

technical field [0001] The invention relates to the technical field of heat exchange networks, in particular to an optimal design method for a heat exchange network considering the change in heat capacity of streams. Background technique [0002] At present, there are mainly three types of methods for heat exchange network design: pinch analysis method, mathematical programming method, and stochastic optimization method. Existing methods have the following disadvantages: [0003] Pinch analysis is difficult to use for large-scale practical problems. Mathematical programming methods tend to fall into local optimal solutions. Stochastic optimization methods generally use genetic algorithms or simulated annealing algorithms. If you want to get better results, you may need to run it multiple times, which requires a long solution time. In addition, the existing methods generally take the minimum operating cost or the minimum total cost (operating cost + equipment cost) as the ...

AI Technical Summary

Problems solved by technology

[0003] The pinch analysis method is difficult to use for large-scale practical problems

Mathematical programming methods are prone to fall into local optimal solutions

Stochastic optimization methods generally use genetic algorithms or simulated annealing algorithms. If you want to get better results, you may need to run it multiple times, requiring a long solution time

In addition, the existing methods generally take the minimum operating cost or the minimum total cost (operating cost + equipment cost) as the optimization goal

The existing technology generally does not consider the change of the heat capacity of the stream with temperature, and there is a certain deviation from the actual engineering

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