Multi-objective dynamic optimization of compressor driven adsorption refrigeration system

Due to environmental protection needs, research and development of new refrigeration systems that can effectively utilize natural refrigerants has become a research hotspot. The compressor-driven adsorption refrigeration (CDAC) system has the advantages of simple and compact structure and utilization of natural refrigerants. It is considered to be one of the most promising alternatives. The main evaluation indicators of W.CDAC system are refrigeration performance coefficient COP and unit mass adsorbent refrigeration. Power and other parameters M. However, as the compressor volume flow rate or cycle time changes, COP and SCP are more constrained to compete. The existing research results of CDAC system focus on the research and development of adsorption working fluid pair, the design of adsorption bed and the study of system dynamic characteristics. The dynamic optimization of the compressor speed and cycle time of this type of system has not been developed.

The iterative dynamic programming method (IDP) developed based on the optimization principle is a few methods that can find the global optimal solution, and has less dependence on the initial value selection and constraint function continuity of the control variable. Although IDP calculation efficiency is low, it is easy to realize parallel computing. This paper applies IDP to study the compressor frequency conversion and cycle time optimal control problem in CDAC system.

1 Compressed adsorption refrigeration system and its dynamic model bookmark1 1.1 Compressor-driven adsorption refrigeration system The CDAC system consists of a four-way valve, two adsorption beds and a compressor connecting them. As shown, the adsorbent in bed A is compressed by the fund project: National Natural Science Foundation of China (No. 51106091); Shandong University Independent Innovation Fund (NO.2012ZD018)), male, doctoral student, mainly engaged in adsorption refrigeration and optimization control the study. Corresponding author: Lai Yanhua, professor, "

The machine suction desorbs and absorbs heat and cools, and the refrigerant is adsorbed by the adsorbent in the bed B through the compressor boosting model to release heat to the environment. After the adsorption/desorption is completed, the four-way valve is redirected to change the roles of the beds A and B, and the second half of the refrigeration cycle is performed.

The adsorbent bed herein is a sleeve type structure. The inner tube is an outer fin tube, the fin is filled with an adsorbent, the tube is a heat transfer fluid passage, and a refrigerant transfer passage is left between the adsorbent and the outer tube. The adsorbent working medium is a kind of expandable graphite/cerium chloride-ammonia with good heat conductivity, the mass fraction of barium chloride is 58.7%, and the standard reaction enthalpy becomes 37k/mol NH3, among which, the state adsorption amount.

The adsorbent equilibrium state adsorption amount Xeq is obtained by the PrX equation to obtain the molar amount of the refrigerant; i is a gas constant. According to the law of mass and energy conservation, the mass and temperature equations of the gas in the adsorbent bed can be obtained.

Adsorption process: the agent is more than the constant pressure heat capacity; fceff is the effective heat transfer coefficient of the adsorbent and the heat transfer fluid. When the volume flow rate is always at the maximum value throughout the cycle time, when the SCPmax is obtained and the COPmax is obtained, the volume flow is obtained. The rate is kept at a maximum for a very short time at the beginning of the cycle, and then the time is kept to a minimum. It is shown that the pre-cooling process of the adsorbent in the desorbed bed is obviously faster than the pre-cooling process with the minimum volume flow rate, which is the main reason for the high initial volume flow rate.

If the cycle time is used as the control variable, then the end time of the system operation is not fixed, assuming that the continuous system is discretized into M segments, and new transformers (4) and T are introduced, so that '(, (4) = ffc > 0. Objective function: due to The optimization goal is multi-objective. Here, combining the ideas of the maximal and minimum methods and the weighting method, the multi-objective problem is transformed into a single-objective problem.

Then: the value when the maximum COP is reached.

Because the calculation process is relatively large, this paper uses a parallel algorithm.

In order to prevent the objective function from converge to the local optimal value, a multi-cycle solving method is adopted. The parameters are set to: the number of process segments A / = 20; each control variable M discrete points is 11; the shrinkage factor is 0.8; the expansion factor is . 95; the number of iterations per loop is 15; the number of loops gives a weighting coefficient o=0.5, which maintains a stable convergence trend before the optimization process, which indicates that IDP is an excellent algorithm that is easy to obtain the global optimal solution.

In most cases, the CDAC system requires COP and SCP performance to operate in a balanced manner. It is the 0.5B inch volume flow optimization curve for both COP and weight. In order to achieve rapid pre-cooling of the adsorbent in the desorption bed, the highest volumetric flow rate is run at the beginning of the cycle; during most of the cycle, the volumetric flow rate is maintained at a relatively low volumetric flow rate, optimized by the target scp Limit, the volume flow rate increases slightly with time; at the end of the cycle, because the pressure ratio is too high, subject to the optimization target cop, the volume flow rate reaches the iteration number iteration number and the optimal value distance changes with the number of iterations

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