Thermodynamic Analysis and Optimization of a Cooling and Power Cogeneration System using GMDH and Genetic Algorithm

In today's world, the demand for energy is increasing and energy conversion must be done with the lowest environmental consequences to use energy resources. The use of electrical energy in this way was very effective, and major supply sources of the world will be provided through the application of thermodynamic cycles that have been established to work in heat engines. In this regard, a Cooling and Power Cogeneration System has been examined specifically on a limited scale. This study tries to use this type of system and dual operating fluids in order to extract optimal results for these cycles. The aim of this study is finding the system performance by changing the number of selected entries (backpressure turbine, condenser temperature and super heater temperature). The proposed combined thermodynamic cycle is based on Rankine Cycle and absorption refrigeration cycle with ammonia recovery. This cycle uses a mixture of water and ammonia as the working fluid. With the proposal of the cycle and by changing the turbine inlet pressure from 18 to 32 Bar and temperature changes for condenser input from 330 to 360 degrees, as well as temperatures ranging from 400 to 500 degrees for a turbine inlet are used to study the impact of effective parameters using a thermodynamic analysis toward the reviews of turbine work and the amount of cooling and the overall efficiency using EES software in each component of the systems. The results show that change in some of these parameters causes an improvement in one of the outputs performances and destroys the other. Thus the need for a multi-objective optimization will be felt. In this regard, in order to determine the output functions, first, by changing the input parameters in the selected range, 500 data output will be created and then by using the method of grouping, numerical data will be specified in the number of output function Information based on the input. Then, by using genetic algorithms and MATLAB software, simultaneous optimization (turbine power and cooling capacity and efficiency of the system) will be done for the input Selective variables. According to the results of the optimization, it turns out that in order to achieve the system performance in the production of all three objective functions, the temperatures in system design must have the lowest level range. In addition, if there is no need to produce cold, the mentioned temperatures can be adjusted in high levels of range