Optimization of a three-bed adsorption chiller by genetic algorithms and neural networks

The presented non-iterative approach gives quick and accurate results as an answer to the input data sets. The CC of the chiller, evaluated using the developed model, is in good agreement with the experimental data. Maximum relative error between measured and calculated data is lower than ±10%. The developed model permits to study the influence of operating parameters on the cooling capacity of the chiller. For the considered range of input parameters the highest cooling capacity which can be obtained by the heat pump is equal 93.3 kW. The method constitutes an alternative, easy-to-apply and useful, complementary technique, comparing to the other techniques of data handling, including the complex of numerical and analytical methods as well as high costs of empirical experiments. The model can be applied for optimizations purposes and can constitute a sub model or a separate module in engineering calculations, capable to predict the CC of the Tri-bed twin-evaporator adsorption cooler, integrated into multigenerative systems.