In this paper, the feasibility of a multi-layer artificial neural network to predict both the cooling capacity and the COP of an adsorption chiller working in a real pilot plant is presented. The ANN was trained to accurately predict the performance of the device using data acquired over several years of operation. The number of neurons used by the ANN should be selected individually depending on the size of the training base. The optimal number of datasets in a training base is suggested to be 35. The predicted cooling capacity curves for a given adsorption chiller driven by the district heating are presented. Predictions of the artificial neural network used show good correlation with experimental results, with the mean relative deviation as low as 1.36%. The character of the cooling capacity curve is physically accurate, and during normal operation for cooling capacities ≥8 kW, the errors rarely exceed 1%.
We show how to use numerical analysis of short-time range experimental data for predicting the limit steady-state value of the investigated parameter. In this article the approach has been applied to a specific, although typical, thermal problem: determining the average steady-state temperature of a heater in the convective and radiative heat exchange with the environment. First, we describe a heat exchange experiment aimed at obtaining temperature experimental data in both short and long time range. Then we present a methodology for applying two methods, i.e., neural networks and least squares approximations, for obtaining predictions about the steady-state temperature values based on short time experimental data. The aim of the study is to compare the predictions to each other and to the long time experimental values, with the aim of determining the applicability range of the two methods.
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