PERFORMANCE PREDICTION OF AN ADIABATIC SOLAR LIQUID DESICCANT REGENERATOR USING ARTIFICIAL NEURAL NETWORK

This paper presents an artificial neural network (ANN) algorithm developed and trained to predict the performance of a solar powered adiabatic packed tower regenerator using LiBr desiccant. A reinforced technique of supervised learning based on the error correction principle rule coupled with the perceptron convergence theorem was used. The input parameters to the algorithm were temperature, flow rates and humidity ratio of both air and desiccant fluid and their respective outputs used to determine regenerator effectiveness and moisture removal rate. The optimum performance of the ANN algorithm was shown by structures 6-4-4-1 and 6-14-1 for moisture removal rate (MRR) and effectiveness respectively. Upon comparison, the predicted and experimental MRR profiles aligned perfectly during training with a maximum and mean difference of 0.18 g/s and 0.11 g/s. The regenerator effectiveness profiles also agreed well with a few negligible disparities with a mean and maximum difference of 0.6 % and 1 %. With respect to humidity ratio, the algorithm predicted the experimental MRR values to maximum and mean accuracies of 0.0925 % and - 0.012 %. The maximum and mean accuracies of 4.14 % and 0.53 % were realized in the prediction of experimental effectiveness by the neural network algorithm. The ANN model precisely predicted the experimental MRR with respect to inlet desiccant temperature with an average deviation of -0.5290 % while the highest difference was 3.496 % between predicted and measured temperature. With change in inlet desiccant temperature, the ANN predicted and experimental values revealed maximum and mean deviations of 2.61 % and 0.21 %. While the regenerator moisture removal rate varied proportionally with the air temperature, the predicted MRR values matched perfectly with the measured data with a mean and highest difference of -0.12 % and 3.2 %. In all the aforementioned cases, the mean and maximum differences between the ANN model and experimental values were way below the allowable limit of 5 % hence the algorithm was deemed to be successful and could find use in air conditioning scenarios.