This paper presents an artificial neural network (ANN) algorithm developed and trained to predict the performance of a solar powered adiabatic packed tower re-generator using LiBr desiccant. | Performance prediction of an adiabatic solar liquid desiccant regenerator using artificial neural network International Journal of Mechanical Engineering and Technology IJMET Volume 10 Issue 03 March 2019 pp. 496-511. Article ID IJMET_10_03_052 Available online at http ijmet JType IJMET amp VType 10 amp IType 3 ISSN Print 0976-6340 and ISSN Online 0976-6359 IAEME Publication Scopus Indexed PERFORMANCE PREDICTION OF AN ADIABATIC SOLAR LIQUID DESICCANT REGENERATOR USING ARTIFICIAL NEURAL NETWORK Andrew Y. A. Oyieke and Freddie L. Inambao Green Energy Solutions Research group Discipline of Mechanical Engineering University of KwaZulu-Natal Mazisi Kunene Road Glenwood Durban 4041 South Africa. Corresponding author ABSTRACT This paper presents an artificial neural network ANN algorithm developed and trained to predict the performance of a solar powered adiabatic packed tower re- generator 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 g s and g s. The regenerator effectiveness profiles also agreed well with a few negligible disparities with a mean and maximum difference of and 1 . With respect to humidity ratio the algorithm predicted the experimental MRR values to maximum and mean accuracies of and - . The maximum and mean accuracies of and were realized in the prediction of experimental .
