Productivity forecasting of solar distiller integrated with evacuated tubes and external condenser using artificial intelligence model and moth-flame optimizer

• Published in: Case studies in thermal engineering Vol. 28; p. 101671
• Main Authors: Elsheikh, Ammar H., Panchal, Hitesh, Ahmadein, Mahmoud, Mosleh, Ahmed O., Sadasivuni, Kishor Kumar, Alsaleh, Naser A.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01-12-2021; Elsevier
• Subjects: Evacuated tubes; External condenser; Forecasting; LSTM neural Network; Moth-flame optimizer; Solar distiller; Solar distiller; External condenser; Forecasting; Evacuated tubes; LSTM neural Network; Moth-flame optimizer
• ISSN: 2214-157X; 2214-157X
• DOI: 10.1016/j.csite.2021.101671

This paper aims at developing an artificial intelligence model to forecast the water yield of a modified solar distiller integrated with evacuated tubes and an external condenser. The model consists of a hybrid long short-term memory (LSTM) model optimized by a moth-flame optimizer (MFO) used as a subroutine to obtain the optimal internal parameters of the LSTM model that maximize the forecasting accuracy. The model performance was compared with that of the standalone LSTM model. Both developed models were trained and tested using experimental data of the modified distiller and a conventional distiller. The thermal performance of both distillers is also compared in this article. The maximum daily distillate output achieved for the modified distiller was 3920 l/m2. The forecasted data of both models were compared using several statistical measures. For all measurements, LSTM-MFO outperformed standalone LSTM. The determination coefficient of the forecasted data using LSTM-MFO reached a high value of 0.999 for both solar distillers.