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المؤلفElsheikh, Ammar H.
المؤلفPanchal, Hitesh
المؤلفAhmadein, Mahmoud
المؤلفMosleh, Ahmed O.
المؤلفSadasivuni, Kishor Kumar
المؤلفAlsaleh, Naser A.
تاريخ الإتاحة2022-03-23T06:35:44Z
تاريخ النشر2021
اسم المنشورCase Studies in Thermal Engineering
المصدرScopus
المعرّفhttp://dx.doi.org/10.1016/j.csite.2021.101671
معرّف المصادر الموحدhttp://hdl.handle.net/10576/28551
الملخصThis 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.
راعي المشروعThe authors extend their appreciation to the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University for funding this work through Research Group no. RG-21-12-03.
اللغةen
الناشرElsevier Ltd
الموضوعEvacuated tubes
External condenser
Forecasting
LSTM neural Network
Moth-flame optimizer
Solar distiller
العنوانProductivity forecasting of solar distiller integrated with evacuated tubes and external condenser using artificial intelligence model and moth-flame optimizer
النوعArticle
رقم المجلد28
dc.accessType Open Access


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