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

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 s...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Ammar H. Elsheikh, Hitesh Panchal, Mahmoud Ahmadein, Ahmed O. Mosleh, Kishor Kumar Sadasivuni, Naser A. Alsaleh
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Solar distiller Evacuated tubes External condenser Forecasting Moth-flame optimizer LSTM neural Network Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/7b27b3610543451bab385dc651ffc0fe
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:7b27b3610543451bab385dc651ffc0fe
record_format	dspace
spelling	oai:doaj.org-article:7b27b3610543451bab385dc651ffc0fe2021-12-04T04:34:16ZProductivity forecasting of solar distiller integrated with evacuated tubes and external condenser using artificial intelligence model and moth-flame optimizer2214-157X10.1016/j.csite.2021.101671https://doaj.org/article/7b27b3610543451bab385dc651ffc0fe2021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2214157X21008340https://doaj.org/toc/2214-157XThis 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.Ammar H. ElsheikhHitesh PanchalMahmoud AhmadeinAhmed O. MoslehKishor Kumar SadasivuniNaser A. AlsalehElsevierarticleSolar distillerEvacuated tubesExternal condenserForecastingMoth-flame optimizerLSTM neural NetworkEngineering (General). Civil engineering (General)TA1-2040ENCase Studies in Thermal Engineering, Vol 28, Iss , Pp 101671- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Solar distiller Evacuated tubes External condenser Forecasting Moth-flame optimizer LSTM neural Network Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	Solar distiller Evacuated tubes External condenser Forecasting Moth-flame optimizer LSTM neural Network Engineering (General). Civil engineering (General) TA1-2040 Ammar H. Elsheikh Hitesh Panchal Mahmoud Ahmadein Ahmed O. Mosleh Kishor Kumar Sadasivuni Naser A. Alsaleh Productivity forecasting of solar distiller integrated with evacuated tubes and external condenser using artificial intelligence model and moth-flame optimizer
description	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.
format	article
author	Ammar H. Elsheikh Hitesh Panchal Mahmoud Ahmadein Ahmed O. Mosleh Kishor Kumar Sadasivuni Naser A. Alsaleh
author_facet	Ammar H. Elsheikh Hitesh Panchal Mahmoud Ahmadein Ahmed O. Mosleh Kishor Kumar Sadasivuni Naser A. Alsaleh
author_sort	Ammar H. Elsheikh
title	Productivity forecasting of solar distiller integrated with evacuated tubes and external condenser using artificial intelligence model and moth-flame optimizer
title_short	Productivity forecasting of solar distiller integrated with evacuated tubes and external condenser using artificial intelligence model and moth-flame optimizer
title_full	Productivity forecasting of solar distiller integrated with evacuated tubes and external condenser using artificial intelligence model and moth-flame optimizer
title_fullStr	Productivity forecasting of solar distiller integrated with evacuated tubes and external condenser using artificial intelligence model and moth-flame optimizer
title_full_unstemmed	Productivity forecasting of solar distiller integrated with evacuated tubes and external condenser using artificial intelligence model and moth-flame optimizer
title_sort	productivity forecasting of solar distiller integrated with evacuated tubes and external condenser using artificial intelligence model and moth-flame optimizer
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/7b27b3610543451bab385dc651ffc0fe
work_keys_str_mv	AT ammarhelsheikh productivityforecastingofsolardistillerintegratedwithevacuatedtubesandexternalcondenserusingartificialintelligencemodelandmothflameoptimizer AT hiteshpanchal productivityforecastingofsolardistillerintegratedwithevacuatedtubesandexternalcondenserusingartificialintelligencemodelandmothflameoptimizer AT mahmoudahmadein productivityforecastingofsolardistillerintegratedwithevacuatedtubesandexternalcondenserusingartificialintelligencemodelandmothflameoptimizer AT ahmedomosleh productivityforecastingofsolardistillerintegratedwithevacuatedtubesandexternalcondenserusingartificialintelligencemodelandmothflameoptimizer AT kishorkumarsadasivuni productivityforecastingofsolardistillerintegratedwithevacuatedtubesandexternalcondenserusingartificialintelligencemodelandmothflameoptimizer AT naseraalsaleh productivityforecastingofsolardistillerintegratedwithevacuatedtubesandexternalcondenserusingartificialintelligencemodelandmothflameoptimizer
_version_	1718372977026793472

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

Ejemplares similares