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Utilization of LSTM neural network for water production forecasting of a stepped solar still with a corrugated absorber plate

Utilization of LSTM neural network for water production forecasting of a stepped solar still with a corrugated absorber plate
Utilization of LSTM neural network for water production forecasting of a stepped solar still with a corrugated absorber plate
This study introduces a long short-term memory (LSTM) neural network model to forecast the freshwater yield of a stepped solar still and a conventional one. The stepped solar still was equiped by a copper corrugated absorber plate. The thermal performance of the stepped solar still is compared with that of conventional single slope solar still. The heat transfer coefficients of convection, evaporation, and radiation process have been evaluated. The exergy and energy efficiencies of both solar stills have been also evaluated. The yield of the stepped solar still is enhanced by about 128 % compared with that of conventional solar still. Then, the proposed LSTM neural network method is utilized to forecast the hourly yield of the investigated solar stills. Field experimental data was used to train and test the developed model. The freshwater yield was used in a time series form to train the proposed model. The forecasting accuracy of the proposed model was compared with those obtained by conventional autoregressive integrated moving average (ARIMA) and was evaluated using different statistical assessment measures. The coefficient of determination of the forecasted results has a high value of 0.97 and 0.99 for the conventional and the stepped solar still, respectively.
Corrugated absorber plate, Forecasting, LSTM neural network, Stepped solar still
0957-5820
273-282
Elsheikh, Ammar H.
e95d74f4-05af-4d46-9313-4da313769ffd
Katekar, Vikrant P.
af7993e8-8bc6-417b-9550-c6241449234b
Muskens, Otto
2284101a-f9ef-4d79-8951-a6cda5bfc7f9
Deshmukh, Sandip S.
75299b64-96bd-4a4d-bba2-ecda53ae727d
Elaziz, Mohamed Abd
cf67d73d-daee-4e6c-add1-94774d345a82
Dabour, Sherif M.
b04be85e-2d25-4fee-841f-451ec673e2d1
Elsheikh, Ammar H.
e95d74f4-05af-4d46-9313-4da313769ffd
Katekar, Vikrant P.
af7993e8-8bc6-417b-9550-c6241449234b
Muskens, Otto
2284101a-f9ef-4d79-8951-a6cda5bfc7f9
Deshmukh, Sandip S.
75299b64-96bd-4a4d-bba2-ecda53ae727d
Elaziz, Mohamed Abd
cf67d73d-daee-4e6c-add1-94774d345a82
Dabour, Sherif M.
b04be85e-2d25-4fee-841f-451ec673e2d1

Elsheikh, Ammar H., Katekar, Vikrant P., Muskens, Otto, Deshmukh, Sandip S., Elaziz, Mohamed Abd and Dabour, Sherif M. (2021) Utilization of LSTM neural network for water production forecasting of a stepped solar still with a corrugated absorber plate. Process Safety and Environmental Protection, 148, 273-282. (doi:10.1016/j.psep.2020.09.068).

Record type: Article

Abstract

This study introduces a long short-term memory (LSTM) neural network model to forecast the freshwater yield of a stepped solar still and a conventional one. The stepped solar still was equiped by a copper corrugated absorber plate. The thermal performance of the stepped solar still is compared with that of conventional single slope solar still. The heat transfer coefficients of convection, evaporation, and radiation process have been evaluated. The exergy and energy efficiencies of both solar stills have been also evaluated. The yield of the stepped solar still is enhanced by about 128 % compared with that of conventional solar still. Then, the proposed LSTM neural network method is utilized to forecast the hourly yield of the investigated solar stills. Field experimental data was used to train and test the developed model. The freshwater yield was used in a time series form to train the proposed model. The forecasting accuracy of the proposed model was compared with those obtained by conventional autoregressive integrated moving average (ARIMA) and was evaluated using different statistical assessment measures. The coefficient of determination of the forecasted results has a high value of 0.97 and 0.99 for the conventional and the stepped solar still, respectively.

Text
Utilization of LSTM neural network for water production forecasting of a stepped solar still with a corrugated absorber plate - Accepted Manuscript
Restricted to Repository staff only until 13 October 2021.
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More information

Accepted/In Press date: 29 September 2020
e-pub ahead of print date: 13 October 2020
Published date: April 2021
Keywords: Corrugated absorber plate, Forecasting, LSTM neural network, Stepped solar still

Identifiers

Local EPrints ID: 445130
URI: http://eprints.soton.ac.uk/id/eprint/445130
ISSN: 0957-5820
PURE UUID: bc5aeca0-691e-4297-9b45-f4b7e7e47db1
ORCID for Otto Muskens: ORCID iD orcid.org/0000-0003-0693-5504

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Date deposited: 20 Nov 2020 17:32
Last modified: 24 Nov 2020 17:35

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Contributors

Author: Ammar H. Elsheikh
Author: Vikrant P. Katekar
Author: Otto Muskens ORCID iD
Author: Sandip S. Deshmukh
Author: Mohamed Abd Elaziz
Author: Sherif M. Dabour

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