| 000 | 03264nlm1a2200433 4500 | ||
|---|---|---|---|
| 001 | 663395 | ||
| 005 | 20231030041853.0 | ||
| 035 | _a(RuTPU)RU\TPU\network\34564 | ||
| 035 | _aRU\TPU\network\33290 | ||
| 090 | _a663395 | ||
| 100 | _a20210209a2021 k y0engy50 ba | ||
| 101 | 0 | _aeng | |
| 102 | _aNL | ||
| 135 | _adrcn ---uucaa | ||
| 181 | 0 | _ai | |
| 182 | 0 | _ab | |
| 200 | 1 |
_aUtilization of LSTM neural network for water production forecasting of a stepped solar still with a corrugated absorber plate _fA. H. Elsheikh, V. P. Katekar, O. L. Muskens [et al.] |
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| 203 |
_aText _celectronic |
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| 300 | _aTitle screen | ||
| 330 | _aThis 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. | ||
| 333 | _aРежим доступа: по договору с организацией-держателем ресурса | ||
| 461 | _tProcess Safety and Environmental Protection | ||
| 463 |
_tVol. 148 _v[P. 273-282] _d2021 |
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| 610 | 1 | _aэлектронный ресурс | |
| 610 | 1 | _aтруды учёных ТПУ | |
| 610 | 1 | _astepped solar still | |
| 610 | 1 | _acorrugated absorber plate | |
| 610 | 1 | _aforecasting | |
| 610 | 1 | _aLSTM neural network | |
| 701 | 1 |
_aElsheikh _bA. H. _gAmmar |
|
| 701 | 1 |
_aKatekar _bV. P. _gVikrant |
|
| 701 | 1 |
_aMuskens _bO. L. _gOtto |
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| 701 | 1 |
_aDeshmukh _bS. S. _gSandip |
|
| 701 | 1 |
_aMokhamed Elsaed _bA. M. _cSpecialist in the field of informatics and computer technology _cProfessor of Tomsk Polytechnic University _f1987- _gAkhmed Mokhamed _2stltpush _3(RuTPU)RU\TPU\pers\46943 |
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| 701 | 1 |
_aDabour _bS. M. _gSherif |
|
| 712 | 0 | 2 |
_aНациональный исследовательский Томский политехнический университет _bИнженерная школа информационных технологий и робототехники _bОтделение информационных технологий _h7951 _2stltpush _3(RuTPU)RU\TPU\col\23515 |
| 801 | 2 |
_aRU _b63413507 _c20210902 _gRCR |
|
| 856 | 4 | _uhttps://doi.org/10.1016/j.psep.2020.09.068 | |
| 942 | _cCF | ||