Performance predication of a solar assisted desiccant air conditioning system using radial basis function neural network: An integrated machine learning approach
<p>In this Paper solar desiccant air conditioning system integrated with cross flow Maisotsenko cycle (M-cycle) indirect evaporative cooler is used to investigate the performance of whole system in different range of parameters. Solar evacuated tube electric heater is used to supply the regene...
محفوظ في:
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| مؤلفون آخرون: | , , , |
| منشور في: |
2024
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| _version_ | 1864513509709053952 |
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| author | Sibghat Ullah (19325794) |
| author2 | Muzaffar Ali (387100) Muhammad Fahad Sheikh (14593224) Ghulam Qadar Chaudhary (19325797) Laoucine Kerbache (17148370) |
| author2_role | author author author author |
| author_facet | Sibghat Ullah (19325794) Muzaffar Ali (387100) Muhammad Fahad Sheikh (14593224) Ghulam Qadar Chaudhary (19325797) Laoucine Kerbache (17148370) |
| author_role | author |
| dc.creator.none.fl_str_mv | Sibghat Ullah (19325794) Muzaffar Ali (387100) Muhammad Fahad Sheikh (14593224) Ghulam Qadar Chaudhary (19325797) Laoucine Kerbache (17148370) |
| dc.date.none.fl_str_mv | 2024-05-09T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.heliyon.2024.e29777 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Performance_predication_of_a_solar_assisted_desiccant_air_conditioning_system_using_radial_basis_function_neural_network_An_integrated_machine_learning_approach/26491036 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Environmental engineering Information and computing sciences Machine learning M-cycle Desiccant Evaporative cooling Solar thermal system Artificial neural network |
| dc.title.none.fl_str_mv | Performance predication of a solar assisted desiccant air conditioning system using radial basis function neural network: An integrated machine learning approach |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>In this Paper solar desiccant air conditioning system integrated with cross flow Maisotsenko cycle (M-cycle) indirect evaporative cooler is used to investigate the performance of whole system in different range of parameters. Solar evacuated tube electric heater is used to supply the regeneration temperature to the desiccant wheel, whereas, Desiccant Wheel (DW) and M-cycle is used to handle latent load and sensible load separately. Major contribution of this research is to predict system level performance parameters of a Solar Assisted Desiccant Air Conditioning (Sol-DAC) system using Radial Basis Function Neural Network (RBF-NN) under real transient experimental inlet conditions. Nine parameters are mainly considered as input parameters to train the RBF-NN model, which are, supply Air temperature at the process side of desiccant wheel, supply air humidity ratio at process side of the desiccant wheel, outlet temperature from the desiccant wheel at process side, outlet humidity ratio from the desiccant wheel at process side, regeneration temperature at regeneration side of the DW, outlet temperature from the heat recovery wheel at process side, outlet humidity ratio out from the Heat Recovery Wheel (HRW) at process side, temperature before heat recovery wheel regeneration side of the system, humidity ratio before heat recovery wheel regeneration side of the system. Four parameters are considered as the output of the RBF-NN model, namely: output temperature, output humidity, Cooling Capacity (CC), and Coefficient of Performance (COP). The results of the RBF-NN model shows that the best Mean Squared Error (MSE) and Regression coefficient (R) for outlet temperature prediction are 0.00998279 and 0.99832 when regeneration temperature is 70 °C and inlet humidity at 18 g/kg. Best MSE and R for predication of outlet humidity are 0.0102932 and 0.99485 when the regeneration temperature is 70 °C and inlet humidity at 16 g/kg. Best MSE and R for predication of COP are 0.0106691 and 0.9981 when the regeneration temperature is 70 °C and inlet humidity 12 g/kg. Best MSE and R for predication of CC are 0.0144943 and 0.99711 when the regeneration temperature is 70 °C and inlet humidity 14 g/kg. Experimental and predicted performance parameters were in close agreement and showed minimal deviation. Investigations of predicted results revealed that trained RBF-NN model was capable of predicting the trend of output result under the varying input condition.</p><h2>Other Information</h2> <p> Published in: Heliyon<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.heliyon.2024.e29777" target="_blank">https://dx.doi.org/10.1016/j.heliyon.2024.e29777</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_19a556941bb14f691a32ff3c2c88f7f0 |
| identifier_str_mv | 10.1016/j.heliyon.2024.e29777 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/26491036 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Performance predication of a solar assisted desiccant air conditioning system using radial basis function neural network: An integrated machine learning approachSibghat Ullah (19325794)Muzaffar Ali (387100)Muhammad Fahad Sheikh (14593224)Ghulam Qadar Chaudhary (19325797)Laoucine Kerbache (17148370)EngineeringEnvironmental engineeringInformation and computing sciencesMachine learningM-cycleDesiccantEvaporative coolingSolar thermal systemArtificial neural network<p>In this Paper solar desiccant air conditioning system integrated with cross flow Maisotsenko cycle (M-cycle) indirect evaporative cooler is used to investigate the performance of whole system in different range of parameters. Solar evacuated tube electric heater is used to supply the regeneration temperature to the desiccant wheel, whereas, Desiccant Wheel (DW) and M-cycle is used to handle latent load and sensible load separately. Major contribution of this research is to predict system level performance parameters of a Solar Assisted Desiccant Air Conditioning (Sol-DAC) system using Radial Basis Function Neural Network (RBF-NN) under real transient experimental inlet conditions. Nine parameters are mainly considered as input parameters to train the RBF-NN model, which are, supply Air temperature at the process side of desiccant wheel, supply air humidity ratio at process side of the desiccant wheel, outlet temperature from the desiccant wheel at process side, outlet humidity ratio from the desiccant wheel at process side, regeneration temperature at regeneration side of the DW, outlet temperature from the heat recovery wheel at process side, outlet humidity ratio out from the Heat Recovery Wheel (HRW) at process side, temperature before heat recovery wheel regeneration side of the system, humidity ratio before heat recovery wheel regeneration side of the system. Four parameters are considered as the output of the RBF-NN model, namely: output temperature, output humidity, Cooling Capacity (CC), and Coefficient of Performance (COP). The results of the RBF-NN model shows that the best Mean Squared Error (MSE) and Regression coefficient (R) for outlet temperature prediction are 0.00998279 and 0.99832 when regeneration temperature is 70 °C and inlet humidity at 18 g/kg. Best MSE and R for predication of outlet humidity are 0.0102932 and 0.99485 when the regeneration temperature is 70 °C and inlet humidity at 16 g/kg. Best MSE and R for predication of COP are 0.0106691 and 0.9981 when the regeneration temperature is 70 °C and inlet humidity 12 g/kg. Best MSE and R for predication of CC are 0.0144943 and 0.99711 when the regeneration temperature is 70 °C and inlet humidity 14 g/kg. Experimental and predicted performance parameters were in close agreement and showed minimal deviation. Investigations of predicted results revealed that trained RBF-NN model was capable of predicting the trend of output result under the varying input condition.</p><h2>Other Information</h2> <p> Published in: Heliyon<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.heliyon.2024.e29777" target="_blank">https://dx.doi.org/10.1016/j.heliyon.2024.e29777</a></p>2024-05-09T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.heliyon.2024.e29777https://figshare.com/articles/journal_contribution/Performance_predication_of_a_solar_assisted_desiccant_air_conditioning_system_using_radial_basis_function_neural_network_An_integrated_machine_learning_approach/26491036CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/264910362024-05-09T03:00:00Z |
| spellingShingle | Performance predication of a solar assisted desiccant air conditioning system using radial basis function neural network: An integrated machine learning approach Sibghat Ullah (19325794) Engineering Environmental engineering Information and computing sciences Machine learning M-cycle Desiccant Evaporative cooling Solar thermal system Artificial neural network |
| status_str | publishedVersion |
| title | Performance predication of a solar assisted desiccant air conditioning system using radial basis function neural network: An integrated machine learning approach |
| title_full | Performance predication of a solar assisted desiccant air conditioning system using radial basis function neural network: An integrated machine learning approach |
| title_fullStr | Performance predication of a solar assisted desiccant air conditioning system using radial basis function neural network: An integrated machine learning approach |
| title_full_unstemmed | Performance predication of a solar assisted desiccant air conditioning system using radial basis function neural network: An integrated machine learning approach |
| title_short | Performance predication of a solar assisted desiccant air conditioning system using radial basis function neural network: An integrated machine learning approach |
| title_sort | Performance predication of a solar assisted desiccant air conditioning system using radial basis function neural network: An integrated machine learning approach |
| topic | Engineering Environmental engineering Information and computing sciences Machine learning M-cycle Desiccant Evaporative cooling Solar thermal system Artificial neural network |