Exergy assessment of infrared assisted air impingement dryer using response surface methodology, Back Propagation-Artificial Neural Network, and multi-objective genetic algorithm

This study deals with the exergy analysis of the thin-layer drying process of apple fruit via an infrared-assisted air impingement dryer. In the study, process conditions, namely, drying temperature (50–70 °C), slice thickness (2–6 mm), and recirculation ratio (10–90 %) were considered as independen...

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Main Authors: Chinmayee Parida, Pramod Kumar Sahoo, Rabiya Nasir, Liaqat Ali Waseem, Aqil Tariq, Muhammad Aslam, Wesam Atef Hatamleh
Format: Article
Language:English
Published: Elsevier 2024-01-01
Series:Case Studies in Thermal Engineering
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S2214157X2301242X
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author Chinmayee Parida
Pramod Kumar Sahoo
Rabiya Nasir
Liaqat Ali Waseem
Aqil Tariq
Muhammad Aslam
Wesam Atef Hatamleh
author_facet Chinmayee Parida
Pramod Kumar Sahoo
Rabiya Nasir
Liaqat Ali Waseem
Aqil Tariq
Muhammad Aslam
Wesam Atef Hatamleh
author_sort Chinmayee Parida
collection DOAJ
description This study deals with the exergy analysis of the thin-layer drying process of apple fruit via an infrared-assisted air impingement dryer. In the study, process conditions, namely, drying temperature (50–70 °C), slice thickness (2–6 mm), and recirculation ratio (10–90 %) were considered as independent parameters. The impacts of process parameters were studied over the responses, namely, exergy efficiency, exergy loss, improvement potential, and sustainability index. A comparative study was conducted between a Back-Propagation Artificial Neural Network (BP-ANN) coupled with a multi-objective genetic algorithm (MOGA) and Response Surface Methodology (RSM). It was found that both BP-ANN and RSM had good prediction ability, but BP-ANN performed slightly better with higher R2, lower RMSE, and MAE values. The optimized conditions for BP-ANN-MOGA were found to be a temperature of 50 °C, slice thickness of 3.9 mm, and recirculation ratio of 76.38 %, which yielded a response of exergy efficiency of 62.23 %, exergy loss of 221 kJ, an improvement potential of 105 kJ, and a sustainability index of 2.65. This study showed a better exergy assessment of the developed hybrid dryer from a thermodynamic point of view.
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spelling doaj.art-5ee2115f10ce456ab199f6272cba846c2024-01-12T04:57:02ZengElsevierCase Studies in Thermal Engineering2214-157X2024-01-0153103936Exergy assessment of infrared assisted air impingement dryer using response surface methodology, Back Propagation-Artificial Neural Network, and multi-objective genetic algorithmChinmayee Parida0Pramod Kumar Sahoo1Rabiya Nasir2Liaqat Ali Waseem3Aqil Tariq4Muhammad Aslam5Wesam Atef Hatamleh6Division of Agricultural Engineering, ICAR - Indian Agricultural Research Institute, New Delhi, 110012, IndiaDivision of Agricultural Engineering, ICAR - Indian Agricultural Research Institute, New Delhi, 110012, IndiaDepartment of Environmental Science, The University of Lahore, PakistanDepartment of Geography, Government College University Faisalabad, Punjab, 38000, PakistanDepartment of Wildlife, Fisheries and Aquaculture, Mississippi State University, Mississippi, 39762, USA; Corresponding author.Department of Computer Science, Aberystwyth University, UKDepartment of Computer Science, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh, 11543, Saudi ArabiaThis study deals with the exergy analysis of the thin-layer drying process of apple fruit via an infrared-assisted air impingement dryer. In the study, process conditions, namely, drying temperature (50–70 °C), slice thickness (2–6 mm), and recirculation ratio (10–90 %) were considered as independent parameters. The impacts of process parameters were studied over the responses, namely, exergy efficiency, exergy loss, improvement potential, and sustainability index. A comparative study was conducted between a Back-Propagation Artificial Neural Network (BP-ANN) coupled with a multi-objective genetic algorithm (MOGA) and Response Surface Methodology (RSM). It was found that both BP-ANN and RSM had good prediction ability, but BP-ANN performed slightly better with higher R2, lower RMSE, and MAE values. The optimized conditions for BP-ANN-MOGA were found to be a temperature of 50 °C, slice thickness of 3.9 mm, and recirculation ratio of 76.38 %, which yielded a response of exergy efficiency of 62.23 %, exergy loss of 221 kJ, an improvement potential of 105 kJ, and a sustainability index of 2.65. This study showed a better exergy assessment of the developed hybrid dryer from a thermodynamic point of view.http://www.sciencedirect.com/science/article/pii/S2214157X2301242XInfrared-assisted hybrid dryerExergyRSMBP-ANN-MOGA
spellingShingle Chinmayee Parida
Pramod Kumar Sahoo
Rabiya Nasir
Liaqat Ali Waseem
Aqil Tariq
Muhammad Aslam
Wesam Atef Hatamleh
Exergy assessment of infrared assisted air impingement dryer using response surface methodology, Back Propagation-Artificial Neural Network, and multi-objective genetic algorithm
Case Studies in Thermal Engineering
Infrared-assisted hybrid dryer
Exergy
RSM
BP-ANN-MOGA
title Exergy assessment of infrared assisted air impingement dryer using response surface methodology, Back Propagation-Artificial Neural Network, and multi-objective genetic algorithm
title_full Exergy assessment of infrared assisted air impingement dryer using response surface methodology, Back Propagation-Artificial Neural Network, and multi-objective genetic algorithm
title_fullStr Exergy assessment of infrared assisted air impingement dryer using response surface methodology, Back Propagation-Artificial Neural Network, and multi-objective genetic algorithm
title_full_unstemmed Exergy assessment of infrared assisted air impingement dryer using response surface methodology, Back Propagation-Artificial Neural Network, and multi-objective genetic algorithm
title_short Exergy assessment of infrared assisted air impingement dryer using response surface methodology, Back Propagation-Artificial Neural Network, and multi-objective genetic algorithm
title_sort exergy assessment of infrared assisted air impingement dryer using response surface methodology back propagation artificial neural network and multi objective genetic algorithm
topic Infrared-assisted hybrid dryer
Exergy
RSM
BP-ANN-MOGA
url http://www.sciencedirect.com/science/article/pii/S2214157X2301242X
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