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Assessment of an energy efficient closed loop heat pump dryer for high moisture contents materials: An experimental investigation and AI based modelling

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  • Hamid, Khalid
  • Sajjad, Uzair
  • Yang, Kai Shing
  • Wu, Shih-Kuo
  • Wang, Chi-Chuan

Abstract

The aim of this study is to discuss the experimental performance analysis and deep learning based modelling of moist sodium polyacrylate material (also known as Orbeez) in a closed-loop heat pump dryer using R-134a as a secondary fluid. The experiments are performed on different weights of Orbeez at a constant air flow rate to calculate different performance parameters like coefficient of performance of the heat pump, drying rate, heat transfer rate by condenser, moisture extraction rate, and specific moisture extraction rate. The higher test loads such as 6, 7, and 8 kg are found better in terms of maximum coefficient of performance (5.2–5.8) and heat transfer rate (0.56–0.64 kW). Similarly, the higher test loads such as 6, 7, and 8 kg yield the highest moisture extraction rate (∼0.66–0.75 kg/h), specific moisture extraction rate (∼2.15–2.27 kg/kWh), and weight reduction (∼91%). The water removal rate depends on the moisture diffusivity, and it increases with an increase in the drying air temperature and drying air velocity. In addition, a deep learning model considering the most influential dryer inlet conditions (air temperature, air relative humidity, and airflow rate), time, and weight as the input features to estimate the dryer outlet conditions and weight reduction for assessment of drying kinetics of the considered material. A high accuracy (coefficient of determination = 0.997) makes it a simple, cost effective, and reliable method to predict the drying performance of various materials with a closed loop heat pump dryer.

Suggested Citation

  • Hamid, Khalid & Sajjad, Uzair & Yang, Kai Shing & Wu, Shih-Kuo & Wang, Chi-Chuan, 2022. "Assessment of an energy efficient closed loop heat pump dryer for high moisture contents materials: An experimental investigation and AI based modelling," Energy, Elsevier, vol. 238(PB).
    • Handle: RePEc:eee:energy:v:238:y:2022:i:pb:s0360544221020673
    DOI: 10.1016/j.energy.2021.121819
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    1. Hafiz M. Asfahan & Uzair Sajjad & Muhammad Sultan & Imtiyaz Hussain & Khalid Hamid & Mubasher Ali & Chi-Chuan Wang & Redmond R. Shamshiri & Muhammad Usman Khan, 2021. "Artificial Intelligence for the Prediction of the Thermal Performance of Evaporative Cooling Systems," Energies, MDPI, vol. 14(13), pages 1-20, July.
    2. Chua, K.J. & Chou, S.K. & Yang, W.M., 2010. "Advances in heat pump systems: A review," Applied Energy, Elsevier, vol. 87(12), pages 3611-3624, December.
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    2. Sergio Bobbo & Giulia Lombardo & Davide Menegazzo & Laura Vallese & Laura Fedele, 2024. "A Technological Update on Heat Pumps for Industrial Applications," Energies, MDPI, vol. 17(19), pages 1-55, October.
    3. Uzair Sajjad & Imtiyaz Hussain & Muhammad Sultan & Sadaf Mehdi & Chi-Chuan Wang & Kashif Rasool & Sayed M. Saleh & Ashraf Y. Elnaggar & Enas E. Hussein, 2021. "Determining the Factors Affecting the Boiling Heat Transfer Coefficient of Sintered Coated Porous Surfaces," Sustainability, MDPI, vol. 13(22), pages 1-19, November.
    4. Solomzi Marco Ngalonkulu & Zhongjie Huan, 2024. "Viability of an Open-Loop Heat Pump Drying System in South African Climatic Conditions," Energies, MDPI, vol. 17(10), pages 1-14, May.
    5. Deymi-Dashtebayaz, Mahdi & Davoodi, Vajihe & Khutornaya, Julia & Sergienko, Olga, 2023. "Parametric analysis and multi-objective optimization of a heat pump dryer based on working conditions and using different refrigerants," Energy, Elsevier, vol. 284(C).
    6. Liu, Xuexiang & Liu, Haowen & Zhao, Xudong & Han, Zhonghe & Cui, Yu & Yu, Min, 2022. "A novel neural network and grey correlation analysis method for computation of the heat transfer limit of a loop heat pipe (LHP)," Energy, Elsevier, vol. 259(C).
    7. Damir Đaković & Miroslav Kljajić & Nikola Milivojević & Đorđije Doder & Aleksandar S. Anđelković, 2023. "Review of Energy-Related Machine Learning Applications in Drying Processes," Energies, MDPI, vol. 17(1), pages 1-38, December.
    8. Antonio Quijano & Celena Lorenzo & Luis Narvarte, 2023. "Economic Assessment of a PV-HP System for Drying Alfalfa in The North of Spain," Energies, MDPI, vol. 16(8), pages 1-19, April.
    9. Rehman, Tauseef-ur & Sajjad, Uzair & Lamrani, Bilal & Shahsavar, Amin & Ali, Hafiz Muhammad & Yan, Wei-Mon & Park, Cheol Woo, 2024. "Investigation on the thermal control and performance of PCM–porous media-integrated heat sink systems: Deep neural network modelling employing experimental correlations," Renewable Energy, Elsevier, vol. 220(C).
    10. Joshua Adeniyi Depiver & Sabuj Mallik, 2023. "An Empirical Study on Convective Drying of Ginger Rhizomes Leveraging Environmental Stress Chambers and Linear Heat Conduction Methodology," Agriculture, MDPI, vol. 13(7), pages 1-28, June.
    11. Showkat Ahmad Bhat & Nen-Fu Huang & Imtiyaz Hussain & Farzana Bibi & Uzair Sajjad & Muhammad Sultan & Abdullah Saad Alsubaie & Khaled H. Mahmoud, 2021. "On the Classification of a Greenhouse Environment for a Rose Crop Based on AI-Based Surrogate Models," Sustainability, MDPI, vol. 13(21), pages 1-18, November.

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