Heat recovery wheels represent key components in air handling units (AHU) that can be used in commercial and industrial building air-conditioning-systems for energy saving. For example, in health facilities, heat transfer process is to be applied in air-conditioning systems for heat recovery of the exhaust (return) air from the patient's room without contamination. Thus, heat recovery wheels are much suitable for such applications. Heat recovery wheels are also known as heat conservation wheels. A conservation wheel consists of a rotor with permeable storage mass fitted in a casing, which operates intermittently between two sections of hot and cold fluids. The rotor is driven by a low-speed electric motor. Thus, the two streams of exhaust and fresh air are alternately passed through the wheel. The present investigation considers computationally the different parameters that affect the operation of heat recovery wheels. These parameters signify actual operating conditions such as flow velocity, shape of cross-section of flow path, and wall material. Moreover, both the artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) techniques were utilized to predict the critical characteristics of the heat exchange system. These artificial intelligence techniques use the present computational results as training and verification data.
| Published in |
American Journal of Energy Engineering (Volume 3, Issue 4-1)
This article belongs to the Special Issue Fire, Energy and Thermal Real-Life Challenges |
| DOI | 10.11648/j.ajee.s.2015030401.16 |
| Page(s) | 79-94 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2015. Published by Science Publishing Group |
Heat Recovery Wheels, Air Handling Units, Computational Study, Artificial Intelligence
| [1] | S. B. Riffat, and G., Gan, "Determination of Effectiveness of Heat-pipe Heat Recovery for Naturally-ventilated Buildings", Applied Thermal Engineering, Vol. 18, No. 3-4, pp. 121-130, 1998. |
| [2] | L. A. Sphaier, and W. M. Worek, "Analysis of Heat and Mass Transfer in Porous Sorbents Used in Rotary Regenerators", International Journal of Heat and Mass Transfer, Vol. 47, pp. 3415-3430, 2004. |
| [3] | G. J. Softah, Numerical Analysis and Neuro-Fuzzy Investigation of the Performance of Heat Recovery Wheels in AHU Systems, M.Sc. Graduation Project, Mechanical Engineering Department, Umm Al-Qura University, Saudi Arabia, Spring 2014. |
| [4] | A. M. Wafiah, Computational and Neural Investigation of the Operation of Heat Exchange Wheels in AHU Systems, M.Sc. Graduation Project, Mechanical Engineering Department, Umm Al-Qura University, Saudi Arabia, Spring 2014. |
| [5] | S. Yamaguchi, and K. Saito, "Numerical and Experimental Performance Analysis of Rotary Desiccant Wheels", International Journal of Heat and Mass transfer, Vol. 60, pp. 51-60, 2013. |
| [6] | W. Shurcliff, "Air-to-air Heat Exchangers for Houses", Annual Review Energy, Vol. 13, pp. 1-22, 1988. |
| [7] | P. Mazzei, F. Minichiello, and D. Palma, "Desiccant HVAC Systems for Commercial Buildings", Applied Thermal Engineering, Vol. 22, No. 5, pp. 545-560, 2002. |
| [8] | 2005 ASHRAE Handbook Fundamentals SI Edition, ASHRAE, USA, 2005. |
| [9] | L. Pérez-Lombard, J. Ortiz, and C. Pout, "A Review on Buildings Energy Consumption Information", Energy and Buildings, Vol. 40, No. 3, pp. 394-398, 2008. |
| [10] | K. K. W. Wan, D. H. W. Li, D. Liu, J. C. Lam, "Future Trends of Building Heating and Cooling Loads and Energy Consumption in Different Climates," Building and Environment, Vol. 46, No. 1, pp. 223-234, 2011. |
| [11] | L. Shao, S. B. Riffat, and G. Gan, "Heat Recovery with Low Pressure Loss for Natural Ventilation", Energy and Buildings, Vol. 28, No. 2, pp. 179-184, 1998. |
| [12] | A. Pesaran, and A. F. Mills, "Moisture Transport in Silica Gel Packed Bed I: Theoretical Study", International Journal of Heat and Mass Transfer, Vol. 30, pp. 1037-1049, 1987. |
| [13] | A. Pesaran, and A. F. Mills, "Moisture Transport in Silica Gel Packed Bed II: Experimental Study", International Journal of Heat and Mass Transfer, Vol. 30, pp. 1051-1060, 1987. |
| [14] | W. Zheng, and W. M. Worek, "Numerical Simulation of Combined Heat and Mass Transfer Process in a Rotary Dehumidifier", Numerical Heat Transfer Part A, Vol. 23, pp. 211-232, 1993. |
| [15] | Y. J. Dai, R. Z. Wang, and H. F. Zhang, "Parameter Analysis to Improve Rotary Desiccant Dehumidification Using a Mathematical Model", International Journal of Thermal Sciences, Vol. 40 pp. 400-408, 2001. |
| [16] | J. L. Niu, and L. Z. Zhang, "Performance Comparisons of Desiccant Wheels for Air Dehumidification and Enthalpy Recovery", Applied Thermal Engineering, Vol. 22, No. 12, pp. 1347-1367, 2002. |
| [17] | F. E. Nia, D. V. Paassen, and M. H. Saidi, "Modeling and Simulation of Desiccant wheel for Air Conditioning", Energy and Buildings, Vol. 38, pp.1230-1239, 2006. |
| [18] | G. Heidarinejad, H. P. Shahri, and S. Delfani, "The Effect of Geometrical Characteristics of Desiccant Wheel on Its Performance", International Journal of Engineering Transactions B: Application, Vol. 22, No. 1, pp. 63-75, 2009. |
| [19] | A. Kodama, T. Hirayama, M. Goto, T. Hirose, and R. E. Critoph, "The Use of Psychometric Charts for the Optimization of a Thermal Swing Desiccant Wheel", Applied Thermal Engineering, Vol. 21, No. 16, pp. 1657-1674, 2001. |
| [20] | T. S. Ge, Y. Li, R. Z. Wang, and Y. J. Dai, "A Review of the Mathematical Models for Predicting Rotary Desiccant Wheel, Renew. Sustain. Energy Revs., Vol. 12, No. 6, pp. 1485-1528, 2008. |
| [21] | E. M. Sterling, A. Arundel, and T. D. Sterling, "Criteria for Human Exposure to Humidity in Occupied Buildings", ASHRAE Trans., Vol. 91, No. 1, pp. 611-622, 1985. |
| [22] | M. H. Ahmed, N. M. Kattab, and M. Fouad, "Evaluation and Optimization of Solar Desiccant Wheel Performance", Renewable Energy, Vol. 30, No. 3, pp. 305-325, 2005. |
| [23] | X. J. Zhang, Y. J. Dai, and R. Z. Wang, "A Simulation Study of Heat and Mass Transfer in a Honeycombed Rotary Desiccant Dehumidifier", Applied Thermal Engineering, Vol. 23, No. 8, pp. 989-1003, 2003. |
| [24] | L. G. Harriman III, "The Basics of Commercial Desiccant Systems", Heating/Piping/Air Conditioning, Vol. 66, No. 7, pp. 77-85. 1994. |
| [25] | H. M. Henning, T. Erpenbeck, C. Hindenberg, and I. S. Santamiria, "The Potential of Solar Energy Use in Desiccant Cooling Cycles", International Journal of Refrigeration, Vol. 24, pp. 220-229, 2001. |
| [26] | P. Mavroudaki, C. B. Beggs, P. A. Sleigh, and S. P. Haliiday, "The Potential for Solar Powered Single-stage Desiccant Cooling in Southern Europe", Applied Thermal Engineering, Vol. 22, pp. 1129-1140, 2002. |
| [27] | S. P. Halliday, C. B. Beggs, and P. A. Sleigh, "The Use of Solar Desiccant Cooling in the UK: a Feasibility Study", Applied Thermal Engineering, Vol. 22, pp. 1327-1338, 2002. |
| [28] | J. Y. San, and S. C. Hsiau, "Effect of Axial Solid Heat Conduction and Mass Diffusion in a Rotary Heat and Mass Regenerator", Int. J. Heat Mass Transfer, Vol. 36, No. 8, pp. 2051-2059, 1993. |
| [29] | R. B. Holmberg, "Combined Heat and Mass Transfer in Regenerators with Hygroscopic Materials", ASME J. Heat Transfer, Vol. 101, pp. 205-210, 1979. |
| [30] | D. Charoensupaya, and W. M. Worek, "Effect of Adsorbent Heat and Mass Transfer Resistances on Performance of an Open Cycle Adiabatic Desiccant Cooling System", Heat Recov. Sys. CHP, Vol. 8, No. 6, pp. 537-548, 1988. |
| [31] | N. Nassif, "Modeling and Optimization of HVAC Systems Using Artificial Neural Network and Genetic Algorithm", Building Simulation, Vol. 7, No. 3, pp. 237-245, June 2014. |
| [32] | Z. Li, X. Xu, S. Deng, and D. Pan, "A Novel Neural Network Aided Fuzzy Logic Controller for a Variable Speed (VS) Direct Expansion (DX) Air Conditioning (A/C) System", Applied Thermal Engineering, Vol. 78, pp. 9-23, 2015. |
| [33] | B. C. Ng, I. Z. M. Darus, H. Jamaluddin, and H. M. Kamar, "Application of Adaptive Neural Predictive Control for an Automotive Air Conditioning System", Applied Thermal Engineering, Vol. 73, No. 1, pp. 1244-1254, 2014. |
| [34] | H. M. Factor, and G. Grossman, "A Packed Bed Dehumidifier/Regenerator for Solar Air Conditioning with Liquid Desiccants", Solar Energy, Vol. 24, No. 6, pp. 541-550, 1980. |
| [35] | A. Al-Alili, Y. Hwang, and R. Radermacher, "Review of Solar Thermal Air Conditioning Technologies", International Journal of Refrigeration, Vol. 39, pp. 4-22, 2014. |
| [36] | A. F. Abdel Gawad, "Numerical and Neural Study of the Turbulent Flow around Sharp-Edged Bodies", 2002 Joint US ASME-European Fluids Engineering Division Summer Meeting, Montreal, Canada, July 14-18, 2002. |
| [37] | O. E. Abdellatif, and A. F. Abdel Gawad, "Experimental, Numerical and Neural Investigation of the Aerodynamic Characteristics for Two-Dimensional Wings in Ground Effect", Al-Azhar Engineering 7th International Conference, Cairo, Egypt, 7-10 April, 2003. |
| [38] | A. F. Abdel Gawad, "Study of Both Airflow and Thermal Fields in a Room Using K- Modelling and Neural Networks", Al-Azhar Engineering 7th International Conference, Cairo, Egypt, 7-10 April, 2003. |
| [39] | A. F. Abdel Gawad, M. M. Nassief, and N. M. Gurguis, "Numerical and Neural Study of Flow and Heat Transfer Across an Array of Integrated Circuit Components", Journal of Engineering and Applied Science (JEAS), Vol. 52, No. 5, pp. 981-1000, October 2005. |
| [40] | A. F. Abdel Gawad, "Computational and Neuro-Fuzzy Study of the Effect of Small Objects on the Flow and Thermal Fields of Bluff Bodies", 8th Biennial ASME conference on Engineering Systems Design and Analysis (ESDA2006), Torino, Italy July 4-7, 2006. |
| [41] | A. F. Abdel Gawad, "Investigation of The Dilution of Outfall Discharges Using Computational and Neuro-Fuzzy Techniques", 2007 ASME International Mechanical Engineering Congress & Exposition (IMECE2007), Seattle, Washington, USA, 5-11 November 2007. |
| [42] | A. Hosseinzadeh, and M. Karimi, "Prediction of J-Integral Dependence to Residual Stress and Crack Depth on NACA 0012-34 Using FE and ANN", Engineering Solid Mechanics, Vol. 3, No. 2, pp. 103-110, 2015. |
| [43] | Z. Wu, R. V. N. Melnik, and F. Borup, "Model-based Analysis and Simulation of Regenerative Heat Wheel", Energy and Buildings, Vol. 38, pp. 502-514, 2006. |
| [44] | A. M. Wafiah, A. F. Abdel Gawad, and M. N. Radhwi, "Computational Investigation of the Operation of Heat Conservation Wheels in AHU-Systems", Umm Al-Qura University Journal of Engineering and Islamic Architecture (UQU-UJEA), Vol. 5, No. 2, Ragab 1435 H - May 2014. |
| [45] | G. J. Softah, M. N. Radhwi, and A. F. Abdel Gawad, "A Parametric Study of the Performance of Heat Recovery Wheels in HVAC System", Umm Al-Qura University Journal of Engineering and Islamic Architecture (UQU-UJEA), Vol. 5, No. 2, Ragab 1435 H - May 2014. |
| [46] | Fluent guide manual, 2011. |
| [47] | R. Parsons, ASHRAE Handbook 2005: Fundamentals, American Society of Heating, Refrigeration and Air-Conditioning Engineers. |
| [48] | Matlab guide manual, 2011. |
APA Style
Ahmed F. Abdel Gawad, Muhammad N. Radhwi, Asim M. Wafiah, Ghassan J. Softah. (2015). Computational and Artificial Intelligence Study of the Parameters Affecting the Performance of Heat Recovery Wheels. American Journal of Energy Engineering, 3(4-1), 79-94. https://doi.org/10.11648/j.ajee.s.2015030401.16
ACS Style
Ahmed F. Abdel Gawad; Muhammad N. Radhwi; Asim M. Wafiah; Ghassan J. Softah. Computational and Artificial Intelligence Study of the Parameters Affecting the Performance of Heat Recovery Wheels. Am. J. Energy Eng. 2015, 3(4-1), 79-94. doi: 10.11648/j.ajee.s.2015030401.16
AMA Style
Ahmed F. Abdel Gawad, Muhammad N. Radhwi, Asim M. Wafiah, Ghassan J. Softah. Computational and Artificial Intelligence Study of the Parameters Affecting the Performance of Heat Recovery Wheels. Am J Energy Eng. 2015;3(4-1):79-94. doi: 10.11648/j.ajee.s.2015030401.16
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Download@article{10.11648/j.ajee.s.2015030401.16,
author = {Ahmed F. Abdel Gawad and Muhammad N. Radhwi and Asim M. Wafiah and Ghassan J. Softah},
title = {Computational and Artificial Intelligence Study of the Parameters Affecting the Performance of Heat Recovery Wheels},
journal = {American Journal of Energy Engineering},
volume = {3},
number = {4-1},
pages = {79-94},
doi = {10.11648/j.ajee.s.2015030401.16},
url = {https://doi.org/10.11648/j.ajee.s.2015030401.16},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajee.s.2015030401.16},
abstract = {Heat recovery wheels represent key components in air handling units (AHU) that can be used in commercial and industrial building air-conditioning-systems for energy saving. For example, in health facilities, heat transfer process is to be applied in air-conditioning systems for heat recovery of the exhaust (return) air from the patient's room without contamination. Thus, heat recovery wheels are much suitable for such applications. Heat recovery wheels are also known as heat conservation wheels. A conservation wheel consists of a rotor with permeable storage mass fitted in a casing, which operates intermittently between two sections of hot and cold fluids. The rotor is driven by a low-speed electric motor. Thus, the two streams of exhaust and fresh air are alternately passed through the wheel. The present investigation considers computationally the different parameters that affect the operation of heat recovery wheels. These parameters signify actual operating conditions such as flow velocity, shape of cross-section of flow path, and wall material. Moreover, both the artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) techniques were utilized to predict the critical characteristics of the heat exchange system. These artificial intelligence techniques use the present computational results as training and verification data.},
year = {2015}
}
TY - JOUR T1 - Computational and Artificial Intelligence Study of the Parameters Affecting the Performance of Heat Recovery Wheels AU - Ahmed F. Abdel Gawad AU - Muhammad N. Radhwi AU - Asim M. Wafiah AU - Ghassan J. Softah Y1 - 2015/07/14 PY - 2015 N1 - https://doi.org/10.11648/j.ajee.s.2015030401.16 DO - 10.11648/j.ajee.s.2015030401.16 T2 - American Journal of Energy Engineering JF - American Journal of Energy Engineering JO - American Journal of Energy Engineering SP - 79 EP - 94 PB - Science Publishing Group SN - 2329-163X UR - https://doi.org/10.11648/j.ajee.s.2015030401.16 AB - Heat recovery wheels represent key components in air handling units (AHU) that can be used in commercial and industrial building air-conditioning-systems for energy saving. For example, in health facilities, heat transfer process is to be applied in air-conditioning systems for heat recovery of the exhaust (return) air from the patient's room without contamination. Thus, heat recovery wheels are much suitable for such applications. Heat recovery wheels are also known as heat conservation wheels. A conservation wheel consists of a rotor with permeable storage mass fitted in a casing, which operates intermittently between two sections of hot and cold fluids. The rotor is driven by a low-speed electric motor. Thus, the two streams of exhaust and fresh air are alternately passed through the wheel. The present investigation considers computationally the different parameters that affect the operation of heat recovery wheels. These parameters signify actual operating conditions such as flow velocity, shape of cross-section of flow path, and wall material. Moreover, both the artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) techniques were utilized to predict the critical characteristics of the heat exchange system. These artificial intelligence techniques use the present computational results as training and verification data. VL - 3 IS - 4-1 ER -
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