Murugesan Mohanraj

Bio: Murugesan Mohanraj is an academic researcher from Hindustan University. The author has contributed to research in topics: Heat pump & Coefficient of performance. The author has an hindex of 26, co-authored 74 publications receiving 2750 citations. Previous affiliations of Murugesan Mohanraj include Al-Farabi University & National Institute of Technology Calicut.

Applications of artificial neural networks for refrigeration, air-conditioning and heat pump systems—A review

Abstract: In this paper, an attempt has been made to review the applications of artificial neural networks (ANN) for energy and exergy analysis of refrigeration, air conditioning and heat pump (RACHP) systems. The studies reported are categorized into eight groups as follows: (i) vapour compression systems (ii) RACHP systems components, (iii) vapour absorption systems, (iv) prediction of refrigerant properties (v) control of RACHP systems, (vi) phase change characteristics of refrigerants, (vii) heat ventilation air conditioning (HVAC) systems and (viii) other special purpose heating and cooling applications. More than 90 published articles in this area are reviewed. Additionally, the limitations with ANN models are highlighted. This paper concludes that ANN can be successfully applied in the field of RACHP systems with acceptable accuracy.

Renewable energy source water pumping systems—A literature review

Abstract: The research developments with renewable energy source water pumping systems (RESWPSs) are reviewed in this paper. The reported investigations are categorized into five major groups as follows: (i) solar photovoltaic water pumping systems (SPWPSs), (ii) solar thermal water pumping systems (STWPSs), (iii) wind energy water pumping systems (WEWPSs), (iv) biomass water pumping systems (BWPSs) and (v) hybrid renewable energy water pumping systems (HREWPSs). More than a hundred published articles related to RESWPSs are briefly reviewed. Additionally, the limitations with RESWPSs and further research needs are described. This paper concludes that renewable energy sources (RESs) play a vital role in reducing the consumption of conventional energy sources and its environmental impacts for water pumping applications.

Artificial neural networks

Abstract: Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Review on life cycle assessment of energy payback and greenhouse gas emission of solar photovoltaic systems

Abstract: This paper aims to examine the sustainability and environmental performance of PV-based electricity generation systems by conducting a thorough review of the life cycle assessment (LCA) studies of five common photovoltaic (PV) systems, i.e., mono-crystalline (mono-Si), multi-crystalline (multi-Si), amorphous silicon (a-Si), CdTe thin film (CdTe) and CIS thin film (CIS), and some advanced PV systems. The results show that, among the five common PV systems, the CdTe PV system presents the best environmental performance in terms of energy payback time (EPBT) and greenhouse gases (GHG) emission rate due to its low life-cycle energy requirement and relatively high conversion efficiency. Meanwhile, the mono-Si PV system demonstrates the worst because of its high energy intensity during the solar cells’ production process. The EPBT and GHG emission rate of thin film PV systems are within the range of 0.75–3.5 years and 10.5–50 g CO 2 -eq./kW h, respectively. In general, the EPBT of mono-Si PV systems range from 1.7 to 2.7 years with GHG emission rate from 29 to 45 g CO 2 -eq./kW h, which is an order of magnitude smaller than that of fossil-based electricity. This paper also reviews the EPBT and GHG emission rates of some advanced PV systems, such as high-concentration, heterojunction and dye-sensitized technologies. The EBPT of high-concentration PV system is lower, ranging from 0.7 to 2.0 years, but the CO 2 emission rate of dye-sensitized PV system is higher than the ones of other PV systems at the moment. The LCA results show that PV technologies are already proved to be very sustainable and environmental-friendly in the state of the art. With the emerging of new manufacturing technologies, the environmental performance of PV technologies is expected to be further improved in the near future. In addition, considering the existing limitations in the previous LCA studies, a few suggestions are recommended.

Solar-energy drying systems: A review

Abstract: In many countries of the world, the use of solar thermal systems in the agricultural area to conserve vegetables, fruits, coffee and other crops has shown to be practical, economical and the responsible approach environmentally. Solar heating systems to dry food and other crops can improve the quality of the product, while reducing wasted produce and traditional fuels—thus improving the quality of life, however the availability of good information is lacking in many of the countries where solar food processing systems are most needed. Solar food dryers are available in a range of size and design and are used for drying various food products. It is found that various types of driers are available to suit the needs of farmers. Therefore, selection of dryers for a particular application is largely a decision based on what is available and the types of dryers currently used widely. A comprehensive review of the various designs, details of construction and operational principles of the wide variety of practically realized designs of solar-energy drying systems reported previously is presented. A systematic approach for the classification of solar-energy dryers has been evolved. Two generic groups of solar-energy dryers can be identified, viz. passive or natural-circulation solar-energy dryers and active or forced-convection solar-energy dryers. Some very recent developments in solar drying technology are highlighted.