@article{scholars12019,
            year = {2019},
         journal = {International Journal of Advanced Trends in Computer Science and Engineering},
       publisher = {World Academy of Research in Science and Engineering},
           pages = {98--102},
          number = {1.3 S1},
          volume = {8},
            note = {cited By 1},
             doi = {10.30534/ijatcse/2019/1981.32019},
           title = {Exergy and regression analysis for optimization of electric chillers at a gas district cooling plant},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074075167&doi=10.30534\%2fijatcse\%2f2019\%2f1981.32019&partnerID=40&md5=32295416972047ee7801cb40a9634aef},
        abstract = {At present, most plant engineers experience uncertainty on the optimal performance of electric chillers (ECs) even after repairs or maintenance services. This is evidence to a district cooling plant, where the current performance of ECs are not consistent and optimized causing energy losses and low efficiency of the equipment. Thus, an analytical model of the ECs under steady state plant operating condition is needed in order to achieve better optimization and to improve equipment efficiency. To increase the exergy efficiency and coefficient of performance of ECs, an exergy-based analytical model was developed by implementing ANOVA and regression analysis on significant parameters. The findings showed that for the exergy efficiency, the actual efficiency is lower due to insufficient chilled water flowrate and low chilled water return temperature. The EC efficiency can be optimized at COP of 3.1 when the cooling load is 311.41 RT/h for an electric consumption of 353.28 kW/h. Hence, this paper exhibit the ability of the exergy-based analytical model to accurately predict the actual performance of the equipment and provide optimizing strategies can be implemented by the plant. {\^A}{\copyright} 2019, World Academy of Research in Science and Engineering. All rights reserved.},
          author = {Abdul Karim, Z. A. and Afiq Sidqi, M. and Majid, M. A. A. and Muhammad, M. and Aoki, H. and Xiaoming, Z.},
            issn = {22783091}
}