Water-energy nexus: Condition monitoring and the performance optimization of a hybrid cooling system

Alimoradi, 2021, Experimental investigation on improvement of wet cooling tower efficiency with diverse packing compaction using ANN-PSO algorithm, Energies, 14, 167, 10.3390/en14010167 Asvapoositkul, 2014, Comparative evaluation of hybrid (dry/wet) cooling tower performance, Appl. Therm. Eng., 71, 83, 10.1016/j.applthermaleng.2014.06.023 Biancalani, 2021, Assessing SDG indicator 6.4. 2 ‘level of water stress’ at major basins level, UCL Open Environ. Prepr., 10.14324/111.444/ucloe.000026 Chapman-Blench, 2012, Traderevolution: Training for Traders. AuthorHouse Conejo, 2020, A review of the current environmental challenges of the steel industry and its value chain, J. Environ. Manage., 259, 10.1016/j.jenvman.2019.109782 Cortinovis, 2009, A systemic approach for optimal cooling tower operation, Energy Convers. Manag., 50, 2200, 10.1016/j.enconman.2009.04.033 Drgoňa, 2018, Approximate model predictive building control via machine learning, Appl. Energy, 218, 199, 10.1016/j.apenergy.2018.02.156 Galal, 2020, The efficiency of rainwater harvesting systems in the lebanese coastal zone, Archit. Plan. J., 26, 2 Giraldo Carvajal, 2017, Development of optimization tools for the systematic exploration of industrial symbiosis opportunities in the process industry, Universitat Politècnica de Catalunya Golkar, 2019, Determination of optimum hybrid cooling wet/dry parameters and control system in off design condition: Case study, Appl. Therm. Eng., 149, 132, 10.1016/j.applthermaleng.2018.12.017 Gorjian, 2015, Solar desalination: A sustainable solution to water crisis in Iran, Renew. Sustain. energy Rev., 48, 571, 10.1016/j.rser.2015.04.009 Guo, 2017, Parallel hybrid model for mechanical draft counter flow wet-cooling tower, Appl. Therm. Eng., 125, 1379, 10.1016/j.applthermaleng.2017.07.138 Hosoz, 2007, Performance prediction of a cooling tower using artificial neural network, Energy Convers. Manag., 48, 1349, 10.1016/j.enconman.2006.06.024 Hu, 2018, Thermodynamic characteristics of thermal power plant with hybrid (dry/wet) cooling system, Energy, 147, 729, 10.1016/j.energy.2018.01.074 Kozicki, 2019, The survival of mankind requires a Water Quality and Quantity Index (WQQI) and Water Applied Testing and Environmental Research (WATER) Centers, World Water Policy, 5, 55, 10.1002/wwp2.12007 Kroger, 2004 Lv, 2018, Analysis and optimization of open circulating cooling water system, Water, 10, 1592, 10.3390/w10111592 Merkel, 1925 Norouzi, 2020, The sun food-water-energy nexus governance model a case study for Iran, Water-Energy Nexus, 3, 72, 10.1016/j.wen.2020.05.005 Petruchik, 2002, Mathematical modeling of evaporative cooling of water in a mechanical-draft tower, J. Eng. Phys. Thermophys., 75, 1332, 10.1023/A:1022110809044 Qi, 2016, Performance prediction of a shower cooling tower using wavelet neural network, Appl. Therm. Eng., 108, 475, 10.1016/j.applthermaleng.2016.07.117 Samani, 2018, Combined cycle power plant with indirect dry cooling tower forecasting using artificial neural network, Decis. Sci. Lett., 7, 131, 10.5267/j.dsl.2017.6.004 Schulze, 2019, Cooling tower management in manufacturing companies: A cyber-physical system approach, J. Clean. Prod., 211, 428, 10.1016/j.jclepro.2018.11.184 T. Schulze, “Gleichungsorientierte Modellierung der Wärme-und Stoffübertragungsprozesse in Verdunstungskühltürmen,” 2015. Singla, 2016, Tower characteristics correlation and parameter retrieval in wet-cooling tower with expanded wire mesh packing, Appl. Therm. Eng., 96, 240, 10.1016/j.applthermaleng.2015.11.063 Thiede, 2017, The water–energy nexus in manufacturing systems: Framework and systematic improvement approach, CIRP Ann., 66, 49, 10.1016/j.cirp.2017.04.108 Wang, 2013, Discrete model-based operation of cooling tower based on statistical analysis, Energy Convers. Manag., 73, 226, 10.1016/j.enconman.2013.04.025 Xu, 2015, Performance prediction of mechanical draft wet cooling tower using artificial neural network, Adv. Mater. Res., 1070-1072, 1994, 10.4028/www.scientific.net/AMR.1070-1072.1994