Optimization of a novel carbon dioxide cogeneration system using artificial neural network and multi-objective genetic algorithm

• Published in: Applied thermal engineering 2014-03, Vol.64 (1-2), p.293-306
• Main Authors: Jamali, Arash, Ahmadi, Pouria, Mohd Jaafar, Mohammad Nazri
• Format: Article
• Language: English
• Subjects: Applied sciences; Artificial neural network; Carbon dioxide; CO2; Combined cycle; Combined power plants; Devices using thermal energy; Ejector; Energy; Energy. Thermal use of fuels; Evolutionary; Exact sciences and technology; Exergy; Genetic algorithm; Genetic algorithms; Heat exchanger; Heat exchangers; Heat exchangers (included heat transformers, condensers, cooling towers); Heat transfer; Heating; Installations for energy generation and conversion: thermal and electrical energy; Optimization; Theoretical studies. Data and constants. Metering; Thermal engineering
• ISSN: 1359-4311
• DOI: 10.1016/j.applthermaleng.2013.11.071

In this research study, a combined cycle based on the Brayton power cycle and the ejector expansion refrigeration cycle is proposed. The proposed cycle can provide heating, cooling and power simultaneously. One of the benefits of such a system is to be driven by low temperature heat sources and using CO2 as working fluid. In order to enhance the understanding of the current work, a comprehensive parametric study and exergy analysis are conducted to determine the effects of the thermodynamic parameters on the system performance and the exergy destruction rate in the components. The suggested cycle can save the energy around 46% in comparison with a system producing cooling, power and hot water separately. On the other hand, to optimize a system to meet the load requirement, the surface area of the heat exchangers is determined and optimized. The results of this section can be used when a compact system is also an objective function. Along with a comprehensive parametric study and exergy analysis, a complete optimization study is carried out using a multi-objective evolutionary based genetic algorithm considering two different objective functions, heat exchangers size (to be minimized) and exergy efficiency (to be maximized). The Pareto front of the optimization problem and a correlation between exergy efficiency and total heat exchangers length is presented in order to predict the trend of optimized points. The suggested system can be a promising combined system for buildings and outland regions. •Energy and exergy analysis of a novel CHP system are reported.•A comprehensive parametric study is conducted to enhance the understanding of the system performance.•Apply a multi-objective optimization technique based on a code developed in the Matlab software program using an evolutionary algorithm.