Multi-objective optimization of a two-stage micro-turbine for combined heat and power production

Abstract

In recent years, reducing the cost of energy production and transmitting electricity in remote areas of the distribution network have attracted many researchers’ attention. One of the methods to fulfill these objectives is using a gas micro-turbine cycle. In this paper, a two-stage micro-turbine with an intercooler was used to produce electricity and heat simultaneously. In this system, the impacts of the effective input parameters, such as compressor pressure ratio, bypass ratio, and re-cooperator yield on cycle performance were studied given that the values obtained from cycle modeling were not continuous, and using GMDH-type neural system (as one of the most widely used neural networks with high potential to model complex data), the desired objective functions were estimated and then simultaneous optimization of the objective functions were implemented. It was shown that the maximum electrical exergy efficiency is 58% and maximum thermal exergy efficiency is 18%.