Experimental evaluation and ANN modeling of a recuperative micro gas turbine burning mixtures of natural gas and biogas

Abstract

Previously published studies have addressed modifications to the engines when operating with biogas, i.e. a low heating value fuel. This study focuses on mapping out the possible biogas share in a fuel mixture of biogas and natural gas in micro combined heat and power (CHP) installations without any engine modifications. This contributes to a reduction in CO2 emissions from existing CHP installations and makes it possible to avoid a costly upgrade of biogas to the natural gas quality as well as engine modifications. Moreover, this approach allows the use of natural gas as a “fallback” solution in the case of eventual variations of the biogas composition and or shortage of biogas, providing improved availability.In this study, the performance and emissions of a commercial 100kW micro gas turbine (MGT) at full and part loads are experimentally evaluated when fed by varying mixtures of natural gas and biogas. The MGT is equipped with additional instrumentation, and a gas mixing station is used to supply the demanded fuel mixtures from zero biogas to the maximum possible level by diluting natural gas with CO2. A typical biogas composition with 0.6 CH4 and 0.4 CO2 (in mole fraction) was used as reference, and corresponding biogas content in the supplied mixtures was computed.This paper presents the test rig setup used for the experimental activities and reports the results, demonstrating the impact of burning a mixture of biogas and natural gas on the performance and emissions of the MGT. The results indicate that the electrical efficiency is almost unchanged and no significant changes were observed in operating parameters, comparing with the natural gas fired case. It was also shown that burning a mixture of natural gas and biogas contributes to a significant reduction in CO2 emissions from the plant by about 19% at full load operation. Given the extensive data obtained during the experimental tests, a data-driven model based on an artificial neural network (ANN) was developed to simulate the performance of the MGT. The mean relative error (MRE) was used to evaluate the prediction accuracy of the developed ANN model with respect to experimental data which were not used during the training. It was demonstrated that the ANN model can predict the performance parameters of the MGT with high accuracy and the error of most samples is less than 1%. A graphical user interface (GUI) was created for the ANN model in Microsoft Visual Basic. The GUI is presented as a user-friendly tool for modeling and condition monitoring of the plant.