RESUMO
In this paper, operational data of a natural gas-fired generation engine at 2 MW of power is presented. This engine is used as part of the power supply system of a flexible packaging transformation and conversion plant. This plant, besides having the power supply generated by the engine, receives electrical energy from the network. The data collected from this engine corresponds to measurements taken before, during and after engine stops, whether due to engine maintenance stops, engine failures or external power grid failures. The measurement was made every 10 seconds, and for the storage of these data a data acquisition software was used, which, besides allowing to take these data, shows in real time the electrical behavior of the electrical supply system, as well as the mechanical behavior of the engine.
RESUMO
This paper presents the application of a systematic methodology to obtain a semi-physical model of phenomenological base for a 2 MW internal combustion engine to generate electric power operating with natural gas, as a function of the average thermodynamic value normally measured in industrial applications. Specifically, the application of the methodology is focused on the cylinders, exhaust manifold, and turbocharger turbine sections. The proposed model was validated with actual operating data, obtaining an error rate not exceeding 5%, which allow a thermal characterization of the Jenbacher JMS 612 GS-N based on the model. A parametric analysis is conducted; considering the volumetric efficiency, the output electric power, the effective efficiency, the exhaust gas temperature, the turbine mass flow, the specific fuel consumption under the nominal operation conditions, which is 1.16 bar in the gas pressure, 65 °C in the cooling water temperature, 35 °C in the average ambient temperature, and 1500 rpm. The results of this model can be used to evaluate the thermodynamic performance parameters of waste heat recovery systems. On the other hand, new control strategies and the implementation of state observers for the detection and diagnosis of failures can be developed based on the proposed model.