Edoardo Platania
Autonomous identification of fuel parameters for adaptive model-based control of large gas engines.
Rel. Federico Millo, Andrea Piano. Politecnico di Torino, Corso di laurea magistrale in Mechatronic Engineering (Ingegneria Meccatronica), 2025
Abstract
The operational efficiency and performance of gas engines, such as those manufactured by INNIO Jenbacher GmbH & Co OG in Jenbach, are affected by a multitude of factors, including engine type, fuel quality, geographic location, systems aging, and other boundary conditions. These engines are increasingly used in different environments and subjected to various gas conditions, necessitating a robustness to gas quality changes during operation. However, the large number of possible variations represents a significant challenge in the complete parameterization of the controller. This thesis presents the adaptation and implementation of an algorithm for the autonomous identification of parameters that characterize the gas type and quality of fuel, from now referred to as Fuel Parameters Estimator or FPE.
The algorithm was developed using MATLAB and Simulink via a combination of theoretical modeling and simulation
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