Advanced Model-based Control of B36:45 LNG Engines
Keywords:B36:45 LNG engine, MPC, optimal operation
AbstractThe framework of model predictive control is used in this paper to optimally control the operation of an B36:45 LNG engine. The model of the engine is based on real life data from an installed B36:45 gas engine in a power plant. Stored data from the plant was used to develop a state space model of the process consisting of 2 manipulatable variables, 3 measured disturbances and 6 measured outputs. The goal is to use global ignition timing and the charge air pressure as control variables to minimize the heat rate while considering constraints on the measured outputs. Heat rate of the engine is directly related to engine performance efﬁciency. Results show that a model based controller has the potential to be used as an advanced controller for optimal operation of this engine.
Rohit V. Koli. Model predictive control of modern high-degree-of-freedom turbocharged spark ignited engines with external cooled egr. PhD Dissertation, Clemson University, 1981.
Jun Lu, Weifeng Ma, Yongjun Han, Yuke Gao, Zhaoyuan Guo, Xin Li, and Honglei Niu. Model predictive engine control using support vector machine. In 2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), pages 1569–1573, 2015. doi:10.1109/CYBER.2015.7288179.
Jim B. Luther. Advanced neural network engine control. PhD Dissertation, Technical University of Denmark, 2002.
J. Richalet. Industrial applications of model based predictive control. Automatica, 29(5):1251–1274, 1993.
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