Development of Predictive Model based Control Scheme for a Molten Carbonate Fuel Cell (MCFC) Process Tae Young Kim, Beom Seok Kim, Tae Chang Park, and Yeong Koo Yeo*
International Journal of Control, Automation, and Systems, vol. 16, no. 2, pp.791-803, 2018
Abstract : "To improve availability and performance of fuel cells, the operating temperature of a molten carbonate
fuel cells (MCFC) stack should be strictly maintained within a specified operation range and an efficient control
technique should be employed to meet this objective. While most of modern control strategies are based on process
models, many existing models for a MCFC process are not ready to be applied in synthesis and operation of control
systems. In this study, auto-regressive moving average (ARMA) model, least square support vector machine (LSSVM)
model and artificial neural network (ANN) model for the MCFC system are developed based on input-output
operating data. Among these models, the ARMA model showed the best tracking performance. A model predictive
control (MPC) method for the operation of a MCFC process is developed based on the proposed ARMA model.
For the purpose of comparison, a MPC scheme based on the linearized rigorous model for a MCFC process is
developed. Results of numerical simulations show that MPC based on the ARMA model exhibits better control
performance than that based on the linearized rigorous model."
Keyword :
ARMA modeling, model predictive control, molten carbonate fuel cells, rigorous model.
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