Development of Predictive Model based Control Scheme for a Molten Carbonate Fuel Cell (MCFC) Process
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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.
KeywordsARMA modeling model predictive control molten carbonate fuel cells rigorous model
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