In practice

model-plant mismatch(MPM) is a key factor that results in performance deterioration in model predictive control(MPC). Traditional correlation analysis between the prediction residual and the manipulated variable of a channel is usually affected by other manipulated variables and disturbance. The result of this process is unreliable

thus unable to locate the MPM accurately. Based on the above problems

partial correlation analysis is used to calculate the correlation between the prediction residual and the manipulated variable of each channel

under the premise of removing the effect of other manipulated variables and disturbance. The MPM problem is converted to a distribution problem of partial correlation coefficients in a certain interval. Whether a channel is mismatched is judged by observing the distribution graph of partial correlation coefficients. The experimental validation on the Shell tower demonstrates the effectiveness of this method.