A method for updating a model of a process plant in a model predictive controller of a distributed control system of the process plant, the method comprising: a) assessing a deviation of the operating performance level from a desired performance level of the process plant; b) diagnosing a model predictive control (MPC) for a model plant mismatch (MPM), by updating the model in the model predictive controller and of the MPC thereof; wherein diagnosing the said MPC is characterized by the following steps: i) determining model prediction errors in relation to MPM, wherein determining the model prediction errors in relation to MPM comprises - adding white noise to manipulated variables of the MPC in the process plant, - providing set points with white noise to the controllers in the process plant and to the model of the MPC, and - estimating the model prediction errors by calculating the difference between output values of the process plant and of an existing model of the MPC; ii) quantifying the MPM using non zero lag correlation coefficients between the model prediction errors and each of the manipulated variables of the MPC, said manipulated variables of the MPC being the set point values with added white noise of the controllers in the process plant; and iii) updating the model in the said model predictive controller for operation of the process plant. a) assessing a deviation of the operating performance level from a desired performance level of the process plant; b) diagnosing a model predictive control (MPC) for a model plant mismatch (MPM), by updating the model in the model predictive controller and of the MPC thereof; wherein diagnosing the said MPC is characterized by the following steps: i) determining model prediction errors in relation to MPM, wherein determining the model prediction errors in relation to MPM comprises - adding white noise to manipulated variables of the MPC in the process plant, - providing set points with white noise to the controllers in the process plant and to the model of the MPC, and - estimating the model prediction errors by calculating the difference between output values of the process plant and of an existing model of the MPC; ii) quantifying the MPM using non zero lag correlation coefficients between the model prediction errors and each of the manipulated variables of the MPC, said manipulated variables of the MPC being the set point values with added white noise of the controllers in the process plant; and iii) updating the model in the said model predictive controller for operation of the process plant. i) determining model prediction errors in relation to MPM, wherein determining the model prediction errors in relation to MPM comprises - adding white noise to manipulated variables of the MPC in the process plant, - providing set points with white noise to the controllers in the process plant and to the model of the MPC, and - estimating the model prediction errors by calculating the difference between output values of the process plant and of an existing model of the MPC; - adding white noise to manipulated variables of the MPC in the process plant, - providing set points with white noise to the controllers in the process plant and to the model of the MPC, and - estimating the model prediction errors by calculating the difference between output values of the process plant and of an existing model of the MPC; ii) quantifying the MPM using non zero lag correlation coefficients between the model prediction errors and each of the manipulated variables of the MPC, said manipulated variables of the MPC being the set point values with added white noise of the controllers in the process plant; and iii) updating the model in the said model predictive controller for operation of the process plant. <EOS>