The economic performance of real-time optimization system is influenced by the accuracy of the model.If the process model deviates from the process plant

it would lead to the offset between the true plant optimum and the predicted optimum and decrease the efficiency of real-time optimization.So it is important to use parameter estimation to make sure the least deviation of the optimum from the true plant.The definition of parameter estimability is proposed for parameter estimation problem with finite measurement information based on the definition of system estimability.The necessary conditions for parameter estimability are stated in both linear and nonlinear systems based on matrix analysis.If the parameters of system are inestimable

the parameter estimability can be changed by fixing some of the modelparameters.The relationship between parameter estimability and the number of data sets is discussed.The effectiveness of the proposed analysis can be demonstrated by the results of linear and nonlinear systems.