The detection and identification of gross errors

especially measurement bias

plays a vital role in data reconciliation for nonlinear dynamic systems. Although parameter estimation method has been proved to be a powerful tool for bias identification

without a reliable and efficient bias detection strategy

the method is limited in efficiency and cannot be applied widely. In this paper

a new bias detection strategy is constructed to detect the presence of measurement bias and its occurrence time. With the help of this strategy

the number of parameters to be estimated is greatly reduced

and sequential detections and iterations are also avoided. In addition

the number of decision variables of the optimization model is reduced

through which the influence of the parameters estimated is reduced. By incorporating the strategy into the parameter estimation model

a new methodology named IPEBD (Improved Parameter Estimation method with Bias Detection strategy) is constructed. Simulation studies on a continuous stirred tank reactor (CSTR) and the Tennessee Eastman (TE) problem show that IPEBD is efficient for eliminating random errors

measurement biases and outliers contained in dynamic process data.