Accurate predictive modeling of response variables under dynamic condition without the use of past response data

One promising attribute of the dynamic predictive modeling method introduced by Rollins et al. [D.K. Rollins, J. Liang, P. Smith, Accurate simplistic predictive modeling of nonlinear dynamic processes, ISA Transactions 37(4) (1998) 193-203] is its ability to accurately predict output response without the use of online output data. The proposed method only needs online input data to accurately predict output behavior once the semi-empirical model has been identified using offline data. This ability is critical to chemical processes because many output variables (such as chemical composition) are often measured infrequently, inaccurately, or not at all. In addition, in the presence of extremely high measurement noise of the output variable, this work will demonstrate very accurate predictive performance. Finally, this article will show that the method of Rollins et al. can predict better without the use of output data than with the use of output data in the case of large measurement variance. Thus, the proposed method is being recommended for its accuracy, especially in situations where online output response data is limited or inaccurate.