Accurate predictive modeling of response variables under dynamic conditionwithout the use of past response data

One promising attribute of the dynamic predictive modeling method introduce d 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 withou t the use of online output data. The proposed method only needs online inpu t data to accurately predict output behavior once the semi-empirical model has been identified using offline data. This ability is critical to chemica l processes because many output variables (such as chemical composition) ar e often measured infrequently, inaccurately, or not at all. In addition, in the presence of extremely high measurement noise of the output variable, t his work will demonstrate very accurate predictive performance. Finally, th is article will show that the method of Rollins et al. can predict better w ithout 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 recommend ed for its accuracy, especially in situations where online output response data is limited or inaccurate. (C) 2000 Elsevier Science Ltd. All rights re served.