Estimating a minimum set of physically based dynamic parameters to enhance statistical inference in block-oriented modeling

In process identification (i.e., dynamic model development) information on the precision and reliability of a parameter estimate is conveyed y a confidence interval. The best confidence interval is the one with the shortest width for a given level of confidence. Confidence intervals iden as the standard error increases or as the number of estimated parameters increases. When the value of a parameter is needed for physical nderstanding of process characteristics, its precision and reliability, i.e., certainty, is crucial. Parameter certainty increases as the number of stimated parameters decreases because this causes confidence intervals to shorten and confidence levels to increase. Hence, this article focuses n maximizing parameter certainty of physically interpretable dynamic parameters under block-oriented modeling by obtaining accurate values or all the dynamic parameters from a minimum set of estimated parameters. This objective is accomplished by the development of a procedure hat identifies equivalent sets of parameters and estimates one parameter for each set. For a seven (7) input, five (5) output, simulated CSTR, its 4 physically based dynamic parameters were accurately determined from 23 estimated parameters that resulted in an increase in confidence level rom 50% to 99.9% for a fixed interval width. 2007 Elsevier Ltd. All rights reserved.