STAGE EFFICIENCY ESTIMATION BY MODIFIED MIMT USING NLP

Abstract

Unmeasured process variables or parameters caused by cost consideration or technical infeasibility can be mostly estimated using data reconcilation techniques. Since, however, the gross errors possibly present in the process measurements deteriorate the data reconcilation results, the reconciled estimates may be biased solutions that are different from the true calues. In this paper, the enhanced data reconcilation and gross error detection method, modified MIMT using NLP, was applied to a flash distillation system. It calculated the recontiled calues of the measurements as well as the optimal estimated of stage effifiencies which were not measured. These techniques using NLP showed the robustness when compared to the conventional algorithms using linearization techniques.