Abstract
Past work has shown that results analysis based on knowledge of the common cause measurement variability and the transmission of this variability in the real-time optimization (RTO) loop to the recommended operating conditions can be applied to an RTO system to increase operating profit and to decrease unnecessary operations changes. An extension to results analysis which detects and diagnoses the cause of ill-conditioning in parameter updating, via singular value decomposition, is developed here and is applied to a case study. The case study has shown that it is possible to evaluate conditioning on-line. With an appropriate selection of an updater control limit, a hypothesis test can prevent ill-conditioned parameter estimates from being used for plant optimization.
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