Low-Variance Parameter Estimation Approach for Real-Time Optimization of Noisy Process Systems

Abstract

Uncertainty is inherent to the measurement and modeling of process systems, where it can have significant impacts on the efficacy of optimization techniques. This work proposes a scheme to address uncertainty as it pertains to real-time optimization (RTO), where noisy measurements are used to estimate model parameters and account for model uncertainty. The parameter estimation (PE) step that accompanies RTO requires plant measurements that are often noisy; this can cause the propagation of noise to the parameter estimates, which may result in poor RTO performance. An information content (IC) metric for choosing the most information-rich measurements and an algorithm to select a favorable subset of measurements, as well as filtering for erroneous parameters, are proposed in this work to improve the PE problem performance. The resulting low-variance PE (lv-PE) algorithm yields parameter estimates that are closer to the true parameter values over many RTO periods. The proposed scheme is tested against a regular RTO/PE on a forced circulation evaporator and the Williams–Otto CSTR. The former case displays the effect of the proposed scheme in avoiding constraint violations, while the latter case shows the economic improvements that the proposed scheme can yield. Both case studies show a reduction in estimate variability with respect to the traditional PE approach; thus, the proposed framework is attractive for the optimization of noisy process systems.

Topics & Concepts