Intelligent state estimation for fault tolerant integrated frequent RTO and adaptive nonlinear MPC

Combinations of real-time optimization (RTO) and model predictive control (MPC) have been widely employed in the process industry for tracking the economic optimum in the face of drifting disturbances and parameters. Online update of model parameters is a critical step in the implementation of RTO. In this work, an intelligent state and parameter estimation approach is developed by combining a fault diagnosis approach with a simultaneous state and parameter estimator. Since faults are more likely to develop as slow drifts, a nonlinear generalized likelihood ratio (GLR) approach available in the literature is modified by considering ramp models for the progression of faults with time. When a fault is isolated by the fault diagnosis and identification (FDI) component, the magnitude of the isolated fault is refined using a moving window state and parameter estimator until the fault magnitude continues to change. The estimation of fault magnitudes is carried out only when required and triggered by the fault identification scheme. Thus, the subset of parameters/faults that are being estimated online can change with time. The intelligent state and parameter estimator is further combined with an online optimizing control scheme consisting of integrated frequent RTO and adaptive MPC. The integrated optimizing control scheme has embedded intelligence to auto-correct models used for estimation, control, and optimization and decide whether the detected changes require the invocation of RTO. The proposed approach employs a single model to carry out four different tasks: process monitoring, state and parameter estimation, nonlinear predictive control, and real-time optimization. This eliminates difficulties that can arise due to model mismatch between different components of the online optimizing control scheme. The efficacy of the proposed scheme is investigated using the benchmark Williams Otto reactor and a continuously operated fermenter. The economic optimum operating point of these systems is sensitive to mean shifts in unmeasured disturbances or system parameters. The proposed ramp model based approach successfully isolates the parameter/ unmeasured disturbance/ sensor bias/ actuator bias that has undergone slow drift and tracks the shifting economic optimum without significant delays. Thus, the proposed integrated approach has the ability to handle normal and abnormal operating envelopes of the system.