A Novel Hierarchical Control Structure with Controlled Variable Adaptation

Abstract

For control and optimization of chemical processes, the traditional hierarchical control structure (HCS), where an optimizer in the real-time optimization (RTO) layer updates the set-points of controlled variables (CVs) in the lower control layer, has been well-acknowledged and widely adopted in industrial applications. However, a common drawback of such an HCS is that the speed for a plant to converge to an optimal operation is slow because the optimizer has to wait for the process to settle from one steady-state to another to get an accurate disturbance estimation before making any changes to the set-points. In this Article, a novel HCS based on the concepts of controlled variable adaptation (CVA) and nonoptimality detection is proposed. In the CVA strategy, the CVs are determined and adapted on the basis of a so-called just-in-time regression algorithm to approximate the necessary conditions of optimality (NCO), which makes the self-optimizing performance adaptive to operating condition changes. For nonoptimality detection, we apply the theory of statistical process monitoring to monitor the optimality of process operation, where the nonoptimal statuses are treated as a special kind of process faults. The detection results are used as a prerequisite to activate the CVA. With these techniques, the proposed CVA-based HCS exhibits the following distinct features: (1) The regulatory control layer has an ability to approach a near optimal operation automatically through selfoptimizing control, thus accomplishing the majority of the optimization task, and the speed of the process converging to an optimal status is fast. (2) The RTO layer extends the self-optimizing operation range via adapting CVs in the lower control layer, rather than their set-points as in a traditional HCS. (3) The activation of CVA is neither regular nor periodic, but only evoked when it is necessary. Two case studies are provided to demonstrate the basic characteristics and advantages of the proposed CVA-based HCS.

Integrating self-optimizing control and real-time optimization using zone control MPC

“…3) is implemented using a classic quadratic control objective function with a finite prediction horizon of p intervals, m input movements, and using the vector of predicted artificial SOC variablesc(k): (15). This leads to the control objective function in terms of ū k in (16) and its quadratic form in (17).…”

“…In [17], a "necessary conditions of optimality" (NCO) tracking procedure [18] was used in the upper layer, and SOC was applied in the lower layer. Ye et al [17] developed a new hierarchical control structure, integrating SOC and RTO. They used the NCO as controlled variables and developed a statistical criterion of non-optimality to decide when the controlled variable should be updated.…”

Run‐to‐run optimization of batch processes with self‐optimizing control strategy

“…The NCO approximation method used a function of measurement variables as NCO estimators, which leads to the NCO indirectly satisfied via CV tracking. However, this method risks fitting for those operating points far away from the optimum that are not closely relevant to optimal operation . More recently, a new global SOC method was developed by approximately minimizing the global average loss in the entire uncertainty space .…”

Model predictive control for the self-optimized operation in wastewater treatment plants: Analysis of dynamic issues

“…Model-free NCO tracking procedure using finite perturbations to calculate the gradients has been developed in Srinivasan et al (2008). The regression-based approach (Ye et al, 2013) and its extension to hierarchical control (Ye et al, 2014) provide a new methodology to determine CVs approximating the necessary conditions of optimality (NCO) in the whole operating region, achieving near-optimal operation globally, enlarging the operation region where the economic loss is acceptable. In Jäschke and Skogestad (2011), it is shown that NCO tracking in the optimization layer and SOC in the lower control layer are complementary methodologies because unexpected disturbances, which are not rejected by SOC, can be handled by the model free NCO tracking procedure.…”