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Self-Optimizing Control Strategy for Distributed Parameter Systems
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DOI：10.1021/acs.iecr.3c01086.png)
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Abstract
En 中文
Real-time optimization (RTO) has been a popular approachfor addressingoptimal operation by repetitively optimizing online. However, thisapproach is impractical for distributed parameter systems (DPSs) dueto its expensive computational cost. Self-optimizing control (SOC)has emerged as a promising alternative strategy, which achieves acceptableoptimality by controlling the designed controlled variables (CVs)at constant set points without reoptimizing. Motivated by this, theexisting lumped parameter SOC method is for the first time expandedto a typical class of DPSs based on a high-fidelity discrete-spacemodel. Starting from an infinite-order system itself, the SOC problemis constructed as the minimal loss functional regarding the CVs throughthe operator operation and variational method. To obtain an operationalanalytical solution of CVs, the optimization problem is reformulatedbased on the discrete-space system derived by the finite difference.Considering the variety of sensor and actuator placements, the CVsare analytically designed with the minimal static loss through thestacking matrix approach. Finally, a case study of a plug-flow reactoris presented to demonstrate the feasibility of the proposed approachto achieve optimal operation for DPSs.
Keywords:
MODEL-PREDICTIVE CONTROL
OPTIMAL MEASUREMENT COMBINATIONS
CONTROLLED VARIABLES
OPTIMAL SELECTION
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