Automated synthesis of control configurations for process networks based on structural coupling

Abstract

Use the concept of relative degree to synthesize control configurations with favorable structural coupling. Propose an integer optimization formulation to synthesize fully decentralized control configurations. Propose a hierarchical clustering procedure to generate block decentralized configuration candidates. Illustrate the application of the method to a complex process network. a b s t r a c tIn this paper, a method to systematically synthesize control configurations with favorable structural coupling is developed, using relative degree as a measure of such coupling. Initially, an integer optimization problem is formulated to identify optimal distributions of inputs and outputs (decentralized control configurations) that minimize the overall structural coupling in the network. Then, a hierarchical clustering procedure, which allows identifying groups of inputs and outputs that are strongly connected topologically (block decentralized control configurations), is proposed. The application of the method is illustrated through an example process network.

Sustainability and process control: A survey and perspective

“…Advances have been made in the context of algorithm development to guarantee closed-loop feasibility, stability and optimality [41][42][43][44][45][46][47][48][49], communication architectures and issues [50][51][52][53][54][55][56][57][58][59], cooperative MPC formulations that utilize a global objective function [60][61][62][63][64][65] and agent-based approaches [66][67][68][69]. An open problem in this area despite some recent progress is the systematic decomposition of the integrated system into the distributed architecture [70][71][72][73]. The development of robust distributed control architectures that facilitate flexible operation of integrated systems in response to dynamic market conditions, and can handle missynchronization of shared data (imprecisely dated measurements) are other important open problems.…”

Reduction of Second-Order Network Systems With Structure Preservation

“…The idea of hierarchical clustering has been extensively used in many fields, including pattern recognition, data compression, computer graphics, and process networks, see [16], [32], [33]. This paper is the first one that introduces this clustering algorithm to model reduction of network systems, and defines the distance by the norm of transfer functions.…”

Control structure selection based on economics: Generalization of the back‐off methodology

“…Global optimality cannot be guaranteed in this case due to nonconvexities in the nonlinear model of the plant. An important advantage of the nonlinear formulation (11) is that it constitutes a case of simultaneous design and control formulation where continuous design decision variables can be revised together with the selection of the control structure so as to improve dynamic economics. Binary design variables that are related to the structural optimization of the plant (D) can be included in formulation (11) but this is correct as far as the structural design decisions do not interfere with the structural decisions related to the control structure.…”

“…Formulation (11), conversely, is a mixed integer nonlinear problem (MINLP) for which efficient solution algorithms have become available in the last 10-15 years. A good initial point can always be found easily by using the solution of problem (2) and, as the disturbances considered are normally very close to their nominal values, convergence is expected to be rapid.…”

“…A large number of alternative methodologies for the solution of the problem of selecting regulatory structures have also been developed. [9][10][11][12][13][14][15][16] The back-off methodology is based on the assumption that the optimal steady state operating point is defined by the intersection of active constraints. This is a well established fact that is critical to the design of successful control systems.…”