ReviewDistributed control and optimization of process system networks: A review and perspective☆
Introduction
Large-scale and complex systems have become prevalent in chemical and energy industries. Such systems arise from the integration of process units and plants as a result of sustainability motivations, specifically, economic efficiency, energy recycle, and carbon and water footprint reduction [1], [2]. Examples of such complex systems include chemical plants with recycles [3], [4], crude oil refineries [5], smart grids and microgrids [6], integrated heating, ventilation and air conditioning (HVAC) systems [7], and supply chains [8], [9].
Process control and optimization technologies are crucial to the implementation of such sustainable solutions in the chemical, energy and other relevant industry sectors. However, control and optimization become difficult for these large-scale and complex systems due to the size of their models and the existence of phenomena that emerge at a network level, such as multi-time-scale dynamics [10], multi-level uncertainties [11], and propagation of disturbances [12]. These challenges to the control and optimization of process networks call for theoretical efforts to deal with each individual phenomenon, and a computational architecture for control and optimization that accounts for their networked nature.
A promising approach to this end is to make the control and optimization decisions in a distributed manner. Specifically, a distributed architecture of control and optimization has the following meaning:
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The entire process system, as a network, is decomposed into several constituent subsystems.
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A computing agent (controller, optimizer) is associated with each subsystem and is responsible for making the decision in the corresponding subsystem.
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Some information is exchanged between the agents to coordinate their decisions towards the control or optimization objective of the entire network.
A distributed architecture is different from a centralized one, in the sense that the decisions are made by multiple agents rather than a single agent. Compared to its centralized counterpart, distributed control and optimization are more scalable and parallelizable, and hence can be more efficient computationally. Moreover, a distributed architecture is flexible to the entering, exiting, and changes of subsystems (“plug-and-play” property [13]), and can be also combined with distributed fault detection for a fault-tolerant control architecture [14], [15]. It is also different from a decentralized architecture (which appears in traditional process control) due to the existence of some coordination mechanism, which generally enables the agents to account for other subsystems and arrive at desirable or optimal decisions for the overall network.
This paper provides a comprehensive and up-to-date review of distributed control and optimization. We aim to present the fundamental ideas and guide the readers through the most significant developments in recent years. The paper is organized as follows. We first introduce in Section 2 the preliminary concepts for the distributed architecture of control and optimization. Two major issues in developing a distributed control or optimization scheme, namely the methods of decomposing the network into subsystems, and the methods of performing optimization based on a network decomposition, are reviewed in Section 3 and Section 4, respectively. A case study on a reactor–separator process network is shown in Section 5. We discuss some important future directions in Section 6. Conclusions are made in Section 7.
Section snippets
Control and optimization
In the operations of process systems, many decisions need to be made. Decision making problems are organized in a hierarchy which contains the following problems on different time and space scales [8], [16]:
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Control — input variables (flow rates, heat exchange rates, etc.) are manipulated to stabilize the system states (temperature, pressure, etc.) at their setpoints or track reference trajectories in a time scale of minutes to hours.
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Real-time optimization — the setpoints or reference
Methods of Network Decomposition
The prerequisite to implement any distributed control or optimization scheme is to decompose networks into subsystems. That is, for control and optimization problems of process systems, the constituent objects in their definitions (inputs and states, optimization variables and/or constraints) need to be first assigned to different groups. Such a decomposition is not always trivial or arbitrary. As will be seen in the case study (Section 5), the network decomposition has a significant effect on
Methods of Decomposition-based Optimization
In the previous section we have presented methods of network decomposition. Once a decomposition is determined for the process, one can solve large-scale optimization problems by employing distributed optimization algorithms, which prescribe how the distributed agents update their decisions and how the information is transferred among the agents. In this section, we introduce the basics of the two major classes of formulations and algorithms of performing distributed optimization, namely block
Case Study on a Benchmark Process
In the works of our group, we have examined network decomposition and distributed optimization methods applied to the distributed MPC of a benchmark reactor–separator process [186], [187]. This system can be regarded as a minimal process network for distributed control and used for a demonstration of concept. For more complex processes, such as those appearing in oil refineries, in-depth investigations need to be carried out and reported.
As illustrated in Fig. 10, the process comprises two
Future Directions
In the previous sections, we have reviewed up-to-date methods of distributed control and optimization. For the future development and industrial application of distributed control and optimization methods, in our opinion, the following directions are of importance:
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The communication network-induced issues need to be addressed in a more unifying framework that incorporates nonlinear systems and optimization-based, distributed agent decisions in a closed loop.
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The network decomposition and
Conclusions
Large-scale, complex process networks are prevalent in chemical and other industries due to economics and sustainability considerations. Distributed control and optimization have been identified as a crucial decision-making paradigm for the operation of such complex networks, and studied extensively in the recent years. In this review paper, we first introduced the basic concepts of distributed control and optimization. We reviewed two most important aspects of distributed control and
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Supported by Division of Chemical, Bioengineering, Environmental and Transport Systems (CBET) of the National Science Foundation (NSF) of the United States of America.