Abstract
Distributed real-time optimization (RTO) enables optimal operation of large-scale process systems with common resources shared across several clusters. Typically in distributed RTO, the different subsystems are optimized locally, and a centralized master problem is used to coordinate the different subsystems in order to reach system-wide optimal operation. This is especially beneficial in industrial symbiosis, where only limited information can be shared between the different clusters. However, one of the main challenges with this approach is the need to solve numerical optimization problems online for each subsystem. With the recent surge of interest in feedback optimizing control, where the optimization problem is converted into a feedback control problem, this paper proposes a distributed feedback-based RTO (DFRTO) framework for optimal resource sharing in an industrial symbiotic setting. In this approach, a master coordinator updates the shadow price for the shared resource, and the different subsystems locally optimize their operation using feedback control for the given shadow price. The proposed framework is shown to converge to a stationary point of the system-wide optimization problem, and is demonstrated using an industrial symbiotic offshore oil and gas production system with shared resources.
Highlights
In the face of growing competition, stringent emission regulations, and increased necessity for sustainable manufacturing, there is a clear need to focus on energy and resource efficiency in order to reduce waste
We focus on steady-state real time optimization, and the reader is referred to [3] for a comprehensive compilation of literature on distributed model predictive control (MPC)
In the proposed distributed feedback-based real-time optimization (RTO), we do not iterate between the master and subproblem, since xi[t] is a real time measurement, and not the solution to a numerical optimization problem
Summary
In the face of growing competition, stringent emission regulations, and increased necessity for sustainable manufacturing, there is a clear need to focus on energy and resource efficiency in order to reduce waste. Finding a feasible and optimal operation for a large-scale system is challenging and typically requires information about the entire process, in the form of models, real time measurements, local constraints and the economic objective. This challenge is only amplified in an industrial symbiotic setting with shared resources, since the different companies might be reluctant to share information across the different organizations, for example due to intellectual property rights, trade secrets, and market competitiveness. This approach may not be suitable for industrial symbiosis [1]
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