Nonlinear model predictive control of energy-integrated process systems

Bâldea, Michael; Touretzky, Cara R.

doi:10.1016/j.sysconle.2013.05.006

Cited by 27 publications

(12 citation statements)

References 33 publications

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“…If stability is established for the individual unit operations with dynamics much faster than that of the process, then the stability of the system will only depend on the slow, process-wide dynamics. [42][43][44][45] For the case studies below, we found that time constants spanning several orders of magnitude tend to have good simulation results. Phase separations evolve at the fastest time scale, the energy balances evolve on a time scale one order of magnitude greater, the parameter integrations and intraunit flow dynamics occur at a time scale two orders of magnitude greater, and process wide dynamics evolve at a time scale three orders of magnitude larger.…”

Section: Flowsheet Simulationmentioning

confidence: 79%

“…Broadly speaking, physical phenomena such as the establishment of vapor-liquid equilibrium, mass and heat transfer occur rather quickly; unit operations dynamics evolve over an intermediate time horizon, 38,41 and process wide dynamics are relatively slow. [42][43][44] Conversely, the rates of chemical reactions can span the spectrum of the aforementioned time scales, and the contribution of chemical reactions to the process model can be adjusted via the parameter continuation strategy outlined above.…”

Section: Flowsheet Simulationmentioning

confidence: 99%

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Equation‐oriented flowsheet simulation and optimization using pseudo‐transient models

2014

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Tight integration through material and energy recycling is essential to the energy efficiency and economic viability of process and energy systems. Equation-oriented (EO) steady-state process simulation and optimization are key enablers in the optimal design of integrated processes. A new process modeling and simulation concept based on pseudotransient continuation is introduced. An algorithm for reformulating the steady-state models of process unit operations as differential-algebraic equation systems that are statically equivalent with the original model is presented. These pseudo-transient models improve the convergence of EO process flowsheet simulations by expanding the convergence basin. This concept is used to build a library of pseudo-transient models for common process unit operations, and this modeling concept seamlessly integrates with a previously developed time-relaxation optimization algorithm. Two design case studies are presented to validate the proposed framework.The framework developed in this article consists of converting the algebraic model into a pseudo-transient process model with an equivalent steady-state solution, which can be obtained by a DAE time integration.The pseudo-transient models described above were implemented in gPROMS. 57 In the following sections, we 4116

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Section: Flowsheet Simulationmentioning

confidence: 79%

Section: Flowsheet Simulationmentioning

confidence: 99%

Equation‐oriented flowsheet simulation and optimization using pseudo‐transient models

2014

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“…In addition to the integration of multiple generating units in the HRES, including wind turbines, PV panels, backup generators, and storage, as a means to handle generation uncertainties, another important approach to improve the overall system reliability of energy supply is to use receding horizon optimization, which is a method of repeated on-line optimization of the control inputs to plan actions over a finite time-span into the future [37,38]. This approach will be pursued in the section to achieve optimized operation of the HRES.…”

Section: Operational Optimizationmentioning

confidence: 99%

Operational optimization and demand response of hybrid renewable energy systems

2015

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“…A bypass stream is taken from the feed and used to control the reactor inlet temperature. Such processes have been shown to exhibit complex nonlinear dynamics and potential instability (see, e.g., [14], [15], [16], [17]), and, as such, addressing the design of the process concurrently with that of the control system is of paramount importance. The objective of this study is to determine size of the reactor and FEHE that provide an optimal dynamic response in the presence of disturbances in the feed temperature T f , caused by a process upstream.…”

Section: Case Studymentioning

confidence: 99%

Conservativeness and stability properties in the optimal design of dynamical systems under uncertainty

Wang

Bâldea

2014

2014 American Control Conference

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In this paper, we study the interdependence between the design of a (process) system and the design of the corresponding controller. This relationship is the subject of a "conservativeness" tradeoff between achieving robust stability and nominal performance in the face of uncertainty in operating conditions. We propose a novel strategy for specifying and fulfilling individual dynamic performance criteria for the dynamics of the process and the associated control system, while maintaining the (bounded input-bounded output-BIBO) stability of the closed-loop system. To this end, we implement a switched control law at the design stage, along with additional constraints that enforce BIBO stability. By establishing a link between the switching conditions and the bandwidth of the closed-loop system, we are able to meet a pre-specified level of process conservativeness (e.g., in terms of size or cost) in process design. Second, we extend our previous results concerning identification-based optimization (IBO) to formulate an efficient algorithm for carrying out the optimization calculations under uncertainty without the need to generate computationally costly scenario sets. We illustrate these ideas with a case study concerning the design of an energy-integrated process.

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Nonlinear model predictive control of energy-integrated process systems

Cited by 27 publications

References 33 publications

Equation‐oriented flowsheet simulation and optimization using pseudo‐transient models

Equation‐oriented flowsheet simulation and optimization using pseudo‐transient models

Operational optimization and demand response of hybrid renewable energy systems

Conservativeness and stability properties in the optimal design of dynamical systems under uncertainty

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