MPC: Current practice and challenges

Darby, Mark L.; Nikolaou, Michael

doi:10.1016/j.conengprac.2011.12.004

Cited by 271 publications

(136 citation statements)

References 65 publications

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“…which is not numerically equivalent to (2). In particular, the representation in (3) does not guarantee conservation of g(x(t)), e.g.…”

Section: Optimization Of Dynamical Systemsmentioning

confidence: 99%

“…mass energy and momentum when solved numerically. We will therefore use the representation in (2) when solving the initial value problem of the fermenter model. The objective function of our optimization is given in Bolza form…”

Section: Optimization Of Dynamical Systemsmentioning

confidence: 99%

“…Recent advances have focused on optimizing the higherlevel objectives, such as economics, directly in the process control layer. Model Predictive Control (MPC) has for long time been the preferred framework in both industry and academia because of its flexibility, performance and ability to handle constraints on the inputs as well as the states [2]. Many researchers have also proposed nonlinear MPC which handles nonlinear systems and constraints.…”

Section: Introductionmentioning

confidence: 99%

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Real-time economic optimization for a fermentation process using Model Predictive Control

Petersen

Jørgensen

2014

2014 European Control Conference (ECC)

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Abstract-Fermentation is a widely used process in production of many foods, beverages, and pharmaceuticals. The main goal of the control system is to maximize profit of the fermentation process, and thus this is also the main goal of this paper. We present a simple dynamic model for a fermentation process and demonstrate its usefulness in economic optimization. The model is formulated as an index-1 differential algebraic equation (DAE), which guarantees conservation of mass and energy in discrete form. The optimization is based on recent advances within Economic Nonlinear Model Predictive Control (E-NMPC), and also utilizes the index-1 DAE model. The E-NMPC uses the single-shooting method and the adjoint method for computation of the optimization gradients. The process constraints are relaxed to soft-constraints on the outputs. Finally we derive the analytical solution to the economic optimization problem and compare it with the numerically determined solution.

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“…which is not numerically equivalent to (2). In particular, the representation in (3) does not guarantee conservation of g(x(t)), e.g.…”

Section: Optimization Of Dynamical Systemsmentioning

confidence: 99%

Section: Optimization Of Dynamical Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Real-time economic optimization for a fermentation process using Model Predictive Control

Petersen

Jørgensen

2014

2014 European Control Conference (ECC)

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“…Controller tuning. Tuning of an MPC controller is, in practice, a complex task given the large number of parameters (error weight, penalty on moves, prediction horizon) that must be appropriately set [12]. Furthermore the integrators added to the model in the MPC give a deliberate plant/model mismatch which renders the analysis more complex [20].…”

Section: Control System Scenariosmentioning

confidence: 99%

“…How should the MPC controller be tuned and, should this be related to the regulatory control layer? [12]. This paper shows an assessment of the controllability implications of heat integration using as a case study an actual industrial process consisting of two distillation columns in series.…”

Section: Introductionmentioning

confidence: 99%

Control assessment for heat integrated systems. An industrial case study for ethanol recovery

Mauricio‐Iglesias

Huusom

Sin

2013

Chemical Engineering and Processing: Process Intensification

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Highlights•Heat integration is needed for energy reduction but can deteriorate controllability •Soundly designed controllers can handle complex processes •The deterioration of controllability was handled by the controllers •Large energy savings are possible with a good dynamic response Abstract:Heat integration is essential for reducing the energy consumption of process industries. However, it may render the dynamic operation more interactive and difficult to control. This paper assesses the implications of heat integration in controllability and performance in energy reduction. The assessment, both on open loop and closed loop, was carried out based on an industrial case study and compared to a modified case without heat integration. Although the heat integrated system Page 2 of 31 A c c e p t e d M a n u s c r i p t 2 displayed a certain deterioration of controllability, the control system made possible an efficient operation. To this goal, different control systems were tested, from a decentralized control system to a model predictive controller. The type of controller used in the process barely influenced the performance of the process since the aim was exclusively to ensure the regulation of the process to fixed setpoints. The reduction of energy consumption achieved thanks to heat integration was considerably larger than the losses due to poor control of the process, confirming the importance of heat integration in energy intensive processes.

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Greybox model for multistage spray drying plants constrained to small datasets

et al. 2020

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Dynamic models of spray drying plants are required for many multivariable control strategies for spray dryers, for example, for model predictive control. Often, the model and its parameters are determined by fitting the model to experimental data. When the experimental data is generated, the experiments disturb normal production and introduce production losses. These losses increase the expenses for determining the model. Therefore, the experimentation time must be limited, which then limits the amount of data available for the model fitting. The limited amount of data restricts the complexity of the spray dryer model. In this article, we propose a greybox model with less complexity than the available models in the literature. The greybox model is derived by combining the process structure, physical relations and data from experimental data. The model is validated with experimental data from an MSD20 pilot plant. Further, the greybox model is compared to a model estimated by subspace identification. The comparison shows that the greybox model has a higher fit to the validation data than the subspace method.

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MPC: Current practice and challenges

Cited by 271 publications

References 65 publications

Real-time economic optimization for a fermentation process using Model Predictive Control

Real-time economic optimization for a fermentation process using Model Predictive Control

Control assessment for heat integrated systems. An industrial case study for ethanol recovery

Greybox model for multistage spray drying plants constrained to small datasets

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