Dynamic optimization of batch processes

Srinivasan, Balasubramaniam; Паланки, Сринивас; Bonvin, Dominique

doi:10.1016/s0098-1354(02)00116-3

Cited by 365 publications

(11 citation statements)

References 73 publications

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“…The original formulation for the run-to-run control applied to insulin bolus dosing and timing is described in Reference [19]. It is based on the application of a constraint control scheme in the run-to-run framework to optimize the operation of batch processes in the chemical industry [21,22].…”

Section: Run-to-run Algorithmmentioning

confidence: 99%

A run‐to‐run framework for prandial insulin dosing: handling real‐life uncertainty

Palerm

Zisser

Jovanovič

et al. 2006

Intl J Robust & Nonlinear

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SUMMARYIndividuals with type 1 diabetes require frequent adjustment of their insulin dose to maintain as near to normal glycaemia as possible. This process is not only burdensome but also, for many, difficult to achieve. As a result, control algorithms to facilitate insulin dosage have been proposed, but have not been completely successful in normalizing glycaemia. Here we present a novel run-to-run control algorithm to adjust the meal-related insulin dose using only post-prandial blood glucose measurements. For each meal independently, the insulin dose is adjusted based on the performance measure for the same meal the previous day. A robustness analysis is performed which considers the sources of uncertainty typically encountered in clinical use. This shows that the system remains stable even with large uncertainty.

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Section: Run-to-run Algorithmmentioning

confidence: 99%

A run‐to‐run framework for prandial insulin dosing: handling real‐life uncertainty

Palerm

Zisser

Jovanovič

et al. 2006

Intl J Robust & Nonlinear

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“…Such trajectories can be obtained in several ways, based on: (i) prior physical or heuristic knowledge of the plant, 61 (ii) some empirical rules, or (iii) optimization approaches that exploit a mathematical model of the plant. 62,63 When optimization-based approaches are applied over nonlinear/complex dynamics, computational expensive procedures are employed offline to obtain sub-optimal (or, if possible, optimal) solutions to the given problem. This work relies on this latter approach, which is discussed in MPC formulation for LTV systems section.…”

Section: This Work Uses the Euclidean Norm As The Distance Function Dmentioning

confidence: 99%

Design of Piecewise Affine and Linear Time-Varying Model Predictive Control Strategies for Advanced Battery Management Systems

Torchio¹,

Magni²,

Braatz³

et al. 2017

J. Electrochem. Soc.

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Advanced Battery Management Systems (ABMSs) are necessary for the optimal and safe operation of Li-ion batteries. This article proposes the design of ABMSs based on Model Predictive Control (MPC). In particular, we consider MPC strategies based on piecewise affine approximations (PWAs) of a first-principles electrochemical battery model known in the literature as the pseudo two-dimensional (P2D) model. The use of model approximations is necessary since the P2D model is too complex to be included in the real-time calculations required by MPC. PWAs allow to well describe the electrochemical phenomena occurring inside the battery. On the other side, the accuracy of such models increases with the number of considered partitions, which also increases the model complexity and the online computational cost of MPC. Linear time-varying (LTV) approximations, which are obtained by linearizing accurate PWAs around a nominal trajectory, are proposed as a way to further reduce online computational costs. The obtained results demonstrate the suitability of MPC based on PWARX and LTV model approximations to provide ABMSs with high performance.

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“…Run-to-run optimization as defined above is a broad category that can be further classified based on how the optimization updates are implemented [24]. Model-explicit optimization uses a model of the system dynamics to compute the local gradient based on current measurements [24,25]. In Ref.…”

Section: Run-to-run Controlmentioning

confidence: 99%

“…References [24,25], and [27] suggest that I be chosen as constructs or parameters with target values that are necessary for optimality, e.g., I as the terminal values for output levels which are constrained to be 0 at t final for an optimal solution. This configuration is designed such that as I is regulated toward its target values, the overall system will then converge to the optimal solution.…”

Section: Run-to-run Controlmentioning

confidence: 99%

“…The optimalitycondition parameterization approach in Refs. [24,25], and [27] is instructive, but many assumptions and simplifications, e.g., regulating only the terminal constraints or assuming nearly linear mapping M, are required for practical implementation. Furthermore, most of the theory was developed for chemical or biological process control, where the dynamics of the system are slower and the control inputs are usually parameterized by arcs and transition times [27].…”

Section: Run-to-run Controlmentioning

confidence: 99%

See 1 more Smart Citation

Run-to-Run Optimization Control Within Exact Inverse Framework for Scan Tracking

Yeoh

Reinhall

Berg

et al. 2017

Journal of Dynamic Systems, Measurement, and Control

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A run-to-run optimization controller uses a reduced set of measurement parameters, in comparison to more general feedback controllers, to converge to the best control point for a repetitive process. A new run-to-run optimization controller is presented for the scanning fiber device used for image acquisition and display. This controller utilizes very sparse measurements to estimate a system energy measure and updates the input parameterizations iteratively within a feedforward with exact-inversion framework. Analysis, simulation, and experimental investigations on the scanning fiber device demonstrate improved scan accuracy over previous methods and automatic controller adaptation to changing operating temperature. A specific application example and quantitative error analyses are provided of a scanning fiber endoscope that maintains high image quality continuously across a 20 °C temperature rise without interruption of the 56 Hz video.

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Dynamic optimization of batch processes

Cited by 365 publications

References 73 publications

A run‐to‐run framework for prandial insulin dosing: handling real‐life uncertainty

A run‐to‐run framework for prandial insulin dosing: handling real‐life uncertainty

Design of Piecewise Affine and Linear Time-Varying Model Predictive Control Strategies for Advanced Battery Management Systems

Run-to-Run Optimization Control Within Exact Inverse Framework for Scan Tracking

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