A Two-Level Strategy of Integrated Dynamic Optimization and Control of Industrial Processes—a Case Study

Kadam, J.V.; Schlegel, Martin; Marquardt, Wolfgang; Tousain, R.L.; Hessem, D.H. van; Berg, Jan van den; Bosgra, О.H.

doi:10.1016/s1570-7946(02)80113-4

Cited by 51 publications

(55 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The reference variables are selected such that they are related to the parts of the NCO such as the remaining endpoint constraintsē and the objective function Φ. τ are the reference start and end times of the sensitivity seeking arcs, which are updated on a run-to-run basis. For brevity, only the pairing equations are given here, we refer to (Kadam et al, 2002) for details on reference trajectory tracking.…”

Section: 1) Linking Parameterized Sensitivity-seeking Dofmentioning

confidence: 99%

Dynamic Real-Time Optimization: From Off-Line Numerical Solution to Measurement-Based Implementation

Kadam

Schlegel

Srinivasan

et al. 2005

IFAC Proceedings Volumes

Self Cite

View full text Add to dashboard Cite

The problem of optimizing a dynamic system in the presence of uncertainty is typically tackled using measurements. The methods widely used in the literature are based on repetitive optimization of a process model. Recently, tracking of the Necessary Conditions of Optimality (NCO tracking) has been proposed as a computationally less expensive alternative, which is based on the derivation of a solution model. So far, the solution model, which contains information on the structure of the input profiles and the set of active constraints, has been derived manually based on physical insight and intuition. In this paper, based on the recent results on the numerical optimization of dynamic systems, we present a systematic and automated approach to come up with a solution model. This concept provides the first steps towards an entirely automated procedure for dynamic optimization under uncertainty via NCO tracking.

show abstract

Section: 1) Linking Parameterized Sensitivity-seeking Dofmentioning

confidence: 99%

Dynamic Real-Time Optimization: From Off-Line Numerical Solution to Measurement-Based Implementation

Kadam

Schlegel

Srinivasan

et al. 2005

IFAC Proceedings Volumes

Self Cite

View full text Add to dashboard Cite

show abstract

“…The optimal control profiles are then determined from the solution of the above dynamic optimization problem and then passed to the underlined MPC layer as trajectory setpoints to follow. The advantages of this formulation in the presence of disturbances have been deeply emphasized in the literature [21,22], also in the case of model-free alternatives [23]. The D-RTO is also seen as a solution for merging economic and control layer, while advances in nonlinear model predictive control and its generalization to deal with economic objective functions taking place [24].…”

Section: Introductionmentioning

confidence: 99%

A Modifier-Adaptation Strategy towards Offset-Free Economic MPC

Vaccari

Pannocchia

2016

Processes

View full text Add to dashboard Cite

Abstract:We address in the paper the problem of designing an economic model predictive control (EMPC) algorithm that asymptotically achieves the optimal performance despite the presence of plant-model mismatch. To motivate the problem, we present an example of a continuous stirred tank reactor in which available EMPC and tracking model predictive control (MPC) algorithms do not reach the optimal steady state operation. We propose to use an offset-free disturbance model and to modify the target optimization problem with a correction term that is iteratively computed to enforce the necessary conditions of optimality in the presence of plant-model mismatch. Then, we show how the proposed formulation behaves on the motivating example, highlighting the role of the stage cost function used in the finite horizon MPC problem.

show abstract

“…The measurements are used to either: (i) Adapt the parameters of a process model and re-optimize it (explicit optimization) (Marlin and Hrymak, 1996;Zang et al, 2001;Kadam et al, 2003), or (ii) directly adapt the inputs (implicit optimization) (Kristic and Wang, 2000;Skogestad, 2000;Srinivasan et al, 2003). Furthermore, in static implicit optimization, it is possible to distinguish between three types of techniques:…”

Section: Introductionmentioning

confidence: 99%

Optimizing control based on output feedback

Gros¹,

Srinivasan²,

Bonvin³

2009

Computers & Chemical Engineering

View full text Add to dashboard Cite

In the framework of process optimization, the use of measurements to compensate the effect of uncertainty has re-emerged as an active area of research. One of the ideas therein is to adapt the inputs in order to track the active constraints and push certain sensitivities to zero. In perturbation-based optimization, the sensitivities are evaluated by perturbation of the inputs and measurement of the cost function, which can be experimentally time consuming. However, since more measurements (typically the outputs) than just the cost function are available, the idea developed in this paper is to incorporate the outputs in a measurement-based optimization framework. This is done using an extension to the neighboring-extremal scheme for the case of output measurements. If measurement noise can be neglected, the approach is shown to converge to the optimum in at most two input updates. The effect of measurement noise is * Current address: Department of Chemical Engineering, Ecole Polytechnique Montreal, Montreal, Canada also investigated. The strength of neighboring-extremal output feedback for optimization is illustrated on a continuous chemical reactor example.

show abstract

A Two-Level Strategy of Integrated Dynamic Optimization and Control of Industrial Processes—a Case Study

Cited by 51 publications

References 1 publication

Dynamic Real-Time Optimization: From Off-Line Numerical Solution to Measurement-Based Implementation

Dynamic Real-Time Optimization: From Off-Line Numerical Solution to Measurement-Based Implementation

A Modifier-Adaptation Strategy towards Offset-Free Economic MPC

Optimizing control based on output feedback

Contact Info

Product

Resources

About