Challenging control problems and emerging technologies in enterprise optimization

Lu, Joseph

doi:10.1016/s0967-0661(03)00006-6

Cited by 67 publications

(10 citation statements)

References 5 publications

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“…The decentralized approach is the most commonly used in the industry because of its robustness and its resiliency to systems failures. Nevertheless, decentralized control does not generally lead to the desired plant-wide optimal operations (Lu (2003); Sun and El-Farra (2008)). A compromise between centralized and decentralized control is desired in order to improve plant operations.…”

Section: Introductionmentioning

confidence: 99%

Coordination of Distributed Model Predictive Controllers for Constrained Dynamic Processes

Marcos

Forbes

Guay

2009

IFAC Proceedings Volumes

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In this paper, a coordinated-distributed model predictive control (MPC) scheme is presented for large-scale discrete-time linear process systems. Coordinated-distributed MPC control aims at enhancing the performance of fully decentralized MPC controllers by achieving the plant-wide optimal operations. The 'price-driven' decomposition-coordination method is used to adjust the operations of the individual processing units in order to satisfy an overall plant performance objective. Newton's method, together with a sensitivity analysis technique, are used to efficiently update the price in the price-driven decomposition-coordination method. The efficiency of the proposed control scheme is evaluated using a model of a fluid catalytic cracking process.

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Section: Introductionmentioning

confidence: 99%

Coordination of Distributed Model Predictive Controllers for Constrained Dynamic Processes

Marcos

Forbes

Guay

2009

IFAC Proceedings Volumes

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“…Thus, two main categories need to be distinguished, though there is a bit of inconsistency in the nomenclature reported in the literature. If only the initial conditions are updated, the schemes are referred to as Model Predictive Control (MPC) [4][5][6][7][8][9][10][11], while Dynamic Real Time Optimization (D-RTO) schemes incorporate adaptation of both initial conditions and parameters [12].…”

Section: Introductionmentioning

confidence: 99%

“…MPC schemes incorporate feedback by re-optimization, when computation is not prohibitive [4][5][6][7]. In this case, the model is not adapted, while a new optimum is computed from the initial conditions obtained from current measurements.…”

Section: Introductionmentioning

confidence: 99%

On the Use of Nonlinear Model Predictive Control without Parameter Adaptation for Batch Processes

Binette¹,

Srinivasan²

2016

Processes

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Optimization techniques are typically used to improve economic performance of batch processes, while meeting product and environmental specifications and safety constraints. Offline methods suffer from the parameters of the model being inaccurate, while re-identification of the parameters may not be possible due to the absence of persistency of excitation. Thus, a practical solution is the Nonlinear Model Predictive Control (NMPC) without parameter adaptation, where the measured states serve as new initial conditions for the re-optimization problem with a diminishing horizon. In such schemes, it is clear that the optimum cannot be reached due to plant-model mismatch. However, this paper goes one step further in showing that such re-optimization could in certain cases, especially with an economic cost, lead to results worse than the offline optimal input. On the other hand, in absence of process noise, for small parametric variations, if the cost function corresponds to tracking a feasible trajectory, re-optimization always improves performance. This shows inherent robustness associated with the tracking cost. A batch reactor example presents and analyzes the different cases. Re-optimizing led to worse results in some cases with an economical cost function, while no such problem occurred while working with a tracking cost.

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“…The objective of EWO is to reduce the costs and inventories for an enterprise by optimizing the operations of supply, manufacturing and distribution activities [3] [4]. In [5], an approach that integrates and coordinates model predictive controllers (MPCs) is suggested for enterprise optimization. However, constructing the models that are required for MPC is often both hard and time demanding for a large-scale system.…”

Section: Introductionmentioning

confidence: 99%

A Method for Improving Plant Availability with Respect to Utilities using Buffer Tanks

Lindholm¹

2011

Artificial Intelligence and Applications / 718: Modelling, Identification, and Control

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Disturbances on utilities, such as steam and cooling water, often cause large revenue losses at industrial sites. These disturbances are often hard to handle, since they commonly are plant-wide disturbances that affect more than one production area at the site. Also, the production areas of the site are often dependent on other production areas because of the flow of product through the site, which makes the effect of disturbances on utilities hard to predict. In this paper, a simple method for decreasing the revenue loss due to disturbances on utilities is presented, where production areas are modeled as either operating at full speed or not operating (on/off), and buffer tanks between areas at the site are utilized. Both choice of levels in the buffer tank and control of the product flow at the site during a disturbance are discussed.

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Challenging control problems and emerging technologies in enterprise optimization

Cited by 67 publications

References 5 publications

Coordination of Distributed Model Predictive Controllers for Constrained Dynamic Processes

Coordination of Distributed Model Predictive Controllers for Constrained Dynamic Processes

On the Use of Nonlinear Model Predictive Control without Parameter Adaptation for Batch Processes

A Method for Improving Plant Availability with Respect to Utilities using Buffer Tanks

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