Economic Nonlinear Model Predictive Control for periodic optimal operation of gas pipeline networks

Gopalakrishnan, Ajit; Biegler, Lorenz T.

doi:10.1016/j.compchemeng.2012.11.011

Cited by 86 publications

(45 citation statements)

References 14 publications

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“…As a matter of fact, in recent years, many efforts have been devoted to investigate Economic MPC variants allowing time-varying costs in several domains of application: management of energy in buildings (Touretzky & Baldea (2014); Ma et al (2014)), control of chemical plants (Ellis & Christofides (2014)) and supervision of distribution networks, such as water networks ), power grids (Hovgaard et al (2010); Cole et al (2014); Adeodu & Chmielewski (2013),) gas networks, etc (Gopalakrishnan & Biegler (2013)). Other Economic MPC approaches dealing either time-varying cost or cyclic plant operations from a theoretical perspective can be found in Ferramosca et al (2014); Limon et al (2014);Huang et al (2012).…”

Section: Introduction and Motivationsmentioning

confidence: 99%

Theoretical advances on Economic Model Predictive Control with time-varying costs

2015

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Economic Model Predictive Control is a technique for optimization of economic revenues arising from controllod dynamical processes that has estabilished itself as a variant of standard Tracking Model Predictive Control. It departs from the latter in that arbitrary cost functions are allowed in the formulation of the stage cost. This paper takes a further step in expanding the applicability of Economic Model Predictive Control by illustrating how the paradigm can be adapted in order to accomodate time-varying or parameter-varying costs.

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Section: Introduction and Motivationsmentioning

confidence: 99%

Theoretical advances on Economic Model Predictive Control with time-varying costs

2015

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“…Marchetti et al [16,43] demonstrated that, upon convergence, the Karush-Kuhn-Tucker (KKT) conditions of the modified problem (27) match the ones of the true process optimization problem (25). Hence, if second-order conditions hold at this point, a local optimum of the real plant can be found by the problem modified as in (27).…”

Section: Rto With Modifier-adaptationmentioning

confidence: 99%

“…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]. In this sense, a receding horizon closed-loop implementation of D-RTO can be also referred to as economic model predictive control [25].…”

Section: Introductionmentioning

confidence: 99%

A Modifier-Adaptation Strategy towards Offset-Free Economic MPC

Vaccari

Pannocchia

2016

Processes

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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.

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“…Real-time optimization (RTO) combined with model predictive control (MPC) approach has been very successful in several manufacturing industries including oil and chemical industries [10][11][12][13][14][15][16][17][18]. As mentioned in Darby et al [16], until 2011, there were 250-300 implementations of RTO technique utilizing commercially available rigorous flow sheet packages based on open-equation modeling techniques.…”

Section: Introductionmentioning

confidence: 99%

Integrated Moving Horizon-Based Dynamic Real-Time Optimization and Hybrid MPC-PID Control of a Direct Compaction Continuous Tablet Manufacturing Process

et al. 2015

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In this manuscript, a moving horizon-based realtime optimization (MH-RTO) has been integrated with a hybrid model predictive control (MPC) system for a continuous tablet manufacturing process for quality by design (QbD)-based efficient continuous manufacturing. In the proposed approach, the integrated MH-RTO provides the optimal operational set points for the tablet production rate in real time. The MH-RTO takes into consideration the capital and operating cost, the market fluctuations, the product inventory, the product quality assurance strategy, the regulatory constraints, and the product rejections. An advanced hybrid model predictive control system then ensures that the required production rate with desired quality is met with minimum resources and time. A robust optimization strategy and an efficient control system have been integrated to achieve the maximum profit. The MH-RTO integrated with a hybrid control strategy ensures the maximum possible profit irrespective of the market demand fluctuations. The basic advantage of the MH-RTO framework is that it takes into consideration the future demand and thus can lead to increased profit compared to a standard real-time optimization approach.

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Economic Nonlinear Model Predictive Control for periodic optimal operation of gas pipeline networks

Cited by 86 publications

References 14 publications

Theoretical advances on Economic Model Predictive Control with time-varying costs

Theoretical advances on Economic Model Predictive Control with time-varying costs

A Modifier-Adaptation Strategy towards Offset-Free Economic MPC

Integrated Moving Horizon-Based Dynamic Real-Time Optimization and Hybrid MPC-PID Control of a Direct Compaction Continuous Tablet Manufacturing Process

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