Improved Unit-Specific Event-Based Continuous-Time Model for Short-Term Scheduling of Continuous Processes:  Rigorous Treatment of Storage Requirements

Shaik, Munawar A.; Floudas, Christodoulos A.

doi:10.1021/ie060480o

Cited by 71 publications

(158 citation statements)

References 30 publications

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“…Some of the proposed models are based on the STN framework; see, for example, Ierapetritou and Floudas (1998), Giannelos and Georgiadis (2002), and Shaik and Floudas (2007). Many other models are based on the RTN framework, e.g.…”

Section: Introductionmentioning

confidence: 99%

A discrete-time scheduling model for continuous power-intensive process networks with various power contracts

Zhang

Sundaramoorthy²,

Grossmann

et al. 2016

Computers & Chemical Engineering

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Please cite this article as: Qi Zhang, Arul Sundaramoorthy, Ignacio E. Grossmann, Jose M. Pinto, A discrete-time scheduling model for continuous power-intensive process networks with various power contracts, Computers and Chemical Engineering (2015), http://dx. A discrete-time scheduling model for continuous power-intensive process networks with various power contracts Discrete-time scheduling model based on the concept of operating modes  Efficient representation of each process by Convex Region Surrogate models  Considers interaction between different processes in the network  Block contract formulation for the modeling of common power contracts  With proposed MILP model, large-scale industrial problems are solved within minutes *HighlightsAbstract Increased volatility in electricity prices and new emerging demand side management opportunities call for efficient tools for the optimal operation of powerintensive processes. In this work, a general discrete-time model is proposed for the scheduling of power-intensive process networks with various power contracts. The proposed model consists of a network of processes represented by Convex Region Surrogate models that are incorporated in a mode-based scheduling formulation, for which a block contract model is considered that allows the modeling of a large variety of commonly used power contracts. The resulting mixed-integer linear programming model is applied to an illustrative example as well as to a real-world industrial test case. The results demonstrate the model's capability in representing the operational flexibility in a process network and different electricity pricing structures. Moreover, because of its computational efficiency, the model holds much promise for its use in a real industrial setting.

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Section: Introductionmentioning

confidence: 99%

A discrete-time scheduling model for continuous power-intensive process networks with various power contracts

Zhang

Sundaramoorthy²,

Grossmann

et al. 2016

Computers & Chemical Engineering

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“…Shaik and Floudas [2] proved to be most significant work for continuous processes in terms of precisely addressing inventory issues and product changeovers. Prior to that, Ierapetritou and Floudas [3] presented work on unit-specific event-based representation for scheduling of continuous and semi-continuous processes.…”

Section: Introductionmentioning

confidence: 99%

“…Giannelos and Georgiadis [4] also used unit-specific event-based representation to model the scheduling problem of continuous process. But these works exhibited limitation vis-a-vis intermediate storage which were later improved by Shaik and Floudas [2] and Shaik et al [5].…”

Section: Introductionmentioning

confidence: 99%

Optimization Model for Scheduling of a Lube-Oil Production Plant

Yadav¹

2014

IJCEA

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“…3 Li et al 3 recently developed a novel unit-specific event-based continuous-time [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21] MINLP formulation for this problem. 3 Li et al 3 recently developed a novel unit-specific event-based continuous-time [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21] MINLP formulation for this problem.…”

Section: Introductionmentioning

confidence: 99%

Scheduling of crude oil operations under demand uncertainty: A robust optimization framework coupled with global optimization

Li¹,

Misener²,

Floudas³

2011

AIChE Journal

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Scheduling of crude oil operations is an important component of overall refinery operations, because crude oil costs account for about 80% of the refinery turnover. The mathematical modeling of blending different crudes in storage tanks results in many bilinear terms, which transform the problem into a challenging, nonconvex, mixed‐integer nonlinear programming (MINLP) optimization model. In practice, uncertainties are unavoidable and include demand fluctuations, ship arrival delays, equipment malfunction, and tank unavailability. In the presence of these uncertainties, an optimal schedule generated using nominal parameter values may often be suboptimal or even become infeasible. In this article, the robust optimization framework proposed by Lin et al. and Janak et al. is extended to develop a deterministic robust counterpart optimization model for demand uncertainty. The recently proposed branch and bound global optimization algorithm with piecewise‐linear underestimation of bilinear terms by Li et al. is also extended to solve the nonconvex MINLP deterministic robust counterpart optimization model and generate robust schedules. Two examples are used to illustrate the capability of the proposed robust optimization approach, and the extended branch and bound global optimization algorithm for demand uncertainty. The computational results demonstrate that the obtained schedules are robust in the presence of demand uncertainty. © 2011 American Institute of Chemical Engineers AIChE J, 58: 2373–2396, 2012

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Improved Unit-Specific Event-Based Continuous-Time Model for Short-Term Scheduling of Continuous Processes: Rigorous Treatment of Storage Requirements

Cited by 71 publications

References 30 publications

A discrete-time scheduling model for continuous power-intensive process networks with various power contracts

A discrete-time scheduling model for continuous power-intensive process networks with various power contracts

Optimization Model for Scheduling of a Lube-Oil Production Plant

Scheduling of crude oil operations under demand uncertainty: A robust optimization framework coupled with global optimization

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