Data-driven construction of Convex Region Surrogate models

Zhang, Qi; Grossmann, Ignacio E.; Sundaramoorthy, Arul; Pinto, José M.

doi:10.1007/s11081-015-9288-8

Cited by 57 publications

(26 citation statements)

References 27 publications

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“…Such a model is A c c e p t e d M a n u s c r i p t generally referred to as a Convex Region Surrogate (CRS) model. For complex processes, CRS models can be constructed by either using a model-based (Sung and Maravelias, 2009) or a data-driven approach (Zhang et al, 2015b).…”

Section: Process Surrogate Modelmentioning

confidence: 99%

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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: Process Surrogate Modelmentioning

confidence: 99%

“…Overproduced gaseous products can be vented through a venting process, and all liquid products can be converted into the corresponding gaseous products through a so-called driox process. The CRS models for each process have been generated by applying the data-driven algorithm proposed Zhang et al (2015b) to real process data.…”

Section: Industrial Case Studymentioning

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

Self Cite

View full text Add to dashboard Cite

show abstract

“…Here, the idea is to approximate the generally nonconvex feasible region of a process or a certain configuration of a process by a set of polytopes with a linear power consumption function associated with each polytope. Such models are referred to as Convex Region Surrogate (CRS) models (Zhang et al, 2016c). The advantage of a CRS model is that it captures nonlinearities and nonconvexities, yet it can be formulated as a set of mixedinteger linear constraints, which do not increase the computational complexity of the scheduling model if it is already formulated as an MILP.…”

Section: Types Of Modelsmentioning

confidence: 99%

Enterprise-wide optimization for industrial demand side management: Fundamentals, advances, and perspectives

Zhang

Grossmann

2016

Chemical Engineering Research and Design

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123

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The active management of electricity demand, also referred to as demand side management (DSM), has been recognized as an effective approach to increase power grid performance and consumer benefits. Being large electricity consumers, the power-intensive process industries play a key role in DSM. In particular, enterprise-wide optimization (EWO) for industrial DSM has emerged as a major area of interest for both researchers and practitioners. In this work, we introduce the reader to the fundamentals of power system economics, provide a definition of DSM that reflects more strongly the consumer's perspective, and present a comprehensive review of existing works on EWO for industrial DSM. The review is organized into four parts, which correspond to the four main challenges that we identify as: (1) accurate modeling of operational flexibility, (2) integration of production and energy management, (3) decision-making across multiple time and space scales, and (4) optimization under uncertainty. Finally, we highlight research gaps and future opportunities in this area.

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“…• Each operating mode is defined by a Convex Region Surrogate (CRS) model (Zhang et al, 2014), in which the feasible region is approximated by a union of convex regions in the form of polytopes. For each convex region, a linear power consumption function with respect to to the production rates is given.…”

Section: Integrated Asu-ces Modelmentioning

confidence: 99%