Pyomo – Optimization Modeling in Python

Hart, William E.; Laird, Carl D.; Watson, Jean‐Paul; Woodruff, David L.

doi:10.1007/978-1-4614-3226-5

Cited by 368 publications

(336 citation statements)

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“…Each problem is formulated in the Pyomo framework for modeling and optimization [61,62]. Nonlinear programming dynamic optimization problems are solved via orthogonal collocation on finite elements [63] with 5 min time discretization and the APOPT and COUENNE MINLP solvers are utilized to solve all mathematical programming problems presented in this work [64,65].…”

Section: Resultsmentioning

confidence: 99%

Economic Benefit from Progressive Integration of Scheduling and Control for Continuous Chemical Processes

et al. 2017

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Performance of integrated production scheduling and advanced process control with disturbances is summarized and reviewed with four progressive stages of scheduling and control integration and responsiveness to disturbances: open-loop segregated scheduling and control, closed-loop segregated scheduling and control, open-loop scheduling with consideration of process dynamics, and closed-loop integrated scheduling and control responsive to process disturbances and market fluctuations. Progressive economic benefit from dynamic rescheduling and integrating scheduling and control is shown on a continuously stirred tank reactor (CSTR) benchmark application in closed-loop simulations over 24 h. A fixed horizon integrated scheduling and control formulation for multi-product, continuous chemical processes is utilized, in which nonlinear model predictive control (NMPC) and continuous-time scheduling are combined.

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Section: Resultsmentioning

confidence: 99%

Economic Benefit from Progressive Integration of Scheduling and Control for Continuous Chemical Processes

et al. 2017

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“…Oemof itself makes heavy use of external modules for optimization problem [26] and data handling [27]. Additional packages for data pre-and post-processing or visualization can be included easily.…”

Section: Methodsmentioning

confidence: 99%

The Open Energy Modelling Framework (oemof) - A novel approach in energy system modelling

Hilpert¹,

Kaldemeyer²,

Krien³

et al. 2017

Preprint

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Energy system models have become indispensable to shape future energy systems by providing insights into different trajectories. However, sustainable systems with high shares of renewable energy are characterised by growing crosssectoral interdependencies and decentralised structures. To capture important properties of increasingly complex energy systems, sophisticated and flexible modelling environments are needed. This paper presents the Open Energy Modelling Framework (oemof) as a novel approach in energy system modelling, representation and analysis. The framework forms a structured set of tools and sub-frameworks to construct comprehensive energy system models and has been published open source under a free licence. Using a collaborative development approach and extensive documentation on different levels, the framework seeks for a maximum level of transparency. Based on a generic graph based description of energy systems it is well suited to flexibly model complex crosssectoral systems ranging from a distributed or urban to a transnational scale.This makes the framework a multi-purpose modelling environment for strategic planning of future energy systems.

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“…The resolution can be done either via sequential or simultaneous approaches: Depending on the problem structure, a combination of a dynamic simulator and an optimization algorithm (rSQP like SNOPT or an evolutionary one) can be a good choice, but modern optimization environments like CasADi (Andersson et al, 2012) or Pyomo, (Hart et al, 2012) offer excellent features, including automatic discretization by orthogonal collocation and automatic differentiation, that facilitate the use of efficient interior point codes such as IPOPT in a simultaneous approach.…”

Section: Estimation Variables Are Considered As Independentmentioning

confidence: 99%

Developing Grey-Box Dynamic Process Models

et al. 2018

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This paper presents a methodology for developing grey models of process systems, that is, models that, being based on fundamental principles and laws of nature, combine them with sub-models obtained from experimental data. The method follows two steps: the first one takes advantage of what is known, while the second uses the data and mixed-integer optimization algorithms to identify the structure and parameters of the remaining parts of the model. The method is illustrated in a challenging biotechnological process: the Acetone-Butanol-Ethanol (ABE) fermentation process.

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Pyomo – Optimization Modeling in Python

Cited by 368 publications

References 0 publications

Economic Benefit from Progressive Integration of Scheduling and Control for Continuous Chemical Processes

Economic Benefit from Progressive Integration of Scheduling and Control for Continuous Chemical Processes

The Open Energy Modelling Framework (oemof) - A novel approach in energy system modelling

Developing Grey-Box Dynamic Process Models

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