Pyomo — Optimization Modeling in Python

Hart, William E.; Laird, Carl D.; Watson, Jean‐Paul; Woodruff, David L.; Hackebeil, Gabriel A.; Nicholson, Bethany L.; Siirola, John Daniel

doi:10.1007/978-3-319-58821-6

Cited by 472 publications

(118 citation statements)

References 0 publications

Supporting

Mentioning

118

Contrasting

Order By: Relevance

“…Values of parameters were estimated using the interior point nonlinear optimization solver IPOPT. The execution was programmed in Python using Pyomo (Hart, Laird, Watson, & Woodruff, ). As the model was used to fit each data set separately, in total, parameter estimation was carried out three times to generate three sets of parameter values.…”

Section: Methodsmentioning

confidence: 99%

Hybrid physics‐based and data‐driven modeling for bioprocess online simulation and optimization

Zhang

Rio‐Chanona

Petsagkourakis

et al. 2019

Biotech & Bioengineering

101

View full text Add to dashboard Cite

Model‐based online optimization has not been widely applied to bioprocesses due to the challenges of modeling complex biological behaviors, low‐quality industrial measurements, and lack of visualization techniques for ongoing processes. This study proposes an innovative hybrid modeling framework which takes advantages of both physics‐based and data‐driven modeling for bioprocess online monitoring, prediction, and optimization. The framework initially generates high‐quality data by correcting raw process measurements via a physics‐based noise filter (a generally available simple kinetic model with high fitting but low predictive performance); then constructs a predictive data‐driven model to identify optimal control actions and predict discrete future bioprocess behaviors. Continuous future process trajectories are subsequently visualized by re‐fitting the simple kinetic model (soft sensor) using the data‐driven model predicted discrete future data points, enabling the accurate monitoring of ongoing processes at any operating time. This framework was tested to maximize fed‐batch microalgal lutein production by combining with different online optimization schemes and compared against the conventional open‐loop optimization technique. The optimal results using the proposed framework were found to be comparable to the theoretically best production, demonstrating its high predictive and flexible capabilities as well as its potential for industrial application.

show abstract

Section: Methodsmentioning

confidence: 99%

Hybrid physics‐based and data‐driven modeling for bioprocess online simulation and optimization

Zhang

Rio‐Chanona

Petsagkourakis

et al. 2019

Biotech & Bioengineering

101

View full text Add to dashboard Cite

show abstract

“…The optimal values of model parameters are estimated by solving the NLP using IPOPT, the state‐of‐the‐art interior point nonlinear optimization solver (Wächter & Biegler, ). This parameter estimation procedure is programmed in the Python optimization environment Pyomo (Laird, Watson, & Woodruff, ). Once the parameters are estimated, the model's simulation results are calculated in Mathematica® 10.…”

Section: Materials and Modeling Methodologymentioning

confidence: 99%

Dynamic modeling of green algae cultivation in a photobioreactor for sustainable biodiesel production

Rio‐Chanona

Liu

Wagner

et al. 2017

Biotech & Bioengineering

View full text Add to dashboard Cite

Biodiesel produced from microalgae has been extensively studied due to its potentially outstanding advantages over traditional transportation fuels. In order to facilitate its industrialization and improve the process profitability, it is vital to construct highly accurate models capable of predicting the complex behavior of the investigated biosystem for process optimization and control, which forms the current research goal. Three original contributions are described in this paper. Firstly, a dynamic model is constructed to simulate the complicated effect of light intensity, nutrient supply and light attenuation on both biomass growth and biolipid production. Secondly, chlorophyll fluorescence, an instantly measurable variable and indicator of photosynthetic activity, is embedded into the model to monitor and update model accuracy especially for the purpose of future process optimal control, and its correlation between intracellular nitrogen content is quantified, which to the best of our knowledge has never been addressed so far. Thirdly, a thorough experimental verification is conducted under different scenarios including both continuous illumination and light/dark cycle conditions to testify the model predictive capability particularly for long-term operation, and it is concluded that the current model is characterized by a high level of predictive capability. Based on the model, the optimal light intensity for algal biomass growth and lipid synthesis is estimated. This work, therefore, paves the way to forward future process design and real-time optimization.

show abstract

“…The model is formulated as NLP from Equation (38) in Pyomo 5.3 and solved by using the interior‐point solver IPOPT 12.9 on an Intel Core i7 CPU @ 2.9 GHz Macbook Pro running MacOS 10.14. HSL_MA97 is used as the linear solver, and Pyomo.DAE is used to automatically discretize the PDE described in the previous section .…”

Section: Pipeline Case Studiesmentioning

confidence: 99%

Dynamic optimization for gas blending in pipeline networks with gas interchangeability control

Kazi

Biegler

et al. 2020

AIChE Journal

View full text Add to dashboard Cite

The increasing penetration of unconventional gas and liquefied natural gas poses an operational challenge on existing regional gas networks for gas quality problems. A new dynamic model for natural gas pipeline network with multiple supplies is presented with a special emphasis on gas interchangeability control. Wobbe index serves as gas interchangeability indicator and is calculated by equations derived from rigorous composition‐based partial differential equations. Disjunctive formulation is applied to represent different modes of gas blending due to gas reversal, and the disjunctive model is then reformulated as a nonsmooth model with min/max and absolute value functions, which is solved by a gradient‐based nonlinear program solver after smooth approximation. Moreover, a heuristic algorithm is proposed to tune the penalty parameters in order to focus on different penalty terms while keeping the model well‐conditioned. The developed model and strategy are first tested with a small pipeline network model and then extended to a large model. The results show that the model can effectively manage gas interchangeability issues in pipeline networks within reasonable CPU time.

show abstract

Pyomo — Optimization Modeling in Python

Cited by 472 publications

References 0 publications

Hybrid physics‐based and data‐driven modeling for bioprocess online simulation and optimization

Hybrid physics‐based and data‐driven modeling for bioprocess online simulation and optimization

Dynamic modeling of green algae cultivation in a photobioreactor for sustainable biodiesel production

Dynamic optimization for gas blending in pipeline networks with gas interchangeability control

Contact Info

Product

Resources

About