Pomodoro: A Novel Toolkit for Dynamic (MultiObjective) Optimization, and Model Based Control and Estimation ⁎ ⁎All the authors are with KU Leuven - Department of Chemical Engineering, BioTeC &amp; OPTEC, Gebroeders de Smetstraat 1, B-9000,Ghent, Belgium.

Bhonsale, Satyajeet; Telen, Dries; Vercammen, Dominique; Vallerio, Mattia; Hufkens, Jan; Nimmegeers, Philippe; Logist, Filip; Impe, Jan Van

doi:10.1016/j.ifacol.2018.03.122

Cited by 10 publications

(5 citation statements)

References 13 publications

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“…For the implementation of the interactive robust multi‐objective optimization problems presented in this work, the ParetoBrowser presented in is used. This interactive multi‐objective optimization toolkit is available as a part of Pomodoro , an open‐source software toolkit which has been developed at KU Leuven/BioTeC+ and which can be downloaded for academic use on .…”

Section: Methodsmentioning

confidence: 99%

“…Pomodoro can be used to solve dynamic optimization problems using CasADi (computer algebra systems for algorithmic differentiation) for the formulation of dynamic optimization problems and for the computation of derivatives. The default discretization strategy to address dynamic optimization problems in Pomodoro is orthogonal collocation, discretizing the controls in piecewise constant control intervals, and for each state, a cubic Lagrange polynomial is used to interpolate between the four collocation points on each interval which are situated at the Radau roots.…”

Section: Methodsmentioning

confidence: 99%

“…In this article, interactive robust multi‐objective optimization of bioprocesses is studied using the ParetoBrowser , which is part of the in‐house developed software toolkit Pomodoro (process optimization multi‐objective optimization, dynamic optimiation, robust optimization) . As a case study, the multi‐objective optimization of a fed‐batch bioreactor is studied for the production of lysine, an essential amino acid, which is often used as a supplement in the feed for livestock and as amino acid supplement for humans.…”

Section: Introductionmentioning

confidence: 99%

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Interactive Multi‐objective Dynamic Optimization of Bioreactors under Parametric Uncertainty

Nimmegeers

Vallerio

Telen

et al. 2018

Chemie Ingenieur Technik

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Model‐based optimization techniques play a key role in achieving a sustainable operation of biochemical processes. Models are an approximation of the real process under study, hence, uncertainty is inherently present and for a sustainable process operation this uncertainty should be accounted for. In practice, optimality with respect to different conflicting objectives is required and multi‐objective optimization is a valuable tool. In this article the sigma point approach is applied to account for parametric uncertainty in the frame of interactive multi‐objective bioprocess optimization.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Interactive Multi‐objective Dynamic Optimization of Bioreactors under Parametric Uncertainty

Nimmegeers

Vallerio

Telen

et al. 2018

Chemie Ingenieur Technik

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“…Using multi-platform communication techniques, e.g., INPROP (Muñoz López et al, 2018), the information on the available feedstocks provided by the bio-inventory tool can be passed to a stand-alone process simulator, like Aspen Plus, in order to simulate the performance of a custom/already existing biorefinery system. Moreover, complementing the optimisation approaches used in the DST, the eventually selected biorefinery design's model can be employed for developing an optimal control strategy of the entire biorefinery system (Bhonsale et al, 2018). De Pinte counts a total of 5,016 clustered building units (i.e., building units located in a area dedicated as clustered or ribbon development) and 654 rural building units.…”

Section: Towards Drafting An Optimal Small-scale Biorefinery Designmentioning

confidence: 99%

Assessing the Local Biowaste Potential of Rural and Developed Areas Using GIS-Data and Clustering Techniques: Towards a Decision Support Tool

et al. 2022

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As the chemical and energy producing industries are steadily transitioning towards more sustainable processing practices, renewable biomass resources are becoming increasingly more valuable. Recently, following the realisation that renewable resources for the chemical and energy industry should not compete with food supplies, the use of plant-based biowaste has significantly gained in interest. Due to its inherently variable composition, diffuse distribution, and seasonality, it is of the utmost importance that (potential) biorefinery exploiters are well informed of the biowaste resources that are available in the vicinity of their (planned) biorefinery. Designing a biorefinery in such a way that it can tailor for the locally available biowaste resources, exhibits several compelling advantages. Apart from significantly reduced logistics costs, the usage of local biowaste can be a reciprocal advantage for both the involved community and the biorefinery. In this paper, a GIS-based (Geo-Information System) bio-inventory toolbox is presented. The toolbox is developed to aid the biorefinery designers and decision makers, e.g., governmental bodies, to get an adequate overview of the locally available plant-based biowaste resources and, linked to this, the expected periodical amounts, their composition, and their seasonality. The toolbox presented in this contribution is the first part of a decision support tool for the development of a locally embedded flexi-feed and small-scale biorefinery, additionally consisting out of a process modelling tool, and an optimisation tool. Both of these additional tools will employ the information obtained from the bio-inventory toolbox to simulate and optimise several suitable biorefinery designs. The eventual goal of the decision support tool is to provide users with several optimised biorefinery designs that are tailored for their local setting. The additional toolboxes are detailed elsewhere.

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“…This combined MHE-MPC strategy has been implemented in the Pomodoro toolkit [21], which is available at https://cit.kuleuven.be/biotec/software/pomodoro (accessed on 11 October 2021). Pomodoro is a toolkit that can be used for solving dynamic (multiobjective) optimization problems and for model-based control and estimation.…”

Section: Combined Mhe/mpc Strategymentioning

confidence: 99%

Metabolic Reaction Network-Based Model Predictive Control of Bioprocesses

et al. 2021

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Bioprocesses are increasingly used for the production of high added value products. Microorganisms are used in bioprocesses to mediate or catalyze the necessary reactions. This makes bioprocesses highly nonlinear and the governing mechanisms are complex. These complex governing mechanisms can be modeled by a metabolic network that comprises all interactions within the cells of the microbial population present in the bioprocess. The current state of the art in bioprocess control is model predictive control based on the use of macroscopic models, solely accounting for substrate, biomass, and product mass balances. These macroscopic models do not account for the underlying mechanisms governing the observed process behavior. Consequently, opportunities are missed to fully exploit the available process knowledge to operate the process in a more sustainable manner. In this article, a procedure is presented for metabolic network-based model predictive control. This procedure uses a combined moving horizon-model predictive control strategy to monitor the flux state and optimize the bioprocess under study. A CSTR bioreactor model has been combined with a small-scale metabolic network to illustrate the performance of the presented procedure.

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Pomodoro: A Novel Toolkit for Dynamic (MultiObjective) Optimization, and Model Based Control and Estimation ⁎ ⁎All the authors are with KU Leuven - Department of Chemical Engineering, BioTeC & OPTEC, Gebroeders de Smetstraat 1, B-9000,Ghent, Belgium.

Cited by 10 publications

References 13 publications

Interactive Multi‐objective Dynamic Optimization of Bioreactors under Parametric Uncertainty

Interactive Multi‐objective Dynamic Optimization of Bioreactors under Parametric Uncertainty

Assessing the Local Biowaste Potential of Rural and Developed Areas Using GIS-Data and Clustering Techniques: Towards a Decision Support Tool

Metabolic Reaction Network-Based Model Predictive Control of Bioprocesses

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