Enfrentando el modelado de bioprocesos: una revisión de las metodologías de modelado

Quintana, Fabián Alberto Ortega; Álvarez, Hernán; Castro, Héctor Antonio Botero

doi:10.18273/revion.v30n1-2017006

Cited by 6 publications

(4 citation statements)

References 84 publications

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“…The procedure was developed by interacting with model users and reviewing published procedures such as those of Basmadjian et al Hangos &Cameron (2001). We reviewed 21 procedures in total (Ortega-Quintana et al, 2017) with the aim of simplifying some steps and looking for a direct sequence easily applicable by junior researchers.…”

Section: Suggested Modeling Procedures For Macroscopic Scalementioning

confidence: 99%

Chemical process modelling from a micro to a macro approach: Closing the gap

Chejne

Camargo-Trillos

Álvarez

et al. 2023

Rev. Acad. Colomb. Cienc. Ex. Fis. Nat.

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The present study discusses the multiscale modeling of chemical processes. Despite the existence of very good models for chemical processes from a macroscopic point of view, they can be limited due to the use of poorly estimated transport and transfer coefficients that reduce the accuracy needed for good technology development. Even if models are supported by fundamental relations, there is an unclear connection between their transport coefficient and molecular dynamics. The microscopic models can model several phenomena at atomic or molecular sizes with extreme precision. However, creating a link between the micro and the macro scale takes a huge computation time, as the way they link is not direct. We study this in detail here.

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Section: Suggested Modeling Procedures For Macroscopic Scalementioning

confidence: 99%

Chemical process modelling from a micro to a macro approach: Closing the gap

Chejne

Camargo-Trillos

Álvarez

et al. 2023

Rev. Acad. Colomb. Cienc. Ex. Fis. Nat.

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“…The equation that best reflects the process is the one that, when used in the simulation of the process, according to the mathematical model of a tubular reactor, produces the smallest deviations with respect to the experimental values. The deviations are estimated as the quotient between the square of the difference between the experimental value and the magnitude calculated by the model and the square of the experimental value for each variable as expressed in equation (3). Although this deviation is determined for the variables substrate concentration, biomass and an overall deviation, the behavior of the substrate concentration variable is of special attention, since the objective of the process is the decontamination of the residual.…”

Section: Ii2 Mathematical Modelling and Simulation Of The Anaerobic P...mentioning

confidence: 99%

“…The degree of specificity and robustness of the response offered by the mathematical model depends on the rigor with which the researcher has developed it [1][2][3]. A great advantage of mathematical models is that it's are a very valuable tool when you want to observe the behavior of a system, but are unwilling or unable to experiment with scaling equipment, particularly with reactors, because of the implications for process safety and feasibility [2] and [3]. Mathematical modeling is a powerful tool that can be used to understand and predict the behavior of biological systems.…”

Section: Introductionmentioning

confidence: 99%

Scale-up by mathematical modeling of anaerobic reactors for yeast wastewater treatment

Barreto,

Rico

2023

ITEGAM

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Wastewater treatment by anaerobic digestion is a viable solution for the discharge of industrial liquid wastes with different levels of contamination. The kinetics of the anaerobic digestion reaction, in laboratory tubular reactors, by a consortium of microorganisms to treat liquid waste from a feed yeast industry does not adjust to a Monod equation, but to a numerical adjustment carried out with the Table Curve 2D program. Where 0.65 d -1 was determined as the maximum cell growth rate and other parameters of the microbial kinetics. The error with which the selected model describes the experimental results is ± 24 mg/L. The mathematical analysis of the models used was carried out in MATLAB ® software, which allowed the determination of a novel dimensionless number, named Gai, which facilitated the analysis, scaling by mathematical modeling and dimensioning of the anaerobic reactors at industrial scale for the treatment of 430 m 3 /d of waste. Tubular bioreactors with a total height of 18,93 m and 2,4 m in diameter were dimensioned. The required flow rate of 2,91 m/h was also determined. The Gai number is a relationship between the governing phenomena of the studied process, the conversion and residence time constants, with two main mechanisms involved in the process: convective flow and conversion. Once it has been demonstrated that there are no diffusional restrictions in the anaerobic leaf.

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“…This subjective character explain the existence of several methodologies for building PBSMs [20, 21, 22, 23, 24, 25]. In our group (KALMAN, Universidad Nacional de Colombia), several studies have been developed [19, 26] to propose the following methodology, described by 10 steps, which are summarized here in the interest of completeness.…”

Section: The Process Of Pbsm Constructionmentioning

confidence: 99%

On Parameter Interpretability of Phenomenological-Based Semiphysical Models

Lema-Perez¹,

Muñoz-Tamayo

García-Tirado

et al. 2018

Preprint

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Empirical and phenomenological based models are used to represent biological and physiological processes. Phenomenological models are derived from the knowledge of the mechanisms that underlie the behaviour of the system under study, while empirical models are derived from analysis of data to quantify relationships between variables of interest. For studying biological systems, the phenomenological modeling approach offers the great advantage of having a structure with variables and parameters with physical meaning that enhance the interpretability of the model and its further used for decision making. The interpretability property of models, however, remains a vague concept. In this study, we tackled the interpretability property for parameters of phenomenological-based models. To our knowledge, this property has not been deeply discussed, perhaps by the implicit assumption that interpretability is inherent to the phenomenological-based models. We propose a conceptual framework to address the parameter interpretability and its implications for parameter identifiability. We use as battle horse a simple but relevant model representing the enzymatic degradation of β−casein by a Lactococcus lactis bacterium.How can we assess the capability of a mathematical model to provide mech-2 anistic insight on the system under study? That is, how the mathematical 3 structure of the model translates and captures the knowledge of the phenomena 4 taking place in the system? To what extent can we interpret mechanistically our 5 model? In biotechnology, biology, and biomedical fields two main approaches ex-6 ist to model processes of interest, namely empirical and phenomenological based 7 modeling. Empirical based models are derived from data, while phenomenolog-8 ical based models are derived from knowledge about the process. In biomedical 9 fields, phenomenological based models are more relevant than empirical based 10 models since, in addition to prediction, their parameters and variables provide 11 information that can be used to perform diagnosis, discriminate clinical risk 12 groups and guide treatment for stratifying patients by disease severity [1, 2]. In 13 spite of this, in the fields mentioned before many models have been developed 14 from an empirical point of view by using black box modeling approaches like 15 machine learning and fuzzy models. Machine learning models, for example, are 16 increasingly used in the field of medicine and healthcare but there is still an 17 inability by humans to understand how those models work and what meaning 18 their parameters have. Some approaches have been proposed to improving the 19 level of explanation and interpretability of such emprirical models, that is to 20 open the black box [3]. The deployment of the above mentioned approaches en-21 counters its first hurdle by the difficulty of formalising the definition of central 22 concepts such as transparency, explanation, and interpretability. In the present 23 work, we focus on the interpretability concept but applied to phenomeno...

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Enfrentando el modelado de bioprocesos: una revisión de las metodologías de modelado

Cited by 6 publications

References 84 publications

Chemical process modelling from a micro to a macro approach: Closing the gap

Chemical process modelling from a micro to a macro approach: Closing the gap

Scale-up by mathematical modeling of anaerobic reactors for yeast wastewater treatment

On Parameter Interpretability of Phenomenological-Based Semiphysical Models

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