Parameter Estimation of Dynamic Beer Fermentation Models.

In this study, two dynamic models of beer fermentation are proposed, and their parameters are estimated using experimental data collected during several batch experiments initiated with different sugar concentrations. Biomass, sugar, ethanol, and vicinal diketone concentrations are measured off-line with an analytical system while two on-line immersed probes deliver temperature, ethanol concentration, and carbon dioxide exhaust rate measurements. Before proceeding to the estimation of the unknown model parameters, a structural identifiability analysis is carried out to investigate the measurement configuration and the kinetic model structure.

Dynamic modeling of Rhodospirillum rubrum PHA production triggered by redox stress during VFA photoheterotrophic assimilations.

Polyhydroxyalkanoates (PHA) represent an environmentally friendly alternative to petroleum based plastics for a broad range of applications from packaging to biomedical devices. In the prospect of an industrial PHA production, it is highly valuable to accurately control the incorporation of different repeating units into the polymer, to produce a polyester with specific material characteristics. In this study, we develop macroscopic dynamic models predicting the polymer production and composition when mixtures containing up to four volatile fatty acids (VFA) are used as substrates.

State estimation of the time-space propagation of COVID-19 using a distributed parameter observer based on a SEIR-type model.

The real-time prediction and estimation of the spread of diseases, such as COVID-19 is of paramount importance as evidenced by the recent pandemic. This work is concerned with the distributed parameter estimation of the time-space propagation of such diseases using a diffusion-reaction epidemiological model of the susceptible-exposed-infected-recovered (SEIR) type. State estimation is based on continuous measurements of the number of infections and deaths per unit of time and of the host spatial domain.

Fast transient optimization of social distancing during Covid-19 pandemics using extremum seeking.

In this work, the application of a model-free extremum seeking strategy is investigated to achieve the hypothetical control of the covid-19 pandemics by acting on social distancing. The advantage of this procedure is that it does not rely on the accurate knowledge of an epidemiological model and takes realistic constraints into account, such as hospital capacities. The simulation study reveals that the convergence has two time scales, with a fast catch of the transient optimum of the measurable cost function, followed by a slow tracking of this optimum following the original SIR dynamics.

Variable selection and parameter estimation of viral amplification in vero cell cultures dedicated to the production of a dengue vaccine.

In this study, a dynamic model of a Vero cell culture-based dengue vaccine production process is developed. The approach consists in describing the process dynamics as functions of the whole living (uninfected and infected) biomass whereas previous works are based on population balance approaches. Based on the assumption that infected biomass evolves faster than other variable, the model can be simplified using a slow-fast approximation.

On the derivation of a simple dynamic model of anaerobic digestion including the evolution of hydrogen.

Hydrogen has been found to be an important intermediate during anaerobic digestion (AD) and a key variable for process monitoring as it gives valuable information about the stability of the reactor. However, simple dynamic models describing the evolution of hydrogen are not commonplace. In this work, such a dynamic model is derived using a systematic data driven-approach, which consists of a principal component analysis to deduce the dimension of the minimal reaction subspace explaining the data, followed by an identification of the kinetic parameters in the least-squares sense.

Macroscopic Dynamic Modeling of Sequential Batch Cultures of Hybridoma Cells: An Experimental Validation.

Hybridoma cells are commonly grown for the production of monoclonal antibodies (MAb). For monitoring and control purposes of the bioreactors, dynamic models of the cultures are required. However these models are difficult to infer from the usually limited amount of available experimental data and do not focus on target protein production optimization.

Dynamic metabolic flux analysis using a convex analysis approach: Application to hybridoma cell cultures in perfusion.

In recent years, dynamic metabolic flux analysis (DMFA) has been developed in order to evaluate the dynamic evolution of the metabolic fluxes. Most of the proposed approaches are dedicated to exactly determined or overdetermined systems. When an underdetermined system is considered, the literature suggests the use of dynamic flux balance analysis (DFBA).

Parameter identification of a dynamic model of CHO cell cultures: an experimental case study.

In this paper, we address the problem of parameter identification in dynamic models of animal cultures, and we propose a step-by-step procedure, which gradually considers more detailed models. This procedure allows subsets of parameters to be estimated at each step, which can be used in the initialization of the next identification step. Finally, the full parameter set can be re-estimated starting from the results of the last step.

Parameter identification of Droop model: an experimental case study.

Mathematical modeling and the development of predictive dynamic models are of paramount importance for the optimization, state estimation, and control of bioprocesses. This study is dedicated to the identification of a simple model of microalgae growth under substrate limitation, i.e.

Anaerobic biodegradability of fish remains: experimental investigation and parameter estimation.

The generation of organic waste associated with aquaculture fish processing has increased significantly in recent decades. The objective of this study is to evaluate the anaerobic biodegradability of several fish processing fractions, as well as water treatment sludge, for tilapia and sturgeon species cultured in recirculated aquaculture systems. After substrate characterization, the ultimate biodegradability and the hydrolytic rate were estimated by fitting a first-order kinetic model with the biogas production profiles.

Design and test of a low-cost RGB sensor for online measurement of microalgae concentration within a photo-bioreactor.

In this study, a low-cost RGB sensor is developed to measure online the microalgae concentration within a photo-bioreactor. Two commercially available devices, i.e.

Design, analysis and validation of a simple dynamic model of a submerged membrane bioreactor.

In this study, a simple dynamic model of a submerged membrane bioreactor (sMBR) is proposed, which would be suitable for process control. In order to validate the proposed model structure, informative data sets are generated using a detailed simulator built in a well-established environment, namely GPS-X. The model properties are studied, including equilibrium points, stability, and slow/fast dynamics (three different time scales).

A dynamical interpretation of strong observability and detectability concepts for nonlinear systems with unknown inputs: application to biochemical processes.

Determination of the observability/detectability properties of a nonlinear system is fundamental to assess the possibility of constructing observers and the properties that can be assigned to them, as e.g., the assignability of the convergence rate.

Long-term hydrolytic capacity evaluation of a thermophilic anaerobic digester treating sewage sludge.

This study presents an evaluation of the hydrolytic activity of a continuous thermophilic anaerobic reactor in long-term operation. The hydrolytic coefficient was estimated by fitting a three-reaction model of the anaerobic digestion process with experimental data obtained from a pilot thermophilic digester operated for about 2 years. The model fitting and the cross-validation indicate that this model can represent the behavior of the system in a proper way; moreover, the results show a variation of the hydrolytic capacity of the system throughout the evaluation period.

Accelerating animal cell growth in perfusion mode by multivariable control: simulation studies.

This study considers the problem of manipulating in an optimal way the perfusion and bleed flow rates of a continuous culture of hybridoma cells, so as to achieve a fast transient start-up and reject potential disturbances. The proposed solution makes use of an analysis of the properties of the steady state solutions of the nonlinear dynamic model of the cell culture, and in particular the relationship between the two main limiting substrates, glucose and glutamine. The solution is implemented using extended prediction self-adaptive control.

Stoichiometric identification with maximum likelihood principal component analysis.

This study presents an effective procedure for the determination of a biologically inspired, black-box model of cultures of microorganisms (including yeasts, bacteria, plant and animal cells) in bioreactors. This procedure is based on sets of experimental data measuring the time-evolution of a few extracellular species concentrations, and makes use of maximum likelihood principal component analysis to determine, independently of the kinetics, an appropriate number of macroscopic reactions and their stoichiometry. In addition, this paper provides a discussion of the geometric interpretation of a stoichiometric matrix and the potential equivalent reaction schemes.

Identification in an anaerobic batch system: global sensitivity analysis, multi-start strategy and optimization criterion selection.

Several mathematical models have been developed in anaerobic digestion systems and a variety of methods have been used for parameter estimation and model validation. However, structural and parametric identifiability questions are relatively seldom addressed in the reported AD modeling studies. This paper presents a 3-step procedure for the reliable estimation of a set of kinetic and stoichiometric parameters in a simplified model of the anaerobic digestion process.

An optimizing start-up strategy for a bio-methanator.

This paper presents an optimizing start-up strategy for a bio-methanator. The goal of the control strategy is to maximize the outflow rate of methane in anaerobic digestion processes, which can be described by a two-population model. The methodology relies on a thorough analysis of the system dynamics and involves the solution of two optimization problems: steady-state optimization for determining the optimal operating point and transient optimization.

Model selection, identification and validation in anaerobic digestion: a review.

Anaerobic digestion enables waste (water) treatment and energy production in the form of biogas. The successful implementation of this process has lead to an increasing interest worldwide. However, anaerobic digestion is a complex biological process, where hundreds of microbial populations are involved, and whose start-up and operation are delicate issues.

Algorithms for sleep-wake identification using actigraphy: a comparative study and new results.

The aim of this study was to investigate two new scoring algorithms employing artificial neural networks and decision trees for distinguishing sleep and wake states in infants using actigraphy and to validate and compare the performance of the proposed algorithms with known actigraphy scoring algorithms. The study employed previously recorded longitudinal physiological infant data set from the Collaborative Home Infant Monitoring Evaluation (CHIME) study conducted between 1994 and 1998 [http://dccwww.bumc.

Dynamic modeling of complex biological systems: a link between metabolic and macroscopic description.

In this study, a class of dynamic models based on metabolic reaction pathways is analyzed, showing that systems with complex intracellular reaction networks can be represented by macroscopic reactions relating extracellular components only. Based on rigorous assumptions, the model reduction procedure is systematic and allows an equivalent 'input-output' representation of the system to be derived. The procedure is illustrated with a few examples.