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Applied Sciences
Special Issues
Modelling and Control of Biotechnological Processes
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Modelling and Control of Biotechnological Processes

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: closed (31 August 2021) | Viewed by 10240

Special Issue Editors

Prof. Dr. Jesús Picó

E-Mail Website
Guest Editor
Instituto de Automática e Informática Industrial, Building 5C, Universitat Politècnica de València, Camí de Vera s/n, 46022 València, Spain
Interests: sliding mode observers for bioprocess estimation; nonlinear adaptive control of bioreactors; feedback control of gene synthetic circuits; robust estimation of metabolic fluxes; multiobjective optimization in systems and synthetic biology; the design, build, test, and learn cycle in synthetic biology; synthetic biology for production of metabolites of interest
Prof. Dr. Hernán De Battista

E-Mail Website
Guest Editor
Universidad Nacional de La Plata-CONICET, Argentina
Interests: nonlinear and switched control; sliding mode observers; control of biotechnological processes; artificial pancreas

Special Issue Information

Dear Colleagues,

Industrial biotechnology has traditionally used enhanced and/or genetically modified microorganisms as cell factories to produce specialty metabolites (e.g. amino acids, vitamins, food additives, biofuels,...) of importance for the health, chemical, food, and energy sectors among others. Other biotechnological processes, like wastewater treatment and bioremediation, are also gaining increasing relevance to cope with current environmental challenges. Bioreactors of different sizes and physical configurations are the workhorses in which characterization, scaling-up, and bioprocessing take place. Therefore, modelling, estimation, and feedback control of bioreactions have received much attention in recent years. However, estimation of the relevant variables and feedback control of bioprocesses, specially in industrial environments, is difficult due to some characteristics of the processes that need to be controlled: (i) lack of knowledge on the key variables of the system representing the physiological state of the culture, (ii) high complexity derived from multi-component non-linear process dynamics, (iii) non-homogeneous conditions, and (iv) large variability.

Recent advances in the dynamic analysis of bioreactors and photo-bioreactors, the design of efficient observers and software sensors to estimate relevant variables, and model-based nonlinear feedback control strategies, along with new on-line techniques to measure and act upon the culture, offer the possibility to improve beyond current industrial practices.

Prof. Jesús Picó
Prof. Hernán De Battista
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Bioprocess control
  • Feedback control
  • Bioreactors
  • Bioproduction
  • Bioremediation
  • Wastewater treatment
  • Photo-bioreactors
  • Continuous and fed-batch operation
  • Turbidostats and chemostats
  • Bioprocess modelling
  • Bioprocess estimation

Published Papers (4 papers)

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Research

17 pages, 1296 KiB  
Article
Gene Expression Space Shapes the Bioprocess Trade-Offs among Titer, Yield and Productivity
by Fernando N. Santos-Navarro, Yadira Boada, Alejandro Vignoni and Jesús Picó
Appl. Sci. 2021, 11(13), 5859; https://doi.org/10.3390/app11135859 - 24 Jun 2021
Cited by 1 | Viewed by 1839
Abstract
Optimal gene expression is central for the development of both bacterial expression systems for heterologous protein production, and microbial cell factories for industrial metabolite production. Our goal is to fulfill industry-level overproduction demands optimally, as measured by the following key performance metrics: titer, [...] Read more.
Optimal gene expression is central for the development of both bacterial expression systems for heterologous protein production, and microbial cell factories for industrial metabolite production. Our goal is to fulfill industry-level overproduction demands optimally, as measured by the following key performance metrics: titer, productivity rate, and yield (TRY). Here we use a multiscale model incorporating the dynamics of (i) the cell population in the bioreactor, (ii) the substrate uptake and (iii) the interaction between the cell host and expression of the protein of interest. Our model predicts cell growth rate and cell mass distribution between enzymes of interest and host enzymes as a function of substrate uptake and the following main lab-accessible gene expression-related characteristics: promoter strength, gene copy number and ribosome binding site strength. We evaluated the differential roles of gene transcription and translation in shaping TRY trade-offs for a wide range of expression levels and the sensitivity of the TRY space to variations in substrate availability. Our results show that, at low expression levels, gene transcription mainly defined TRY, and gene translation had a limited effect; whereas, at high expression levels, TRY depended on the product of both, in agreement with experiments in the literature. Full article
(This article belongs to the Special Issue Modelling and Control of Biotechnological Processes)
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23 pages, 4044 KiB  
Article
ABACO: A New Model of Microalgae-Bacteria Consortia for Biological Treatment of Wastewaters
by Ana Sánchez-Zurano, Enrique Rodríguez-Miranda, José Luis Guzmán, Francisco Gabriel Acién-Fernández, José M. Fernández-Sevilla and Emilio Molina Grima
Appl. Sci. 2021, 11(3), 998; https://doi.org/10.3390/app11030998 - 22 Jan 2021
Cited by 44 | Viewed by 3525
Abstract
Microalgae-bacteria consortia have been proposed as alternatives to conventional biological processes to treat different types of wastewaters, including animal slurry. In this work, a microalgae-bacteria consortia (ABACO) model for wastewater treatment is proposed, it being calibrated and validated using pig slurry. The model [...] Read more.
Microalgae-bacteria consortia have been proposed as alternatives to conventional biological processes to treat different types of wastewaters, including animal slurry. In this work, a microalgae-bacteria consortia (ABACO) model for wastewater treatment is proposed, it being calibrated and validated using pig slurry. The model includes the most relevant features of microalgae, such as light dependence, endogenous respiration, and growth and nutrient consumption as a function of nutrient availability (especially inorganic carbon), in addition to the already reported features of heterotrophic and nitrifying bacteria. The interrelation between the different populations is also included in the model, in addition to the simultaneous release and consumption of the most relevant compounds, such as oxygen and carbon dioxide. The implementation of the model has been performed in MATLAB software; the calibration of model parameters was carried out using genetic algorithms. The ABACO model allows one to simulate the dynamics of different components in the system, and the relative proportions of microalgae, heterotrophic bacteria, and nitrifying bacteria. The percentage of each microbial population obtained with the model was confirmed by respirometric techniques. The proposed model is a powerful tool for the development of microalgae-related wastewater treatment processes, both to maximize the production of microalgal biomass and to optimize the wastewater treatment capacity. Full article
(This article belongs to the Special Issue Modelling and Control of Biotechnological Processes)
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23 pages, 2925 KiB  
Article
Model Reference Adaptive Control for Milk Fermentation in Batch Bioreactors
by Jožef Ritonja, Andreja Goršek and Darja Pečar
Appl. Sci. 2020, 10(24), 9118; https://doi.org/10.3390/app10249118 - 20 Dec 2020
Cited by 4 | Viewed by 2300
Abstract
This paper presents the advanced control theory’s original utilisation to realise a system that controls the fermentation process in batch bioreactors. Proper fermentation control is essential for quality fermentation products and the economical operation of bioreactors. Batch bioreactors are very popular due to [...] Read more.
This paper presents the advanced control theory’s original utilisation to realise a system that controls the fermentation process in batch bioreactors. Proper fermentation control is essential for quality fermentation products and the economical operation of bioreactors. Batch bioreactors are very popular due to their simple construction. However, this simplicity presents limitations in implementing control systems that would ensure a controlled fermentation process. Batch bioreactors do not allow the inflow/outflow of substances during operation. Therefore, we have developed a control system based on a stirrer drive instead of material flow. The newly developed control system ensures tracking of the fermentation product time course to the reference trajectory by changing the stirrer’s speed. Firstly, the paper presents the derivation of the enhanced mathematical model suitable for developing a control system. A linearisation and eigenvalue analysis of this model were made. Due to the time-consuming determination of the fermentation model and the variation of the controlled plant during operation, the use of adaptive control is advantageous. Secondly, a comparison of different adaptive approaches was made. The model reference adaptive control was selected on this basis. The control theory is presented, and the control realisation described. Experimental results obtained with the laboratory batch bioreactor confirm the advantages of the proposed adaptive approach compared to the conventional PI-control. Full article
(This article belongs to the Special Issue Modelling and Control of Biotechnological Processes)
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12 pages, 2082 KiB  
Article
Fuzzy Logic-Based Adaptive Control of Specific Growth Rate in Fed-Batch Biotechnological Processes. A Simulation Study
by Mantas Butkus, Jolanta Repšytė and Vytautas Galvanauskas
Appl. Sci. 2020, 10(19), 6818; https://doi.org/10.3390/app10196818 - 29 Sep 2020
Cited by 7 | Viewed by 1996
Abstract
This article presents the development and application of a distinct adaptive control algorithm that is based on fuzzy logic and was used to control the specific growth rate (SGR) in a fed-batch biotechnological process. The developed control algorithm was compared with two adaptive [...] Read more.
This article presents the development and application of a distinct adaptive control algorithm that is based on fuzzy logic and was used to control the specific growth rate (SGR) in a fed-batch biotechnological process. The developed control algorithm was compared with two adaptive control systems that were based on a model-free adaptive technique and gain scheduling technique. A typical mathematical model of recombinant Escherichia coli fed-batch cultivation process was selected to evaluate the performance of the fuzzy-based control algorithm. The investigated control techniques performed similarly when considering the whole process duration. The adaptive PI controller with fuzzy-based parameter adaptation demonstrated advantages over the previously mentioned algorithms—especially when compensating the deviations of the SGR. These deviations usually occur when the equipment malfunctions or process disturbances take place. The fuzzy-based control system was stable within the investigated ranges. It was determined that, regarding control quality, the investigated control algorithms are suited to control the SGR in a fed-batch biotechnological process. However, substrate feeding rate manipulation and limitation needs to be used. Taking into account the time needed to design and tune the controller, the developed controller is suitable for practical applications when expert knowledge is available. The proposed algorithm can be further adapted and developed to control the SGR in other cell cultivations while running the process under substrate limitation conditions. Full article
(This article belongs to the Special Issue Modelling and Control of Biotechnological Processes)
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