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Modeling, Optimization and Control in Algal Biotechnology
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Modeling, Optimization and Control in Algal Biotechnology

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A4: Bio-Energy".

Deadline for manuscript submissions: closed (20 July 2022) | Viewed by 19948

Special Issue Editors

Prof. Dr. Štěpán Papáček

E-Mail Website
Guest Editor
The Institute of Information Theory and Automation of the Czech Academy of Sciences, 18200 Prague, Czech Republic
Interests: mathematical modeling; optimization methods; optimal control; mathematical biology; photobioreactors; micro and macroalgae cultivation; integrated multitrophic aquaculture (IMTA) systems
Prof. Dr. Francisco Gabriel Acién Fernández

E-Mail Website
Guest Editor
Department of Chemical Engineering-CIESOL, University of Almeria, Almería, Spain
Interests: bioprocesses; algae biotechnology; waste recycling
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. José M. Fernández-Sevilla

E-Mail Website
Guest Editor
Department of Chemical Engineering, Universidad de Almería, 04071 Almería, Spain
Interests: wastewater treatment using microalgae and bacteria consortia; microalgae photosynthesis; biomass production; bioethanol production, CFD numerical simulations

Special Issue Information

Dear Colleagues,

This Special Issue, entitled ‘Modeling, Optimization, and Control in Algal Biotechnology (Applications of General Principles and Techniques)’, aims to publish a set of articles that present ‘success stories’ of the application of general principles and techniques of mathematical modeling, numerical simulation, optimization, and control theory in the field of algal biotechnology. We intend to showcase the very best insightful and influential examples of the cultivation and utilization of both micro- and macroalgae in a variety of industrial processes.

We would like to include articles that will form a useful benchmark against which other articles are measured. Energies readers and authors are encouraged to send their very best work to be showcased. The key criteria for manuscript acceptance will be novelty and the potential contribution to the field.

Prof. Dr. Štěpán Papáček
Prof. Dr. Francisco Gabriel Acién Fernández
Prof. Dr. José M. Fernández-Sevilla
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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. Energies 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 2600 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

  • algae
  • microalgae
  • macroalgae
  • modeling
  • optimization
  • numerical simulation
  • control theory
  • algae biofuels
  • CFD simulations

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Published Papers (4 papers)

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Research

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18 pages, 1360 KiB  
Article
Machine Learning Methods Modeling Carbohydrate-Enriched Cyanobacteria Biomass Production in Wastewater Treatment Systems
by Héctor Rodríguez-Rángel, Dulce María Arias, Luis Alberto Morales-Rosales, Victor Gonzalez-Huitron, Mario Valenzuela Partida and Joan García
Energies 2022, 15(7), 2500; https://doi.org/10.3390/en15072500 - 29 Mar 2022
Cited by 37 | Viewed by 4634
Abstract
One-stage production of carbohydrate-enriched microalgae biomass in wastewater is a promising option to obtain biofuels. Understanding the interaction of water quality parameters such as nutrients, carbon, internal carbohydrates, and microbial composition in the culture is crucial for efficient operation and viable large-scale cultivation. [...] Read more.
One-stage production of carbohydrate-enriched microalgae biomass in wastewater is a promising option to obtain biofuels. Understanding the interaction of water quality parameters such as nutrients, carbon, internal carbohydrates, and microbial composition in the culture is crucial for efficient operation and viable large-scale cultivation. Bioprocess models are an essential tool for studying the simultaneous effect of complex factors on carbohydrate accumulation, optimizing the process, and reducing operational costs. In this sense, we use a dataset obtained from an empirical model that analyzed the accumulation of carbohydrates in a single process (simultaneous growth and accumulation) from real wastewater. In this experiment, there were no ideal conditions (limiting nutrient conditions), but rather these limitations are guaranteed by the operating conditions (hydraulic retention times/nutrient or carbon loads). Thus, the model integrates 18 variables that are affected and not only carbohydrates. The effect of these variables directly influences the accumulation of carbohydrates. Therefore, this paper analyzes artificial intelligence (AI) algorithms to develop a model to forecast biomass production in wastewater treatment systems. Carbohydrates were modeled using five artificial intelligence methods: (1) Artificial Neural Networks (ANNs), (2) Convolutional Neural Networks (CNN), (3) Long Short-Term Memory Network (LSTMs), (4) K-Nearest Neighbors (kNN), and (5) Random Forest (RF)). The AI methods allow learning how several components interact and if their combinations work faster than building the physical experiments over the same period of time. After comparing the five learning models, the CNN-1D model obtained the best results with an MSE (Mean Squared Error) = 0.0028. This result shows that the model adequately approximates the system’s dynamics. Full article
(This article belongs to the Special Issue Modeling, Optimization and Control in Algal Biotechnology)
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11 pages, 338 KiB  
Article
Advanced Computational Fluid Dynamics Study of the Dissolved Oxygen Concentration within a Thin-Layer Cascade Reactor for Microalgae Cultivation
by Karel Petera, Štěpán Papáček, Cristian Inostroza González, José María Fernández-Sevilla and Francisco Gabriel Acién Fernández
Energies 2021, 14(21), 7284; https://doi.org/10.3390/en14217284 - 3 Nov 2021
Cited by 8 | Viewed by 3739
Abstract
High concentration of dissolved oxygen within microalgae cultures reduces the performance of corresponding microalgae cultivation system (MCS). The main aim of this study is to provide a reliable computational fluid dynamics (CFD)-based methodology enabling to simulate two relevant phenomena governing the distribution of [...] Read more.
High concentration of dissolved oxygen within microalgae cultures reduces the performance of corresponding microalgae cultivation system (MCS). The main aim of this study is to provide a reliable computational fluid dynamics (CFD)-based methodology enabling to simulate two relevant phenomena governing the distribution of dissolved oxygen within MCS: (i) mass transfer through the liquid–air interface and (ii) oxygen evolution due to microalgae photosynthesis including the inhibition by the same dissolved oxygen. On an open thin-layer cascade (TLC) reactor, a benchmark numerical study to assess the oxygen distribution was conducted. While the mass transfer phenomenon is embedded within CFD code ANSYS Fluent, the oxygen evolution rate has to be implemented via user-defined function (UDF). To validate our methodology, experimental data for dissolved oxygen distribution within the 80 meter long open thin-layer cascade reactor are compared against numerical results. Moreover, the consistency of numerical results with theoretical expectations has been shown on the newly derived differential equation describing the balance of dissolved oxygen along the longitudinal direction of TLC. We argue that employing our methodology, the dissolved oxygen distribution within any MCS can be reliably determined in silico, and eventually optimized or/and controlled. Full article
(This article belongs to the Special Issue Modeling, Optimization and Control in Algal Biotechnology)
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28 pages, 3012 KiB  
Article
Hydrodynamics and Mass Transfer in a Concentric Internal Jet-Loop Airlift Bioreactor Equipped with a Deflector
by Radek Šulc and Jan Dymák
Energies 2021, 14(14), 4329; https://doi.org/10.3390/en14144329 - 18 Jul 2021
Cited by 5 | Viewed by 2963
Abstract
The gas–liquid hydrodynamics and mass transfer were studied in a concentric tube internal jet-loop airlift reactor with a conical bottom. Comparing with a standard design, the gas separator was equipped with an adjustable deflector placed above the riser. The effect of riser superficial [...] Read more.
The gas–liquid hydrodynamics and mass transfer were studied in a concentric tube internal jet-loop airlift reactor with a conical bottom. Comparing with a standard design, the gas separator was equipped with an adjustable deflector placed above the riser. The effect of riser superficial gas velocity uSGR on the total gas holdup εGT, homogenization time tH, and overall volumetric liquid-phase mass transfer coefficient kLa was investigated in a laboratory bioreactor, of 300 mm in inner diameter, in a two-phase air–water system and three-phase air–water–PVC–particle system with the volumetric solid fraction of 1% for various deflector clearances. The airlift was operated in the range of riser superficial gas velocity from 0.011 to 0.045 m/s. For the gas–liquid system, when reducing the deflector clearance, the total gas holdup decreased, the homogenization time increased twice compared to the highest deflector clearance tested, and the overall volumetric mass transfer coefficient slightly increased by 10–17%. The presence of a solid phase shortened the homogenization time, especially for lower uSGR and deflector clearance, and reduced the mass transfer coefficient by 15–35%. Compared to the gas–liquid system, the noticeable effect of deflector clearance was found for the kLa coefficient, which was found approx. 20–29% higher for the lowest tested deflector clearance. Full article
(This article belongs to the Special Issue Modeling, Optimization and Control in Algal Biotechnology)
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Review

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27 pages, 376 KiB  
Review
On-Line Monitoring of Biological Parameters in Microalgal Bioprocesses Using Optical Methods
by Ivo Havlik, Sascha Beutel, Thomas Scheper and Kenneth F. Reardon
Energies 2022, 15(3), 875; https://doi.org/10.3390/en15030875 - 25 Jan 2022
Cited by 45 | Viewed by 6574
Abstract
Microalgae are promising sources of fuels and other chemicals. To operate microalgal cultivations efficiently, process control based on monitoring of process variables is needed. On-line sensing has important advantages over off-line and other analytical and sensing methods in minimizing the measurement delay. Consequently, [...] Read more.
Microalgae are promising sources of fuels and other chemicals. To operate microalgal cultivations efficiently, process control based on monitoring of process variables is needed. On-line sensing has important advantages over off-line and other analytical and sensing methods in minimizing the measurement delay. Consequently, on-line, in-situ sensors are preferred. In this respect, optical sensors occupy a central position since they are versatile and readily implemented in an on-line format. In biotechnological processes, measurements are performed in three phases (gaseous, liquid and solid (biomass)), and monitored process variables can be classified as physical, chemical and biological. On-line sensing technologies that rely on standard industrial sensors employed in chemical processes are already well-established for monitoring the physical and chemical environment of an algal cultivation. In contrast, on-line sensors for the process variables of the biological phase, whether biomass, intracellular or extracellular products, or the physiological state of living cells, are at an earlier developmental stage and are the focus of this review. On-line monitoring of biological process variables is much more difficult and sometimes impossible and must rely on indirect measurement and extensive data processing. In contrast to other recent reviews, this review concentrates on current methods and technologies for monitoring of biological parameters in microalgal cultivations that are suitable for the on-line and in-situ implementation. These parameters include cell concentration, chlorophyll content, irradiance, and lipid and pigment concentration and are measured using NMR, IR spectrophotometry, dielectric scattering, and multispectral methods. An important part of the review is the computer-aided monitoring of microalgal cultivations in the form of software sensors, the use of multi-parameter measurements in mathematical process models, fuzzy logic and artificial neural networks. In the future, software sensors will play an increasing role in the real-time estimation of biological variables because of their flexibility and extendibility. Full article
(This article belongs to the Special Issue Modeling, Optimization and Control in Algal Biotechnology)
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