A Monitoring, Modeling and Decision Support System (DSS) for a Microalgae Production Plant based on Internet of Things Structure

Esposito, Serena; Cafiero, Antonio Francisco; Giannino, Francesco; Mazzoleni, Stefano; Diano, Marcello

doi:10.1016/j.procs.2017.08.316

Cited by 15 publications

(13 citation statements)

References 14 publications

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“…Since the growth and productivity of microalgae cultivation is so tightly correlated with light conditions, better engineering solutions must be developed regarding light distribution, mass transfer, and hydrodynamics (Posten, 2009; de Vree et al ., 2016; Esposito et al ., 2017; Araújo et al ., 2021). Operational costs are still too high and dependent on the local conditions and the metabolites produced for commercialization (Posten, 2009; Araújo et al ., 2021).…”

Section: Case‐study: Microalgae Productionmentioning

confidence: 99%

“…Microalgae cultivation can be a complex system with several biological, fluid dynamics, environmental, and nutritional conditions playing a crucial role in the productivity and viability of the industrial process. Therefore, the development of prediction models that allow in‐/online monitoring of substrates and products is fundamental for process control, enabling the possibility to take decisions and actions in real‐time (Esposito et al ., 2017).…”

Section: Case‐study: Microalgae Productionmentioning

confidence: 99%

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Perspectives of fluorescence spectroscopy for online monitoring in microalgae industry

Sá

Ferrer-Ledo

Gao

et al. 2022

Microbial Biotechnology

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Summary Microalgae industrial production is viewed as a solution for alternative production of nutraceuticals, cosmetics, biofertilizers, and biopolymers. Throughout the years, several technological advances have been implemented, increasing the competitiveness of microalgae industry. However, online monitoring and real‐time process control of a microalgae production factory still require further development. In this mini‐review, non‐destructive tools for online monitoring of cellular agriculture applications are described. Still, the focus is on the use of fluorescence spectroscopy to monitor several parameters (cell concentration, pigments, and lipids) in the microalgae industry. The development presented makes it the most promising solution for monitoring up‐and downstream processes, different biological parameters simultaneously, and different microalgae species. The improvements needed for industrial application of this technology are also discussed.

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Section: Case‐study: Microalgae Productionmentioning

confidence: 99%

Section: Case‐study: Microalgae Productionmentioning

confidence: 99%

Perspectives of fluorescence spectroscopy for online monitoring in microalgae industry

Sá

Ferrer-Ledo

Gao

et al. 2022

Microbial Biotechnology

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“…Meanwhile, the two different culturing system"s parameters [14], [17] can be measured and monitored through the Internet but not corrected. An IoTbased Spirulina [18] and microalgae [19]- [20] culturing system where its parameters can be monitored and corrected through the Internet were developed but the growth of the algae was measured using turbidity value [18] and microalgae biomass [19]- [20], respectively. The said limitations are summarized and listed as shown in Table I.…”

Section: Challenges and Limitations Of Existing Approachesmentioning

confidence: 99%

IoT-based Closed Algal Cultivation System with Vision System for Cell Count through ImageJ via Raspberry Pi

Tolentino¹,

Belarmino²,

Chan³

et al. 2021

IJACSA

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Spirulina platensis and other microalgae are now being considered in the different fields of research. It is due to the former's endless potential let alone, its high protein content. That is why, a stable demand of microalga production is now necessary. To achieve a high-protein spirulina, its cultivation using closed algal cultivation requires monitoring and maintenance of the bio-environmental factors and parameters affecting its growth to provide a stable and efficient production of microalgae. Meanwhile, laboratories that culture spirulina determine its cell count through manually counting the cells under a microscopea tedious work. This establishes the need to construct a device that cultivates spirulina with maintaining and cell counting capabilities. Thus, the proponents developed a culturing device that has three main systems. The first system is tasked to maintain the bio-environmental parameters, such as the pH level, temperature, and light. The second system on the other hand speaks of the cell counting system through ImageJ's Image Processing. This system verifies the cell count and growth through counting the filaments of the spirulina. Lastly, a corresponding Android application, which was developed using Firebase and Android Studio, displays real-time values of the culture's parameter. Results show that the device was able to stabilize its parameters. Also, red LEDs exhibited 28.43% higher approximate cell count than red-blue LEDs. With this, the quality of the Spirulina that was produced throughout the study was improved. Lastly, the use of ImageJ's image processing feature showed no significant difference with manual counting. It also releases the results multiple times faster than the manual counting. Thus, being a better alternative to manual cell counting.

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“…This model can be used as a tool for designing raceways before construction thereby saving time and costs. Modeling also makes it easier for raceway operators to evaluate the impact of various changes such as depth, temperature and flow velocity without reducing the productivity of microalgae biomass [12]. This paper describes the application of Cellular Automata modeling particularly the Biological Lattice Gas Cellular Automaton (BIO-LGCA) to model the optimal conditions and productivity of microalgae biomass production.…”

Section: Introductionmentioning

confidence: 99%

Cellular Automata Machine Modeling to obtain optimal conditions and productivity of microalgae biomass

Arkeman

Haris

Saefurahman

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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Microalgae is a promising source of superior biomass for sustainable food sources, high value products, and 3rd generation biofuel. The main factors affecting growth, carbon fixation and biomass production of microalgae are species type, CO2 supply, nutrients, light, temperature, pH and mixing. This paper discusses the precision farming approach in microalgae biomass production using the Cellular Automata Machine modeling, i.e., Probabilistic Cellular Automata. Cellular Automata is a decentralized computing model that provides a useful platform to model the complex dynamic systems such as growth of cells. The application of this method on a large-scale microalgae production system can simulate and model the characteristics of microalgae growth and biomass. In conclusion, the Biological Lattice Gas Cellular Automaton (BIO-LGCA) has been proposed as modeling tools to obtain the optimal conditions and maximum productivity in microalgae biomass production.

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A Monitoring, Modeling and Decision Support System (DSS) for a Microalgae Production Plant based on Internet of Things Structure

Cited by 15 publications

References 14 publications

Perspectives of fluorescence spectroscopy for online monitoring in microalgae industry

Perspectives of fluorescence spectroscopy for online monitoring in microalgae industry

IoT-based Closed Algal Cultivation System with Vision System for Cell Count through ImageJ via Raspberry Pi

Cellular Automata Machine Modeling to obtain optimal conditions and productivity of microalgae biomass

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