A new model to analyze the temperature effect on the microalgae performance at large scale raceway reactors

Process design and economic analysis for the production of microalgae from anaerobic digestates in open raceway ponds

A model based framework was established for large scale assessment of microalgae production using anaerobically digested effluent considering varied climatic parameters such as solar irradiance and air temperature. The aim of this research was to identify the optimum monthly average culture depth operation to minimize the cost of producing microalgae grown on anaerobic digestion effluents rich in ammoniacal nitrogen with concentration of 248 mg L-1. First, a productivity model combined with a thermal model was developed to simulate microalgae productivity in open raceway ponds as a function of climatic variables. Second, by combining the comprehensive open pond model with other harvesting equipment, the final techno economic model was developed to produce a microalgae product with 20 wt% biomass content and treated water with <1 mg L-1 ammoniacal nitrogen. The optimization approach on culture depth for outdoor open raceway ponds managed to reduce the cost of microalgae production grown in anaerobic digested wastewater up to 16 %, being a suitable solution for the production of low cost microalgae (1.7 AUD kg-1 dry weight) at possible scale of 1300 t dry weight microalgae yr-1.

Microalgal biorefineries: Advancement in machine learning tools for sustainable biofuel production and value-added products recovery

The microalgae can be converted into biofuels, biochemicals, and bioactive compounds in a biorefinery. Recently, designing and executing more viable and sustainable biofuel production from microalgal biomass is one of the vital challenges in the development of biorefinery. Scalable cultivation of microalgae is mandatory for commercializing and industrializing the biorefinery. The intrinsic complication in cultivation of microalgae is the physiological and operational factors that renders challenging impact to enable a smooth and profitable operation. However, this aim can only be successful via a simulation prospect. Machine learning tools provides advanced approaches for evaluating, predicting, and controlling uncertainties in microalgal biorefinery for sustainable biofuel production. The present review provides a critical evaluation of the most progressing machine learning tools that validate a potential to be employed in microalgal biorefinery. These tools are highly potential for their extensive evaluation on microalgal screening and classification. However, the application of these tools for optimization of microalgal biomass cultivation in industries in order to increase the biomass production, is still in its initial stages. Integrated hybrid machine learning tools can aid the industries to function efficiently with least resources. Some of the challenges, and perspectives of machine learning tools are discussed. Besides, future prospects are also emphasized. Though, most of the research reports on machine learning tools are not appropriate to gather generalized information, standard protocols and strategies must be developed to design generalized machine learning tools. On a whole, this review offers a perspective information about digitalized microalgal exploitation in a microalgal biorefinery.

Artificial intelligence and machine learning tools for high-performance microalgal wastewater treatment and algal biorefinery: A critical review

The increased water scarcity, depletion of freshwater resources, and rising environmental awareness are stressing for the development of sustainable wastewater treatment processes. Microalgae-based wastewater treatment has resulted in a paradigm shift in our approach toward nutrient removal and simultaneous resource recovery from wastewater. Wastewater treatment and the generation of biofuels and bioproducts from microalgae can be coupled to promote the circular economy synergistically. A microalgal biorefinery transforms microalgal biomass into biofuels, bioactive chemicals, and biomaterials. The large-scale cultivation of microalgae is essential for the commercialization and industrialization of microalgae biorefinery. However, the inherent complexity of microalgal cultivation parameters regarding physiological and illumination parameters renders it challenging to facilitate a smooth and cost-effective operation. Artificial intelligence (AI)/machine learning algorithms (MLA) offer innovative strategies for assessing, predicting, and regulating uncertainties in algal wastewater treatment and biorefinery. The current study presents a critical review of the most promising AI/MLAs that demonstrate a potential to be applied in microalgal technologies. The most commonly used MLAs include artificial neural networks, support vector machine, genetic algorithms, decision tree, and random forest algorithms. Recent developments in AI have made it possible to combine cutting-edge techniques from AI research fields with microalgae for accurate analysis of large datasets. MLAs have been extensively studied for their potential in microalgae detection and classification. However, the ML application in microalgal industries, such as optimizing microalgae cultivation for increased biomass productivity, is still in its infancy. Incorporating smart AI/ML-enabled Internet of Things (IoT) based technologies can help the microalgal industries to operate effectively with minimum resources. Future research directions are also highlighted, and some of the challenges and perspectives of AI/ML are outlined. As the world is entering the digitalized industrial era, this review provides an insightful discussion about intelligent microalgal wastewater treatment and biorefinery for researchers in the field of microalgae.

Process engineering strategy for large scale outdoor cultivation of Tetradesmus obliquus CT02 coupled with pH guided CO2 feeding

A novel CO₂ tolerant microalga Tetradesmus obliquus CT02, was previously evaluated to be a suitable bio refinery platform for synthesis of bioactive molecules, biodiesel, and biofertilizer. In the present study, a process engineering strategy was developed targeting improved growth performance of the strain at large scale under fluctuating outdoor environmental conditions. The strategy relies on maintaining pH of the culture at its optimal value via cascade control with CO₂ feeding. The strategy was developed at laboratory scale bubble column photobioreactor under diurnal variation of simulated sunlight intensity and was further validated through growth performance of the strain under outdoor conditions in a 100 L airlift bioreactor. Under laboratory condition, 53.3% and 85.16% improvement in biomass concentration (1.87 g L^-1) and productivity (114.8 mg L^-1 day^-1) was achieved as compared to the uncontrolled pH, respectively. The strategy demonstrated a significant improvement in biomass concentration and productivity by 225.7% and 121.6% respectively, compared to the pH uncontrolled batch, even under outdoor fluctuating environmental condition.

Biorefinery Approach Applied to the Production of Food Colourants and Biostimulants from Oscillatoria sp

Simple Summary

In this study, a biorefinery based on Oscillatoria sp. is developed to produce high-value compounds such as C-phycocyanin, used in food colourant applications, and biostimulants, used in agriculture-related applications. The results confirm that C-phycocyanin concentrations ranging from 22 to 106 mg/L produce colours similar to commercial products; moreover, the safety of the extracted C-phycocyanin was confirmed through toxicity tests. The leftover biomass was confirmed as a biostimulant, with the results confirming a relevant auxin-like positive effect. Finally, an economic analysis was conducted to evaluate different scenarios, with results confirming this as the best scenario from an economic standpoint.

Abstract

In this study, a biorefinery based on Oscillatoria sp. is developed to produce high-value compounds such as C-phycocyanin, used in food colourant applications, and biostimulants, used in agriculture-related applications. First, the Oscillatoria biomass production was optimized at a pilot scale in an open raceway reactor, with biomass productivities equivalent to 52 t/ha·year being achieved using regular fertilizers as the nutrient source. The biomass produced contained 0.5% C-phycocyanins, 95% of which were obtained after freeze–thawing and extraction at pH 6.5 and ionic strength (FI) 100 mM, with a purity ratio of 0.71 achieved in the final extract. This purity ratio allows for use of the extract directly as a food colourant. Then, the extract’s colourant capacity on different beverages was evaluated. The results confirm that C-phycocyanin concentrations ranging from 22 to 106 mg/L produce colours similar to commercial products, thus avoiding the need for synthetic colourants. The colour remained stable for up to 12 days. Moreover, the safety of the extracted C-phycocyanin was confirmed through toxicity tests. The waste biomass was evaluated for use as a biostimulant, with the results confirming a relevant auxin-like positive effect. Finally, an economic analysis was conducted to evaluate different scenarios. The results confirm that the production of both C-phycocyanin and biostimulants is the best scenario from an economic standpoint. Therefore, the developed biomass processing scheme provides an opportunity to expand the range of commercial applications for microalgae-related processes.

How Heat Transfer Indirectly Affects Performance of Algae-Bacteria Raceways

Oxygenation in wastewater treatment leads to a high energy demand. High-rate algal-bacterial ponds (HRABP) have often been considered an interesting solution to reduce this energy cost, as the oxygen is provided by microalgae during photosynthesis. These complex dynamic processes are subject to solar fluxes and consequently permanent fluctuations in light and temperature. The process efficiency therefore highly depends on the location and the period of the year. In addition, the temperature response can be strongly affected by the process configuration (set-up, water depth). Raised pilot-scale raceways are typically used in experimental campaigns, while raceways lying on the ground are the standard reactor configuration for industrial-scale applications. It is therefore important to assess what the consequences are for the temperature patterns of the different reactor configurations and the water levels. The long-term validated algae-bacteria (ALBA) model was used to represent algae-bacteria dynamics in HRABPs. The model was previously validated over 600 days of outdoor measurements, at two different locations and for the four seasons. However, the first version of the model, like all the existing algae-bacteria models, was not fully predictive, since, to be run, it required the measurement of water temperature. The ALBA model was therefore updated, coupling it with a physical model that predicts the temperature evolution in the HRABP. A heat transfer model was developed, and it was able to accurately predict the temperature during the year (with a standard error of 1.5 ∘C). The full predictive model, using the temperature predictions, degraded the model's predictive performances by less than 3%. N2O predictions were affected by ±7%, highlighting the sensitivity of nitrification to temperature The temperature response for two different process configurations were then compared. The biological process can be subjected to different temperature dynamics, with more extreme temperature events when the raceway does not lie on the ground and for thinner depths. Such a situation is more likely to lead to culture crashes.

A seasonal simulation approach for culture depth influence on the temperature for different characterized microalgae strains

Irradiance and temperature are among the most important variables that affect microalgae growth, being both difficult to control in outdoor raceway reactors utilized for large-scale production of microalgae biomass. They are mainly a function of the location of the reactors, thus, producing certain strains of microalgae in inappropriate places conduces to the failure of the systems. To be able to determine important parameters of any microalgae strains on the performance of the culture, such as the influence of irradiance and temperature, is a powerful tool in decision-making processes. In addition, whatever the strain and location, operation strategies must be defined for each specific case, such as the imposed dilution rate and culture depth, both influencing the light availability and temperature of the culture as major variables determining the biomass productivity. In this paper, a simulation-based methodology is proposed to establish the influence of season and culture depth on the 1-year age irradiance and temperature of the culture, and thus on the biomass productivity of different microalgae strains. Up to five of the most frequently produced strains, such as Spirulina platensis, Chlorella vulgaris, Nannochloropsis gaditana, Isochrysis galbana and Scenedesmus almeriensis have been considered. The challenge is to develop an easy-to-manage decision-making tool for the optimal design and operation of large-scale microalgae facilities. Especially, dates for microalgae production and culture depth at which the reactors must be operated will be provided, being valid for any microalgae strain. The proposed methodology will largely contribute to the risk of investment in this field, then to enlarge the relevance of the microalgae production industry. This article is protected by copyright. All rights reserved.