Microalgae In Photobioreactors Research Articles

Kinetics model with experimental validation for optimal microalgae generation in double-skin façades

Microalgae photobioreactors integrated into double-skin façades in cold climates enhance growth conditions for biomass generation, CO2 fixation, and O2 production, while reducing thermal loads. However, shading imposed by double-skin façade's assembly limits light interception for the photobioreactors. This study employs kinetics and shading models to estimate variations in biomass concentration within the light-constrained photobioreactor. To validate the accuracy and reliability of the model, the results were compared to experimental data collected by the authors from a full-scale constructed system. Biomass concentration was monitored using a developed RGB image-based monitoring technique. Direct search methods were employed to optimize the design and operational variables of the microalgae photobioreactors, with the goal of maximizing annual areal and volumetric productivities across 14 Canadian cities under various climatic conditions. Hooke-Jeeves algorithm was 65.6 % faster than Powell's conjugate direction method. This showed the system's adaptability by generating 1.3–3.0 kg/m2.year of biomass in different climates. The study established a framework for optimizing microalgae photobioreactors into façades to maximize biomass harvest.

Synergistic optimization of baffles and aeration to improve the Light/Dark cycle of microalgae photobioreactor for enhanced nitrogen removal performance: Computational fluid dynamics and experimental verification

Microalgae photobioreactor (PBR) is a kind of efficient wastewater treatment system for nitrogen removal. However, there is still an urgent need for process optimization of PBR. Especially, the synergistic effect and optimization of light and flow state poses a challenge. In this study, the computational fluid dynamics is employed for simulating the optimization of the number and length of the internal baffles, as well as the aeration rate of PBR, which in turn leads to the optimal growth of microalgae and efficient nitrogen removal. After optimization, the Light/Dark cycle of the reactor B is shortened by 51.6 %, and the biomass increases from 0.06 g/L to 3.94 g/L. In addition, the removal rate of NH4+-N increased by 106.0 % to 1.56 mg L−1 h−1. This work provides a feasible method for optimizing the design and operational parameters of PBR aiming the engineering application.

Multi-criteria analysis of the continuous operation of a membrane photobioreactor to treat sewage: Modeling and sensitivity analysis

Microalgae-based wastewater remediation is aligned with circular economy principals but successful large-scale facilities are scarce due to the limited knowledge of the interactions between all relevant variables and the scarce development of control and automation systems. Machine learning (ML) methods have the potential predict performance in microalgae photobioreactors, with the aim to optimize it. Some attempts have been performed under controlled lab conditions which prevents the direct application of their results to industrial scale. This study aims to develop ML models for the prediction of nutrient removal, biomass production and photosynthetic efficiency using a database obtained from 1.5-y operation of a pilot-scale membrane photobioreactor (MPBR) that treated sewage. A total of 14 inputs and 6 outputs were selected. Random forests (RF), boosted trees (BT), multilayer perceptron (MLP), support vector machine methods (SVM) were tested. The lowest prediction errors were obtained with the MLP model, allowing the developed tool to be used as an alternative to mechanistic models. Large ranges of data were used, considering the variability of factors in the diurnal cycle whose influence on the variability of the data is usually neglected. Using global sensitivity analysis (GSA), input-output relationships were verified, reflecting relevant number of variables showing significant values of Shapley Indices. Additionaly, partial dependence plots showed both linear and nonlinear depending on the selected inputs and outputs. Finally, using ML models, multi-criteria optimization of operating parameters was performed for two variants: a) optimization of operating parameters (HRT, SRT, and air flowrate (Fair)); and b) optimization of operating parameters and influent nutrient loads (HRT, SRT, Fair, nitrogen and phosphorus loading rates).

Produced water treatment by semi-continuous sequential bioreactor and microalgae photobioreactor

Produced water (PW) from oil and gas exploration adversely affects aquatic life and living organisms, necessitating treatment before discharge to meet effluent permissible limits. This study first used activated sludge to pretreat PW in a sequential batch reactor (SBR). The pretreated PW then entered a 13 L photobioreactor (PBR) containing Scenedesmus obliquus microalgae culture. Initially, 10% of the PW mixed with 90% microalgae culture in the PBR. After the exponential growth of the microalgae, an additional 25% of PW was added to the PBR without extra nutrients. This study reported the growth performance of microalgae in the PBR as well as the reduction in effluent’s total organic carbon (TOC), total dissolved solids (TDS), electrical conductivity (EC), and heavy metals content. The results demonstrated removal efficiencies of 64% for TOC, 49.8% for TDS, and 49.1% for EC. The results also showed reductions in barium, iron, and manganese in the effluent by 95, 76, and 52%, respectively.

Optimization models for photosynthetic bioenergy generation in building façades

Façade design optimization has a significant impact on promoting resilient solar harvesting methods in urban areas. This study introduces a new framework toward the optimal inclusion of microalgae photobioreactors in different façade geometries by developing a detailed chemical kinetics model aided by optimization techniques. Although algal bioreactive façades in recent literature are restricted to flat façades, the present study reveals that bioenergy generation can be enhanced by up to 29.6 % through optimized folded façades. Hooke-Jeeves algorithm demonstrates remarkable computational efficiency in optimizing such façades, achieving the optimal outcome with only 88 function evaluations. However, the Hooke-Jeeves algorithm has limitations when applied to free-form façades by displaying noticeable instability and reporting an optimal value approximately 40 % lower than Genetic Algorithms. Hooke-Jeeves algorithm has the possibility to conclude misleading outcomes as optimal points. This is mitigated by applying multiple initial points and verifying the outcomes with graphical optimization. In terms of energy performance, free-form façades have higher irradiation of up to 210 W/m2 compared to 180 W/m2 for flat façades. Optimal distribution of renewable modules on free-form façades increases energy generation by 14.3 %. South-facing façades produce the highest bioenergy at 2.4 W/m2, while west and east orientations generate the lowest at 0.8 W/m2.

Intensifying outdoor cultivation of oleaginous microalgae in photobioreactors for simultaneous wastewater bioremediation and low-cost production of renewable biodiesel feedstocks

Oleaginous microalgae are gaining increasing attention for simultaneous wastewater bioremediation and biofuel feedstock production. In this study, four oleaginous species of microalgae including Chlamydomonas sp. WWP, Scenedesmus sp. HP56, Haematococcus sp., and Nannochloropsis sp., were screened for their growth and lipid production at 30, 35, and 40 °C, under light intensity at 3, 4, and 5 klux. Among the strains screened, Haematococcus sp. grew best and gave comparable high growth at 30 °C and 35 °C (0.7–0.8 g/L) indicating its thermotolerant characteristic, and the optimal light intensity was found at 4 klux. Haematococcus sp. also grew well using unsterile secondary effluent from the seafood processing plant and produced high biomass (0.53–0.57 g/L) and lipids (0.17–0.19 g/L), especially in the column photobioreactor (CP). The repeated-batch outdoor cultivation mode showed that CP equipped with four-blade baffles was most efficient for the production of biomass (0.40–0.63 g/L) and lipids (0.15–0.30 g/L) during 4 cycles. The microalga also removed total nitrogen and total phosphorus from secondary effluent by 70.59% and 94.97%, respectively. The fatty acid compositions in microalgal lipids were C16-C18, especially palmitic acid, oleic acid, and linolenic acid, which show superior fuel properties, suggesting their promising use as biodiesel feedstocks. These strategies may have a significant impact not only on bioremediation of industrial effluent but also low-cost production of microalgae-based biodiesel feedstocks.

Performance evaluation of a two-phase anaerobic reactor coupled with microalgae photobioreactors for slaughterhouse wastewater treatment in Ethiopia

Performance evaluation of a two-phase anaerobic reactor coupled with microalgae photobioreactors for slaughterhouse wastewater treatment in Ethiopia

Microalgae cultivation using ammonia and carbon dioxide concentrations typical of pig barns

ABSTRACT While global population growth drives increased production efficiency in animal agriculture, there is a growing demand for environmentally friendly practices, particularly in reducing air pollutant emissions from concentrated animal feeding operations. This study explores the potential of cultivating microalgae in photobioreactors (PBRs) as an eco-friendly and cost-effective approach to mitigate NH3 and CO2 emissions from pig barns. Unlike traditional physicochemical mitigation systems, microalgae offer a renewable solution by converting pollutants into valuable biomass. The research focused on Scenedesmus dimorphus growth under typical NH3 and CO2 concentrations found in the indoor air of pig barns. Four NH3 (0, 12, 25, and 50 ppm) and four CO2 concentrations (350, 1200, 2350, and 3500 ppm) were tested using photobioreactors. Results showed a maximum specific growth rate of 0.83 d−1 with 12 ppm NH3 and 3500 ppm CO2. The dry biomass concentration was significantly higher (1.16 ± 0.08 g L−1; p < 0.01) at 25 ppm NH3 and 2350 ppm CO2 than other test conditions. S. dimorphus demonstrated the peak NH3 and CO2 fixation rates (23.8 ± 2.26 mg NH3 L−1 d−1 and 432.24 ± 41.09 mg CO2 L−1 d−1) at 25 ppm NH3 and 2350 ppm CO2. These findings support the feasibility of using algae to effectively remove air pollutants in pig barns, thereby improving indoor air quality.

Data-driven recurrent neural networks modeling of cyanobacteria growth in bubble columns reactors under sparging with CO2-enriched Air

Cultivating carbohydrate-enriched microalgae in photobioreactors is a recognized method for converting atmospheric carbon dioxide (CO2) into valuable resources, offering a solution to combat industrial CO2 emissions. Achieving global carbon neutrality by 2050 necessitates an annual reduction of approximately 1.4 gigatons of CO2. To address this challenge, we developed a machine learning-based black box model using data from the Institute of Advanced Materials for Sustainable Manufacturing at ITESM. This dataset encompasses hourly operational parameters collected over eleven months, including temperature, pressure, and flow rate. These parameters were essential for a microalgal cultivation experiment using the cyanobacterium Desertifilum tharense and CO2-enriched air. Two 100-L photobioreactor systems were employed. Leveraging the sequential dataset, we deployed a long short-term memory network (LSTM) to predict CO2 percentages at reactor outlets, indirectly indicating algae biomass production. Seven input variables were selected on the basis of their impact on system behavior. Using advanced machine learning time series mathematical models can boost process intensification tasks through advanced process optimization, simulation, and control goals.

Neural Networks for Monitoring Microalgae Biomass in Building Façades

The substantial impact of buildings on CO2 emissions and climate change highlights the urgent need to adopt sustainable measures. Microalgae photobioreactors have shown potential as a renewable feedstock for biomass generation and CO2 absorption. Conventional biomass prediction methods are usually laborious and time-intensive, and sacrifice biomass for measurement. This research addresses these challenges by developing a novel approach using Feed-forward Neural Networks and Convolutional Neural Networks for accurate biomass prediction. Feed-forward Neural Networks demonstrated a positive linear relationship between predicted and valid biomass values of 0.99 on average. Convolutional Neural Networks improved performance over time with an accuracy of up to 97%. This research advanced biomass prediction methodologies and supported optimizing photobioreactors performance for enhanced energy efficiency and biomass generation.

A Microalgae Photobioreactor System for Indoor Air Remediation: Empirical Examination of the CO2 Absorption Performance of Spirulina maxima in a NaHCO3-Reduced Medium

Microalgae-based photobioreactors (PBRs) have gained attention as a sustainable solution for indoor air quality (IAQ) control. This study investigates indoor CO2 absorption performance of Spirulina maxima (S. maxima) in NaHCO3-limited cultivation (standard: NaHCO3-free medium = 1:1 v/v%) of a lab-scale PBR system. Cultivation performance of three medium amendments (standard, 50% NaHCO3, and NaHCO3-free) was compared by observing cell growth for 30 days in a controlled environment. Empirical examinations were conducted to evaluate the algal CO2 uptake, and overall system performance in the culture volumes of 2, 4, and 7 L and natural indoor CO2 concentration of ~1100 ppm. We found CO2 was reduced by ~55%, in an air chamber of 0.064 m3, showing the greatest mitigation rate (~20%) on Day 4. Under a high concentration of CO2 (10,000 ppm), the CO2 levels were decreased up to ~90% before saturation. This research provides valuable insights into the development of S. maxima-activated IAQ control systems for airtight buildings.

Understanding regulation in complex environments: a route to enhance photosynthetic light-reactions in microalgae photobioreactors

Microalgae are recognized as a sustainable source of biomass to produce a wide range of bioproducts. To maximize the positive environmental impact and achieve economic competitiveness of microalgae-based products, it is however still essential to improve the biomass productivity during large-scale cultivation. Microalgae large-scale cultures are generally limited by light availability and thus the efficiency in conversion of radiation energy into biomass is a major factor impacting productivity. Natural light is a highly variable environmental parameter, and it constantly changes following seasons, time of day, and weather conditions. The artificial environment of large-scale microalgae cultures generates a further layer of complexity added to these natural light dynamics. In fact, because of biomass density and cell self-shading, light is unevenly distributed in the mass culture. Moreover, because of mixing, cells move between different parts of the volume, generating abrupt fluctuations in light exposure. Although microalgae evolved various regulatory mechanisms to cope with dynamic light conditions, these are not adapted to respond to the complex mixture of natural and artificial fluctuations commonly encountered in large-scale cultures, often causing reduction in photosynthetic efficiency. In the past years, genetic approaches to improve the light reactions of photosynthesis have been explored to optimise the composition and regulation of the photosynthetic machinery to large-scale cultivation. These approaches have shown promising results at the laboratory scale but have yet to be fully proven at the industrial scale. This can be explained by the fact that the complexity of the cultivation environment on microalgae photosynthesis and its impact on productivity is underestimated. This work aims for a systematic discussion on the complex role played by the growth environment in determining microalgae photosynthetic performances upon cultivation at industrial scale, with the objective of maximizing the impact of genetic modifications and ultimately fully realize the potential of microalgae for biomass productivity.

Active disturbance rejection control: an application to continuous microalgae photobioreactors

AbstractBACKGROUNDMathematical modelling is a widely employed approach for investigating the growth behaviour of microalgae. As a result, the development of model‐based controllers to regulate process variables has garnered increasing attention. However, despite the significant efforts invested in this area, control performance can be adversely affected by unmodelled dynamics and disturbances.RESULTSTwo active disturbance rejection controllers (ADRC) were designed to enable robust tracking of biomass concentration in continuous microalgae photobioreactors, with reduced reliance on the mathematical model of the system. The controllers were tuned to achieve a nonovershoot response and minimize settling time based on the culture's characteristics. Simulations were performed using optimal setpoints specific to each model. The results showcased a maximum output signal deviation of ±2.2%, ±7.8% and ± 7.62% for the Dunaliella tertiolecta, Isochrysis affinis galbana and Chlorella vulgaris models, respectively, regardless of the presence of simulated disturbances.CONCLUSIONSThe findings of this study significantly contribute to the advancement of the field of sustainable microalgae production. By introducing less dependent model‐based controllers, this research enhances the feasibility of implementing robust control strategies. These controllers require only knowledge of the equation system's order and the control gain function, simplifying the design process. This approach effectively addresses control performance degradation arising from unmodelled dynamics and disturbances. The ability to maintain desired process variables through ADRC controllers not only ensures improved control performance, but also supports the cultivation of specific microalgal species, when an accurate model is not available, thus promoting the overall progress and viability of microalgae biomass production. © 2023 Society of Chemical Industry.

An evaluation of light wavelengths, intensity and control for the production of microalgae in photobioreactors: a review

An evaluation of light wavelengths, intensity and control for the production of microalgae in photobioreactors: a review

Methodology for the fast direct estimation of spectral radiative transport properties in microalgae photobioreactors

Light distribution within photobioreactors is critical for the photosynthetic activity of microalgae in biotechnological processes. The radiative transport properties allow determining the radiant energy distribution, which is highly useful when designing, optimizing, and scaling photobioreactors. As radiation transport is strongly affected by process-dependent phenomena, simple, fast, and accurate in-situ estimation methodologies are of paramount importance. This paper presents a methodology for estimating microalgae cultures’ spectral radiative transport properties (i.e., the extinction coefficient, single-scattering albedo, and scattering phase function), considering measurements of incoming, transmitted, and dispersed energies by the microalgae culture within a modular spherical system. The experimental and numerical methods presented allow us to quickly and independently estimate the spectral radiative transport properties in a single setup. The methodology shows acceptable accuracy when estimating the spectral radiative transport properties of Chlorella vulgaris (400–700 nm). Thus, the proposed methodology has the potential for in-process monitoring and real-time analysis of light transport phenomena in photobioreactors.

Energy efficient glazing: A comparison of microalgae photobioreactor and Iranian Orosi window designs

Orosi is a traditional Iranian window which is made of colorful glasses embedded in wooden latticed frame. This window was designed in traditional courtyard houses located in central Iran and behaved as a passive strategy to control light intensity, provide privacy and aesthetics. However, this type of window is rarely used nowadays due to a lack of acoustic and thermal insulation and high construction costs. This paper explores the possibility of using double-glazed window with colored microalgae inspired by Orosi to improve its light performance and energy consumption compared to the traditional Orosi window design A series of experimental tests and a set of computer simulations are conducted to evaluate and compare the two designs. The experimental results showed that while the light intensity in the indoor space is approximately equal to the corresponding magnitude measured for the Orosi window, the inside temperature witnessed a small increase approximately 1° Celsuis. Likewise, relative humidity, which has a significant impact on thermal comfort perceived by occupants, increased from 25% to nearly 70% in the algae window. Based on results obtained, nearly 500 ppm of CO2 was absorbed due to the low height of the photobioreactor. Results of computer simulations indicated that the amount of annual energy consumption in terms of cooling electric energy, heating fuel energy and electric equipment within the two simulated models were similar. However, the best performance of the colored algae window in terms of UDI was observed when the microalgae concentration was between 40% and 50%.

A pilot scale study on the treatment of rare earth tailings (REEs) wastewater with low C/N ratio using microalgae photobioreactor

Microalgae appear to be a promising and ecologically safe way for nutrients removal from rare earth tailings (REEs) wastewater with CO2 fixation and added benefits of resource recovery and recycling. In this study, a pilot scale (50 L) co-flocculating microalgae photobioreactor (Ma-PBR) as constructed and operated for 140 days to treat REEs wastewater with low C/N ratio of 0.51–0.56. The removal rate of ammonia nitrogen (NH4+-N) reached 88.04% and the effluent residual concentration was as low as 9.91 mg/L that have met the Emission Standards of Pollutants from Rare Earths Industry (GB 26451–2011). Timely supplementation of trace elements was necessary to maintain the activity of microalgae and then prolonged the operation time. The dominant phyla in co-flocculating microalgae was Chlorophyta, the relative abundance of which was higher than 80%. Tetradesmus belonging to Chlorophyceae was the dominant genus with relative abundance of 80.35%. The results provided a practical support for the scaling-up of Ma-PBR to treat REEs wastewater.

The Influence of Forward Osmosis Module Configuration on Nutrients Removal and Microalgae Harvesting in Osmotic Photobioreactor

Microalgae have attracted great interest recently due to their potential for nutrients removal from wastewater, renewable biodiesel production and bioactive compounds extraction. However, one major challenge in microalgal bioremediation and the algal biofuel process is the high energy cost of separating microalgae from water. Our previous studies demonstrated that forward osmosis (FO) is a promising technology for microalgae harvesting and dewatering due to its low energy consumption and easy fouling control. In the present study, two FO module configurations (side-stream and submerged) were integrated with microalgae (C. vulgaris) photobioreactor (PBR) in order to evaluate the system performance, including nutrients removal, algae harvesting efficiency and membrane fouling. After 7 days of operation, both systems showed effective nutrients removal. A total of 92.9%, 100% and 98.7% of PO4-P, NH3-N and TN were removed in the PBR integrated with the submerged FO module, and 82%, 96% and 94.8% of PO4-P, NH3-N and TN were removed in the PBR integrated with the side-stream FO module. The better nutrients removal efficiency is attributed to the greater algae biomass in the submerged FO-PBR where in situ biomass dewatering was conducted. The side-stream FO module showed more severe permeate flux loss and biomass loss (less dewatering efficiency) due to algae deposition onto the membrane. This is likely caused by the higher initial water flux associated with the side-stream FO configuration, resulting in more foulants being transported to the membrane surface. However, the side-stream FO module showed better fouling mitigation by simple hydraulic flushing than the submerged FO module, which is not convenient for conducting cleaning without interrupting the PBR operation. Taken together, our results suggest that side-stream FO configuration may provide a viable way to integrate with PBR for a microalgae-based treatment. The present work provides novel insights into the efficient operation of a FO-PBR for more sustainable wastewater treatment and effective microalgae harvesting.

Computational fluid dynamics using finite volume method: A numerical model for Double Skin Façades with renewable energy source in cold climates

Double-skin façades often rely on proper cavity design, which unleashes a significant potential for inhabitation and renewable energy integration in cold climates. A co-model between Matlab and Ansys Fluent was developed to investigate the performance of a new typology for such façades. The steady-state energy balance equations for heat transfer were introduced with governing non-isothermal turbulent flow such that energy was conserved at each element of the assembly. Internal and mixed-mode ventilation schemes were utilized with multiple cavity widths and boundary conditions. The numerical model was validated against experimental measurements where the average temperature deviation was 1.9 °C. For the renewable energy source, the system utilized photovoltaics and microalgae modules which were brought in contact with the occupied space in order to counter the heat loss to the cold ambient. Results showed that double-skin façades were more energy efficient than an insulated double-glazed assembly regardless of the cavity ventilation scheme. The flow of heat transfer was reversed from 82.1 W/m2 loss to the surrounding to 35.7 W/m2 gain into the occupied space. Microalgae photobioreactors were found to have higher heating potential by generating 8.1% more than that of photovoltaics. Introducing ventilation into the cavity led to enhanced photovoltaics efficiency by 7.5% associated with a minimum drop in the cavity's temperature from 16.3 °C to −6.8 °C. Photobioreactors' functionability was enhanced by feeding water at a mean flow of 0.26 kg/s. The study recommended utilizing adjustable external air vents that can be regulated depending on the ambient temperature and the personal occupancy level in the cavity whenever its size fits.

Volatile Compounds of Algal Biomass Pyrolysis

The use and transformation of biomass into highly valuable products is a key element in circular economy models. The purpose of this research was to characterise the volatile compounds and the temperature at which they are emitted during the thermal decomposition by pyrolysis of algal biomass while looking at three different types: (A1) endemic microalgae consortium, (A2) photobioreactor microalgae consortium and (A3) Caribbean macroalgae consortium. Furthermore, the ultimate (CHON) and proximate (humidity, volatile solids and ashes) compositions of the algal biomass were determined. Some volatile species were identified as having potential industrial interest for use as precursors and intermediaries, such as commercially used aromatic compounds which if not suitably managed can be harmful to our health and the environment. It is concluded that the pyrolysis of algal biomass shows potential for the generation of valuable products. The information generated is useful, especially the temperature at which volatility occurs, in order to access the valuable compounds offered by the algal biomasses, and under the concept of biorefinery convert the issue of biomass disposal into a sustainable source of raw materials.