Monitoring algal growth rates and estimating microalgae concentration in photobioreactor systems are critical for optimizing production efficiency. Traditional methods—such as microscopy, fluorescence, flow cytometry, spectroscopy, and macroscopic approaches—while accurate, are often costly, time-consuming, labor-intensive, and susceptible to contamination or production interference. To overcome these limitations, this study proposes an automated, real-time, and cost-effective solution by integrating machine learning with image-based analysis. We evaluated the performance of Decision Trees (DTS), Random Forests (RF), Gradient Boosting Machines (GBM), and K-Nearest Neighbors (k-NN) algorithms using RGB color histograms extracted from images of Scenedesmus dimorphus cultures. Ground truth data were obtained via manual cell enumeration under a microscope and dry biomass measurements. Among the models tested, DTS achieved the highest accuracy for cell count prediction (R² = 0.77), while RF demonstrated superior performance for dry biomass estimation (R² = 0.66). Compared to conventional methods, the proposed ML-based approach offers a low-cost, non-invasive, and scalable alternative that significantly reduces manual effort and response time. These findings highlight the potential of machine learning–driven imaging systems for continuous, real-time monitoring in industrial-scale microalgae cultivation.

This research study investigates bubble and liquid circulation patterns in a vertical column photobioreactor (PBR) both experimentally as well as computationally using computational fluid dynamics (CFD). Dispersed gas-liquid flow in the rectangular bubble column PBR are modeled using Eulerian-Lagrangian approach. A low Reynolds number k-epsilon CFD model is used to describe the flow pattern near the wall. A flat surface bubble column PBR is used to achieve sufficient light penetration into the system. Bubble size distribution measurements were completed using a high-speed digital camera. Operating parameters, bubble flow patterns, and internal hydrodynamics of a bubble column reactor were studied, and the numerical simulations presented for the hydrodynamics in a bubble column PBR account for bubble phenomena that have not been sufficiently accounted for in previous research. Bubble size and shape affect the hydrodynamics as does bubble interaction with other bubbles (multiple bubbles in a flow versus single bubbles and wall effects on bubble(s) that are not symmetrical or bubbles not centered on the reactor cross-section). Understanding the bubble movement patterns will aid in predicting other design parameters like mass transfer (bubble to liquid and liquid to bubble), heat transfer (within the PBR and between the PBR and environment surrounding the PBR), and interaction forces inside the PBR. The computational results are validated with experimental data and from current literature.

This research investigates the placement of spargers on thermofluid effects within a column photobioreactor (PBR) using computational fluid dynamics (CFD). This study compares two configurations, each with three rows of spargers spaced at different widths: one with spargers spaced 7.62 cm apart and the other spaced 10.16 cm apart. These spargers are modeled in a PBR with overall dimensions of 34.29 cm in length, 15.25 cm in width, and 34.28 cm in height. The objective of this research is to predict the performance of PBRs using CFD, which can be used to improve the design of PBRs used to grow microalgae for biofuels and bioproducts.

The objective of this research is to predict how the placement of structural light guides within a column photobioreactor (PBR) affects the fluid mechanics and heat transfer of the system. The photobioreactor studied is modeled and analyzed using computational fluid dynamics (CFD). A bubble sparger provides carbon dioxide and mixing. Five different arrays and eight carbon dioxide volumetric flow rates are tested. Based on this setup, the bubble flow patterns, velocities, and temperature distributions are analyzed and the recommended placement of light guides is determined for predicting the growing conditions suitable for microalgae and other photosynthetic organisms.

Numerous technologies have been investigated for mitigating air pollutant emissions from swine barns. Among them, algal photobioreactors (PBRs) can remove and utilize air pollutants such as CO₂ and NH₃ from barn exhaust. However, a challenge to PBR operation is that it involves multiple system input parameters and output goals. A key question is then how to determine the appropriate CO₂ and NH₃ concentrations in this case. Conventional statistical methods are inadequate for handling this complex problem. Multi-criteria decision-making (MCDM) emerges as a practical methodology for comparison and can be utilized to rank different CO₂–NH₃ interactions based on their environmental and biological performance. By employing MCDM methods, producers can effectively control the ratio of CO₂ and NH₃ concentrations, enabling them to identify the optimal range of operating parameters for various housing types, ensuring efficient pollutant mitigation. In this study, a multi-criteria decision-making (MCDM) approach was employed to support operation management. Specifically, influent CO₂ and NH₃ concentrations were optimized for three scenarios (the best biological, environmental, and overall performance), using a combination of two MCDM techniques. This study is anticipated to facilitate the system analysis and optimization of algae-based phytoremediation processes.

This research investigates the effects of the sparger on flow patterns and heat transfer within a column photobioreactor (PBR) using computational fluid dynamics (CFD). This study compares two types of spargers: a porous membrane, which occupies the entire floor of the reactor, and a single sparger, which is located along the centerline of the PBR floor. The PBR is modeled using the Lagrangian–Eulerian approach. The objective of this research is to predict the performance of PBRs using CFD models, which can be used to improve the design of PBRs used to grow microalgae that are used to produce biofuels and bioproducts.