Spectroradiometric detection of competitor diatoms and the grazer Poteriochromonas in algal cultures

Non-destructive monitoring of microalgae biofilms

Biofilm-based cultivation systems are emerging as a promising technology for microalgae production. However, efficient and non-invasive monitoring routines are still lacking. Here, a protocol to monitor microalgae biofilms based on reflectance indices (RIs) is proposed. This framework was developed using a rotating biofilm system for astaxanthin production by cultivating Haematococcus pluvialis on cotton carriers. Biofilm traits such as biomass, astaxanthin, and chlorophyll were characterized under different light and nutrient regimes. Reflectance spectra were collected to identify the spectral bands and the RIs that correlated the most with those biofilm traits. Robust linear models built on more than 170 spectra were selected and validated on an independent dataset. Astaxanthin content could be precisely predicted over a dynamic range from 0 to 4% of dry weight, regardless of the cultivation conditions. This study demonstrates the strength of reflectance spectroscopy as a non-invasive tool to improve the operational efficiency of microalgae biofilm-based technology.

Real-Time Sensor Data Profile-Based Deep Learning Method Applied to Open Raceway Pond Microalgal Productivity Prediction

Microalgal biotechnology holds the potential for renewable biofuels, bioproducts, and carbon capture applications due to unparalleled photosynthetic efficiency and diversity. Outdoor open raceway pond (ORP) cultivation enables utilization of sunlight and atmospheric carbon dioxide to drive microalgal biomass synthesis for production of bioproducts including biofuels; however, environmental conditions are highly dynamic and fluctuate both diurnally and seasonally, making ORP productivity prediction challenging without time-intensive physical measurements and location-specific calibrations. Here, for the first time, we present an image-based deep learning method for the prediction of ORP productivity. Our method is based on parameter profile plot images of sensor parameters, including pH, dissolved oxygen, temperature, photosynthetically active radiation, and total dissolved solids. These parameters can be remotely monitored without physical interaction with ORPs. We apply the model to data we generated during the Unified Field Studies of the Algae Testbed Public-Private-Partnership (ATP3 UFS), the largest publicly available ORP data set to date, which includes millions of sensor records and 598 productivities from 32 ORPs operated in 5 states in the United States. We demonstrate that this approach significantly outperforms an average value based traditional machine learning method (R2 = 0.77 ≫ R2 = 0.39) without considering bioprocess parameters (e.g., biomass density, hydraulic retention time, and nutrient concentrations). We then evaluate the sensitivity of image and monitoring data resolutions and input parameter variations. Our results demonstrate ORP productivity can be effectively predicted from remote monitoring data, providing an inexpensive tool for microalgal production and operational forecasting.

On-Line Monitoring of Biological Parameters in Microalgal Bioprocesses Using Optical Methods

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.

Barriers to microalgal mass cultivation

Economically successful microalgal mass cultivation is dependent on overcoming several barriers that contribute to the cost of production. The severity of these barriers is dependent on the market value of the final product. These barriers prevent the commercially viable production of algal biofuels but are also faced by any producers of any algal product. General barriers include the cost of water and limits on recycling, costs and recycling of nutrients, CO₂ utilization, energy costs associated with harvesting and biomass loss due to biocontamination and pond crashes. In this paper, recent advances in overcoming these barriers are discussed.