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.

The Minus Approach Can Redefine the Standard of Practice of Drinking Water Treatment.

Chlorine-based disinfection for drinking water treatment (DWT) was one of the 20th century's great public health achievements, as it substantially reduced the risk of acute microbial waterborne disease. However, today's chlorinated drinking water is not unambiguously safe; trace levels of regulated and unregulated disinfection byproducts (DBPs), and other known, unknown, and emerging contaminants (KUECs), present chronic risks that make them essential removal targets. Because conventional chemical-based DWT processes do little to remove DBPs or KUECs, alternative approaches are needed to minimize risks by removing DBP precursors and KUECs that are ubiquitous in water supplies. We present the "Minus Approach" as a toolbox of practices and technologies to mitigate KUECs and DBPs without compromising microbiological safety. The Minus Approach reduces problem-causing chemical addition treatment (i.e., the conventional "Plus Approach") by producing biologically stable water containing pathogens at levels having negligible human health risk and substantially lower concentrations of KUECs and DBPs. Aside from ozonation, the Minus Approach avoids primary chemical-based coagulants, disinfectants, and advanced oxidation processes. The Minus Approach focuses on bank filtration, biofiltration, adsorption, and membranes to biologically and physically remove DBP precursors, KUECs, and pathogens; consequently, water purveyors can use ultraviolet light at key locations in conjunction with smaller dosages of secondary chemical disinfectants to minimize microbial regrowth in distribution systems. We describe how the Minus Approach contrasts with the conventional Plus Approach, integrates with artificial intelligence, and can ultimately improve the sustainability performance of water treatment. Finally, we consider barriers to adoption of the Minus Approach.

Revolutionizing Membrane Design Using Machine Learning-Bayesian Optimization.

Polymeric membrane design is a multidimensional process involving selection of membrane materials and optimization of fabrication conditions from an infinite candidate space. It is impossible to explore the entire space by trial-and-error experimentation. Here, we present a membrane design strategy utilizing machine learning-based Bayesian optimization to precisely identify the optimal combinations of unexplored monomers and their fabrication conditions from an infinite space. We developed ML models to accurately predict water permeability and salt rejection from membrane monomer types (represented by the Morgan fingerprint) and fabrication conditions. We applied Bayesian optimization on the built ML model to inversely identify sets of monomer/fabrication condition combinations with the potential to break the upper bound for water/salt selectivity and permeability. We fabricated eight membranes under the identified combinations and found that they exceeded the present upper bound. Our findings demonstrate that ML-based Bayesian optimization represents a paradigm shift for next-generation separation membrane design.

The Selective Use of Hypochlorite to Prevent Pond Crashes for Algae-Biofuel Production.

Although algae-biofuels have many advantages including high areal productivity, algae can be preyed upon by amoebas, protozoans, ciliates, and rotifers, particularly in open pond systems. Thus, these higher organisms need to be controlled. In this study, Chlorella kessleri was used as the algal culture and Brachionus calyciflorus as the source of predation. The effect of sodium hypochlorite (bleach) was tested with the goal of totally inhibiting the rotifer while causing minor inhibition to the alga. The 24-hr LC(50) for B. calyciflorus in spring water was 0.198 mg Cl/L while the 24-hr LC(50) for C. kessleri was 0.321 mg Cl/L. However, chlorine dissipates rapidly as the algae serves as reductant. Results showed a chlorine dosage between 0.45 to 0.6 mg Cl/L and a dosing interval of two hours created the necessary chlorine concentrations to inhibit predation while letting the algae grow; thus giving algae farmers a tool to prevent pond crashes.

Use of Copper to Selectively Inhibit Brachionus calyciflorus (Predator) Growth in Chlorella kessleri (Prey) Mass Cultures for Algae Biodiesel Production.

A single Brachionus rotifer can consume thousands of algae cells per hour causing an algae pond to crash within days of infection. Thus, there is a great need to reduce rotifers in order for algal biofuel production to become reality. Copper can selectively inhibit rotifers in algae ponds, thereby protecting the algae crop. Differential toxicity tests were conducted to compare the copper sensitivity of a model rotifer-B. calyciflorus and an alga, C. kessleri. The rotifer LC50 was <0.1 ppm while the alga was not affected up to 5 ppm Cu(II). The low pH of the rotifer stomach may make it more sensitive to copper. However, when these cultures were combined, a copper concentration of 1.5 ppm was needed to inhibit the rotifer as the alga bound the copper, decreasing its bioavailability. Copper (X ppm) had no effect on downstream fatty acid methyl ester extraction.

The Use of the Schizonticidal Agent Quinine Sulfate to Prevent Pond Crashes for Algal-Biofuel Production.

Algal biofuels are investigated as a promising alternative to petroleum fuel sources to satisfy transportation demand. Despite the high growth rate of algae, predation by rotifers, ciliates, golden algae, and other predators will cause an algae in open ponds to crash. In this study, Chlorella kessleri was used as a model alga and the freshwater rotifer, Brachionus calyciflorus, as a model predator. The goal of this study was to test the selective toxicity of the chemical, quinine sulfate (QS), on both the alga and the rotifer in order to fully inhibit the rotifer while minimizing its impact on algal growth. The QS LC50 for B. calyciflorus was 17 µM while C. kessleri growth was not inhibited at concentrations <25 µM. In co-culture, complete inhibition of rotifers was observed when the QS concentration was 7.7 µM, while algal growth was not affected. QS applications to produce 1 million gallons of biodiesel in one year are estimated to be $0.04/gallon or ~1% of Bioenergy Technologies Office's (BETO) projected cost of $5/gge (gallon gasoline equivalent). This provides algae farmers an important tool to manage grazing predators in algae mass cultures and avoid pond crashes.

Effect of centrifugation on water recycling and algal growth to enable algae biodiesel production.

The latest research shows that algal biofuels, at the production levels mandated in the Energy Independence and Security Act of 2007, will place significant demands on water and compete with agriculture meant for food production. Thus, there is a great need to recycle water while producing algal biofuels. This study shows that when using a synthetic medium, soluble algal products, bacteria, and other inhibitors can be removed by centrifugation and enable water recycling. Average water recovery reached 84% and water could be recycled at least 10 times without reducing algal growth.