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.