A Novel Polyculture Growth Model of Native Microalgal Communities to Estimate Biomass Productivity for Biofuel Production

Abstract

Native polyculture microalgae is a promising scheme to produce microalgal biomass as biofuel feedstock in an open raceway pond. However, predicting biomass productivity of native polycultures microalgae is incredibly complicated. Therefore, developing polyculture growth model to forecast biomass yield is indispensable for commercial-scale production. This research aims to develop a polyculture growth model for native microalgal communities in the Minamisoma algae plant and to estimate biomass and biocrude oil productivity in a semicontinuous open raceway pond.The model was built based on monoculture growth of polyculture species and it is later formulated using species growth, polyculture factor (k value ), initial concentration, light intensity, and temperature. In order to calculate species growth, a simplified Monod model was applied. In the simulation, 115 samples of the 2014-2015 field dataset were used for model training, and 70 samples of the 2017 field dataset were used for model validation. The model simulation on biomass concentration showed that the polyculture growth model with k value had a root-mean-square error of 0.12, whereas model validation provided a better result with a root-mean-square error of 0.08. Biomass productivity forecast showed maximum productivity of 18.87 g/m 2 /d in June with an annual average of 13.59 g/m 2 /d. Biocrude oil yield forecast indicated that hydrothermal liquefaction process was more suitable with a maximum productivity of 0.59 g/m 2 /d compared with solvent extraction which was only 0.19 g/m 2 /d.With satisfactory root-mean-square errors less than 0.3, this polyculture growth model can be applied to forecast the productivity of native microalgae.

Stronger together: A workflow to design new fish polycultures

“…For example, species can be co-farmed in different ratios (e.g., 16,137 ) and using different management practices, 138,139 which can change the combination's feasibility or potential benefits of polyculture. This places a premium on optimising system parameters, which could be attempted empirically, but a model-assisted approach is preferable (for a similar strategy for crops, see, 140,141 and for aquaculture, see 142,143 ). The prototyping stage thus first develops an initial prototype of the polyculture that has the same characteristics as those of the bioassays (Step 2), before modelling the functioning of the initial prototype to identify which parameters to optimise in the next version.…”