A parametric logistic equation with light flux and medium concentration for cultivation planning of microalgae

A kinetic-assisted growth curve prediction method for Chlamydomonas reinhardtii incorporating transfer learning

Traditional predictions of microalgal growth states rely on empirical or easily implementable kinetic models, leading to significant biases and elevated cost. This study proposes a kinetic-assisted machine learning method for predicting the growth curve of microalgal biomass under small sample conditions. Firstly, a microalgae growth kinetic model is constructed based on the logistic model. A two-stage kinetic fitting strategy is specified to account for the light-dark ratio. The Box-Behnken method is employed for experimental design. Then, using Two-stage TrAdaboost.R2 algorithm, the kinetic model is utilized as the source domain, and the experimental design data serves as the target domain for training machine learning models. The results indicate that the proposed method outperforms a single machine learning model in terms of prediction and has the potential to rapidly estimate microalgal growth trends under different conditions and accurately predict harvested biomass, potentially reducing the need for laborious, expensive, and time-consuming laboratory trials.

Air-liquid interface cultivation of Navicula incerta using hollow fiber membranes

Microalgae cultivation in open ponds requires a large footprint, while most photobioreactors need improvement in the ratio of surface to volume and energy consumption. In this study, polyethersulfone (PES) and poly(vinylidene fluoride) (PVDF) hollow fiber membranes with a large surface area were rearranged into open-ended and dead-ended configurations to improve the air-liquid interface cultivation of Navicula incerta. N. incerta were successfully grown on the porous membrane surface with the nutrients circulating inside the lumen. Fourier-transform infrared spectra showed the accumulation of polysaccharides, proteins and humic acids. Hydrophilic polysaccharides reduced water contact angles on PES and PVDF membranes to 37.2 ± 2.6° and 55.7 ± 3.3°, respectively. However, the porosity of PES (80.1 ± 1.1%) and PVDF (61.3 ± 4.5%) membranes were not significantly affected even after cultivation and harvesting of N. incerta. Scanning electron images further confirmed that N. incerta, cell debris and extracellular organic matter accumulated on the membrane. With large pores and a hydrophobic surface, PVDF hollow fiber membranes offered a greater improvement in N. incerta cell growth rate compared to PES hollow fiber membranes despite using different configurations. In the dead-ended configuration, they even attained the greatest improvement in N. incerta growth rate, up to 54.0%. However, PES hollow fiber membranes only achieved improvement in harvesting efficiency within the range of 18.7-38.0% due to weak cell adhesion. PVDF hollow fiber membranes significantly promoted the growth of microalgae N. incerta through the air-liquid interface system, leading to potential applications in wastewater treatment.