Industrial waste disrupts the natural production of
microalgae cultures. Cultivation of microalgae in a controlled
environment highly results to biomass with lower contamination
necessary as high-valued economic product. In response to the
emerging challenges of sustainable energy production, the
integration of computational intelligence and biosystems
engineering is considered as an open research area. In this study,
Chlorella vulgaris microalgae were cultivated in BG-11 growth
medium on three customized surface-mount light bioreactors that
are equipped with digital camera for growth monitoring in terms
of accumulated biomass surface area and color reflectance
intensity via IoT. Feature-based machine learning models
predicted microalgae growth area in terms of water temperature,
pH level and turbidity, and light intensity. Microalgae cultures
were exposed to combinations of white artificial light source of
2000 ± 1000 lux and water temperature of 27 ± 5°C using Peltier
plate to discriminate biomass growth within a 30-day cultivation
period. A total of nine environmental conditions were employed to
clearly discriminate the impacts of environmental stressors to
microalgae growth. Combined neighborhood component analysis
and ReliefF was used to select high impact color features of C, Ye,
M, H, and S with biomass area. Electromagnetism-like
mechanism optimized-RBNN bested RNN and generalized
processing regression with R2
of 0.985 and RMSE of 6.262. There
is also considerable growth in biomass surface area for certain
combinations of light intensity and water temperature (2125 ± 625
lux and 28.75 ± 3.25°C), and turbidity and water pH
concentrations (3.85 ± 0.15 NTU and 8.025 ± 0.775). However, the
photobioreactor with 27°C and 2000 lux exposure is considered
having the exact optimum controlled environment condition in
cultivating Chlorella vulgaris based on the generated growth in
biomass surface area of 38.314%. This developed intelligent
system is scalable for seamless microalgae production of any
strands for renewable energy resource.