Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/163734
Title: Artificial intelligence model for monitoring biomass growth in semi-batch Chlorella vulgaris cultivation
Authors: Peter, Angela Paul
Chew, Kit Wayne
Pandey, Ashok
Lau, Sie Yon
Rajendran, Saravanan
Ting, Huong Yong
Munawaroh, Heli Siti Halimatul
Phuong, Nguyen Van
Show, Pau Loke
Keywords: Engineering::Chemical engineering
Issue Date: 2023
Source: Peter, A. P., Chew, K. W., Pandey, A., Lau, S. Y., Rajendran, S., Ting, H. Y., Munawaroh, H. S. H., Phuong, N. V. & Show, P. L. (2023). Artificial intelligence model for monitoring biomass growth in semi-batch Chlorella vulgaris cultivation. Fuel, 333(Part 2), 126438-. https://dx.doi.org/10.1016/j.fuel.2022.126438
Journal: Fuel
Abstract: There is a great demand for a clean, economical, and long-term energy source, due to the depletion of fossil fuels. Large-scale production of microalgae biomass for biofuel production is likely attributable to several challenges, including the high cost of photobioreactors, the need for a sustainable medium for optimum development, and time-consuming algal growth monitoring techniques. Firstly, the research novelty aims at improving the strategy of recycling culture media for semi-batch cultivation of Chlorella vulgaris. Two cycles were performed with varying amounts of recycled medium replacement to evaluate algal growth and biochemical content. As compared to all other culture ratio combinations, the mixing ratio of recycled medium to fresh medium is at 40 % (40RB) combination yielded the greatest biomass growth (4.52 g/L), lipid (317.40 mg/g), protein (280.57 mg/g), and carbohydrate (451.37 mg/g) content. Next, custom vision was applied to Chlorella vulgaris maturing stages, and a unique digital architecture framework was developed. The iteration model delivers result interpretation with an accuracy of more than 92 % of every data set based on the trained Model Performance.
URI: https://hdl.handle.net/10356/163734
ISSN: 0016-2361
DOI: 10.1016/j.fuel.2022.126438
Rights: © 2022 Elsevier Ltd. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:CCEB Journal Articles

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