Chlorella vulgaris FSP-E cultivation in waste molasses: Photo-to-property estimation by artificial intelligence

Guo Yong Yew, Boon Keat Puah, Kit Wayne Chew, Sin Yong Teng, Pau Loke Show, The Hong Phong Nguyen

Research output: Contribution to journalArticlepeer-review

39 Scopus citations

Abstract

This progress of industry revolution, which involves reutilizing waste materials and simplifying complex procedures of analysis through artificial intelligent (AI), are the current interest in automated industries. There are two main objectives, firstly, the use of waste molasses from sugar mills as a cultivation medium for microalgae and nutrients extraction. The biomass in 15% of the molasses medium without carbon dioxide aeration during cultivation obtained the highest dry cell weight at 1206.43 mg/L. Protein content in the biomass of 10% molasses cultivation medium is 20.60%, which is higher compared to commercial mediums. Secondly, the exploitation of the deep colouration properties of molasses-cultivated microalgae, a novel photo-to-property estimation was performed by k-Nearest Neighbour (k-NN) algorithm through RGB model pixel raster in the images to rapidly determine the biomass concentration, nitrogen concentration and pH without use of tedious analytical processes. The k-value at 4 was studied in normalized Root-Mean-Square-Error (RMSE) for biomass concentration at 0.10, nitrate at 0.11, and pH at 0.02 for a sequence of days.

Original languageBritish English
Article number126230
JournalChemical Engineering Journal
Volume402
DOIs
StatePublished - 15 Dec 2020

Keywords

  • Artificial intelligence
  • Chlorella sp.
  • Image analyze algorithm
  • Microalgae
  • Microalgae cultivation
  • Molasses

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