title: Laser reflectance measurement for the online monitoring of Chlorella sorokiniana biomass concentration.
abstract: Fast and reliable methods to determine biomass concentration are necessary to facilitate the large scale production of microalgae. A method for the rapid estimation of Chlorella sorokiniana biomass concentration was developed. The method translates the suspension particle size spectrum gathered though laser reflectance into biomass concentration by means of two machine learning modelling techniques. In each case, the model hyper-parameters were selected applying a simulated annealing algorithm. The results show that dry biomass concentration can be estimated with a very good accuracy (R2=0.87). The presented method seems to be suited to perform fast estimations of biomass concentration in suspensions of microalgae cultivated in moderately turbid media with tendency to aggregate.
authors.affiliation: Chemical Engineering Department, Complutentse University of Madrid, Faculty of Chemical Sciences, Avda. Complutense s/n, Madrid 28040, Spain. Electronic address: plopezex@ucm.es.
authors.forename: Patricio
authors.initials: P
authors.lastname: López Expósito
authors.affiliation: Chemical Engineering Department, Complutentse University of Madrid, Faculty of Chemical Sciences, Avda. Complutense s/n, Madrid 28040, Spain.
authors.forename: Angeles
authors.initials: A
authors.lastname: Blanco Suárez
authors.affiliation: Chemical Engineering Department, Complutentse University of Madrid, Faculty of Chemical Sciences, Avda. Complutense s/n, Madrid 28040, Spain.
authors.forename: Carlos
authors.initials: C
authors.lastname: Negro Álvarez
mesh_terms: D018533:Biomass; D001709:Biotechnology / Q000379:methods; D002138:Calibration; D002478:Cells, Cultured; D002708:Chlorella / Q000737:chemistry* / Q000254:growth & development*; D004784:Environmental Monitoring / Q000379:methods; D005427:Flocculation; D007834:Lasers; D009862:Online Systems; D058433:Photobioreactors; D062065:Wastewater
chemical_list: D062065:Wastewater
keywords: Bioprocess monitoring; Cell aggregates; Fast estimation dry biomass concentration; Machine learning regression; On-line monitoring microalgal cultures