A mathematical model for carbon fixation and nutrient removal by an algal photobioreactor. (22nd October 2016)

• Record Type: Journal Article
• Authors: Al Ketife, Ahmed M.D.; Judd, Simon; Znad, Hussein
• Is Part Of: Chemical engineering science. Volume 153(2016)
• Page Start: 354
• Page End: 362
• Publication Date: 2016-10-22
• Subjects: Mathematical model -- Chlorella vulgaris -- Photobioreactor -- Light attenuation -- Growth kinetics; Chemical engineering -- Periodicals; Génie chimique -- Périodiques; Chemical engineering; Periodicals; Electronic journals; 660
• DOI: 10.1016/j.ces.2016.07.042
• Languages: English
• ISSNs: 0009-2509

Abstract

Abstract: A comprehensive mathematical modeling method for Chlorella vulgaris (Cv) has been developed to assess the influence of nutrient concentration (total nitrogen TN= 28–207 and total phosphorus TP= 6–8 mg L −1 ) and irradiation intensity ( I = 100–250 μE) at feed gas CO2 concentrations ( C c, g ) of 0.04-5%. The model encompasses gas-to-liquid mass transfer, algal uptake of carbon dioxide ( C d ), nutrient removal efficiency (RE for TN and TP), and the growth biokinetics of Cv with reference to the specific growth rate µ in d −1 . The model was validated using experimental data on the Cv species growth in an externally illuminated photobioreactor (PBR). The fitted parameters of the model were found to be in good agreement with experimental data obtained over the range of cultivation conditions explored. The mathematical model accurately reproduced the dynamic profiles of the algal biomass and nutrient (TN and TP) concentrations, and light attenuation at different input C c, g values. The proposed model may therefore be used for predicting algal growth and nutrient RE for this algal species, permitting both process optimization and scale-up. Highlights: A comprehensive model assessing PBR performance was developed. Only a single set of parameters were utilized in the proposed model. The model accurately predicted the dynamic nutrient removal in the medium. μmax and biomass loss rate ( Kd ) were the most sensitive parameters in the model. The model provides a powerful tool for process optimization and scale-up.