Browsing by Author "Liyanaarachchi, V. C"

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Co-production of fucoxanthin, docosahexaenoic acid (DHA) and bioethanol from the marine microalga Tisochrysis lutea
(Elsevier, 2021-12-01) Premaratne, M; Liyanaarachchi, V. C; Nimarshana, P. H. V; Ariyadasa, T. U; Malik, A; Attalage, R. A
The marine microalga Tisochrysis lutea is renowned for its ability to synthesize fucoxanthin and docosahexaenoic acid (DHA), which are nutritionally valuable high-value compounds. Although numerous studies in literature have assessed fucoxanthin and DHA production by T. lutea, very few studies have evaluated the feasibility of comprehensively utilizing biomass for co-production of these metabolites within the framework of biorefineries. To this end, the current study focused on the synthesis of fucoxanthin and DHA by cultivation of T. lutea under two different initial nitrate concentrations (1x: 882 µM, 3x: 2,646 µM) and three different light intensities (LL: 50 µmol/m2/s; ML: 100 µmol/m2/s; HL: 150 µmol/m2/s). The maximum fucoxanthin yield of 8.80 ± 0.30 mg/L (14.43 ± 0.52 mg/g) and DHA yield of 7.08 ± 0.02 mg/L (11.90 ± 0.14 mg/g) were achieved in the 3x HL culture at the end of 16 days of cultivation. Thereafter, a novel process of biphasic solvent extraction using ethanol/n-hexane/water (10:9:1 v/v/v) was utilized for co-extraction 97.96 ± 0.54% fucoxanthin and 74.11 ± 1.49% DHA from 3x HL biomass, and products were separated into two fractions. Fermentation of the residual biomass obtained from co-extraction resulted in a bioethanol yield of 48.49 ± 0.58 mg/g. Accordingly, the current study demonstrated the potential of T. lutea as a feedstock for biorefineries.
Embargo
Development of an artificial neural network model to simulate the growth of microalga Chlorella vulgaris incorporating the effect of micronutrients
(Elsevier, 2020-03-20) Liyanaarachchi, V. C; Nishshanka, G. K. S. H; Nimarshana, P. H.V; Ariyadasa, T. U; Attalage, R. A
Artificial neural network (ANN) models can be trained to simulate the dynamic behavior of biological systems. In the present study, an ANN model was developed upon multilayer perceptron neural network architecture with 23-20-1 configuration to predict the cell concentration of microalga Chlorella vulgaris at a given time. Irradiance level, photoperiod, temperature, air flow rate, CO2 percentage of the air stream, initial cell concentration, cultivation time and the nutrient concentrations of the media were considered as the input variables of the model. Resilient backpropagation learning algorithm was used to train the model by means of 484 experimental data belonging to four studies. Bias and accuracy factors of the developed model fall into the range of 0.95–1.11 indicating the model has an excellent prediction ability. Parity plot showed a good agreement between the predicted and experimental values with R2 = 0.98. Relative importance of the inputs was evaluated using Garson’s algorithm. The results of the study indicated that CO2 supply had the highest impact on the growth of C. vulgaris within the selected range of input parameters. Among macronutrients and micronutrients, highest influence was demonstrated by nitrogen and copper respectively.