Process intensification for the enhancement of growth and chlorophyll molecules of isolated Chlorella thermophila: A systematic experimental and optimization approach

In our current work, we have optimized six physicochemical parameters (light intensity, light period, pH, inoculum size, culture period, and salt concentration) toward growth and chlorophyll synthesis using isolated fresh water microalgae Chlorella thermophila [contains ∼6% (w/w on dry biomass basis...

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Published inPreparative biochemistry & biotechnology Vol. 53; no. 6; pp. 634 - 652
Main Authors Sarkar, Sreya, Sarkar, Sambit, Bhowmick, Tridib Kumar, Gayen, Kalyan
Format Journal Article
LanguageEnglish
Published England Taylor & Francis 03.07.2023
Taylor & Francis Ltd
Subjects
Algae
algorithms
Artificial neural network
Artificial neural networks
Biofuels
Biomass
Biosynthesis
Biotechnology
carbon
Carbon sources
Chlorella
Chlorella thermophila
Chlorophyll
experimental design
Fresh water
freshwater
genetic algorithm
Genetic algorithms
Inoculum
Light intensity
Luminous intensity
microalgae
Neural networks
nitrogen
Nutrients
Optimization
Orthogonal arrays
Parameters
photophase
Photosynthesis
Physicochemical properties
process engineering
Process intensification
Productivity
salt concentration
Sucrose
Taguchi orthogonal array
Urea
genetic algorithm
chlorophyll
Chlorella thermophila
Artificial neural network
Taguchi orthogonal array
process engineering
Online AccessGet full text
ISSN1082-6068
1532-2297
1532-2297
DOI10.1080/10826068.2022.2119578

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Abstract In our current work, we have optimized six physicochemical parameters (light intensity, light period, pH, inoculum size, culture period, and salt concentration) toward growth and chlorophyll synthesis using isolated fresh water microalgae Chlorella thermophila [contains ∼6% (w/w on dry biomass basis) chlorophyll]. Here, both experimental and computational [Taguchi orthogonal array (TOA), artificial neural network (ANN), and genetic algorithm (GA)] approaches were employed for the process intensification. Results revealed that the content of biomass and chlorophyll were enhanced by 118% and 95%, respectively, with productivity enhancement of 30% for biomass and 61% for chlorophyll from the optimization of physicochemical parameters. Further, optimum light intensity was found to be 128 µmol m −2  s −1 after conducting experiments in optimized chemical and physicochemical conditions, contributing to the enhancement of productivity of 46% for biomass and 106% for chlorophyll. Urea was found to be the most effective nitrogen source with an increase of 70% and 160% biomass and chlorophyll productivity, respectively. Moreover, sucrose as a carbon source contributed to an increase of 97% and 264% biomass and chlorophyll productivity.
AbstractList In our current work, we have optimized six physicochemical parameters (light intensity, light period, pH, inoculum size, culture period, and salt concentration) toward growth and chlorophyll synthesis using isolated fresh water microalgae Chlorella thermophila [contains ∼6% (w/w on dry biomass basis) chlorophyll]. Here, both experimental and computational [Taguchi orthogonal array (TOA), artificial neural network (ANN), and genetic algorithm (GA)] approaches were employed for the process intensification. Results revealed that the content of biomass and chlorophyll were enhanced by 118% and 95%, respectively, with productivity enhancement of 30% for biomass and 61% for chlorophyll from the optimization of physicochemical parameters. Further, optimum light intensity was found to be 128 µmol m⁻² s⁻¹ after conducting experiments in optimized chemical and physicochemical conditions, contributing to the enhancement of productivity of 46% for biomass and 106% for chlorophyll. Urea was found to be the most effective nitrogen source with an increase of 70% and 160% biomass and chlorophyll productivity, respectively. Moreover, sucrose as a carbon source contributed to an increase of 97% and 264% biomass and chlorophyll productivity.
In our current work, we have optimized six physicochemical parameters (light intensity, light period, pH, inoculum size, culture period, and salt concentration) toward growth and chlorophyll synthesis using isolated fresh water microalgae Chlorella thermophila [contains ∼6% (w/w on dry biomass basis) chlorophyll]. Here, both experimental and computational [Taguchi orthogonal array (TOA), artificial neural network (ANN), and genetic algorithm (GA)] approaches were employed for the process intensification. Results revealed that the content of biomass and chlorophyll were enhanced by 118% and 95%, respectively, with productivity enhancement of 30% for biomass and 61% for chlorophyll from the optimization of physicochemical parameters. Further, optimum light intensity was found to be 128 µmol m −2  s −1 after conducting experiments in optimized chemical and physicochemical conditions, contributing to the enhancement of productivity of 46% for biomass and 106% for chlorophyll. Urea was found to be the most effective nitrogen source with an increase of 70% and 160% biomass and chlorophyll productivity, respectively. Moreover, sucrose as a carbon source contributed to an increase of 97% and 264% biomass and chlorophyll productivity.
In our current work, we have optimized six physicochemical parameters (light intensity, light period, pH, inoculum size, culture period, and salt concentration) toward growth and chlorophyll synthesis using isolated fresh water microalgae Chlorella thermophila [contains ∼6% (w/w on dry biomass basis) chlorophyll]. Here, both experimental and computational [Taguchi orthogonal array (TOA), artificial neural network (ANN), and genetic algorithm (GA)] approaches were employed for the process intensification. Results revealed that the content of biomass and chlorophyll were enhanced by 118% and 95%, respectively, with productivity enhancement of 30% for biomass and 61% for chlorophyll from the optimization of physicochemical parameters. Further, optimum light intensity was found to be 128 µmol m-2 s-1 after conducting experiments in optimized chemical and physicochemical conditions, contributing to the enhancement of productivity of 46% for biomass and 106% for chlorophyll. Urea was found to be the most effective nitrogen source with an increase of 70% and 160% biomass and chlorophyll productivity, respectively. Moreover, sucrose as a carbon source contributed to an increase of 97% and 264% biomass and chlorophyll productivity.In our current work, we have optimized six physicochemical parameters (light intensity, light period, pH, inoculum size, culture period, and salt concentration) toward growth and chlorophyll synthesis using isolated fresh water microalgae Chlorella thermophila [contains ∼6% (w/w on dry biomass basis) chlorophyll]. Here, both experimental and computational [Taguchi orthogonal array (TOA), artificial neural network (ANN), and genetic algorithm (GA)] approaches were employed for the process intensification. Results revealed that the content of biomass and chlorophyll were enhanced by 118% and 95%, respectively, with productivity enhancement of 30% for biomass and 61% for chlorophyll from the optimization of physicochemical parameters. Further, optimum light intensity was found to be 128 µmol m-2 s-1 after conducting experiments in optimized chemical and physicochemical conditions, contributing to the enhancement of productivity of 46% for biomass and 106% for chlorophyll. Urea was found to be the most effective nitrogen source with an increase of 70% and 160% biomass and chlorophyll productivity, respectively. Moreover, sucrose as a carbon source contributed to an increase of 97% and 264% biomass and chlorophyll productivity.
In our current work, we have optimized six physicochemical parameters (light intensity, light period, pH, inoculum size, culture period, and salt concentration) toward growth and chlorophyll synthesis using isolated fresh water microalgae [contains ∼6% (w/w on dry biomass basis) chlorophyll]. Here, both experimental and computational [Taguchi orthogonal array (TOA), artificial neural network (ANN), and genetic algorithm (GA)] approaches were employed for the process intensification. Results revealed that the content of biomass and chlorophyll were enhanced by 118% and 95%, respectively, with productivity enhancement of 30% for biomass and 61% for chlorophyll from the optimization of physicochemical parameters. Further, optimum light intensity was found to be 128 µmol m  s after conducting experiments in optimized chemical and physicochemical conditions, contributing to the enhancement of productivity of 46% for biomass and 106% for chlorophyll. Urea was found to be the most effective nitrogen source with an increase of 70% and 160% biomass and chlorophyll productivity, respectively. Moreover, sucrose as a carbon source contributed to an increase of 97% and 264% biomass and chlorophyll productivity.
In our current work, we have optimized six physicochemical parameters (light intensity, light period, pH, inoculum size, culture period, and salt concentration) toward growth and chlorophyll synthesis using isolated fresh water microalgae Chlorella thermophila [contains ∼6% (w/w on dry biomass basis) chlorophyll]. Here, both experimental and computational [Taguchi orthogonal array (TOA), artificial neural network (ANN), and genetic algorithm (GA)] approaches were employed for the process intensification. Results revealed that the content of biomass and chlorophyll were enhanced by 118% and 95%, respectively, with productivity enhancement of 30% for biomass and 61% for chlorophyll from the optimization of physicochemical parameters. Further, optimum light intensity was found to be 128 µmol m−2 s−1 after conducting experiments in optimized chemical and physicochemical conditions, contributing to the enhancement of productivity of 46% for biomass and 106% for chlorophyll. Urea was found to be the most effective nitrogen source with an increase of 70% and 160% biomass and chlorophyll productivity, respectively. Moreover, sucrose as a carbon source contributed to an increase of 97% and 264% biomass and chlorophyll productivity.
Author Gayen, Kalyan
Bhowmick, Tridib Kumar
Sarkar, Sreya
Sarkar, Sambit
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/36093762$$D View this record in MEDLINE/PubMed
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crossref_primary_10_1080_10826068_2023_2241898
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Keywords genetic algorithm
chlorophyll
Chlorella thermophila
Artificial neural network
Taguchi orthogonal array
process engineering
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Snippet In our current work, we have optimized six physicochemical parameters (light intensity, light period, pH, inoculum size, culture period, and salt...
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SubjectTerms Algae
algorithms
Artificial neural network
Artificial neural networks
Biofuels
Biomass
Biosynthesis
Biotechnology
carbon
Carbon sources
Chlorella
Chlorella thermophila
Chlorophyll
experimental design
Fresh water
freshwater
genetic algorithm
Genetic algorithms
Inoculum
Light intensity
Luminous intensity
microalgae
Neural networks
nitrogen
Nutrients
Optimization
Orthogonal arrays
Parameters
photophase
Photosynthesis
Physicochemical properties
process engineering
Process intensification
Productivity
salt concentration
Sucrose
Taguchi orthogonal array
Urea
Title Process intensification for the enhancement of growth and chlorophyll molecules of isolated Chlorella thermophila: A systematic experimental and optimization approach
URI https://www.tandfonline.com/doi/abs/10.1080/10826068.2022.2119578
https://www.ncbi.nlm.nih.gov/pubmed/36093762
https://www.proquest.com/docview/2832173732
https://www.proquest.com/docview/2713305515
https://www.proquest.com/docview/2849890332
Volume 53
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