Modeling of carbon dioxide fixation by microalgae using hybrid artificial intelligence (AI) and fuzzy logic (FL) methods and optimization by genetic algorithm (GA)

In this study, we are reporting a novel prediction model for forecasting the carbon dioxide (CO 2 ) fixation of microalgae which is based on the hybrid approach of adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithm (GA). The CO 2 fixation rate of various algal strains was collected...

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Published inEnvironmental science and pollution research international Vol. 30; no. 10; pp. 24927 - 24948
Main Authors Kushwaha, Omkar Singh, Uthayakumar, Haripriyan, Kumaresan, Karthigaiselvan
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2023
Subjects
Algorithms
Aquatic Pollution
Artificial Intelligence
Atmospheric Protection/Air Quality Control/Air Pollution
Carbon Dioxide
carbon dioxide fixation
Chemical and Bioengineering for Sustainable Environment
Earth and Environmental Science
Ecotoxicology
Environment
Environmental Chemistry
Environmental Health
Fuzzy Logic
industrial applications
Microalgae
nitrogen
phosphorus
prediction
statistical analysis
temperature
Waste Water Technology
Water Management
Water Pollution Control
Industrial scale-up
Genetic algorithm
fixation rate
Adaptive neuro-fuzzy inference system
CO
Carbon capture
And root mean square error
CO2 fixation rate
Online AccessGet full text
ISSN1614-7499
1614-7499
DOI10.1007/s11356-022-19683-0

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Abstract In this study, we are reporting a novel prediction model for forecasting the carbon dioxide (CO 2 ) fixation of microalgae which is based on the hybrid approach of adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithm (GA). The CO 2 fixation rate of various algal strains was collected and the cultivation conditions of the microalgae such as temperature, pH, CO 2 %, and amount of nitrogen and phosphorous (mg/L) were taken as the input variables, while the CO 2 fixation rate was taken as the output variable. The optimization of ANFIS parameters and the formation of the optimized fuzzy model structure were performed by genetic algorithm (GA) using MATLAB in order to achieve optimum prediction capability and industrial applicability. The best-fitting model was figured out using statistical analysis parameters such as root mean square error (RMSE), coefficient of regression ( R 2 ), and average absolute relative deviation (AARD). According to the analysis, GA-ANFIS model depicted a greater prediction capability over ANFIS model. The RMSE, R 2 , and AARD for GA-ANFIS were observed to be 0.000431, 0.97865, and 0.044354 in the training phase and 0.00056, 0.98457, and 0.032156 in the testing phase, respectively, for the GA-ANFIS Model. As a result, it can be concluded that the proposed GA-ANFIS model is an efficient technique having a very high potential to accurately predict the CO 2 fixation rate.
AbstractList In this study, we are reporting a novel prediction model for forecasting the carbon dioxide (CO ) fixation of microalgae which is based on the hybrid approach of adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithm (GA). The CO fixation rate of various algal strains was collected and the cultivation conditions of the microalgae such as temperature, pH, CO %, and amount of nitrogen and phosphorous (mg/L) were taken as the input variables, while the CO fixation rate was taken as the output variable. The optimization of ANFIS parameters and the formation of the optimized fuzzy model structure were performed by genetic algorithm (GA) using MATLAB in order to achieve optimum prediction capability and industrial applicability. The best-fitting model was figured out using statistical analysis parameters such as root mean square error (RMSE), coefficient of regression (R ), and average absolute relative deviation (AARD). According to the analysis, GA-ANFIS model depicted a greater prediction capability over ANFIS model. The RMSE, R , and AARD for GA-ANFIS were observed to be 0.000431, 0.97865, and 0.044354 in the training phase and 0.00056, 0.98457, and 0.032156 in the testing phase, respectively, for the GA-ANFIS Model. As a result, it can be concluded that the proposed GA-ANFIS model is an efficient technique having a very high potential to accurately predict the CO fixation rate.
In this study, we are reporting a novel prediction model for forecasting the carbon dioxide (CO₂) fixation of microalgae which is based on the hybrid approach of adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithm (GA). The CO₂ fixation rate of various algal strains was collected and the cultivation conditions of the microalgae such as temperature, pH, CO₂ %, and amount of nitrogen and phosphorous (mg/L) were taken as the input variables, while the CO₂ fixation rate was taken as the output variable. The optimization of ANFIS parameters and the formation of the optimized fuzzy model structure were performed by genetic algorithm (GA) using MATLAB in order to achieve optimum prediction capability and industrial applicability. The best-fitting model was figured out using statistical analysis parameters such as root mean square error (RMSE), coefficient of regression (R²), and average absolute relative deviation (AARD). According to the analysis, GA-ANFIS model depicted a greater prediction capability over ANFIS model. The RMSE, R², and AARD for GA-ANFIS were observed to be 0.000431, 0.97865, and 0.044354 in the training phase and 0.00056, 0.98457, and 0.032156 in the testing phase, respectively, for the GA-ANFIS Model. As a result, it can be concluded that the proposed GA-ANFIS model is an efficient technique having a very high potential to accurately predict the CO₂ fixation rate.
In this study, we are reporting a novel prediction model for forecasting the carbon dioxide (CO 2 ) fixation of microalgae which is based on the hybrid approach of adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithm (GA). The CO 2 fixation rate of various algal strains was collected and the cultivation conditions of the microalgae such as temperature, pH, CO 2 %, and amount of nitrogen and phosphorous (mg/L) were taken as the input variables, while the CO 2 fixation rate was taken as the output variable. The optimization of ANFIS parameters and the formation of the optimized fuzzy model structure were performed by genetic algorithm (GA) using MATLAB in order to achieve optimum prediction capability and industrial applicability. The best-fitting model was figured out using statistical analysis parameters such as root mean square error (RMSE), coefficient of regression ( R 2 ), and average absolute relative deviation (AARD). According to the analysis, GA-ANFIS model depicted a greater prediction capability over ANFIS model. The RMSE, R 2 , and AARD for GA-ANFIS were observed to be 0.000431, 0.97865, and 0.044354 in the training phase and 0.00056, 0.98457, and 0.032156 in the testing phase, respectively, for the GA-ANFIS Model. As a result, it can be concluded that the proposed GA-ANFIS model is an efficient technique having a very high potential to accurately predict the CO 2 fixation rate.
In this study, we are reporting a novel prediction model for forecasting the carbon dioxide (CO2) fixation of microalgae which is based on the hybrid approach of adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithm (GA). The CO2 fixation rate of various algal strains was collected and the cultivation conditions of the microalgae such as temperature, pH, CO2 %, and amount of nitrogen and phosphorous (mg/L) were taken as the input variables, while the CO2 fixation rate was taken as the output variable. The optimization of ANFIS parameters and the formation of the optimized fuzzy model structure were performed by genetic algorithm (GA) using MATLAB in order to achieve optimum prediction capability and industrial applicability. The best-fitting model was figured out using statistical analysis parameters such as root mean square error (RMSE), coefficient of regression (R2), and average absolute relative deviation (AARD). According to the analysis, GA-ANFIS model depicted a greater prediction capability over ANFIS model. The RMSE, R2, and AARD for GA-ANFIS were observed to be 0.000431, 0.97865, and 0.044354 in the training phase and 0.00056, 0.98457, and 0.032156 in the testing phase, respectively, for the GA-ANFIS Model. As a result, it can be concluded that the proposed GA-ANFIS model is an efficient technique having a very high potential to accurately predict the CO2 fixation rate.In this study, we are reporting a novel prediction model for forecasting the carbon dioxide (CO2) fixation of microalgae which is based on the hybrid approach of adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithm (GA). The CO2 fixation rate of various algal strains was collected and the cultivation conditions of the microalgae such as temperature, pH, CO2 %, and amount of nitrogen and phosphorous (mg/L) were taken as the input variables, while the CO2 fixation rate was taken as the output variable. The optimization of ANFIS parameters and the formation of the optimized fuzzy model structure were performed by genetic algorithm (GA) using MATLAB in order to achieve optimum prediction capability and industrial applicability. The best-fitting model was figured out using statistical analysis parameters such as root mean square error (RMSE), coefficient of regression (R2), and average absolute relative deviation (AARD). According to the analysis, GA-ANFIS model depicted a greater prediction capability over ANFIS model. The RMSE, R2, and AARD for GA-ANFIS were observed to be 0.000431, 0.97865, and 0.044354 in the training phase and 0.00056, 0.98457, and 0.032156 in the testing phase, respectively, for the GA-ANFIS Model. As a result, it can be concluded that the proposed GA-ANFIS model is an efficient technique having a very high potential to accurately predict the CO2 fixation rate.
Author Uthayakumar, Haripriyan
Kushwaha, Omkar Singh
Kumaresan, Karthigaiselvan
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  orcidid: 0000-0001-7128-3245
  surname: Kushwaha
  fullname: Kushwaha, Omkar Singh
  email: kushwaha.iitmadras@gmail.com
  organization: Department of Chemical Engineering, Indian Institute of Technology
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  givenname: Haripriyan
  surname: Uthayakumar
  fullname: Uthayakumar, Haripriyan
  organization: Department of Chemical Engineering, Anna University, Department of Chemical Engineering and Sciences, Swinburne University of Technology
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  givenname: Karthigaiselvan
  surname: Kumaresan
  fullname: Kumaresan, Karthigaiselvan
  organization: Department of Chemical Engineering, Anna University
BackLink https://www.ncbi.nlm.nih.gov/pubmed/35349067$$D View this record in MEDLINE/PubMed
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2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
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Thu Apr 24 23:06:48 EDT 2025
Fri Feb 21 02:45:39 EST 2025
IsPeerReviewed true
IsScholarly true
Issue 10
Keywords Industrial scale-up
Genetic algorithm
fixation rate
Adaptive neuro-fuzzy inference system
CO
Carbon capture
And root mean square error
CO2 fixation rate
Language English
License 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
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PublicationTitle Environmental science and pollution research international
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  publication-title: J Environ Manag
  doi: 10.1016/j.jenvman.2019.06.085
– volume: 16
  start-page: 287
  issue: 3
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  publication-title: Biochem Eng J
  doi: 10.1016/S1369-703X(03)00072-X
– volume: 12
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  publication-title: Appl Soft Comput
  doi: 10.1016/j.asoc.2011.10.020
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Snippet In this study, we are reporting a novel prediction model for forecasting the carbon dioxide (CO 2 ) fixation of microalgae which is based on the hybrid...
In this study, we are reporting a novel prediction model for forecasting the carbon dioxide (CO ) fixation of microalgae which is based on the hybrid approach...
In this study, we are reporting a novel prediction model for forecasting the carbon dioxide (CO2) fixation of microalgae which is based on the hybrid approach...
In this study, we are reporting a novel prediction model for forecasting the carbon dioxide (CO₂) fixation of microalgae which is based on the hybrid approach...
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SubjectTerms Algorithms
Aquatic Pollution
Artificial Intelligence
Atmospheric Protection/Air Quality Control/Air Pollution
Carbon Dioxide
carbon dioxide fixation
Chemical and Bioengineering for Sustainable Environment
Earth and Environmental Science
Ecotoxicology
Environment
Environmental Chemistry
Environmental Health
Fuzzy Logic
industrial applications
Microalgae
nitrogen
phosphorus
prediction
statistical analysis
temperature
Waste Water Technology
Water Management
Water Pollution Control
Title Modeling of carbon dioxide fixation by microalgae using hybrid artificial intelligence (AI) and fuzzy logic (FL) methods and optimization by genetic algorithm (GA)
URI https://link.springer.com/article/10.1007/s11356-022-19683-0
https://www.ncbi.nlm.nih.gov/pubmed/35349067
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https://www.proquest.com/docview/2887612820
Volume 30
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