<abstract xmlns="http://www.w3.org/1999/xhtml">Azolla algae currently represent a major threat to the environment and rice cultivation fields. Various studies have shown that one of the practical solutions is to turn this threat into an opportunity for biomass energy production. This research investigates the production potential of methane in Azolla. Test was conducted in a completely randomized design (CRD) with Mini Tap software. Three different C/N ratios, 30, 34 and 38, two levels of thermal pre-treatment (raw and steamed Azolla) and two levels of chemical pre-treatment (NaOH 0% and 9%) were used and effects of each case on the methane production rate was studied. The highest amount of methane (247.7 ml·g−1vs) was produced at C/N ratio of 30 with application of NaOH (9%) and no thermal pre-treatment (raw Azolla). ANOVA analysis showed that interactions of the effective variables were significant, but the trend was not incremental or decreasing; therefore, ANFIS and stepwise regression modelling were used. The results showed that the ANFIS model provides more accurate mapping between the empirical and predicted values than the regression model. Furthermore, among examined models (triangular; trapezoidal; Gaussian; bell-shaped models), the best model for methane production prediction was Gaussian linear membership function (<italic>R</italic>2 = 0.997 and ε (%) = 0.32). According to the regression model, the thermal-chemical factor is the most efficient in prediction of the model (<italic>b</italic> = 35.6).</abstract>

Azolla algae currently represent a major threat to the environment and rice cultivation fields. Various studies have shown that one of the practical solutions is to turn this threat into an opportunity for biomass energy production. This research investigates the production potential of methane in Azolla. Test was conducted in a completely randomized design (CRD) with Mini Tap software. Three different C/N ratios, 30, 34 and 38, two levels of thermal pre-treatment (raw and steamed Azolla) and two levels of chemical pre-treatment (NaOH 0% and 9%) were used and effects of each case on the methane production rate was studied. The highest amount of methane (247.7 ml·g−1vs) was produced at C/N ratio of 30 with application of NaOH (9%) and no thermal pre-treatment (raw Azolla). ANOVA analysis showed that interactions of the effective variables were significant, but the trend was not incremental or decreasing; therefore, ANFIS and stepwise regression modelling were used. The results showed that the ANFIS model provides more accurate mapping between the empirical and predicted values than the regression model. Furthermore, among examined models (triangular; trapezoidal; Gaussian; bell-shaped models), the best model for methane production prediction was Gaussian linear membership function (R2 = 0.997 and ε (%) = 0.32). According to the regression model, the thermal-chemical factor is the most efficient in prediction of the model (b = 35.6).

Methane Production Potential of Azolla Under Different Ratios of C/N, Chemical and Thermal Pre-Treatment

Acta Technologica Agriculturae

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Azolla algae currently represent a major threat to the environment and rice cultivation fields. Various studies have shown that one of the practical solutions...

Methane Production Potential of Azolla Under Different Ratios of C/N, Chemical and Thermal Pre-Treatment

Published Online: Aug 27, 2020

Page range: 126 - 131

DOI: https://doi.org/10.2478/ata-2020-0020

Keywords
Azolla algae, ANFIS, C/N ratio, regression model

© 2020 Majid Fardmanesh et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Methane Production Potential of Azolla Under Different Ratios of C/N, Chemical and Thermal Pre-Treatment

Published Online: Aug 27, 2020

Page range: 126 - 131

DOI: https://doi.org/10.2478/ata-2020-0020

KeywordsAzolla algae, ANFIS, C/N ratio, regression model

© 2020 Majid Fardmanesh et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Keywords
Azolla algae, ANFIS, C/N ratio, regression model