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https://doi.org/10.7287/peerj.preprints.26564v1

Biochemical conversion of fruit rind of Telfairia occidentalis (Fluted Pumpkin) and poultry manure

This study evaluated the potentials of Fluted pumpkin fruit rind and poultry manure for biogas generation. Mechanical and thermo-alkaline pre-treatments were applied to two samples labelled ‘O’ and ‘P’ while the third sample (Q) had no thermo-alkaline treatment. The physicochemical characteristics of the substrates revealed richness in nutrients and mineral elements. The modelling was done using the Response Surface Methodology and Artificial Neural Networks and statistical prediction showed the process optimal conditions to be 30.02 o C, 7.90, 20.03 days, 5.94 g/kg and 4.01 g/kg for temperature, pH, retention time, total solids and volatile solids. Using the above set values, the biogas yield was predicted to be 2614.1, 2289.9 and 1003.3 10-3m3/kg VS for digestions ‘O’, ‘P’ and ‘Q’ respectively. The results showed that use of combination of pre-treatment methods enhanced the biogas yield in the pre-treated substrates. Analysis of the gas composition showed 66.5 ± 2.5 % Methane, 25 ± 1% Carbon dioxide; 58.5 ± 2.5 % Methane, 26 ± 1% Carbon dioxide; 54.5 ± 1.5 % Methane, 28 ± 2% Carbon dioxide for the three experiments respectively. All the obtained values show the models had a high predictive ability. However, the coefficient of determination (R2) for RSM was lower compared to that of ANN which is an indication that ANNs model is more accurate than RSM model in predicting biogas generation from the anaerobic co-digestion of rind of Fluted pumpkin and poultry manure. The substrates should be further used for energy generation.



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Biochemical conversion of fruit rind of Telfairia occidentalis (Fluted Pumpkin) and poultry manure

https://doi.org/10.7287/peerj.preprints.26564v1

This study evaluated the potentials of Fluted pumpkin fruit rind and poultry manure for biogas generation. Mechanical and thermo-alkaline pre-treatments were applied to two samples labelled ‘O’ and ‘P’ while the third sample (Q) had no thermo-alkaline treatment. The physicochemical characteristics of the substrates revealed richness in nutrients and mineral elements. The modelling was done using the Response Surface Methodology and Artificial Neural Networks and statistical prediction showed the process optimal conditions to be 30.02 o C, 7.90, 20.03 days, 5.94 g/kg and 4.01 g/kg for temperature, pH, retention time, total solids and volatile solids. Using the above set values, the biogas yield was predicted to be 2614.1, 2289.9 and 1003.3 10-3m3/kg VS for digestions ‘O’, ‘P’ and ‘Q’ respectively. The results showed that use of combination of pre-treatment methods enhanced the biogas yield in the pre-treated substrates. Analysis of the gas composition showed 66.5 ± 2.5 % Methane, 25 ± 1% Carbon dioxide; 58.5 ± 2.5 % Methane, 26 ± 1% Carbon dioxide; 54.5 ± 1.5 % Methane, 28 ± 2% Carbon dioxide for the three experiments respectively. All the obtained values show the models had a high predictive ability. However, the coefficient of determination (R2) for RSM was lower compared to that of ANN which is an indication that ANNs model is more accurate than RSM model in predicting biogas generation from the anaerobic co-digestion of rind of Fluted pumpkin and poultry manure. The substrates should be further used for energy generation.



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https://doi.org/10.7287/peerj.preprints.26564v1

Biochemical conversion of fruit rind of Telfairia occidentalis (Fluted Pumpkin) and poultry manure

This study evaluated the potentials of Fluted pumpkin fruit rind and poultry manure for biogas generation. Mechanical and thermo-alkaline pre-treatments were applied to two samples labelled ‘O’ and ‘P’ while the third sample (Q) had no thermo-alkaline treatment. The physicochemical characteristics of the substrates revealed richness in nutrients and mineral elements. The modelling was done using the Response Surface Methodology and Artificial Neural Networks and statistical prediction showed the process optimal conditions to be 30.02 o C, 7.90, 20.03 days, 5.94 g/kg and 4.01 g/kg for temperature, pH, retention time, total solids and volatile solids. Using the above set values, the biogas yield was predicted to be 2614.1, 2289.9 and 1003.3 10-3m3/kg VS for digestions ‘O’, ‘P’ and ‘Q’ respectively. The results showed that use of combination of pre-treatment methods enhanced the biogas yield in the pre-treated substrates. Analysis of the gas composition showed 66.5 ± 2.5 % Methane, 25 ± 1% Carbon dioxide; 58.5 ± 2.5 % Methane, 26 ± 1% Carbon dioxide; 54.5 ± 1.5 % Methane, 28 ± 2% Carbon dioxide for the three experiments respectively. All the obtained values show the models had a high predictive ability. However, the coefficient of determination (R2) for RSM was lower compared to that of ANN which is an indication that ANNs model is more accurate than RSM model in predicting biogas generation from the anaerobic co-digestion of rind of Fluted pumpkin and poultry manure. The substrates should be further used for energy generation.

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      This study evaluated the potentials of Fluted pumpkin fruit rind and poultry manure for biogas generation. Mechanical and thermo-alkaline pre-treatments were applied to two samples labelled ‘O’ and ‘P’ while the third sample (Q) had no thermo-alkaline treatment. The physicochemical characteristics of the substrates revealed richness in nutrients and mineral elements. The modelling was done using the Response Surface Methodology and Artificial Neural Networks and statistical prediction showed the process optimal conditions to be 30.02 o C, 7.90, 20.03 days, 5.94 g/kg and 4.01 g/kg for temperature, pH, retention time, total solids and volatile solids. Using the above set values, the biogas yield was predicted to be 2614.1, 2289.9 and 1003.3 10-3m3/kg VS for digestions ‘O’, ‘P’ and ‘Q’ respectively. The results showed that use of combination of pre-treatment methods enhanced the biogas yield in the pre-treated substrates. Analysis of the gas composition showed 66.5 ± 2.5 % Methane, 25 ± 1% Carbon dioxide; 58.5 ± 2.5 % Methane, 26 ± 1% Carbon dioxide; 54.5 ± 1.5 % Methane, 28 ± 2% Carbon dioxide for the three experiments respectively. All the obtained values show the models had a high predictive ability. However, the coefficient of determination (R2) for RSM was lower compared to that of ANN which is an indication that ANNs model is more accurate than RSM model in predicting biogas generation from the anaerobic co-digestion of rind of Fluted pumpkin and poultry manure. The substrates should be further used for energy generation.
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