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Optimization of temperature and pretreatments for methane yield of hazelnut shells using the response surface methodology
In this study, NaOH pretreatment, H2SO4 pretreatment, thermal pretreatment and production temperature were optimized to ensure maximum methane yield from hazelnut shells (HS) using the response surface methodology (RSM). A Box-Behnken design was achieved with four different independent variables and...
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Published in: | Fuel (Guildford) 2020-07, Vol.271, p.117585, Article 117585 |
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Main Authors: | , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | In this study, NaOH pretreatment, H2SO4 pretreatment, thermal pretreatment and production temperature were optimized to ensure maximum methane yield from hazelnut shells (HS) using the response surface methodology (RSM). A Box-Behnken design was achieved with four different independent variables and one dependent variable (methane yield). A total of 29 tests were performed after pretreatment according to the RSM design and to different production temperatures, suggesting optimum values for NaOH pretreatment, H2SO4 pretreatment, thermal pretreatment and production temperature were 3.5% w/v, 2.56% v/v, 145.66 °C and 34.65 °C, respectively. Under these conditions, the RSM-predicted methane yield was 215.896 mL/g volatile solid (VS). The high R2 value (0.9904) showed that the model could be applied effectively in the digestion of HS for the predicted methane yield according to the production temperature and pretreatments. In addition, lignocellulosic solubilisation was tested after pretreatment of the reactors according to the RSM operating conditions, which showed that the highest cellulose, hemicellulose and lignin solubilisation that could be achieved was 38.7% w/w (R10), 36.9% w/w (R22) and 50.5% w/w (R10), respectively. The modified Gompertz model supported the experimental cumulative methane yields (CMYs). |
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ISSN: | 0016-2361 1873-7153 |
DOI: | 10.1016/j.fuel.2020.117585 |