Effects of pre-treatments and co-digestion on biogas production from Okra waste

被引:24
|
作者
Ugwu, S. N. [1 ]
Enweremadu, C. C. [1 ]
机构
[1] Univ South Africa, Mech & Ind Engn, Florida Campus, ZA-1709 Johannesburg, South Africa
基金
新加坡国家研究基金会;
关键词
ARTIFICIAL NEURAL-NETWORK; ENHANCING ANAEROBIC-DIGESTION; METHANE POTENTIAL BMP; INDUSTRIAL SLUDGES; YIELD;
D O I
10.1063/1.5049530
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The effects of pre-treatment and co-digestion on the biogas yield of okra wastes are the focus of this paper. Okra wastes (O) were treated with 5% alkaline (NaOH) (OA), microwave (OMW), and microwave thorn alkaline. Sheep slurry (S) was co-digested with okra waste in varying proportions [25% (OS25), 50% (OS50), and 100% (S)]. Biomethane potential tests using 500 ml bioreactors were carried out in the batch mode at a hydraulic retention time of 25 days under mesophilic conditions (37 degrees C). The cumulative biogas yield from the alkaline treated sample (OA) increased by 45.87% when compared to the control (O), and other pre-treated samples were statistically the same with untreated okra waste at the 95% confidence level. Samples co-digested with sheep slurry recorded a low biogas yield. The results of the kinetic studies performed with the measured cumulative biogas yield showed that while the modified Gompertz model presented a better prediction range (0.66%-2.48%), the first-order kinetic model produced a better goodness of fit (<10) and R-2 values when compared to the modified Gompertz model. The results of the Artificial neural network (ANN) performed were compared with the earlier used kinetic models, and it was observed that all the performance indicators (R-2, RMSE and % prediction difference) from the ANN model best predicted the biomethane yields. Published under license by AIP Publishing.
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页数:10
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