生物炭在含牛粪、食物垃圾和稻草的厌氧沼气池中作为甲烷强化催化剂的实验和统计研究

IF 3.9
P. Sivakumar , R. Saravanane , S. Mohan , B. Sankar
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引用次数: 0

摘要

人们对用更可持续的能源来满足日益增长的能源需求越来越感兴趣。生物质能有潜力成为化石燃料的可持续和环境友好型替代品,并有助于在不久的将来实现净零排放。本研究提出了一种经济可行的方法,通过混合消化牛粪(CD)、食物垃圾(FW)和稻草(RS),并添加椰子壳生物炭(BC)来提高沼气效率。本研究旨在通过监测pH、温度、总固形物(TS)、挥发性固形物(VS)、挥发性脂肪酸(VFA)、碳氮比(C/N)等影响因素的变化,研究生物炭添加过程中沼气产量的变化。添加生物炭通过转化H2S和CO2等中间体来稳定pH和温度。由于生物炭的缓冲能力,它也显著增加了VFA的积累和降解。加生物炭的混合物甲烷产率显著高于不加生物炭的混合物。含生物炭的混合物CD 30: FW 50:RS 20甲烷产率峰值为165.08 mL。利用响应面法(RSM)建立的统计模型预测甲烷产量的准确率为99.07 %,统计显著性水平为0.05。通过与现有的Gompertz动力学模型的比较,验证了RSM模型的准确性。RSM模型和Gompertz模型的性能评价误差指标、相关系数(R)和均方根误差(RMSE)结果分别为0.966、0.925和62.89 mL/gVS、87.24 mL/gVS,表明本研究建立的RSM模型具有较好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Biochar as a catalyst for methane enhancement in anaerobic digestor containing cow dung, food waste, and rice straw: An experimental and statistical study
There is a growing interest in meeting the rising energy demand from a more sustainable source. Biomass energy has the potential to act as a sustainable and environmentally friendly alternative to fossil fuels and help achieve net-zero emissions in the near future. This study proposes an economically feasible method to enhance biogas efficiency by co-digesting cow dung (CD), food waste (FW), rice straw (RS), with the addition of Coconut husk Bio-Char (BC). The present research aims to study the variation in the biogas yield from biochar addition by monitoring the alteration in the influential parameters such as pH, temperature, total solids (TS), volatile solids (VS), volatile fatty acids (VFA), and carbon to nitrogen ratio (C/N). The biochar addition stabilized both pH and temperature due to its intrinsic properties by transforming intermediates like H2S and CO2. It also significantly increased the VFA accumulation and degradation attributed to the buffering ability of the biochar. The methane yield of blends with biochar was significantly higher than that of the blends without biochar. The mixture CD 30: FW 50:RS 20 containing biochar showed a peak methane yield of 165.08 mL. The statistical model developed using response surface methodology (RSM) predicted the methane yield with an accuracy of 99.07 % and a statistical significance level of 0.05. The accuracy of the RSM model was validated by comparing it with the existing Gompertz kinetic model. The performance evaluation error metrics, Coefficient of Correlation (R), and Root Mean Square Error (RMSE) results were observed to be 0.966, 0.925, and 62.89 mL/gVS, 87.24 mL/gVS for RSM model and Gompertz model, indicating the superior performance of the RSM model developed in this study.
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