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首页> 外文期刊>Energy Conversion & Management >Optimization of pretreatment, process performance, mass and energy balance in the anaerobic digestion of Arachis hypogaea (Peanut) hull
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Optimization of pretreatment, process performance, mass and energy balance in the anaerobic digestion of Arachis hypogaea (Peanut) hull

机译：花生厌氧消化过程中预处理，工艺性能，质量和能量平衡的优化

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The potential of a major bioresource (Peanut hull) for biogas generation was evaluated. A sample was pretreated using combinations of mechanical and thermo-alkaline procedures using the Central Composite Design (CCD) for the optimization of the pretreatment temperature and time while another sample was treated without thermo-alkaline methods. The physico-chemical and microbial characteristics of the A. hypogaea hull and the rumen contents were carried out using standard methods. The actual biogas yields were 1739.20 m(3)/kg TSfed and 1100.50 m(3)/kg TSfed with desirability values of 91 and 100% for the pretreated and untreated experiments respectively. The methane and carbon dioxide content of biogas from both experiments as revealed by Gas chromatography were 61.5 +/- 2.5%; 24 +/- 1% and 51 +/- 2%; 25 2% respectively. The optimization of important process parameters in the anaerobic digestion were done using CCD of Response Surface Methodology (RSM) and the Artificial Neural Networks (ANNs) and the optimal values for each of the five major parameters optimized are as follows: Temperature = 30.00 degrees C, pH = 7.50, Retention time = 30.00 day, Total solids =12.00 g/kg and Volatile solids = 4.00 g/kg. Taking these values into account, the predicted biogas yield for RSM was 1819.89 m(3)/kg TSfed and 1743.6 m(3)/kg TSfed for ANNs in the thermo-alkaline pretreated experiment. For the experiment without pretreatment, the RSM predicted yield was 1119.54 m(3)/kg TSfed while that of ANNs was 1103.40 m(3)/kg TSfed. In all there was a 38.5% increase in predicted biogas yield in the experiment with pretreatment over that of untreated A. hypogaea hull. Based on the coefficient of determination (R-2), the mean error and predicted biogas yields, ANNs was found more accurate and is recommended for the optimization of biogas generation from the substrate used in this study. Further usage of peanut hull for biofuels is encouraged. (C) 2017 Elsevier Ltd. All rights reserved.
机译：评价了产生沼气的主要生物资源（花生壳）的潜力。使用中央复合设计（CCD）使用机械和热碱性程序的组合对样品进行预处理，以优化预处理温度和时间，而另一种样品则不使用热碱性方法进行处理。使用标准方法进行了A. hypogaea壳的理化和微生物特性以及瘤胃含量的测定。实际沼气产量分别为1739.20 m（3）/ kg TSfed和1100.50 m（3）/ kg TSfed，分别对预处理和未经处理的实验的期望值为91和100％。气相色谱显示，两个实验中沼气的甲烷和二氧化碳含量均为61.5 +/- 2.5％； 24 +/- 1％和51 +/- 2％; 25 2％。使用响应表面方法（RSM）和人工神经网络（ANN）的CCD对厌氧消化中的重要工艺参数进行了优化，优化后的五个主要参数的最佳值分别为：温度= 30.00摄氏度，pH ＝ 7.50，保留时间＝ 30.00天，总固体＝ 12.00g / kg，挥发性固体＝ 4.00g / kg。考虑到这些值，在热碱预处理实验中，RSM的预测沼气产量为1819.89 m（3）/ kg TSfed和1743.6 m（3）/ kg TSfed。对于未经预处理的实验，RSM预测的产量为1119.54 m（3）/ kg TSfed，而人工神经网络的产量为1103.40 m（3）/ kg TSfed。总体而言，与未处理过的拟南芥壳相比，经过预处理的实验预计的沼气产量提高了38.5％。根据确定系数（R-2），平均误差和预测的沼气产量，发现人工神经网络更为准确，建议将其用于优化本研究中使用的底物产生沼气。鼓励将花生壳进一步用作生物燃料。 （C）2017 Elsevier Ltd.保留所有权利。

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