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首页> 外文期刊>Waste Management >Modelling the anaerobic digestion of solid organic waste - Substrate characterisation method for ADM1 using a combined biochemical and kinetic parameter estimation approach
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Modelling the anaerobic digestion of solid organic waste - Substrate characterisation method for ADM1 using a combined biochemical and kinetic parameter estimation approach

机译:固体有机废物厌氧消化的模拟-生化和动力学参数联合估算方法对ADM1的底物表征方法。

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摘要

This work proposes a novel and rigorous substrate characterisation methodology to be used with ADM1 to simulate the anaerobic digestion of solid organic waste. The proposed method uses data from both direct substrate analysis and the methane production from laboratory scale anaerobic digestion experiments and involves assessment of four substrate fractionation models. The models partition the organic matter into a mixture of particulate and soluble fractions with the decision on the most suitable model being made on quality of fit between experimental and simulated data and the uncertainty of the calibrated parameters. The method was tested using samples of domestic green and food waste and using experimental data from both short batch tests and longer semi-continuous trials. The results showed that in general an increased fractionation model complexity led to better fit but with increased uncertainty. When using batch test data the most suitable model for green waste included one particulate and one soluble fraction, whereas for food waste two particulate fractions were needed. With richer semi-continuous datasets, the parameter estimation resulted in less uncertainty therefore allowing the description of the substrate with a more complex model. The resulting substrate characterisations and fractionation models obtained from batch test data, for both waste samples, were used to validate the method using semi-continuous experimental data and showed good prediction of methane production, biogas composition, total and volatile solids, ammonia and alkalinity.
机译:这项工作提出了一种新颖且严格的底物表征方法,该方法可与ADM1一起用于模拟固体有机废物的厌氧消化。所提出的方法使用直接底物分析和实验室规模厌氧消化实验产生的甲烷数据,并涉及对四种底物分馏模型的评估。这些模型将有机物分为颗粒部分和可溶性部分的混合物,并根据实验和模拟数据之间的拟合质量以及校准参数的不确定性来确定最合适的模型。使用家庭绿色食品和食物垃圾样品以及短期分批测试和较长的半连续试验的实验数据对方法进行了测试。结果表明,总的来说,分馏模型复杂度的增加会导致拟合效果更好,但不确定性也会增加。使用批处理测试数据时,最适合绿色废物的模型包括一个颗粒和一个可溶部分,而对于食品废物,则需要两个颗粒部分。使用更丰富的半连续数据集,参数估计可以减少不确定性,因此可以使用更复杂的模型来描述基材。从批次测试数据获得的两种废物样品的底物表征和分馏模型均用于半连续实验数据验证该方法,并显示出对甲烷产量,沼气组成,总固体和挥发性固体,氨和碱度的良好预测。

著录项

  • 来源
    《Waste Management》 |2016年第7期|40-54|共15页
  • 作者单位

    Energy Research Institute, School of Chemical and Process Engineering, University of Leeds, LS2 9JT, UK;

    Energy Engineering Group, Mechanical Engineering, University of Sheffield, S10 2TN, UK;

    Energy Engineering Group, Mechanical Engineering, University of Sheffield, S10 2TN, UK;

    Energy Engineering Group, Mechanical Engineering, University of Sheffield, S10 2TN, UK;

    Energy Engineering Group, Mechanical Engineering, University of Sheffield, S10 2TN, UK;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Anaerobic digestion; ADM1; Model inputs; Substrate description; Food waste; Green waste;

    机译:厌氧消化;ADM1;模型输入;基材描述;食物浪费;绿色废物;

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