The biochemical methane potential (BMP) test is significant for the landfill industry as it provides a means to evaluate the gas potential, and therefore potential degradability, of both incoming and in-place municipal solid waste (MSW). However, the BMP test is not standardized making comparison of BMP results across sites problematic. For example, the BMP test duration has historically ranged from 20 days to several months with most current BMP tests lasting 60 days. However, the gas generation data can potentially be modelled for any of those durations to produce a prediction of the ultimate BMP value (BMPULT). Currently, the predicted BMPULT values of 23 long-duration (115–150 days) BMP tests were used to determine the required quantity of data (i.e. number of days) needed to produce an accurate BMPULT prediction. Results showed that no single test duration produced both accurate and efficient results, so a novel performance-based endpoint was proposed. The relative change in predicted BMPULT values with respect to time (dBMPULT/d t) was chosen as a potential performance-based completion metric. Results indicate that once the absolute normalized dBMPULT/d t value is within <2.5, <1.5 and <0.6% day−1 that the predicted BMPULT is within 20, 10 and 5% of the true BMPULT, respectively. Overall, the use of performance-based metrics for determining BMP test completion will allow for the collection of partial data sets, reduced experimental times and verification of results.

中文翻译：

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一种评价和验证填埋城市固体废物生化甲烷潜力测试完成情况的方法

生化甲烷潜力 (BMP) 测试对于垃圾填埋场行业具有重要意义，因为它提供了一种评估进入和就地城市固体废物 (MSW) 的气体潜力以及潜在降解性的方法。然而，BMP 测试并未标准化，使得跨站点的 BMP 结果比较存在问题。例如，BMP 测试持续时间历来从 20 天到几个月不等，当前大多数 BMP 测试持续 60 天。然而，可以对任何这些持续时间的气体生成数据进行建模，以生成最终 BMP 值的预测 (BMP超低温试验）。目前，预测的 BMP超低温试验使用 23 个长期（115-150 天）BMP 测试的值来确定生成准确的 BMP 所需的数据量（即天数）超低温试验预言。结果表明，没有一个测试持续时间能够产生既准确又有效的结果，因此提出了一种新的基于性能的终点。预测 BMP 的相对变化超低温试验相对于时间的值 (dBMP超低温试验/dt）被选为潜在的基于绩效的完成指标。结果表明，一旦绝对归一化 dBMP超低温试验/dt 值在 <2.5、<1.5 和 <0.6% 天以内−1预测的 BMP超低温试验与真实 BMP 的误差在 20%、10% 和 5% 以内超低温试验， 分别。总体而言，使用基于性能的指标来确定 BMP 测试完成情况将允许收集部分数据集、减少实验时间并验证结果。