While dozens of studies have attempted to estimate the Monod kinetic parameters of microbial reductive dechlorination, published values in the literature vary by 2–6 orders of magnitude. This lack of consensus can be attributed in part to limitations of both experimental design and parameter estimation techniques. To address these issues, Hamiltonian Monte Carlo was used to produce more than one million sets of realistic simulated microcosm data under a variety of experimental conditions. These data were then employed in model fitting experiments using a number of parameter estimation algorithms for determining Monod kinetic parameters. Analysis of data from conventional triplicate microcosms yielded parameter estimates characterized by high collinearity, resulting in poor estimation accuracy and precision. Additionally, confidence intervals computed by commonly used classical regression analysis techniques contained true parameter values much less frequently than their nominal confidence levels. Use of an alternative experimental design, requiring the same number of analyses as conventional experiments but comprised of microcosms with varying initial chlorinated ethene concentrations, is shown to result in order-of-magnitude decreases in parameter uncertainty. A Metropolis algorithm which can be run on a typical personal computer is demonstrated to return more reliable parameter interval estimates.

中文翻译：

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连续莫诺动力学参数的可靠估计

虽然数十项研究试图估计微生物还原脱氯的莫诺动力学参数，但文献中公布的值相差 2-6 个数量级。这种缺乏共识的部分原因是实验设计和参数估计技术的局限性。为了解决这些问题，使用哈密顿蒙特卡罗在各种实验条件下生成了超过一百万组真实的模拟微观世界数据。然后将这些数据用于模型拟合实验，使用多种参数估计算法来确定莫诺动力学参数。对传统三重微观世界数据的分析产生了具有高共线性的参数估计，导致估计准确度和精确度较差。此外，通过常用的经典回归分析技术计算的置信区间包含真实参数值的频率远低于其名义置信水平。使用替代实验设计，需要与常规实验相同数量的分析，但由具有不同初始氯化乙烯浓度的微观世界组成，结果显示参数不确定性会出现数量级的下降。可以在典型个人计算机上运行的 Metropolis 算法被证明可以返回更可靠的参数区间估计。