Global sensitivity analysis and uncertainty quantification for a mathematical model of dry anaerobic digestion in plug-flow reactors.

IF 2.6 4区 工程技术 Q1 Mathematics
Daniele Bernardo Panaro, Andrea Trucchia, Vincenzo Luongo, Maria Rosaria Mattei, Luigi Frunzo
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引用次数: 0

Abstract

In many applications, complex biological phenomena can be reproduced via structured mathematical models, which depend on numerous biotic and abiotic input parameters, whose effect on model outputs can be of paramount importance. The calibration of model parameters is crucial to obtain the best fit between simulated and experimental data. Sensitivity analysis and uncertainty quantification constitute essential tools in the field of biological systems modeling. Despite the significant number of applications of sensitivity analysis in wet anaerobic digestion, there are no examples of global sensitivity analysis for mathematical models describing the dry anaerobic digestion in plug-flow reactors. For the first time, the present study explores the global sensitivity analysis and uncertainty quantification for a plug-flow reactor model. The investigated model accounts for the mass$ / $volume variation that takes place in these systems as a result of solid waste conversion in gaseous value-added compounds. A preliminary screening based on the Morris' method allowed for the definition of three different groups of parameters. A surrogate model was constructed to investigate the relation between input and output parameters without running demanding simulations from scratch. The obtained Sobol' indices allowed to perform the quantitative global sensitivity analysis. Finally, the uncertainty quantification results led to the definition of the probability density function related to the investigated quantity of interest. The study showed that the net methane production is mostly sensitive to the values of the conversion parameter related to the particulate biodegradable volatile solids in acetic acid $ k_1 $ and to the kinetic parameter describing the acetic acid uptake $ k_2 $. The application of these techniques led to helpful information for model calibration and validation.

塞流反应器干式厌氧消化数学模型的全局敏感性分析和不确定性量化。
在许多应用中,复杂的生物现象可以通过结构化的数学模型来再现,而数学模型取决于许多生物和非生物输入参数,这些参数对模型输出的影响至关重要。模型参数的校准对于获得模拟数据和实验数据之间的最佳拟合效果至关重要。灵敏度分析和不确定性量化是生物系统建模领域的重要工具。尽管敏感性分析在湿法厌氧消化中有大量应用,但还没有对描述塞流式反应器中干法厌氧消化的数学模型进行全局敏感性分析的实例。本研究首次探讨了塞流式反应器模型的全局敏感性分析和不确定性量化。所研究的模型考虑了这些系统中固体废物转化为气态增值化合物时产生的质量/体积变化。根据莫里斯方法进行的初步筛选确定了三组不同的参数。为了研究输入和输出参数之间的关系,我们建立了一个替代模型,而无需从头开始进行苛刻的模拟。根据所获得的索博尔指数,可以对全局敏感性进行定量分析。最后,根据不确定性量化结果,确定了与所研究的相关数量有关的概率密度函数。研究表明,甲烷净产量对醋酸中可生物降解挥发性固体颗粒相关的转换参数 k_1 $ 和描述醋酸吸收的动力学参数 k_2 $ 的值最为敏感。这些技术的应用为模型校准和验证提供了有用的信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Mathematical Biosciences and Engineering
Mathematical Biosciences and Engineering 工程技术-数学跨学科应用
CiteScore
3.90
自引率
7.70%
发文量
586
审稿时长
>12 weeks
期刊介绍: Mathematical Biosciences and Engineering (MBE) is an interdisciplinary Open Access journal promoting cutting-edge research, technology transfer and knowledge translation about complex data and information processing. MBE publishes Research articles (long and original research); Communications (short and novel research); Expository papers; Technology Transfer and Knowledge Translation reports (description of new technologies and products); Announcements and Industrial Progress and News (announcements and even advertisement, including major conferences).
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