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在ADM1中纳入粉煤灰效应:使用实际废水对一个简单函数进行校准和验证。

Inclusion of fly ash effects in ADM1: calibration and validation of a simple function using real wastewater.

作者信息

Vargas-Morales Gustavo, Lauzurique Yeney, Huiliñir Cesar

机构信息

CETAQUA, Centro Tecnológico del Agua, Santiago, Chile.

Departamento de Ingeniería Química y Bioprocesos, Universidad de Santiago de Chile, Santiago, Chile.

出版信息

Biodegradation. 2025 May 3;36(3):38. doi: 10.1007/s10532-025-10134-4.

DOI:10.1007/s10532-025-10134-4
PMID:40317375
Abstract

Including a source of trace elements such as fly ash (FA) in the ADM1 has yet to be addressed, including calibration and validation. The present work aimed to propose, calibrate, and validate a simple function for including the FA effect in the ADM1 using real wastewater from the winery industry. The new function and the ADM1 were calibrated with data of daily methane production coming from a biochemical methane potential (BMP) test of winery wastewater without FA and validated for BMPs using five FA concentrations (25, 50, 75, 100, and 150 mg/L). The ADM1 calibration showed that the model fits well with the experimental data, although the parameter values are lower than the range reported in the literature. The calibration of the proposed function fits well with the experimental data under all conditions, including the inhibition and stimulating zones. Lastly, the modified ADM1 could accurately represent the accumulated methane at different FA concentrations, with NRMSE between 0.0635 and 0.1342. Furthermore, the sensitivity analysis shows that the proposed function only requires calibrating one parameter (k). Thus, the proposed model is robust and valid for all the tested conditions.

摘要

在ADM1中纳入微量元素来源(如粉煤灰(FA)),包括校准和验证,这一问题尚未得到解决。本研究旨在提出、校准和验证一个简单函数,以便使用酿酒业的实际废水将FA效应纳入ADM1。新函数和ADM1使用不含FA的酿酒废水的生化甲烷潜力(BMP)测试中的每日甲烷产量数据进行校准,并使用五种FA浓度(25、50、75、100和150mg/L)对BMP进行验证。ADM1校准表明,尽管参数值低于文献报道的范围,但该模型与实验数据拟合良好。所提出函数的校准在所有条件下,包括抑制区和刺激区,都与实验数据拟合良好。最后,改进后的ADM1能够准确表示不同FA浓度下累积的甲烷,归一化均方根误差(NRMSE)在0.0635至0.1342之间。此外,敏感性分析表明,所提出的函数只需要校准一个参数(k)。因此,所提出的模型在所有测试条件下都是稳健且有效的。

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本文引用的文献

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