Soft sensor for substrate characterization through the reverse application of the ADM1 model for anaerobic digestion plant operations

F. Zorrilla, M. C. Sadino-Riquelme, Felipe Hansen, Andrés Donoso-Bravo
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Abstract

Accurately characterizing the substrate used in anaerobic digestion is crucial for predicting the biogas plant's performance. This issue makes particularly challenging the application of modeling in codigestion plants. In this work, a novel methodology called substrate prediction module (SPM) has been developed and tested, using virtual codigestion data. The SPM aims to estimate the inlet properties of the substrate based on the reverse application of the anaerobic digestion model n1 (ADM1). The results show that, while the SPM can estimate some properties of the substrate based on certain output parameters, there are limitations in accurately determining all required variables.
通过将 ADM1 模型反向应用于厌氧消化设备运行,实现基质表征的软传感器
准确描述厌氧消化所用基质的特征对于预测沼气厂的性能至关重要。这个问题使得在代码消化设备中应用建模尤其具有挑战性。在这项工作中,利用虚拟代码消化数据,开发并测试了一种称为基质预测模块(SPM)的新方法。SPM 的目的是在反向应用厌氧消化模型 n1 (ADM1) 的基础上估计基质的入口特性。结果表明,虽然 SPM 可以根据某些输出参数估计基质的某些特性,但在准确确定所有所需变量方面存在局限性。
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