解决TAP反应器脉冲响应分析中的反问题
Resolving the inverse problem in pulse response analysis of TAP reactors
AI总结：
研究TAP反应器脉冲实验中反问题，用局部方波脉冲离散反问题，经截断奇异值分解求解，与傅里叶滤波法对比，结果更好，利于脉冲响应分析自动化及其他离散反问题技术应用。
摘要：
TAP反应器中的脉冲实验是研究气固催化反应瞬态动力学的重要方法。Y程序是从气体出口通量测量推断反应速率R与反应物浓度C关系的无模型分析框架，但因反向追踪扩散传输放大测量噪声而受限。本文明确其反问题，用反问题工具处理，不用特设噪声滤波器预处理测量，结果更好，利于自动化及其他技术应用。
英文摘要：
Pulse experiments in the temporal analysis of products (TAP) reactor are one of the most important methods for studying transient kinetics of gas-solid catalytic reactions. The Y-procedure (Yablonsky et al., Chem. Eng. Sci. 62, 6754, 2007) is a model-free analysis framework for inferring the relationship between the reaction-rate $R$ and the reactant concentration $C$ from measurements of the outlet flux of gas. While elegant in conception, its application is hindered by the amplification of measurement noise that results from having to backtrack diffusive transport from the outlet to the reaction zone. Here, we explicitly recognize the inverse problem inherent in the Y-procedure and treat it using well-developed tools from the field of inverse problems. While previous implementations of the Y-procedure used Fourier-based filtering, we do not pre-process the measurements with an ad hoc noise-filter. Instead, we use a basis of localized square pulses to formulate a discrete inverse problem, whose regularized solution is obtained via the truncated singular value decomposition (TSVD) method. This method requires one to select a cutoff mode number; while we show how the choice of this regularization parameter can be guided by a Picard plot, we also develop an objective selection strategy for state defining experiments, for which $R(C)$ is a single-valued function. We apply our proposed inverse-problem approach to synthetic data corresponding to linear and nonlinear reactions and compare the results with the Fourier-filtration method. The former produces better reconstructions of the $R$ vs $C$ relationship, especially for nonlinear reactions. Our work facilitates the automation of pulse response analyses and enables the application of other discrete inverse-problem techniques, such as Tikhonov regularization or machine-learning methods.
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