Characteristic Investigation on Fluid Signals Based on a Combination of Empirical Mode Decomposition and Hilbert Transform in a Jet Impact-Negative Pressure Deamination Reactor,Industrial & Engineering Chemistry Research

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Characteristic Investigation on Fluid Signals Based on a Combination of Empirical Mode Decomposition and Hilbert Transform in a Jet Impact-Negative Pressure Deamination Reactor
Industrial & Engineering Chemistry Research ( IF 3.8 ) Pub Date : 2024-06-20 , DOI: 10.1021/acs.iecr.4c00627
Xingzong Zhang ₁ , Xinjie Chai ₁ , Facheng Qiu ₁ , Yuxi Hu ₁ , Yingying Dong ₁ , Wensheng Li ₁ , Zhiliang Cheng ₁

Affiliation

The jet impact-negative pressure reactor (JI-NPR) is a novel wastewater treatment technology developed for the efficient removal of high-concentration ammonia nitrogen. However, the complex and transient nature of the flow behavior within the JI-NPR poses significant challenges for understanding the underlying fluid dynamics. In this work, a comprehensive signal-processing framework was developed to elucidate the flow characteristics inside the JI-NPR. First, a flow signal acquisition platform was established to capture the negative pressure signals during the treatment process. The empirical mode decomposition (EMD) technique was then employed to decompose the turbulent flow signals into a series of intrinsic mode functions (IMFs), representing multiscale turbulent eddy characteristics. To mitigate the effects of local noise and abrupt changes, various curve fitting methods, including cubic spline interpolation, piecewise cubic Hermite interpolating polynomial, and Makima interpolation, were utilized to smooth the IMF signals. The Hilbert transform was subsequently applied to extract the instantaneous frequency features of the smoothed IMFs, enabling more accurate quantification of the nonstationary and nonlinear flow behavior. The results revealed that the low-frequency IMFs were associated with the interactions between the wastewater jet and negative pressure, while the high-frequency IMFs reflected the internal dynamic evolution of the fluid. Furthermore, the multiple regression analysis approach was adopted to quantify the relationship between the IMF feature parameters and the critical performance metric of the denitrification efficiency. The decision tree regression model was identified as a particularly suitable technique, as it can flexibly capture both linear and nonlinear dependencies and effectively identify the most influential variables. This integrated approach of EMD, curve fitting, Hilbert transform, and regression analysis methods provides valuable insights into the quantitative impact of multiscale turbulent eddies on the overall performance of the JI-NPR system. These findings are expected to guide targeted optimization of the reactor design to enhance the denitrification efficiency, a crucial goal for the practical application of this wastewater treatment technology.

中文翻译：

基于经验模态分解和希尔伯特变换相结合的射流冲击-负压脱氨反应器流体信号特征研究

射流冲击负压反应器（JI-NPR）是针对高效去除高浓度氨氮而开发的一种新型废水处理技术。然而，JI-NPR 内流动行为的复杂性和瞬态性对理解潜在的流体动力学提出了重大挑战。在这项工作中，开发了一个全面的信号处理框架来阐明 JI-NPR 内部的流动特征。首先，建立流量信号采集平台，采集治疗过程中的负压信号。然后采用经验模态分解（EMD）技术将湍流信号分解为一系列固有模态函数（IMF），代表多尺度湍流涡流特征。为了减轻局部噪声和突变的影响，采用了各种曲线拟合方法，包括三次样条插值、分段三次 Hermite 插值多项式和 Makima 插值，来平滑 IMF 信号。随后应用希尔伯特变换来提取平滑 IMF 的瞬时频率特征，从而能够更准确地量化非平稳和非线性流动行为。结果表明，低频IMF与废水射流和负压之间的相互作用有关，而高频IMF则反映了流体的内部动态演化。此外，采用多元回归分析方法量化IMF特征参数与反硝化效率关键性能指标之间的关系。决策树回归模型被认为是一种特别合适的技术，因为它可以灵活地捕获线性和非线性依赖性，并有效地识别最有影响力的变量。这种 EMD、曲线拟合、希尔伯特变换和回归分析方法的集成方法为多尺度湍流涡流对 JI-NPR 系统整体性能的定量影响提供了宝贵的见解。这些发现有望指导有针对性的反应器设计优化，以提高反硝化效率，这是该废水处理技术实际应用的一个关键目标。

更新日期：2024-06-20

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