Analysis Of Pressure Fluctuation Signals Research Articles

Novel Application of Pretreatment and Diagnostic Method Using Dynamic Pressure Fluctuations to Resolve and Detect Issues Related to Biogenic Residue Ash in Chemical Looping Gasification

Biogenic residues are a promising feedstock to produce liquid biofuels via chemical looping gasification (CLG), but they form ashes with a high inorganic matter content, thus causing agglomeration and deposition in CLG-fluidized beds made of oxygen carriers (OC). The aim of this work is to develop pretreatments for residual biomasses to prevent this issue. Raw forest pine (as a reference material) and wheat straw residues were considered. The latter were pretreated by torrefaction at 250, 260, or 270 °C and through the washing of torrefied biomasses. Torrefaction encouraged a de-chlorinating effect, while washing allowed the removal of 30–40% of S, 60–70% of K, and 40–50% of P. The analysis of pressure fluctuation signals (standard deviations and dominant frequencies) was utilized to verify the improvement of the performance of treated biomass in fluidized beds: three OCs were, respectively, coupled with ashes from all biomasses, then fluidized from 700 to 1000 °C at two and three times the minimum fluidization velocity. The diagnostic method used to analyze pressure fluctuations was shown to be effective for detecting the incipient fading of bubbling fluidization. This phenomenon was related to the agglomeration or the severe fragmentation of OC particles mixed with ashes, thanks to scanning electron microscopy and particle-size measurements. These characterizations and pressure fluctuations analyses confirmed the general improvement of wheat straw performances after pretreatments.

Surge protection system development in centrifugal compressor with an indicative method using numerical simulation of unsteady processes and analysis of pressure fluctuation signals

Nowadays, anti-surge protection system based on parametric method is widely used. The disadvantage of this anti-surge system is the fact that it is not possible to fully ensure the safe operation of the compressor at low flow conditions, because large measurement error will occur. Other method of anti-surge protection is indicative method. This method is what it is necessary to detect occurrence of rotating stall in time on the score of information criterion. Further the control decision is accepted in connection with the approach to the danger zone of surge. Indicative method is more complicated and requires special signal analysis such as wavelet analysis, correlation analysis. Also in addition to the methods of analysis, important is the length of the sample for processing unsteady pressure fluctuations and the method of its displacement on time. It is now possible to mathematically pre-simulate unsteady processes in the flow part of centrifugal compressor.

Pressure fluctuation signal analysis of a hydraulic turbine based on variational mode decomposition

Due to the nonstationarity of the pressure fluctuation signal from a hydraulic turbine draft tube, this research proposes a pressure fluctuation signal analysis method based on variational mode decomposition (VMD). Firstly, the research was used to compare VMD with empirical mode decomposition (EMD) by using a simulation signal, which shows that the VMD method could effectively inhibit mode mixing, but EMD method could not. This could improve the accuracy of such analysis. Then, the VMD method was applied to analyze the real pressure fluctuation signal from a hydraulic turbine. The results showed that VMD produced clearer time–frequency spectra. This allowed accurate analysis of pressure fluctuation signals. This research provided a new method of pressure fluctuation signal analysis for hydraulic turbine draft tubes.

Analysis of pressure fluctuation signals in the underfeed circulating spouted bed

The underfeed circulating spouted bed (UCSB) was designed as a new dry desulfurization reactor. In this article, pressure fluctuations along the axial direction were measured and time, frequency and Shannon entropy analysis of pressure signals were studied under different operation parameters. The results indicate: the pressure fluctuations express different characteristics at various axial locations. And the operation parameters have significant influence on the flow characteristic in the riser. Advancing the spouting velocity or the circulating ratio, the solid concentration in the riser increases and the amplitude of the pressure fluctuations rises. With higher injecting velocity or nozzle position, the gas–solid turbulence in the bottom bed gets more intense. It shows that the existence of injecting flow near the underfeed nozzle has a distinct effect on strengthening the particle mixing. The power spectrum density acquired from the signals can coincidently reflect the dynamic behavior of the gas–solid flow in the riser. Analysis of Shannon entropy also corresponds to the above summarization of flow characteristic very well.

Multifractal detrended fluctuation analysis of pressure fluctuation signals in an impinging entrained-flow gasifier

Abstract On a laboratory-scale testing platform of impinging entrained-flow gasifier with two-burner, the pressure signals are measured at two axial positions with stainless steel water-cooled probes. The probes are traversed radially from the wall to the reactor centerline (spaced 15 cm apart) for six radial positions (0, 3, 6, 9, 12, and 15 cm) to acquire pressure signals. Multifractal detrended fluctuation analysis (MF-DFA) is a method which is accurate and easy to implement, therefore it is used on the pressure fluctuation to examine the multifractal characteristics. It is found that the pressure fluctuation exhibits multifractal characteristics, the analysis of the pressure fluctuation signals under “unstable operating state”, “stable operating state” and also “cold state” suggests that the combustion system is characterized by a dynamical change from heterogeneity toward homogeneity, revealed by a loss of multifractality. Moreover, the analysis confirms the existence of multifractal characteristics in the investigated pressure fluctuation series. Origin of multifractal phenomena of the pressure signal measured in the entrained-flow gasifier is interpreted in terms of the multiplicative cascade process.

Multifractal analysis of pressure fluctuation signals in an impinging entrained-flow gasifier

A study of pressure fluctuation signals in an impinging entrained-flow gasifier was undertaken using multifractal analysis. By applying the method of direct determination of the f( α) singularity spectrum, the values of different radial position pressure fluctuation signals’ singularity spectra were calculated. It was found that the pressure fluctuation exhibited the multifractal characteristics, the comparison of the pressure fluctuation signals from “unstable operating condition” to “stable operating condition” suggests that the combustion system is characterized by a dynamical change from heterogeneity toward homogeneity, revealed by a loss of multifractality. Origin of multifractal phenomena of the pressure signal measured in the entrained-flow gasifier was interpreted in terms of the multiplicative cascade process.

Fluidization regimes in different spouted bed apparatus constructions

In this study, prediction of transition from fixed bed to spouting bed are carried out and different spouting regimes are characterized by analysis of pressure fluctuation signals. Pressure signals are measured in three different constructions of spouted bed apparatuses, operated under ambient conditions and under different spouting regimes. The presented spouted beds are developed for handling of non-spherical particles and poly dispersed and finely dispersed systems. Several flow regimes are found to exist as: fixed bed, channel formation, bubbling formation, stable spouting and slugging bed regimes. Different methods, based on time domain analysis, frequency domain analysis and phase space analysis have been applied to identify the flow regimes. Stable spouting regime is characterized by a low amplitude (respectively, low standard deviation) of pressure fluctuations, maximum in the dominant frequency. Time-series analysis of pressure fluctuations in spouted beds, using techniques from deterministic chaos theory, has revealed that a spouted bed is a chaotic system that can be characterized with a low-dimensional strange attractor.

Characterization of spouted bed regimes using pressure fluctuation signals

This work compares time, frequency and phase space analyses of pressure measurements in different spouted beds. The experiments were carried out in different constructions of spouted bed apparatuses, operated under ambient conditions and under different spouting regimes. Spouted beds are used when the conventional fluidized beds fail to achieve a homogeneous and stable flow regime as, for example, in the case of non-spherical particles and in poly dispersed and finely dispersed systems. Different fluidization regimes in spouted beds have been characterized by the analysis of pressure fluctuation signals. Several flow regimes are found to exist as: fixed bed, channel formation, bubbling formation, stable spouting and slugging bed regimes. Analyses of standard deviation and chaotic time series on pressure fluctuation signals are conducted to determine the transition gas velocities. A treatment technique using the Fast Fourier Transformation of measured pressure fluctuations was developed to create plots describing the bed behaviour evolution from fixed to slugging bed. At the beginning of stable spouting the amplitude of pressure fluctuations is uniform and small.

Flow characteristics in a bubbling fluidized bed at elevated temperature

A bubbling fluidized bed with 0.082 m in diameter and 1.5 m in height was employed to investigate the minimum fluidization velocity and flow dynamics at bed temperature up to 1000 °C. Ashes of three sizes (Geldart B) from pressurized fluidized bed boiler were used as fluidization materials. Experiments show that the minimum fluidization velocity decreases with increasing bed temperature. Pressure fluctuation signals were analyzed by using power spectral density function (PSDF), chaos, and wavelet analysis. The major frequency of pressure fluctuation signals is in the range from 1 to 4 Hz in the fluidized state. It demonstrates that a bubbling fluidized bed at high temperature is a deterministic chaos system since all the largest Lyapunov exponents are positive. The correlation dimension and Kolmogorov entropy increase with an increase in the fluidization number, and then they vary little with increasing fluidization gas velocity. By the wavelet transform, the fluctuating pressure signals in the bed can be decomposed into its approximations and details at different resolutions. The number of peaks in the scale six detail signal represents the number of bubbles passing through pressure probe measurement region over the sampling time, which agrees with the major frequency obtained from PSDF analysis of pressure fluctuation signals.

Wavelet analysis of pressure fluctuation signals in a gas-solid fluidized bed

Wavelet analysis of pressure fluctuation signals in a gas-solid fluidized bed

Wavelet analysis of pressure fluctuation signals in a gas-solid fluidized bed

Wavelet analysis of pressure fluctuation signals in a gas-solid fluidized bed

Wavelet transform analysis of pressure fluctuation signals in a pressurized bubble column

Several industrial bubble column reactors are operated at high pressures and it is important to understand its influence on the hydrodynamics and mass transfer. Pressure fluctuation signals in a pressurized bubble column are very complex since a bubble column behaves highly irregularly. The characteristics of the pressure fluctuations have been studied by several analytical methods. The recently developed technique of wavelet transform based on localized wavelet functions is applicable to the analysis of nonlinear or nonstationary pressure fluctuation signals: the time series of pressure fluctuation signals have been analyzed by means of discrete wavelet transform coefficients and multiresolution decomposition. By resorting to this wavelet transform, a pressure signal in a pressurized bubble column can be decomposed into its approximations and details at different resolutions. The energy of the details provides a measure of irregularity of the signal at various resolutions. And this wavelet transform enables us to obtain the frequency content of objects in a pressurized bubble column locally in time. In this work, the wavelet transform technique has been applied to the characterization of the fluctuating pressure signals generated in a pressurized bubble column, which are directly connected with the bubble phenomena in it.

Wavelet transform analysis of pressure fluctuation signals in a three-phase fluidized bed

The wavelet transform based on localized wavelet functions is applicable to analysis of pressure fluctuation signals from different flow regimes of a three-phase fluidized bed, which usually is nonlinear or nonstationary. The pressure fluctuation has been analyzed by resorting to the discrete wavelet transform such as wavelet coefficients, wavelet energy, and time-scale plane. The dominant scale of wavelet coefficients and the highest wavelet energy in the bubble-disintegrating regime are finer than ones in the bubble-coalescence regime. The cells corresponding to fine scale of time-scale plane in bubble-disintegrating regime are more shaded and energetic, while the cells corresponding to coarse scale in bubble-coalescence regime are more energetic. Therefore, the wavelet transform enables us to obtain the frequency content of objects in a three-phase fluidized bed locally in time.

Characterizations of flow regime transitions in a high-pressure bubble column by chaotic time series analysis of pressure fluctuation signals

In this study, experiments are carried out for a high-pressure bubble column with a perforated plate as the distributor under the operating pressure up to 15 MPa . Gas holdups and pressure fluctuation signals are measured. Analyses of standard deviation and chaotic time series on pressure fluctuation signals are conducted to determine the transition gas velocities. The results of chaotic measures, such as the largest Lyapunov exponent, metric entropy and the correlation dimension show that there are bands of transition velocities for each operating pressure. The general trend is that the transition velocities increase as the operating pressure rises. Similar trends are also found for the transition velocities obtained by the standard deviation. All the transition velocities resulting from the chaotic measures and the standard deviation show good agreements.

Predictions of flow transitions in a bubble column by chaotic time series analysis of pressure fluctuation signals

In this study, predictions of flow transitions of a bubble column are carried out by chaotic time series analysis of pressure fluctuation signals. Pressure signals are measured for four different distributor plates with the superficial gas velocity varying from 0 to approximately 0.22 m/ s . Methodology of the chaotic time series analysis is presented, including procedures of the time-delayed phase-space reconstruction and calculation of the chaotic invariants. Four chaotic invariants, which include the largest Lyapunov exponent, metric entropy, correlation dimension and mutual information, of all pressure signals are computed. The critical gas velocities are determined by the sharp increase or decrease of the obtained chaotic invariants at the flow regime transitions. This simple criterion is consistent with the experimental observations. The obtained critical gas velocities by various chaotic measures for the four different distributor plates are quantitatively consistent and are in good agreement with the results by the gas holdup data.

Wavelet analysis of pressure fluctuation signals in a bubbling fluidized bed

Pressure fluctuations of fluidized beds have been used to evaluate the fluidization quality. In bubbling fluidized beds, the bed behavior is characterized by bubbling. The information indicated by the pressure fluctuation signals can be applied to describe those behaviors. The signals representing the characteristics of bubbling can be separated from original signals through discrete Wavelet analysis. Considering the principles of Wavelet, the Scale 4 detail signals can reveal the bubble behaviors in a fluidized bed. The peak frequency of the Scale 4 detail signal stands for the bubbling frequency and the peak amplitude for the bubble size.