CN111007153B - Detection method for gas-liquid dispersion state of jet bubbling reactor - Google Patents
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Abstract
The invention discloses an acoustic emission detection method for gas-liquid dispersion state in a jet bubbling reactor, which comprises the following steps of collecting acoustic signals in the reactor by using an acoustic wave sensor arranged on the outer wall surface of a container, and determining the gas-liquid dispersion state according to the slope k of a characteristic parameter of the acoustic signals along with the Reynolds number change curve of liquid: when k is 0, corresponding to a flooding state; when k is more than 0, corresponding to the state of carrier gas; when k < 0, this corresponds to a completely dispersed state. The method belongs to a non-invasive nondestructive measurement technology, and is high in measurement precision, safe and environment-friendly.
Description
Technical Field
The invention relates to the technical field of petrochemical industry, in particular to a detection method for a gas-liquid dispersion state of a jet bubbling reactor.
Background
In the jet bubbling reactor, the shearing and crushing action of the vertical downward immersed liquid jet on bubbles can realize the high-efficiency mixing of gas and liquid in the reactor. Compared with the traditional stirred tank reactor, the liquid jet flow is utilized to replace the stirring paddle, so that on one hand, the mechanical maintenance cost of the moving equipment can be reduced, and on the other hand, the vibration problem of the stirring paddle is effectively avoided when the liquid circulation volume is large. The gas-liquid dispersion state has an important influence on the performance of the jet bubbling reactor, and poor gas-liquid dispersion effect can reduce the gas-liquid mixing effect, the gas-liquid mass transfer efficiency and the reaction rate, thereby reducing the yield.
At present, the detection of the gas-liquid dispersion state in the jet bubbling reactor is not reported in a public way, but in a gas-liquid stirring kettle, the detection method of the gas-liquid dispersion state mainly comprises an visual detection method, an invasive probe measurement technology, an acoustic emission detection technology and the like. The visual detection method requires the reactor to be transparent, is not suitable for detecting the gas-liquid dispersion state in the industrial reactor, but the invasive probe detection method can damage the flow field and has low precision. As a novel detection means, the acoustic emission detection technology has the characteristics of sensitive detection, real-time online detection, no invasion to a flow field and the like, and is widely applied to detection of multiphase flow systems such as a stirring bed, a fluidized bed, a stirring kettle and the like.
Therefore, the method adopts a non-invasive passive acoustic emission technology, extracts characteristic parameters representing the gas-liquid dispersion state by collecting acoustic signals in the reactor, and obtains a method for rapidly, effectively and real-timely monitoring the gas-liquid dispersion state.
Disclosure of Invention
The invention provides an acoustic emission detection method for gas-liquid dispersion state of a jet bubbling reactor, which has the characteristics of sensitive detection, safety, environmental protection, simplicity, rapidness and the like.
The jet bubbling reactor consists of a reactor cylinder, a liquid nozzle arranged at the top of the reactor cylinder and a gas distributor arranged below the liquid nozzle. Along with the increase of the outlet flow rate of the liquid nozzle, the gas-liquid dispersion state in the reactor sequentially undergoes three flow patterns of gas flooding, carrier gas, complete dispersion and the like, wherein the Reynolds number of the outlet of the liquid nozzle corresponding to a boundary point of the gas flooding state and the carrier gas state is called as a Reynolds number of the liquid at the flood point, and the Reynolds number of the outlet of the liquid nozzle corresponding to the boundary point of the carrier gas state and the complete dispersion state is called as a Reynolds number of the complete dispersion liquid.
The acoustic emission detection of the gas-liquid dispersion state in the jet bubbling reactor comprises the following steps:
(1) at least 1 acoustic wave sensor is arranged on the outer wall of the reactor and used for receiving acoustic wave signals in the reactor;
(2) preprocessing the collected sound wave signals to remove noise, and extracting sound signal characteristic parameters;
(3) determining the gas-liquid dispersion state according to the slope k of the Reynolds number change curve of the acoustic signal characteristic parameter along with the outlet of the liquid nozzle: when k is 0, corresponding to a flooding state; when k is more than 0, corresponding to the state of carrier gas; when k < 0, this corresponds to a completely dispersed state.
The determination of the Reynolds number of the overtime liquid comprises the following steps: gradually increasing the Reynolds number of the liquid at the outlet of the nozzle from zero, recording a change curve of the characteristic parameters of the acoustic signals along with the Reynolds number of the outlet of the liquid nozzle, and calculating a slope k, wherein the corresponding Reynolds number of the liquid is the Reynolds number of the liquid at the overtop when k is changed from zero to a positive value;
the determination of the Reynolds number of the completely dispersed liquid comprises the following steps: gradually increasing the Reynolds number of the liquid at the outlet of the nozzle, recording a change curve of the characteristic parameters of the acoustic signals along with the Reynolds number of the outlet of the liquid nozzle, and calculating a slope k, wherein the Reynolds number of the liquid corresponding to the Reynolds number of the liquid is the Reynolds number of the completely dispersed liquid when k is changed from a positive value to a negative value;
and (3) selecting one or more of smoothing, differentiation, multivariate scattering correction, orthogonal signal correction, Fourier transform, wavelet transform and net analysis signals as the method for removing noise by preprocessing in the step (2). Smoothing can improve the signal-to-noise ratio of the analysis signal, and the most common methods are moving average smoothing and Savizky-Golay polynomial smoothing; the differential can eliminate baseline drift, strengthen spectral band characteristics and overcome spectral band overlapping, the first-order differential can eliminate drift irrelevant to the wavelength, and the second-order differential can take out drift relevant to the wavelength linearly; the Fourier transform can realize the conversion between a spectral domain function and a time domain function, and can be used for carrying out smooth denoising, data compression and information extraction on a sound spectrum; the wavelet transformation transducer decomposes the signal into a plurality of scale components according to different frequencies, and adopts sampling step lengths with corresponding thicknesses for the scale components with different sizes, thereby being capable of focusing on any part in the signal; the basic idea of the net analysis signal algorithm is basically the same as that of the orthogonal signal correction, and information irrelevant to the component to be measured in the sound spectrum array is removed through orthogonal projection.
And carrying out data processing on the denoised acoustic signals to obtain acoustic signal characteristic parameters such as a fluctuation distribution index FI for judging a gas-liquid dispersion state, a statistic value S based on an attractor comparison method, a fault coefficient C based on complexity analysis and the like.
The fluctuation distribution index is defined by using a dimensionless molecular weight distribution index which is defined based on a moment method and describes a molecular weight distribution pattern of a polymer in polymerization reaction engineering for reference, and the FI calculation step is as follows: firstly, the preprocessed acoustic signals are divided into n sections at equal time intervals t, and the standard deviation sigma in the ith section of time is calculatediCalculating the average value X of the standard deviation of the acoustic signal:
secondly, a basic fluctuation parameter of the standard deviation of the acoustic signal is calculated with X as a reference. Two basic fluctuation parameters are defined here: amplitude d of the standard deviation of the acoustic signal exceeding the baseline X at a certain moment and frequency F of occurrence of data points with amplitude d exceeding the baseline Xn(d)。
Finally, a fluctuation parameter lambda is constructed based on a moment method0、λ1And λ2：
The time interval t is related to the relative sizes of the reactor internal diameter and the bubble diameter. When the inner diameter of the reactor is unchanged, the larger the bubble diameter is, the larger the time interval value is, and the smaller the bubble diameter is, the smaller the time interval value is, but the time interval value is larger than the contact time of the bubbles and the wall surface.
The statistic value S based on the attractor comparison method is calculated as shown in the formula (6);
wherein,
being an estimate of the square of the distance between the delay vector profile of the reference time series of the acoustic emission signal and the delay vector profile of the evaluation time series of the acoustic emission signal,
is composed of
The variance of (c). The reference time sequence of the acoustic emission signal is an acoustic emission signal acquired under a pure jet bubbling-free operation condition, or an acoustic emission signal acquired under a pure jet bubbling-free operation condition;
the fault coefficient C based on the complexity analysis is defined as shown in formulas (7) and (8);
wherein, CD2、CK2Fault coefficients, C, represented by the correlation dimension and the K-entropy, respectivelyD2,a、CK2,aRespectively represents the correlation among the acoustic signal chaos characteristic parameters collected under the jet bubbling operation conditionDimension and K-entropy, CD2,0、CK2,0Respectively representing the correlation dimension and the K-entropy in the acoustic signal chaotic characteristic parameters collected under the pure bubbling or pure jet operation condition.
The acoustic wave sensor is selected from an acoustic emission sensor or an acceleration sensor, preferably an acceleration sensor.
The frequency response range of the acoustic wave sensor is 1 Hz-1 MHz, preferably 10 kHz-100 kHz. The sound wave signals collected by the sound wave sensor are amplified by the amplifying device to ensure long-distance signal transmission, and then enter a signal processing device (computer) for processing and analysis after A/D conversion.
One preferred option is to use multiple acoustic wave sensors. The plurality of acoustic sensors can be arranged at different positions, preferably the plurality of acoustic sensors are sequentially arranged on a straight line along the axial direction of the reactor, and more preferably the plurality of acoustic sensors are sequentially arranged along the circumferential direction of the reactor and are at the same axial height.
A more preferable scheme is that the acoustic wave sensor is arranged at any position below the static liquid level of the outer wall surface of the jet bubbling reactor, and is preferably arranged in a static liquid level height area of the outer wall surface of the reactor, which is close to the bottom 1/7-1/2 of the cylinder body of the reactor.
The invention collects the acoustic signal in the jet bubbling reactor and extracts the characteristic parameter of the acoustic signal, and realizes the real-time online detection of the gas-liquid dispersion state in the jet bubbling reactor according to the corresponding relation between the change characteristic of the characteristic parameter and the gas-liquid dispersion state in the reactor. According to the detection result, the reactor can be optimally designed, and the aims of guiding production and improving production efficiency are fulfilled. The invention has the following advantages:
(1) the acoustic detection is a non-invasive detection method, and cannot influence the movement of multiphase fluid or the reaction inside a reactor;
(2) an emission source is not needed, and the acoustic signal is generated in the fluid movement process, so that the device is safe and environment-friendly;
(3) the requirement on the measurement condition is low, and the device can work in all weather under severe environment;
(4) the reaction is sensitive, the measurement error is small, and real-time online detection can be realized.
Drawings
FIG. 1 is a schematic view of an acoustic emission testing device of the present invention;
in the figure, a centrifugal pump 1, a fan 2, a buffer tank 3, a valve 4, a flowmeter 5, a gas distributor 6, a reactor 7, a liquid nozzle 8, a sound wave sensor 9, a main amplifier 10, a data acquisition card 11 and a computer 12 are arranged.
FIG. 2 shows the variation of the wave distribution index FI with the Reynolds number of the liquid jet for different embodiments.
Detailed Description
By adopting the acoustic emission detection device shown in fig. 1, liquid discharged from the bottom of the reactor 7 is metered by the flowmeter 5 under the pumping action of the centrifugal pump 1 and then is sprayed into the reactor 7 through the liquid nozzle 8 to form liquid circulation, the fan 2 blows gas into the reactor 7 from the gas distributor 6 through the flowmeter 5, the acoustic wave sensor 9 receives acoustic signals generated by the movement of the fluid in the reactor, and the acoustic wave sensor is connected into the computer 12 through the main amplifier 10 and the data acquisition card 11. The acoustic wave sensor is selected from one or two of an acoustic emission sensor or an acceleration sensor, and is preferably an acceleration sensor. The frequency response characteristics of the acoustic wave sensors are the same, and the frequency response range is 1Hz to 1MHz, and preferably 10kHz to 100 kHz. When a plurality of sensors are arranged, the sensors are preferably arranged along the axial direction of the reactor in sequence and on a straight line or the sensors are arranged along the circumferential direction of the reactor in sequence and on the same axial height. The acoustic wave sensor is arranged at any position below the static liquid level of the outer wall surface of the jet bubbling reactor, and preferably in a region of the outer wall surface of the reactor close to the static liquid level of 1/7-1/2 of the bottom of the cylinder of the reactor. And analyzing the change rule of the characteristic parameters of the acoustic signals along with the Reynolds number of the liquid jet. In some embodiments of the invention, the characteristic parameter of the acoustic signal analyzed is the fluctuation distribution index FI. In other embodiments of the present invention, the acoustic signal characteristic parameter analyzed is a statistic S based on attractor comparison. In some embodiments of the invention, the analyzed acoustic signal characteristic parameter is a complexity analysis based fault coefficient C.
The present invention will be described in further detail with reference to examples.
Example 1
By adopting the acoustic emission detection device shown in fig. 1, liquid (water) discharged from the bottom of a reactor 7 is metered by a flowmeter 5 under the pumping action of a centrifugal pump 1 and then is sprayed into the reactor 7 through a liquid nozzle 8 to form liquid circulation, a fan 2 blows gas (air) into the reactor 7 from a gas distributor 6 through the flowmeter 5, an acoustic wave sensor 9 receives acoustic signals generated by the movement of the fluid in the reactor, and the acoustic wave sensor is connected to a computer 12 through a main amplifier 10 and a data acquisition card 11.1 acceleration sensor is placed at the height position of the static liquid level of the outer wall surface of the reactor close to the bottom 1/7 of the reactor cylinder, the sampling frequency is 100kHz, and the sampling time is 10 s. The change rule of the fluctuation distribution index of the acoustic signal along with the Reynolds number of the liquid jet is shown in figure 2(a), and the gas-liquid dispersion state is determined by the slope k of the change curve of FI along with the Reynolds number of the outlet of the liquid nozzle: when k is 0, corresponding to a flooding state; when k is more than 0, corresponding to the state of carrier gas; when k < 0, this corresponds to a completely dispersed state. When k is changed from zero to a positive value, the corresponding liquid Reynolds number is the Pan-Point liquid Reynolds number, and the relative deviation with the measurement result of the visual method is 0; when k is changed from a positive value to a negative value, the corresponding liquid Reynolds number is the Reynolds number of the completely dispersed liquid, and the relative deviation from the measurement result of the visual method is 6.57%.
Example 2
The difference from example 1 is that the sampling frequency is 50kHz, and the acceleration sensor is arranged on the outer wall surface of the reactor at a position close to the static liquid level height of the bottom 1/4 of the reactor cylinder. The change rule of FI along with the Reynolds number of the liquid jet is shown in figure 2(b), and the gas-liquid dispersion state is determined by the slope k of the change curve of FI along with the Reynolds number of the outlet of the liquid nozzle: when k is 0, corresponding to a flooding state; when k is more than 0, corresponding to the state of carrier gas; when k < 0, this corresponds to a completely dispersed state. When k is changed from zero to a positive value, the corresponding liquid Reynolds number is the Pan-Point liquid Reynolds number, and the relative deviation with the measurement result of the visual method is 0; and when k is changed from a positive value to a negative value, the corresponding liquid Reynolds number is the Reynolds number of the completely dispersed liquid, and the relative deviation from the measurement result of the visual method is 0.
Example 3
The difference from the embodiment 1 is that the acoustic wave sensor is an acoustic emission sensor, the acoustic emission sensor is arranged on the outer wall surface of the reactor and close to the height position of 1/2 static liquid level of the bottom of the cylinder body of the reactor, and the sampling frequency is 1 MHz. The change rule of FI along with the Reynolds number of the liquid jet is shown in figure 2(c), and the gas-liquid dispersion state is determined by the slope k of the change curve of FI along with the Reynolds number of the outlet of the liquid nozzle: when k is 0, corresponding to a flooding state; when k is more than 0, corresponding to the state of carrier gas; when k < 0, this corresponds to a completely dispersed state. When k is changed from zero to a positive value, the corresponding liquid Reynolds number is the Pan-Point liquid Reynolds number, and the relative deviation with the measurement result of the visual method is 0; when k is changed from a positive value to a negative value, the corresponding liquid Reynolds number is the Reynolds number of the completely dispersed liquid, and the relative deviation from the measurement result of the visual method is 6.57%.
Example 4
The difference from the example 1 is that the number of the acceleration sensors is 4, the sampling frequency is 10kHz, and 4 acoustic wave sensors are uniformly arranged on the outer wall surface of the reactor at the static liquid level position close to the bottom 1/7 of the reactor cylinder. The change rule of the FI mean value of the acoustic signals collected by the 4 sensors along with the Reynolds number of the liquid jet is shown in figure 2(d), and the gas-liquid dispersion state is determined by the slope k of the change curve of the FI mean value along with the Reynolds number of the outlet of the liquid nozzle: when k is 0, corresponding to a flooding state; when k is more than 0, corresponding to the state of carrier gas; when k < 0, this corresponds to a completely dispersed state. Wherein, when k is converted into a positive value from zero, the corresponding liquid Reynolds number is the Pan-Point liquid Reynolds number, and the relative deviation with the visual measurement result is 11.16%; and when k is changed from a positive value to a negative value, the corresponding liquid Reynolds number is the Reynolds number of the completely dispersed liquid, and the relative deviation from the measurement result of the visual method is 0.
Example 5
The difference from the example 1 is that 3 acceleration sensors are arranged along the axial direction of the reactor in sequence and are in a straight line, and 3 acceleration sensors are arranged on the outer wall surface of the reactor in sequence along the axial direction at 1/7, 1/4 and 1/2 positions close to the static liquid level height at the bottom of the reactor cylinder. The change rule of the FI mean value of the acoustic signals collected by the 3 sensors along with the Reynolds number of the liquid jet is shown in fig. 2(e), and the gas-liquid dispersion state is determined by the slope k of the change curve of the FI along with the Reynolds number of the outlet of the liquid nozzle: when k is 0, corresponding to a flooding state; when k is more than 0, corresponding to the state of carrier gas; when k < 0, this corresponds to a completely dispersed state. Wherein, when k is converted into a positive value from zero, the corresponding liquid Reynolds number is the Pan-Point liquid Reynolds number, and the relative deviation with the visual measurement result is 9.95%; when k is changed from a positive value to a negative value, the corresponding liquid Reynolds number is the Reynolds number of the completely dispersed liquid, and the relative deviation from the measurement result of the visual method is 7.3%.
Example 6
The difference from example 1 is that the acoustic signal characteristic parameter analyzed is a statistic S based on an attractor comparison method. Determining the gas-liquid dispersion state according to the slope k of the change curve of the statistic value S along with the Reynolds number of the outlet of the liquid nozzle: when k is 0, corresponding to a flooding state; when k is more than 0, corresponding to the state of carrier gas; when k < 0, this corresponds to a completely dispersed state. Wherein, when k is converted into a positive value from zero, the corresponding liquid Reynolds number is the Pan-Point liquid Reynolds number, and the relative deviation with the measurement result of the visual method is 8.6%; when k is changed from a positive value to a negative value, the corresponding liquid Reynolds number is the Reynolds number of the completely dispersed liquid, and the relative deviation from the measurement result of the visual method is 7.1%.
Example 7
The difference from embodiment 1 is that the analyzed acoustic signal characteristic parameter is a fault coefficient C based on complexity analysis. Determining the gas-liquid dispersion state according to the gradient k of the change curve of the failure coefficient C along with the Reynolds number of the outlet of the liquid nozzle: when k is 0, corresponding to a flooding state; when k is more than 0, corresponding to the state of carrier gas; when k < 0, this corresponds to a completely dispersed state. Wherein, when k is converted into a positive value from zero, the corresponding liquid Reynolds number is the Pan-Point liquid Reynolds number, and the relative deviation with the visual measurement result is 11.1%; when k is changed from a positive value to a negative value, the corresponding liquid Reynolds number is the Reynolds number of the completely dispersed liquid, and the relative deviation from the measurement result of the visual method is 7.7%.
In conclusion, the acoustic emission detection method provided by the invention can be used for identifying the gas-liquid dispersion state in the jet bubbling reactor, and has higher accuracy.
The above-mentioned embodiments are merely preferred embodiments of the present invention, and not intended to limit the present invention, and any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention, and the technical contents of the present invention, which are claimed, are all described in the claims.
Claims (8)
1. A detection method for gas-liquid dispersion state of a jet bubbling reactor is characterized by comprising the following steps: (1) at least 1 acoustic wave sensor is arranged on the outer wall of the reactor and used for receiving acoustic wave signals in the reactor; (2) preprocessing the collected sound wave signals to remove noise, and extracting sound signal characteristic parameters; (3) determining the gas-liquid dispersion state according to the slope k of the Reynolds number change curve of the acoustic signal characteristic parameter along with the outlet of the liquid nozzle: when k is 0, corresponding to a flooding state; when k is more than 0, corresponding to the state of carrier gas; when k is less than 0, corresponding to a completely dispersed state;
the characteristic parameters of the acoustic wave signals are fluctuation distribution indexes FI and/or statistical values S based on an attractor comparison method and/or fault coefficients C based on complexity analysis;
the calculation steps of the fluctuation distribution index FI are as follows: firstly, dividing the preprocessed acoustic signal into n segments at equal time intervals t, and calculating the standard deviation sigma of the acoustic signal in the ith segmentiCalculating the average value X of the standard deviation of the acoustic signal:
secondly, with X as a reference, a basic fluctuation parameter of the standard deviation of the acoustic signal is calculated, where two basic fluctuation parameters are defined: amplitude d of the standard deviation of the acoustic signal exceeding the baseline X at a certain moment and frequency F of occurrence of data points with amplitude d exceeding the baseline Xn(d)；
Finally, a fluctuation parameter lambda is constructed based on a moment method0、λ1And λ2：
2. The method for detecting a gas-liquid dispersion state according to claim 1, further comprising a step of determining a reynolds number of the liquid at a flood point and/or a reynolds number of the liquid at full dispersion as follows: gradually increasing the Reynolds number of the liquid at the outlet of the nozzle from zero, recording a change curve of the characteristic parameters of the acoustic signals along with the Reynolds number of the outlet of the liquid nozzle, and calculating a slope k, wherein the corresponding Reynolds number of the liquid is the Reynolds number of the liquid at the overtop when k is changed from zero to a positive value; and when k is changed from a positive value to a negative value, the corresponding liquid Reynolds number is the completely dispersed liquid Reynolds number.
3. The method of detecting a gas-liquid dispersion state according to claim 1, wherein the time interval t is longer than a contact time of the gas bubbles with the wall surface.
4. The method for detecting a gas-liquid dispersion state according to claim 1, wherein the statistic value S based on the attractor comparison method is calculated as shown in formula (6);
wherein,
being an estimate of the square of the distance between the delay vector profile of the reference time series of the acoustic emission signal and the delay vector profile of the evaluation time series of the acoustic emission signal,
is composed of
The variance of (c).
5. The method for detecting a gas-liquid dispersion state according to claim 1, wherein the complexity analysis-based failure coefficient C is defined as shown in formulas (7) and (8);
wherein, CD2、CK2Fault coefficients, C, represented by the correlation dimension and the K-entropy, respectivelyD2,a、CK2,aRespectively representing the correlation dimension and K-entropy, C in the acoustic signal chaos characteristic parameters collected under the jet bubbling operation conditionD2,0、CK2,0Respectively representing the correlation dimension and the K-entropy in the acoustic signal chaotic characteristic parameters collected under the pure bubbling or pure jet operation condition.
6. The method for detecting a gas-liquid dispersion state according to any one of claims 1 to 5, wherein the acoustic wave sensor is selected from an acoustic emission sensor and an acceleration sensor.
7. The method for detecting a gas-liquid dispersion state according to claim 6, wherein the frequency response range of the acoustic wave sensor is 1Hz to 1 MHz.
8. The method for detecting a gas-liquid dispersion state according to claim 6, wherein the acoustic wave sensor is disposed at any position below a static liquid level on an outer wall surface of the jet bubbling reactor.
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