Electrostatic Sensor Array Research Articles

Investigations into the flow dynamics of mixed biomass particles in a fluidized bed through Hilbert-Huang transformation and data-driven modelling

Flow dynamics of binary particles are investigated to realize the monitoring and optimization of fluidized beds. It is a challenge to accurately classify the mass fraction of mixed biomass, considering the limitations of existing techniques. The data collected from an electrostatic sensor array is analyzed. Cross correlation, empirical mode decomposition (EMD), Hilbert-Huang transform (HHT) are applied to process the signals. Under a higher mass fraction of the wood sawdust, the segregation behavior occurs, and the high energy region of HHT spectrum increases. Furthermore, two data-driven models are trained based on a hybrid wavelet scattering transform and bidirectional long short-term memory (ST-BiLSTM) network and a EMD and BiLSTM (EMD-BiLSTM) network to identify the mass fractions of the mixed biomass, with accuracies of 92% and 99%. The electrostatic sensing combined with the EMD-BiLSTM model is effective to classify the mass fraction of the mixed biomass.

An optimization method of electrostatic sensor array based on Kriging surrogate model and improved non-dominated sorting genetic algorithm with elite strategy algorithm

Array-type electrostatic monitoring is gradually becoming an effective tool for aero-engine fault diagnosis. In this paper, we innovatively apply the surrogate optimization method to the optimization of the sensor array structure in order to meet the need of improving the particle information recognition capability of the electrostatic sensor array (ESA). A structure optimization method of ESA based on the Kriging surrogate model and improved NSGA-II algorithm is proposed. In this paper, a finite element simulation model of ESA is established, and the array optimization problem is abstracted as the solution of a mixed-integer optimization problem. This paper reduces the large-scale numerical simulations in the full-variable space with the help of the Kriging surrogate model. In addition, an improved NSGA-II algorithm for mixed-integer optimization is proposed. The simulation experiment verified that the average absolute error of the sensor before and after optimization for the identification of particle position and charge quantity was reduced by 69.88% and 49.68%, respectively. The array structure optimization method proposed in this paper facilitates the acceleration of the design process of electrostatic sensors and provides a scientific design method for their specific design for airborne application.

Ring-Rod Detection Array for Gas–Solid Flow Pattern Based on Electrostatic Induction Principle

The sensitivity matrix of a ring electrostatic sensor array for gas-solid flow pattern detection presents a significant decrease and poor uniformity at the center of the pipe. To improve the uniformity of the sensitivity matrix, a ring-rod electrostatic array sensor is proposed. Firstly, the structure of the ring-rod array sensor is proposed, with the influence of the number of ring-rod electrodes in the array on the average and uniformity error of sensitivity being analyzed. Secondly, the ring-rod array and ring array in gas-solid flow detection were both simulated, and the influencing factors on their performances were studied. Finally, an experimental platform was setup to evaluate their performance for detecting gas-solid flow patterns under different conditions. Results show that the sensitivity matrix of the ring-rod array electrostatic sensor is more uniform, with its sensitivity error being small, and it shows better potential for detecting the gas-solid flow patterns.

Compressive sensing-based de-aliasing method for array electrostatic signal

PurposeThe aero-engine array electrostatic monitoring technology (AEMT) can provide more and more accurate information about the direct product of the fault, and it is a novel condition monitoring technology that is expected to solve the problem of high false alarm rate of traditional electrostatic monitoring technology. However, aliasing of the array electrostatic signals often occurs, which will greatly affect the accuracy of the information identified by using the electrostatic sensor array. The purpose of this paper is to propose special solutions to the above problems.Design/methodology/approachIn this paper, a method for de-aliasing of array electrostatic signals based on compressive sensing principle is proposed by taking advantage of the sparsity of the distribution of multiple pulse signals that originally constitute aliased signals in the time domain.FindingsThe proposed method is verified by finite element simulation experiments. The simulation experiments show that the proposed method can recover the original pulse signal with an accuracy of 96.0%; when the number of pulse signals does not exceed 5, the proposed method can recover the pulse peak with an average absolute error of less than 5.5%; and the recovered aliased signal time-domain waveform is very similar to the original aliased signal time-domain waveform, indicating that the proposed method is accurate.Originality/valueThe proposed method is one of the key technologies of AEMT.

A gas–solid flow pattern identification algorithm based on cross-rod electrostatic sensor array

The accurate identification of gas–solid two-phase flow patterns is an important but challenging subject for pneumatic conveying. In this study, the sensitivity deficiencies of a single electrode were analysed via finite element analysis and a more sensitive cross-rod electrostatic sensor array structure was designed to measure the flow pattern signals. The experiment used Geldart D particles to verify the feasibility of the designed sensor array. Three types of feature vectors were extracted: the mean value, variance, and energy ratio. To identify the flow pattern accurately, the sine–cosine algorithm (SCA) is exploited to optimise the smoothing factor critical for a probabilistic neural network (PNN), namely SCA-PNN. The identification results show that the identification accuracy of the proposed algorithm outperforms the traditional PNN, the back propagation neural network (BPNN) and the support vector machine (SVM).

New instrument based on electrostatic sensor array for measuring tribo-electrification charging due to single particle impacts.

In the processing of particulate solids, particle-particle and particle-wall collisions can generate electrostatics. This is called contact/impact/frictional electrification and can lead to many problems such as affecting powder flow and explosion hazards. It is necessary to research the tribo-electrification charging due to single particle impacts on a target as the fundamental understanding of tribo-electrification. A new impact charging test rig based on an electrostatic sensor array that can measure charge transfer caused by a single impact between a particle and a target plane has been designed and established. Combined with the electrostatic sensor array, the compressed sensing algorithm is used to estimate not only the spatial position but also the charge amount of particle. The cross-correlation algorithm is used to determine particle's velocity instead of using other devices such as a photodetector. The new instrument allows single particles impacting target planes at different angles with a velocity exceeding 100m/s. An oil calibration test rig has been constructed to verify the proposed methods. The estimation errors of the spatial position and charge amount are both within 5% when the particle is located at the central area of the pipeline and the estimation errors of velocities are within 2%. The impact charging experiments show a special initial charge prior to impact for which no net charge transfer would occur for polymer particles, but the charge would completely transfer for metal particles.

Multi-scale analysis on the pulverized coal flow behaviors under high pressure dense-phase pneumatic conveying

To deeply knowledge of the flow behaviors of pulverized coal particles in dense gas–solid two-phase flow, a multi-scale analysis method based on electrostatic sensor array is applied for the multi-scale characterization of flow behaviors of dense gas–solid flow. The experimental results indicate that: for steady flow, with the increment of conveying pressure difference, the individual particles increase and the particle clusters decrease, the individual particle distribution is always inhomogeneous but the particle cluster distribution tends to be more homogeneous over the cross-section of pipe, while the average flow behavior of pulverized coal particles is always in the relatively static state. For unsteady flow, the average flow behavior of pulverized coal particles is dynamic, and the flow behaviors of the multi-scale flow structures over the cross-section of pipe are all significantly inhomogeneous. Moreover, the effect of particle size on flow behavior of pulverized coal is also investigated and validated.

Identification methods of charged particles based on aero-engine exhaust gas electrostatic sensor array.

This paper presents a study of aero-engine exhaust gas electrostatic sensor array to estimate the spatial position, charge amount and velocity of charged particle. Firstly, this study establishes a mathematical model to analyze the inducing characteristics and obtain the spatial sensitivity distribution of sensor array. Then, Tikhonov regularization and compressed sensing are used to estimate the spatial position and charge amount of particle based on the obtained sensitivity distribution; cross-correlation algorithm is used to determine particle's velocity. An oil calibration test rig is established to verify the proposed methods. Thirteen spatial positions are selected as the test points. The estimation errors of spatial positions and charge amounts are both within 5% when the particles are locating at central area. The errors are higher when the particles are closer to the wall and may exceed 10%. The estimation errors of velocities by using cross-correlation are all within 2%. An air-gun test rig is further established to simulate the high velocity condition and distinguish different kinds of particles such as metal particles and non-metal particles.

Mass Flow Rate Measurement of Pneumatically Conveyed Solids Through Multimodal Sensing and Data-Driven Modeling

Online continuous measurement of the mass flow rate of pneumatically conveyed solids is desirable in the monitoring and optimization of a range of industrial processes, such as food processing, chemical engineering, and power generation. This article introduces a technique for the mass flow rate measurement of pneumatically conveyed solids based on multimodal sensing and data-driven modeling. The multimodal sensing system is comprised of an array of ring-shaped electrostatic sensors, four arrays of arc-shaped electrostatic sensors, and a differential-pressure (DP) transducer. Data-driven models, including artificial neural network (ANN), support vector machine (SVM), and convolutional neural network (CNN), are established through training with statistical features extracted from the postprocessed data from the sensing system. Statistical features are shortlisted based on their importance by calculating the partial mutual information (PMI) between the features and the corresponding reference mass flow rate of solids. Experimental work was conducted on a laboratory-scale rig to train and test the models on both horizontal and vertical pipelines with particle velocity ranging from 10.1 to 36.0 m/s and mass flow rate of solids from 3.2 to 35.8 g/s. Experimental results suggest that the ANN, SVM, and CNN models predict the mass flow rate of solids with a relative error within ±18%, ±14%, and ±8%, respectively, under all test conditions. The predicted mass flow rate measurements with the ANN, SVM, and CNN models are repeatable with a normalized standard deviation within 14%, 8%, and 5%, respectively, under all test conditions.

Mass-Flow-Rate Measurement of Pneumatically Conveyed Particles Through Acoustic Emission Detection and Electrostatic Sensing

Accurate online mass flow rate measurement of pneumatically conveyed particles is desirable to convert a conventional pulverized fuel-fired power station into a smart thermal power plant. This article presents a novel method for the online measurement of the mass flow rate of pulverized fuel through acoustic emission (AE) detection and electrostatic sensing. An integrated sensing head with an AE probe and three sets of electrostatic sensor arrays is developed. The proposed method determines the particle velocity by multichannel cross correlation of the electrostatic signals and extracts the information about mass flow rate from the AE signal arising from impacts of particles with a waveguide protruding into the flow. An analytical model that relates the energy of the AE signals, the particle velocity, and the mass flow rate is established. The sensing head was mounted on the vertical and horizontal sections of a 72-mm bore laboratory-scale test rig conveying fine silica particles. Experimental tests were conducted under a range of flow conditions and installation orientations to assess the performance of the developed measurement system. The results demonstrate that the sensing head should be installed in any orientation away from the elbow on the vertical section of a pipe, while for installation on a horizontal pipe, the waveguide should be in the horizontal direction. The instrumentation system is capable of measuring the mass flow rate of particles in the vertical pipe with a relative error within ±6.5% regardless of the orientation of the sensing head over the mass flow rate from 7 to 25 kg/h and the particle velocity from 12 to 30 m/s, while on the horizontal pipe, the error is within 5.8% when the sensing head is installed with the waveguide in the horizontal direction under the same flow conditions.

Experimental Investigations Into Bubble Characteristics in a Fluidized Bed Through Electrostatic Imaging

Fluidized beds are widely applied in many industrial processes. In order to control and optimize the operation of a fluidized bed, it is necessary to develop reliable methods for the measurement of bubble characteristics to monitor the status of the bed. Electrostatic sensing methods based on the detection of charges on particles have been applied to characterize the particle motion in a fluidized bed. However, there is limited research on the measurement of bubble characteristics using the electrostatic methods due to complex electrostatic phenomena around the bubbles. In this article, an imaging method using a two-dimensional electrostatic sensor array is employed for the experimental investigations into the bubble behaviors in a two-dimensional fluidized bed. The bubble size, shape, rising velocity, and generation frequency are measured. Moreover, an optical imaging system is employed to obtain reference information to evaluate the performance of the electrostatic imaging method. Experimental results show that the bubble characteristics measured from the electrostatic sensor array have a good agreement with the results from the optical imaging system. The relative root mean square error between the bubble shapes measured from the electrostatic sensor array and from the optical system is 0.239 with a standard deviation within 4.7%.

Mass flow rate measurement of solids in a pneumatic conveying pipeline in different orientations

Extensive work has been undertaken for the mass flow rate measurement of solids in a horizontal or vertical pneumatic conveying pipe. However, flow regime of the two-phase flow is highly influenced by different orientations of the pipe, resulting in different characteristics of sensor signals and hence large errors in mass flow rate measurement using conventional methods. This paper presents a novel technique to measure the mass flow rate of pneumatically conveyed particles in different pipe orientations. A range of low-cost sensors, including an array of electrostatic sensors, a differential-pressure transducer, and an accelerometer, are integrated to form a sensing unit. Data-driven models, based on support vector machine (SVM), are developed to take the selected features from post-processed sensor data and infer the mass flow rate of solids in different pipe orientations. The partial mutual information algorithm is applied to quantify the importance of each feature. The firefly algorithm is used to optimize the selection of useful features and tune the learning parameters in SVM models. Experimental tests were conducted on a pneumatic conveying test rig circulating flour over the mass flow rate of solids from 3.2 ​g/s to 35.8 ​g/s in pipe orientations from 0° to 90°. Performance comparisons are made between the conventional SVM model and the optimised SVM models with the training data from horizontal orientation and different orientations, respectively. Results demonstrate that the relative error and repeatability of the measured mass flow rate of solids with the optimized SVM model are both improved to within ±12%.

Turbulent filament properties in L and H-mode regime in the RFX-mod operating as a tokamak

The dynamics of turbulent filaments in L and ELM-free H-mode in the RFX-mod device operating as a tokamak is discussed in the present work. L-H transition and sustained H-mode have been achieved in the RFX-mod with the aid of an external electrode biasing (Spolaore et al 2017 Nucl. Fusion 57 116039). Through advanced statistical techniques, turbulent filaments are detected and tracked from the edge to the scrape-off layer (SOL) in a two-dimension floating potential map (measured with a 2D array of Langmuir probes) related with extreme events of a fixed ion saturation current signal in the SOL. While in L-mode filaments travel almost freely, their motion becomes restricted to the near SOL during the induced biasing H-mode. In this region, the background shear decorrelation time becomes shorter than the convective filament time, favouring its suppression. However, the experimental observation of a nearly ‘trapped’ monopole potential points out the possible role of the vortex selection mechanism. The sign and magnitude of the turbulent parallel vorticity are consistent with the E × B background vorticity in the SOL in this regime, while in L-mode, the inferred turbulent vorticity is always above the background one. The sparse filamentary transport in the far SOL during H-mode is further confirmed from the measurements of a poloidally symmetric array of electrostatic sensors on the wall. Lastly, the radial velocity of filaments in both regimes was discussed in the framework of analytical scalings. Filaments in L-mode exhibit a reasonable agreement with the sheath-limited regime in the cold ion approximation, with a non-negligible associated parallel current density measured with a set of 3-axial pick-up coils installed in the probe head. In H-mode, the collisionality increases near the separatrix while filaments seem to follow the inertial regime before become trapped.

Measurement of velocity and concentration profiles of pneumatically conveyed particles in a square-shaped pipe using electrostatic sensor arrays

Cross-sectional measurement of particle velocity and concentration in a pneumatic conveying pipe is desirable for the characterisation of particle flow dynamics and determination of particle mass flow rate. In this study, an inner-inserted electrostatic sensor array consisting of nine pairs of electrodes is implemented to measure the cross-sectional velocity and concentration profiling of particles over the whole cross section in a square-shaped pipe. Experimental tests were conducted on both vertical and horizontal pipe sections on a test rig under dilute conditions with different air velocities and particle mass flow rates. Test results show that the slope-shaped particle concentration profile changes to an arch-shaped one when the particles flow from a horizontal pipe to a vertical one. The particle velocity profile is arch-shaped in both vertical and horizontal pipes. A comparative study of cross-sectional mean particle velocity and concentration measured by the developed electrostatic sensor arrays is conducted.

Flame Boundary Measurement Using an Electrostatic Sensor Array

The flame boundary is an important geometrical characteristic for the evaluation of flame properties such as heat release rate and radiation. Reliable and accurate measurement of the flame boundary is desirable for the prediction of flame structure and the optimization of combustion systems. Such measurement will inform the designers and operators of the combustion systems. This article presents for the first time a study of using an electrostatic sensor array for flame boundary measurement. The electrostatic sensor is placed in the vicinity of the flame to sense its movement through charge transfer. The principle, design, implementation, and assessment of a measurement system based on this methodology are introduced. Comparative experimental investigations with a digital camera conducted on a laboratory-scale combustion test rig show that the electrostatic sensor can respond to the variation of the distance between the electrode and the flame boundary. Reconstruction of the flame boundary is achieved using a set of distance measurements obtained from a sensor array. For diffusion flames over the range of fuel flow rate 0.60-0.80 L/min and premixed flames over the range of equivalence ratio 1.27-3.81, experimental results show that the measurement system is capable of providing a reliable measurement of the flame boundary. The correlation coefficients under all test conditions are mostly larger than 0.96, the mean relative errors within 7.4%, and the relative root-mean-square errors within 0.09. More accurate flame boundary measurements are achieved for diffusion flames. In addition, the overall polarity of charges in a flame can be determined from the polarity of the sensor signal.

Investigations into the blockage of pulverized fuel pipes on coal-fired boilers using an electrostatic sensor system

The blockage of pulverized fuel (PF) conveying pipes on a coal-fired power plant is a hazard accident. An in-depth understanding of the factors that cause PF pipe blockage provides operators with prior knowledge to avoid such accidents. Two representative primary air pipe blockage events are observed on two commercial boilers, respectively. The PF dynamic parameters of the two cases during the whole blockage processes are monitored by an electrostatic sensor array based instrumentation system. The variation trends of the velocity, mass flow rate, volumetric concentration and flow stability of PF particles in different blockage stages are described in detail. The main reasons for the two blockage events are analyzed based on the geometry of the PF conveying pipes and the dynamic parameters of the PF particles. The results obtained show that on-line monitoring of PF flow is an effective and reliable way for the early detection of PF pipe blockage.

Particle velocity measurement method of dense-phase gas–solid flow based on an electrostatic sensor array

Particle velocity is an important parameter of dense-phase gas–solid flow for both monitoring and control purposes. This paper presents a recent development in the noncontact measurement of solid particle velocity using an electrostatic sensor array (ESA). The electrostatic sensor array with eight electrodes is used to collect electrostatic signals generated by the moving charged particles. A velocity measurement method based on the tomography technique is presented, which measures the velocity of solid particles in the horizontal pipeline. The simulated model of the ESA with eight electrodes is built, and the sensing area of the ESA is mapped onto 13 pixels of equal area. Simulation tests are then conducted to evaluate the feasibility and accuracy of the velocity measurement at different positions. Finally, experimental tests are conducted on the horizontal conveying pipeline of a high- pressure dense-phase pneumatic conveying pilot system. Results indicate that the proposed method is capable of measuring the spatial average velocity of pulverized coal over the cross-sectional area of pipe with a relative error within ±7% for the conditions of particles tending to be of uniform distribution. In addition, the effect of moisture on velocity measurement is investigated.

Online prediction of biomass moisture content in a fluidized bed dryer using electrostatic sensor arrays and the Random Forest method

The inherent moisture content in biomass needs to be dried before it is used for energy production. Fluidized bed dryers (FBD) are widely applied in drying biomass and the moisture content should be monitored continuously to maximise the efficiency of the drying process. In this paper, the moisture content of biomass in a FBD is predicted using electrostatic sensor arrays and a random forest (RF) based ensemble learning method. The features of electrostatic signals in the time and frequency domains, correlation velocity and the outlet temperature and humidity of exhaust air are chosen to be the input of the RF model. Model training is accomplished using the data taken from a lab-scale experimental platform and the hyper-parameters of the RF model are tuned based on the Bayesian optimization algorithm. Finally, comparisons between the online predicted and sampled values of biomass moisture content are conducted. The maximum relative error between the online predicted and reference values is less than 13%, indicating that the RF model provides a viable solution to the online monitoring of the fluidized bed drying process.

Gas⁻Solid Two-Phase Flow Pattern Identification Based on Artificial Neural Network and Electrostatic Sensor Array.

A method for gas–solid two-phase flow pattern identification in horizontal pneumatic conveying pipelines is proposed based on an electrostatic sensor array (ESA) and artificial neural network (ANN). The ESA contains eight identical arc shaped electrodes. Numerical simulation is conducted to discuss the contributions of the electrostatic signals to the flow patterns according to the error recognition rate, and the results show that the amplitudes of the output signals from each electrode of the ESA can give important information on the particle distribution and further infer the flow patterns. In experiments, the average values and standard deviations of the eight output signals’ amplitudes are respectively extracted as the inputs of the ANN to identify four kinds of flow patterns in a pneumatic conveying pipeline, which are fully suspended flow, stratified flow, dune flow and slug flow. Results show that for any one of those two input values, the correct rates of the ANN model are all 100%.

Profiling of pulverized fuel flow in a square-shaped pneumatic conveying pipe using electrostatic sensor arrays

Quantitative profiling of pulverized fuel flow in a pneumatic conveying pipe is essential to balance the fuel distribution and improve the operation flexibility of a coal-fired boiler. In this study, an electrostatic sensing head comprising square-loop shaped electrodes and strip-shaped electrodes is used to characterize the particle flow in a square-shaped pipe under various flow conditions. Cross-correlation and data fusion algorithms are also proposed to process and combine the multi-channel signals from the sensor arrays in the sensing head. Dynamic characteristics of the particle flow in a vertical square-shaped pipe on an experimental platform are presented. Experimental results demonstrate that the cross-sectional profiles of the velocity and volumetric concentration of particles in a square-shaped pneumatic conveying pipe are highly non-uniform.