Wang, T., Yan, Y., Wang, L., Hu, Y. and Zhang, S. (2020). Instantaneous Rotational Speed Measurement Using Image Correlation and Periodicity Determination Algorithms. IEEE Transactions on Instrumentation and Measurement [Online] 69:2924-2937. Available at: https://dx.doi.org/10.1109/TIM.2019.2932154.
Dynamic and accurate measurement of instantaneous rotational speed is desirable in many industrial processes for both condition monitoring and safety control purposes. This paper presents a novel imaging based system for instantaneous rotational speed measurement. The low-cost imaging device focuses on the side surface of a rotating shaft without the use of a marker, entailing benefits of non-contact measurement, low maintenance and wide applicability. Meanwhile, new periodicity determination methods based on the Chirp-Z transform and parabolic interpolation based auto-correlation algorithm are proposed to process the signal of similarity level reconstructed using an image correlation algorithm. Experimental investigations are conducted on a purpose-built test rig to quantify the effects of the periodicity determination algorithm, frame rate, image resolution, exposure time, illumination conditions, and photographic angle on the accuracy and reliability of the measurement system. Experimental results under steady and transient operating conditions demonstrate that the system is capable of providing measurements of a constant or gradually varying speed with a relative error no greater than ±0.6% over a speed range from 100 to 3000 RPM (Revolutions Per Minute). Under step change conditions the proposed system can achieve valid speed measurement with a maximum error of 1.4%.
Bai, L., Pepper, M., Yan, Y., Phillips, M. and Sakel, M. (2020). Low Cost Inertial Sensors for the Motion Track-ing and Orientation Estimation of Human Upper Limbs in Neurological Rehabilitation. IEEE Access [Online]. Available at: http://dx.doi.org/10.1109/ACCESS.2020.2981014.
This paper presents the feasibility of utilizing low cost inertial sensors such as those found in Sony Move, Nintendo Wii (Wii Remote with Wii MotionPlus) and smartphones for upper limb motion mon-itoring in neurorehabilitation. Kalman and complementary filters based on data fusion are used to estimate sensor 3D orientation. Furthermore, a two-segment kinematic model was developed to estimate limb segment position tracking. Performance has been compared with a high-accuracy measurement system using the Xsens MTx. The experimental results show that Sony Move, Wii and smartphones can be used for measuring upper limb orientation, while Sony Move and smartphones can also be used for specific applications of upper limb segment joint orientation and position tracking during neurorehabilitation. Sony Move’s accuracy is within 1.5° for Roll and Pitch and 2.5° for Yaw and position tracking to within 0.5 cm over a 10 cm movement. This accuracy in measurement is thought to be adequate for upper limb orientation and position tracking. Low cost inertial sensors can be used for the accurate assessment/measurement of upper limb movement of patients with neurological disorders and also makes it a low cost replacement for upper limb motion measurements. The low cost inertial sensing systems were shown to be able to accurately measure upper limb joint orienta-tion and position during neurorehabilitation.
Bai, L., Pepper, M., Yan, Y., Phillips, M. and Sakel, M. (2020). Inertial Sensor based Quantitative Assessment of Upper Limb Range of Motion and Functionality before and after Botulinum Toxin: A Pilot Study. Global Journal of Engineering and Technology Advances [Online] 2:35-44. Available at: https://doi.org/10.30574/gjeta.2020.2.3.0008.
Botulinum toxin (BTX) treatment of upper limb is considered effective for upper limb spasticity following stroke and brain injury. Traditional method - Modified Ashworth Scale (MAS) is widely used for assessment of spasticity, however, it suffers from limitations including the lack of objective outcome measures and ignorance of the active movements. This pilot study is to develop a quantitative assessment utilizing inertial sensors tool for upper limb movement measurement and to investigate an objective measure of upper limb function for neurological patients before and after BTX treatment of spasticity. The system we proposed provides kinematic measurements of upper limb segment and joint motion data. In this study, four stroke patients were assessed by our proposed inertial sensing system immediately before and one week after BTX injection. In addition, patients were assessed using clinical assessment scales e.g. MAS, Disability Assessment Scale (DAS) and Motor Assessment Scale. The results showed that elbow Active Range of Motion (AROM) increased by 19 degrees on average and MAS and Motor Assessment Scale scores did not show significant change. The changes of the kinematic measures for patients 1-3 e.g. AROM, Rate of change of elbow joint angle, NJS, MUN and S-ratio all show that the inertial system is able to identify improvement in performance. This inertial sensing system provides additional and novel dynamic motion data for a sensitive and quantitative assessment of response to treatment and the efficacy of post-injection physiotherapy.
Shao, D., Yan, Y., Zhang, W., Sun, S., Sun, C. and Xu, L. (2019). Dynamic measurement of gas volume fraction in a CO2 pipeline through capacitive sensing and data driven modelling. International Journal of Greenhouse Gas Control [Online] 94:102950. Available at: https://doi.org/10.1016/j.ijggc.2019.102950.
Gas volume fraction (GVF) measurement of gas-liquid two-phase CO2 flow is essential in the deployment of carbon capture and storage (CCS) technology. This paper presents a new method to measure the GVF of two-phase CO2 flow using a 12-electrode capacitive sensor. Three data driven models, based on back-propagation neural network (BPNN), radial basis function neural network (RBFNN) and least-squares support vector machine (LS-SVM), respectively, are established using the capacitance data. In the data pre-processing stage, copula functions are applied to select feature variables and generate training datasets for the data driven models. Experiments were conducted on a CO2 gas-liquid two-phase flow rig under steady-state flow conditions with the mass flowrate of liquid CO2 ranging from 200 kg/h to 3100 kg/h and the GVF from 0% to 84%. Due to the flexible operations of the power generation utility with CCS capabilities, dynamic experiments with rapid changes in the GVF were also carried out on the test rig to evaluate the real-time performance of the data driven models. Measurement results under steady-state flow conditions demonstrate that the RBFNN yields relative errors within ±7% and outperforms the other two models. The results under dynamic flow conditions illustrate that the RBFNN can follow the rapid changes in the GVF with an error within ±16%.
Ge, H., Li, X., Li, Y., Lu, G. and Yan, Y. (2019). Biomass Fuel Identification Using Flame Spectroscopy and Tree Model Algorithms. Combustion Science and Technology [Online]. Available at: https://doi.org/10.1080/00102202.2019.1680654.
This paper presents an identification method for types of fuel such as biomass by combining flame spectroscopic monitoring and tree model algorithms. The features of the flame spectra are extracted, including the spectral intensity of flame radicals [OH* (310.85 nm)，CN* (390.00 nm), CH* (430.57 nm) and C2* (515.23 nm, 545.59 nm)], flame radiation intensity and flame radiation energy (integration of spectral intensity). The identification models are built using four tree model algorithms, i.e., decision tree, random forest, extremely randomized trees and gradient boost decision tree. The different type biomass and spectra features of combustion flames are composed of sample pairs to train identification models. Experiments are carried out on a laboratory-scale biomass-air combustion test rig. Four different biomass fuels, including corncob, willow, peanut shell and wheat straw are burnt. The results demonstrate that the identification models proposed is capable of identifying types of biomass fuels correctly with the average identification success rate of 98% in ten trials.
Cui, X., Yan, Y., Hu, Y. and Guo, M. (2019). Performance comparison of acoustic emission sensor arrays in different topologies for the localization of gas leakage on a flat-surface structure. Sensors and Actuators A: Physical [Online] 300:111659. Available at: https://doi.org/10.1016/j.sna.2019.111659.
The topology of the acoustic emission sensor array has an important effect on the performance of the leak localization technique. This paper compares the performances of different topologies of acoustic emission sensor arrays in the localization of gas leakage on a flat-surface structure. The principle of the leak localization is based on the near-field beamforming according to the spherical wave model and the narrowband filtering which can effectively avoid the influence of acoustic dispersion. The effect of different arrangements of the sensing elements in a sensor array on the localization accuracy is investigated and discussed. Eight typical topologies, including line, L-shaped, cross, triangle, star, circular, semi-circular and square shapes, are appraised through computer simulation. Simulation results suggest that all the arrays can perform leak localization but with different accuracies and that the L-shaped array outperforms all other topologies under the similar conditions. Furthermore, the optimal number of sensors in the L-shaped array which can maintain a reasonable accuracy of localization is analyzed. Experimental work was carried out on a laboratory scale test rig to verify and assess the effectiveness of the L-shaped array. The simulation and experimental results demonstrate that the L-shaped array is capable of identifying the location of a leak hole in a plate structure with a reasonably good accuracy.
Hu, Y., Yan, Y., Qian, X. and Zhang, W. (2019). A Comparative Study of Induced and Transferred Charges for Mass Flow Rate Measurement of Pneumatically Conveyed Particles. Powder Technology 356:715-725.
In-line measurement of the mass flow rate of solids in pneumatic conveying pipelines is essential for the efficient and optimized operation of many industrial processes. This paper presents a comparative study of using induced and transferred charges from non-intrusive electrodes exposed to the particle flow for mass flow rate measurement. A novel signal conditioning circuit, which consists of a current sense amplifier and a charge amplifier, is designed to convert the induced and transferred charges into two separate voltage signals. Empirical measurement models that relate the particle velocity, the root-mean-square (r.m.s) magnitude of the induced charge signal and the slope of the transferred charge signal to the mass flow rate are proposed. Experiments were undertaken using electrodes of different widths and under different mass flow rate and particle velocity conditions. Results obtained show that both the r.m.s magnitude of the induced charge signal and the slope of the transferred charge signal increase with the mass flow rate and the velocity of particles. In general, the measurement results using the induced and transferred charges from either the narrow or the wide electrode are similar. However, the method based on the transferred charge is less reliable due to confined sensing area and electrode charging by particles adhered to the electrode surface.
Hu, Y., Guo, M., Yan, Y., Lu, G. and Cheng, X. (2019). Temperature Measurement of Stored Biomass of Different Types and Bulk Densities Using Acoustic Techniques. FUEL [Online]. Available at: https://www.sciencedirect.com/science/article/pii/S0016236119313389.
The internal temperature of stored biomass needs to be measured to suppress the possible self-ignition at biomass-fired power stations. Acoustic sensing has been proven to be a promising approach to measuring the temperature of stored wood pellets online and non-intrusively. In such a temperature measurement system, a characteristic factor is defined to derive the sound speed from measured time of flight of sound waves. The characteristic factor is updated based on its experimental relationship with the biomass temperature during temperature measurement. When the type, particle size, particle density and bulk density of stored biomass change, whether the relationship between the characteristic factor and biomass temperature needs to be recalibrated needs investigation. Therefore, the relationship between the characteristic factor and biomass property is modelled using the empirical equation of Miki. Then the model is used to analyse the impact of the particle size, particle density and bulk density of stored biomass on the relationship. An acoustic sensing system is constructed to investigate the influence of bulk density for different types of biomass. The system is also applied to measure the temperature of four biomass fuels, including wood blocks, wood pellets, wood chips, and wheat straws. Results show that the relative error of temperature measurements for the four types of biomass is within 3.5%, 5.7%, 6.8% and 2.5%, respectively, over the temperature range from 22.1℃ to 74.2℃. The relationship between the characteristic factor and biomass temperature should be re-established for different types of biomass and different particle size distributions.
Reda, K. and Yan, Y. (2019). An Improved Method for the Processing of Signals Contaminated with Strong Common-Mode Periodic Noise in Correlation Velocity Measurement. IEEE Sensors Letters [Online] 3:1-4. Available at: https://ieeexplore.ieee.org/document/8750838?source=authoralert.
Electrostatic sensors have been successfully used for the velocity measurement of pneumatically conveyed particles and the rotational speed measurement. However, the signal from an electrostatic sensor is usually vulnerable and susceptible to contamination in a hostile environment. The acquired original signal may be contaminated by different types of noise that can be within or outside the frequency range of the signal. This paper presents a novel correlation signal processing method to minimise the impact of noise in the signal through a de-noising process and hence improve the performance of correlation-based measurements in general. The method is applied to the rotational speed measurement based on electrostatic sensors in particular. The de-noising process is an essential task in digital signal processing to improve the signal-to-noise ratio before implementing the measurement algorithm. A hybrid de-noising method is proposed to combine a cut-off frequency method to remove the noise components outside the signal bandwidth and a median filter to smooth the signal. Subsequently, the signal is de-noised in the time domain by employing an advanced digital filtering method based on correlation techniques to suppress the noise frequency components mixed with the original signal. The rotational speed measurement system with the proposed technique has proven to be effective in de-noising signals that are buried in noise with which they are correlated. Moreover, the technique is capable of producing more accurate and repeatable measurements with a wider measurement range than the existing system. Experimental results suggest that the relative error of the improved system is mostly within ±0.1% over the speed range of 300 rpm - 3000 rpm and within ±0.2% over the speed range of 40-300 rpm.
Han, X., Zhao, S., Cui, X. and Yan, Y. (2019). Localization of CO\(_2\) gas leakages through acoustic emission multi-sensor fusion based on wavelet-RBFN modeling. Measurement Science and Technology [Online] 30:85007. Available at: https://doi.org/10.1088/1361-6501%2Fab1025.
CO\(_2\) leakage from transmission pipelines in carbon capture and storage systems may seriously endanger the ecological environment and human health. Therefore, there is a pressing need of an accurate and reliable leak localization method for CO\(_2\) pipelines. In this study, a novel method based on the combination of a wavelet packet algorithm and a radial basis function network (RBFN) is proposed to realize the leak location. Multiple acoustic emission (AE) sensors are first deployed to collect leakage signals of CO\(_2\) pipelines. The characteristics of the leakage signals from the AE sensors under different pressures are then analyzed in both time and frequency domains. Further, leakage signals are decomposed into three layers using wavelet decomposition theory. Wavelet packet energy and maximum value, and time difference calculated by cross-correlation are selected as the input feature vectors of the RBFN. Experiments were carried out on a laboratory-scale test rig to verify the validity and correctness of the proposed method. Leakage signals at different positions under different pressures were obtained on the CO\(_2\) pipeline leakage test bench. Compared with the time difference of arrival method, the relative error obtained using the proposed method is less than 2%, which has certain engineering application prospects.
Zhang, Q., Yan, Y., Hu, Y. and Zheng, G. (2019). On-line Size Measurement of Pneumatically Conveyed Particles Through Acoustic Emission Sensing. Powder Technology [Online] 353:195-201. Available at: https://doi.org/10.1016/j.powtec.2019.05.023.
Acoustic emission (AE) methods have been proposed for on-line size measurement of pneumatically conveyed particles in recent years. However, there is limited research on the fundamental mechanism of the AE-based particle sizing technique. In order to achieve more accurate measurement of particle size, the impact between particles and a waveguide should be described in a more realistic way. In this paper, an improved model based on the Stronge impact theory is presented to establish the relationship between the resulting AE signal and the particle size being measured. The improved model is validated with experiments on a single-particle test rig. A total five sets of glass beads with a mean diameter of 0.4, 0.6, 0.8, 1.0 and 1.2 mm, respectively, are used as the test particles with an impact velocity ranging from 22 m/s to 37 m/s. It is proven that the Stronge impact theory is more accurate to describe the collision process than the Hertzian impact theory and is thus more suitable for the particle size inversion, which is validated by comparing the inversion results using these two impact theories. Meanwhile, a good agreement is observed between the measured and reference particle sizes under different experimental conditions. The mean relative error between the measured and reference diameters is mostly within 12%.
Zhang, W., Cheng, X., Hu, Y. and Yan, Y. (2019). Online prediction of biomass moisture content in a fluidized bed dryer using electrostatic sensor arrays and the Random Forest method. Fuel [Online] 239:437-445. Available at: https://doi.org/10.1016/j.fuel.2018.11.049.
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.
Reda, K. and Yan, Y. (2018). Vibration Measurement of an Unbalanced Metallic Shaft Using Electrostatic Sensors. IEEE Transactions on Instrumentation & Measurement [Online] 68:1467-1476. Available at: http://dx.doi.org/10.1109/TIM.2018.2882900.
Vibration measurement of a rotary shaft is essential for the diagnosis and prognosis of industrial rotating machinery. However, the imbalance of a shaft, as quantified through vibration displacement, is the most common cause of machine vibration. The objective of this study is to develop a novel technique through electrostatic sensing for the on-line, continuous and non-contact displacement measurement of a rotary shaft due to imbalance faults. A mathematical model is established to extract useful information about the shaft displacement vibration from the simulated signal in the frequency domain. Experimental tests were conducted on a purpose-built test rig to measure the displacement vibration of the shaft. An eccentric shaft was tested with the output signal from the electrostatic sensor analyzed. The effectiveness of the proposed method is verified through computer simulation and experimental tests. Results obtained indicate that the measurement system yields a relative error of within ±0.6% in the displacement measurement.
Zhang, W., Yan, Y., Shao, D., Liu, S. and Wang, T. (2018). Experimental investigations into the transient behaviours of CO2 in a horizontal pipeline during flexible CCS operations. International Journal of Greenhouse Gas Control [Online] 79:193-199. Available at: https://doi.org/10.1016/j.ijggc.2018.10.013.
Power plants with CCS facilities should be operated flexibly because of the variability in electricity demand. Load change, start-up and shutdown will occur during flexible CCS operations. It is necessary to investigate the transient behaviours of CO2 flow in the pipeline during these operations for optimized operation of CCS plants. However, very limited experimental data for gas-liquid two-phase CO2 under CCS conditions are available. As a result, experimental observations of the CO2 transient behaviours were conducted on a CO2 gas-liquid two-phase flow rig. Load change, start-up and shutdown of a CO2 flow process were replicated on the rig. Coriolis flowmeters and high-speed imaging equipment were used to observe the mass flow rate, thermophysical properties and flow regimes of the CO2 flow. There are significant discrepancies in the mass flow rate of two-phase CO2 between the test value and the reference value during the load change. During the start-up operation, the flow regime transits from liquid slug flow to gas bubbly flow and the mass flow rate from the Coriolis flowmeter presents two-step changes. In addition, the depressurization and evaporation of liquid CO2 in the pipeline were observed during the shutdown operation.
Sun, C., Yan, Y., Zhang, W. and Wang, L. (2018). A dynamic ensemble selection approach to developing softcomputing models for two-phase flow metering. Journal of Physics: Conference Series [Online] 1065:1-4. Available at: http://dx.doi.org/10.1088/1742-6596/1065/9/092022.
This paper presents a dynamic ensemble selection approach to developing least squares support vector regression (LSSVR) models for the flow metering of gas-liquid two-phase CO2. Ensemble models based on flow pattern recognition and dynamic ensemble selection (FPR-DES) are established for the measurement of total mass flowrate and gas volume fraction of two-phase CO2, respectively. The input variables of ensemble models are obtained from a Coriolis flowmeter and a differential-pressure transducer installed on a horizontal test section. Experimental tests were conducted with liquid CO2 mass flowrate ranging from 200 kg/h to 3100 kg/h and gas volume fraction between 3.1% and 88.4%. Performance comparisons between the proposed FPR-DES based ensemble model, bagging based ensemble model and the single LSSVR model are undertaken with experimental data under stratified, bubbly and mist flow conditions. Experimental results suggest that the FPR-DES based ensemble model outperforms the other two models with maximum errors of ±1% and ±10% for the total CO2 mass flowrate and gas volume fraction, respectively.
Wang, T., Yan, Y. and Wang, L. (2018). Vibration measurement using a low-cost imaging sensor and image processing techniques. Journal of Physics: Conference Series [Online]:1-4. Available at: http://dx.doi.org/10.1088/1742-6596/1065/22/222012.
Vibration measurement is essential for the monitoring of rotational machineries in a variety of industries. This paper presents a novel method using a low-cost imaging sensor and image processing techniques to measure the vibration of a rotor. A series of regions of interest in the captured images is selected to extract useful information for vibration measurement. Digital image correlation is applied to evaluate the similarity level of the regions of interest between sequential images and the reference image. Vibration measurement of a rotor is achieved through the spectral analysis of the signal indicating image similarity level. Experimental assessments of the proposed method were conducted on a purpose-built test rig where a commercial eddy current sensor was used to provide reference data. Experimental results demonstrate that the vibration frequencies and their relative amplitudes from the proposed measurement system agree well with those from the reference sensor. Meanwhile, the rotor vibrates at several distinct frequencies that increase with the rotational speed and hence agrees the expected vibration characteristics.
Wang, L., Yan, Y. and Reda, K. (2018). Enhancing the performance of a rotational speed measurement system through data fusion. Journal of Physics: Conference Series [Online] 1065:1-4. Available at: http://dx.doi.org/10.1088/1742-6596/1065/7/072024.
Electrostatic sensors with a single electrode or double electrodes have been applied for rotational speed measurement. In order to improve the performance of the rotational speed measurement system based on double electrostatic sensors, a data fusion algorithm is incorporated in the system. Two independent signals are accessible from the electrostatic sensor with double electrodes. From these signals two independent rotational speed measurements are obtained through auto-correlation processing of each signal and the third rotational speed measurement is also achieved by cross-correlating the two signals. A data fusion algorithm is then applied to optimally combine the three measurements. The system with the data fusion algorithm is capable of producing more accurate and more robust measurements than previous double-sensor system with a wider measurement range. Experimental results suggest that the relative error of the improved system is mostly within ±0.5% over the speed range of 200 rpm - 3000 rpm.
Yan, Y., Shen, Y., Cui, X. and Hu, Y. (2018). Localization of Multiple Leak Sources Using Acoustic Emission Sensors Based on MUSIC Algorithm and Wavelet Packet Analysis. IEEE Sensors Journal [Online] 18:9812-9820. Available at: http://dx.doi.org/10.1109/JSEN.2018.2871720.
Multiple leak sources may occur in a large pressure
vessel that contains corrosive materials or has been in use for a
long period of time. Although a variety of leak localization
methods have been proposed in previous studies, they are capable
of locating only a single leak source. Methods for simultaneous
localization of multiple leak sources are desirable in practical
applications. To address this issue, a novel method using acoustic
emission (AE) sensors in conjunction with MUltiple SIgnal
Classification (MUSIC) algorithm and wavelet packet analysis is
proposed and experimentally assessed. High-frequency AE
sensors are assembled into a linear array to acquire signals from
multiple leak sources. Characteristics of the leak signals are
analyzed in the frequency domain. Wavelet packet analysis is
deployed to extract useful information about the signals from the
frequency band of 50 kHz - 400 kHz. The MUSIC algorithm is
applied to identify the directions of the leak sources through a
space spectrum function. Leak sources are located based on the
directions identified by the AE sensor array placed at different
locations. The performance of the proposed method is evaluated
through experimental tests on a stainless steel flat plate of 100 cm
×100 cm×0.4 cm. The results demonstrate that the method is
capable of locating two leak holes. In addition, the localization
accuracy depends on the leaking pressure. It is demonstrated that
the two leak holes are located within two small areas, respectively,
which are 25.12 cm2 for leak hole 1 and 1.96 cm2 for leak hole 2.
Sun, S., Zhang, W., Sun, J., Cao, Z., Xi, L. and Yan, Y. (2018). Real-time Imaging and Holdup Measurement of Carbon Dioxide under CCS Conditions Using Electrical Capacitance Tomography. IEEE Sensors Journal [Online] 18:1-1. Available at: https://doi.org/10.1109/JSEN.2018.2858448.
This paper presented a method for real-time
cross-sectional imaging and holdup measurement of gas-liquid
two-phase carbon dioxide (CO2) flow using electrical
capacitance tomography (ECT). A high-pressure ECT sensor
with 12 electrodes was constructed and a dedicated digital ECT
system with a data acquisition rate of 757 frames per second was
developed for capacitance measurement. Three widely-used
image reconstruction algorithms were compared for
tomographic imaging and phase holdup measurement.
Experiments were carried out on a DN25 laboratorial scale CO2
two-phase flow rig at a pressure of 6 MPa for the gaseous mass
flowrates from 0 to 430 kg/h and liquid mass flowrates at 515,
1100 and 1900 kg/h. The experimental results show that the
cross-sectional distribution of two-phase CO2 flow can be
monitored using the ECT system, which matches well with the
images captured by a high-speed imaging system. Compared
with the reference gas holdup obtained by the flowmeters in the
single phase gaseous and liquid loops, the absolute accuracy of
the gas holdup measurement can reach 6%, indicating that the
developed system is promising for real-time monitoring of
carbon dioxide in CCS transportation pipelines.
Guo, M., Yan, Y., Hu, Y., Lu, G. and Zhang, J. (2018). Temperature Measurement of Stored Biomass Using Low-frequency Acoustic Waves and Correlation Signal Processing Techniques. Fuel [Online] 227:89-98. Available at: https://dx.doi.org/10.1016/j.fuel.2018.04.062.
As a substitute of traditional fossil fuels, biomass is widely used to generate electricity and heat. The temperature of stored biomass needs to be monitored continuously to prevent the biomass from self-ignition. This paper proposes a non-intrusive method for the temperature measurement of stored biomass based on acoustic sensing techniques. A characteristic factor is introduced to obtain the sound speed in free space from the measured time of flight of acoustic waves in stored biomass. After analysing the relationship between the defined characteristic factor and air temperature, an updating procedure on the characteristic factor is proposed to reduce the influence of air temperature. By measuring the sound speed in free space air temperature is determined which is the same as biomass temperature. The proposed methodology is examined using a single path acoustic system which consists of a source and two sensors. A linear chirp signal with a duration of 0.1 s and frequencies of 200-500 Hz is generated and transmitted through stored biomass pellets. The time of flight of sound waves between the two acoustic sensors is measured through correlation signal processing. The relative error of measurement results using the proposed method is no more than 4.5% over the temperature range from 22? to 48.9?. Factors that affect the temperature measurement are investigated and quantified. The experimental results indicate that the proposed technique is effective for the temperature measurement of stored biomass with a maximum error of 1.5? under all test conditions.
Wang, T., Yan, Y., Wang, L. and Hu, Y. (2018). Rotational Speed Measurement through Image Similarity Evaluation and Spectral Analysis. IEEE Access [Online]:1-12. Available at: https://doi.org/10.1109/ACCESS.2018.2866479.
Accurate and reliable measurement of rotational speed is desirable in a variety of industries.
This paper presents a rotational speed measurement system based on a low-cost imaging device with a
simple marker on the rotor. Sequential images are pre-processed through denoising, histogram equalization
and circle Hough transform, and then processed by similarity evaluation methods to obtain the similarity
level of images. Finally, the rotational speed is obtained through Chirp-Z transform on the restructured
signals. The measurement principle, structure design and performance assessment of the proposed system
are presented. The effects of different influence factors, including frame rate, marker shape and size,
algorithm for image similarity evaluation, illumination conditions, shooting angle and photographic
distance, on the performance of the measurement system are quantified and discussed through a series of
experimental tests on a laboratory test rig. Experimental results suggest that the system is capable of
providing constant rotational speed measurement with a maximum relative error of ±0.6% and a
repeatability of less than 0.6% over a speed range from 100 to 900 RPM. Under varying speed conditions
the proposed system can achieve valid measurement with a relative error within ±1% over the speed range
of 300 to 900 RPM.
Cugley, J., Lu, G., Hossain, M., Yan, Y. and Searle, I. (2018). Visualisation and measurement of flames in a gas-fired multi-burner boiler. Journal of Physics: Conference Series [Online] 1065. Available at: https://dx.doi.org/10.1088%2F1742-6596%2F1065%2F20%2F202009.
The paper presents the development of an instrumentation system for the visualisation and measurement of flames in a gas-fired multi-burner boiler based on digital imaging and spectrometric techniques. The system consists of a rigid optical probe and an optical fibre, a digital camera, a spectrometer and an embedded computer with associated application software. The characteristic parameters of the flame, including size, temperature and oscillation frequency are quantitatively determined based on flame images obtained. The spectral characteristics of the flame are analysed over the spectral range from the ultraviolet to near infrared. The system was evaluated on a gas-fired heat recovery boiler under different operation conditions. Results obtained suggest the promising correlation between computed flame parameters and operation conditions.
Li, X., Li, Y., Lu, G. and Yan, Y. (2018). Biomass Fuel Identification Based on Flame Spectroscopy and Feature Engineering. Proceeding of the CSEE [Online] 38:4474-4481. Available at: http://dx.doi.org/10.13334/j.0258-8013.pcsee.171984.
Flame spectra contain useful information about combustion and hence the spectral features of flame radicals may be used to identify different biomass fuels. A technique for biomass fuel identification is proposed based on the spectral features of flame radicals, feature engineering and improved support vector machine. The spectral intensity signals of biomass flames and flame radicals (OH*(310.85nm), CN*(390.00nm), CH*(430.57nm) and C2*(515.23nm, 545.59nm)) were acquired using a spectrometer. Feature engineering was built, which can accurately reflect the characteristics of sample category, through feature extraction, feature selection based on Filter and feature learning based on dictionary learning. The support vector machine is used to build the identification model, where radial basis kernel parameter ? and error penalty factor C are optimized using an improved grid search algorithm. Experimental results from a laboratory-scale combustion rig show the effectiveness of the proposed method for the identification of biomass fuel.
Yan, Y., Wang, L., Wang, T., Wang, X., Hu, Y. and Duan, Q. (2018). Application of Soft Computing Techniques to Multiphase Flow Measurement: A Review. Flow Measurement and Instrumentation [Online] 60:30-43. Available at: https://doi.org/10.1016/j.flowmeasinst.2018.02.017.
After extensive research and development over the past three decades, a range of techniques have been proposed and developed for online continuous measurement of multiphase flow. In recent years, with the rapid development of computer hardware and machine learning, soft computing techniques have been applied in many engineering disciplines, including indirect measurement of multiphase flow. This paper presents a comprehensive review of the soft computing techniques for multiphase flow metering with a particular focus on the measurement of individual phase flowrates and phase fractions. The paper describes the sensors used and the working principle, modelling and example applications of various soft computing techniques in addition to their merits and limitations. Trends and future developments of soft computing techniques in the field of multiphase flow measurement are also discussed.
Wang, L., Yan, Y., Wang, X., Wang, T., Duan, Q. and Zhang, W. (2017). Mass Flow Measurement of Gas-Liquid Two-Phase CO\(_2\) in CCS Transportation Pipelines using Coriolis Flowmeters. International Journal of Greenhouse Gas Control [Online] 68:269-275. Available at: https://dx.doi.org/10.1016/j.ijggc.2017.11.021.
Carbon Capture and Storage (CCS) is a promising technology that stops the release of CO\(_2\) from industrial processes such as electrical power generation. Accurate measurement of CO\(_2\) flows in a CCS system where CO\(_2\) flow is a gas, liquid, or gas-liquid two-phase mixture is essential for the fiscal purpose and potential leakage detection. This paper presents a novel method based on Coriolis mass flowmeters in conjunction with least squares support vector machine (LSSVM) models to measure gas-liquid two-phase CO\(_2\) flow under CCS conditions. The method uses a classifier to identify the flow pattern and individual LSSVM models for the metering of CO2 mass flowrate and prediction of gas volume fraction of CO\(_2\), respectively. Experimental work was undertaken on a multiphase CO\(_2\) flow test facility. Performance comparisons between the general LSSVM and flow pattern based LSSVM models are conducted. Results demonstrate that Coriolis mass flowmeters with the LSSVM model incorporating flow pattern identification algorithms perform significantly better than those using the general LSSVM model. The mass flowrate measurement of gas-liquid CO\(_2\) is found to yield errors less than ±2% on the horizontal pipeline and ±1.5% on the vertical pipeline, respectively, over flowrates from 250 kg/h to 3200 kg/h. The error in the estimation of CO\(_2\) gas volume fraction is within ±10% over the same range of flow rates.
Shan, L., Kong, M., Bennett, T., Archi, S., Carol, E., Sun, D., Lu, G., Yan, Y. and Liu, H. (2017). Studies on Combustion Behaviours of Single Biomass Particles Using a Visualization Method. BIOMASS & BIOENERGY [Online] 109. Available at: https://doi.org/10.1016/j.biombioe.2017.12.008.
Combustion behaviours of single particles (125-150m) of eucalyptus, pine and olive residue were investigated by means of a transparent drop-tube furnace, electrically heated to 1073 K, and a high-speed camera coupling with a long distance microscope. All three types of biomass samples were found to have two evident combustion phases, i.e., volatile combustion in an envelope flame and subsequent char combustion with high luminance. Yet, due to differences in chemical compositions and properties, their combustion behaviours - were also seen somewhat discrepant. The volatile flame of the olive residue was fainter than that of pine and eucalyptus due to its high ash content. During the char combustion phase, fragmentation took place for most pine particles but only for a few particles of olive residue and eucalyptus. For all three types of biomass samples, the flame size and the average luminous intensity profiles were deduced from the captured combustion video images whilst the combustion burnout times of the volatile matter and char were also calculated and estimated. There were two peak values clearly shown on the profiles of both the flame size and the average luminous intensity during the volatile combustion process of pine and eucalyptus particles, which, according to literature, could not be observed by optical pyrometry. The observed peaks correspond to the devolatilisation of hemicellulose and cellulose. The ratio between the estimated char burnout time and volatile combustion time increases quadratically with the fixed carbon to volatile matter mass ratio, confirming char combustion is much slower than volatile combustion.
Zhang, W., Cheng, X., Hu, Y. and Yan, Y. (2017). Measurement of moisture content in a fluidized bed dryer using an electrostatic sensor array. Powder Technology [Online] 325:49-57. Available at: https://doi.org/10.1016/j.powtec.2017.11.006.
Fluidized bed dryers have been widely applied to dry raw materials or final products due to the advantages of good mixing efficiency and high heat and mass transfer rate. In order to control and optimize the drying process of fluidized bed dryers, it is necessary to develop reliable methods to measure the moisture content of solid particles in the bed. Because of the advantages of non-intrusiveness, simple structure and high sensitivity, an electrostatic sensor array has been developed to monitor the drying process. Experimental investigations were conducted on a lab-scale fluidized bed dryer. The moisture content during the drying process was measured using the sampled particles as reference. It is found that the fluctuation of the electrostatic signals can reflect the change in moisture content. However, the relationship between the fluctuation of the electrostatic signal and the moisture content depends on the air velocity in the dryer. To eliminate the velocity effect on moisture content measurement, a model between the moisture content and the root-mean-square magnitude of the electrostatic signal is established. The effectiveness of the model is validated using experimental results under a range of conditions. The findings indicate that the electrostatic sensor array can measure the moisture content in the bed with a maximum error of ±15%.
Zhang, S., Yan, Y., Qian, X., Huang, R. and Hu, Y. (2017). Homogenization of the Spatial Sensitivity of Electrostatic Sensors for the Flow Measurement of Pneumatically Conveyed Solids in a Square-Shaped Pipe. IEEE Sensors Journal [Online] 17:7516-7525. Available at: https://doi.org/10.1109/JSEN.2017.2758442.
The spatial sensitivity of an electrostatic sensor is recognized as an important factor that affects the accuracy of solids flow measurement in a pneumatic conveying pipe. However, the distribution of the spatial sensitivity is generally inhomogeneous due to the physical structure of the electrostatic sensor and the inherent electrostatic sensing mechanism. This paper proposes a sensitivity homogenization method based on differential measurement, i.e., using the differential outputs from two electrodes with different axial widths. The validity of the sensitivity homogenization method for a square-shaped electrostatic sensing head, which consists of strip-shaped electrode arrays with different widths, is validated through mathematical analysis. Furthermore, the response of the electrostatic sensing head incorporating the sensitivity homogenization method to roping flow regimes was evaluated on a gravity-fed solids flow test rig. Results from both modeling and experimental tests indicate that the homogeneity of the spatial sensitivity is improved significantly. The mean non-uniformity of the outputs from the sensing head is 11.7% as a result of the homogenization method.
Wang, L., Yan, Y. and Reda, K. (2017). Comparison of Single and Double Electrostatic Sensors for Rotational Speed Measurement. Sensors and Actuators A: Physical [Online] 266:46-55. Available at: http://dx.doi.org/10.1016/j.sna.2017.09.014.
Accurate and reliable measurement of rotational speed is crucial in many industrial processes. Recent research provides an alternative approach to rotational speed measurement of dielectric rotors through electrostatic sensing and signal processing. This paper aims to explore the electrostatic phenomenon of rotational machineries, design considerations of the spacing between double electrostatic sensors and effect of dielectric rotors on the performance of the rotational speed measurement systems based on single and double electrostatic sensors. Through a series of experimental tests with rotors of different material types, including polytetrafluoroethylene (PTFE), polyvinyl chloride (PVC) and Nylon, different surface roughness (Ra 3.2 and Ra 6.3) and difference diameters (60 mm and 120 mm), the accuracy and reliability of the two measurement systems are assessed and compared. Experimental results suggest that more electrostatic charge is generated on the PTFE rotors with a larger diameter and coarser surface and hence better performance of the measurement systems. The single-sensor system yields a relative error of within ±1% while the double-sensor system produces an error within ±1.5% over the speed range of 500 - 3000 rpm for all tested rotors. However, the single-sensor system outperforms the double-sensor system at high rotational speeds (>2000 rpm) with a relative error less than ±0.05%.
Zhou, H., Li, Y., Tang, Q., Lu, G. and Yan, Y. (2017). Combining flame monitoring techniques and support vector machine for the online identification of coal blends. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering) [Online] 18:677-689. Available at: http://dx.doi.org/10.1631/jzus.A1600454.
The combustion behavior of two single coals and three coal blends in a 300 kW coal-fired furnace under variable operating conditions was monitored by a flame monitoring system based on image processing and spectral analysis. A similarity coefficient was defined to analyze the similarity of combustion behavior between two different coal types. A total of 20 flame features, extracted by the flame monitoring system, were ranked by weights of their importance estimated using ReliefF, a feature selection algorithm. The mean of the infrared signal was found to have by far the highest importance weight among the flame features. Support vector machine (SVM) was used to identify the coal types. The number of flame features used to build the SVM model was reduced from 20 to 12 by combining the methods of ReliefF and SVM, and computational precision was guaranteed simultaneously. A threshold was found for the relationship between the error rate and similarity coefficient, which were positively correlated. The success rate decreased with increasing similarity coefficient. The results obtained demonstrate that the system can achieve the online identification of coal blends in industry.
Bai, X., Lu, G. and Yan, Y. (2017). Fractal Characteristics of Thin Thermal Mixing Layers in Coal-Fired Flame. Journal of Combustion Science and Technology [Online] 3:225-230. Available at: http://journals.tju.edu.cn/rs/oa/DArticle.aspx?type=view&id=R201606021.
The images of turbulent flame were acquired by using a digital imaging system on an industry-scale pulverized coal-fired test rig?The fractal dimensions of thin thermal mixing layers in flame were computed through morphology-based flame image processing techniques?The effects of the ratios of primary air and secondary/tertiary air on fractal dimensions were characterized?The results presented in this work show that the variations of fractal dimension are closely related to the ratio changes of primary air and secondary/tertiary air. Therefore?the fractal dimensions of flame thin thermal mixing layers can be used as an important indicator for the control and optimization of a combustion process.
Bai, X., Lu, G. and Yan, Y. (2017). Flame image segmentation using multiscale color and wavelet-based texture features. Computer Engineering and Applications (Chinese) [Online] 53:213-219. Available at: http://dx.doi.org/10.3778/j.issn.1002-8331.1610-0083.
Accurate and reliable segmentation of flame images are crucial in vision based monitoring and characterization of flames. It is, however, difficult to maintain the segmentation accuracy while achieving fast processing time due to the impact of the background noise in the images and the variation of operation conditions. To improve the quality of the image segmentation, a flame image segmentation method is proposed based on Multiscale Color and Wavelet-based Textures?MCWT? of the images. By combining the color and texture features, a characteristic matrix is built and then compressed using a local mean method. The outer contour of the flame image under the compressed scale is detected by a cluster technique. Subsequently, the flame edge region under the original scale is determined, following that, the characteristic matrix of the edge region is constructed and classified, and finally, the flame image segmentation is achieved. Flame images captured from an industrial-scale coal-firedtest rig under different operation conditions are segmented to evaluate the proposed method. The test results demonstrate that the performance of segmenting flame images of the proposed method is superior to other traditional methods. It also has been found that the proposed method performs more effectively in segmenting the flame images with Gaussian and pepper and salt noise.
Adefila, K., Yan, Y., Sun, L. and Wang, T. (2017). Flow Measurement of Wet CO2 Using an Averaging Pitot Tube and Coriolis Mass Flowmeters. International Journal of Greenhouse Gas Control [Online] 63:289-295. Available at: https://doi.org/10.1016/j.ijggc.2017.06.005.
The flow measurement of wet-gas is an active field with extensive research background that remains a modern-day challenge. The implication of wet-gas flow conditions is no different in Carbon Capture and Storage (CCS) pipelines. The associated complex flow regime with wet-gas flow makes it difficult to accurately meter the flow rate of the gas phase. Some conventional single-phase flowmeters like the Coriolis, Orifice plate, Ultrasonic, V-Cone, Venturi and Vortex have been tested for this application, usually accompanied with special recommendations. Often, a correlation equation valid within a certain range of specific conditions is required to correct the response of the flowmeter. This paper presents investigations into the suitability and performance of one of the most advanced averaging pitot tubes for the flow measurement of wet CO2 gas. The averaging pitot tube with flow conditioning wing geometry (APT-FCW) was studied and experimentally assessed in earlier work for the flow measurement of pure and dry CO2 within an error of ±1%. Under wet-gas conditions, however, the APT-FCW sensor is found to give an error of up to ±25% and within ±1.5% after appropriate correcting solutions are applied for a liquid fraction of up to 20%.
Hu, Y., Zhang, S., Yan, Y., Wang, L., Qian, X. and Yang, L. (2017). A Smart Electrostatic Sensor for Online Condition Monitoring of Power Transmission Belts. IEEE Transactions on Industrial Electronics [Online] 64:7313-7322. Available at: http://dx.doi.org/10.1109/TIE.2017.2696507.
Online condition monitoring of power transmission belts is essential to keep industrial belt-driven equipment functioning smoothly and reliably. This paper presents a smart electrostatic sensor that monitors belt motion through detection of static charge on the belt. A theoretical model is established using the method of moments for calculation of induced charge on strip-shaped electrodes placed in the vicinity of a belt moving both axially and transversely. The sensor unit converts the induced charge into proportional voltage signals using charge amplifiers and measures belt speed and vibration through cross correlation and spectral analysis, respectively. The performance of the smart electrostatic sensor is validated against a photoelectric rotary encoder and a laser displacement sensor. Comparative experimental results show that the belt speed can be measured with a relative error within ±2% over the range 1.7-15.5 m/s. The electrostatic sensor is capable of measuring the frequencies of transverse vibration accurately. Although absolute displacement cannot be measured due to the uncertain level of charge on the belt, the measurement results of relative vibration magnitudes for different modes and at different belt speeds are reasonably accurate.
Sun, J. and Yan, Y. (2017). Non-intrusive Characterisation of Particle Cluster Behaviours in a Riser through Electrostatic and Vibration Sensing. Chemical Engineering Journal [Online] 323:381-395. Available at: http://doi.org/10.1016/j.cej.2017.04.082.
Particle clusters are important mesoscale flow structures in gas-solid circulating fluidised beds (CFBs). An electrostatic sensing system and two accelerometers are installed on the riser of a CFB test rig to collect signals simultaneously. Cross correlation, Hilbert-Huang transform (HHT), V-statistic analysis, and wavelet transform are applied for signal identification and cluster characterisation near the wall. Solids velocities are obtained through cross correlation. Non-stationary and non-linear characteristics are distinctly exhibited in the Hilbert spectra of the electrostatic and vibration signals, and the cluster dynamic behaviours are represented by the energy distributions of the signal intrinsic mode functions (IMFs). The cycle feature and main cycle frequency of cluster motion are characterised through V-statistic analysis of the vibration signals. Consistent characteristic information about particle clusters is extracted from the electrostatic and vibration signals. Furthermore, a cluster identification criterion for electrostatic signals is proposed, including a fixed and a wavelet dynamic thresholds, based on which the cluster time fraction, average cluster duration time, cluster frequency, and average cluster vertical size are quantified. Especially, the cluster frequency obtained from this criterion agrees well with that from the aforementioned V-statistic analysis. Results from this 3 work provide a new non-intrusive approach to the characterisation of cluster dynamic behaviours and their effects on the flow field.
Daood, S., Ottolini, M., Taylor, S., Ogunyinka, O., Hossain, M., Lu, G., Yan, Y. and Nimmo, W. (2017). Additive technology for pollutant control and efficient coal combustion. Energy and Fuels [Online] 31:5581-5596. Available at: http://dx.doi.org/10.1021/acs.energyfuels.7b00017.
High efficiency and low emissions from pf coal power stations has been the drive behind the development of present and future efficient coal combustion technologies. Upgrading coal, capturing CO2, reducing emission of NOx, SO2 and particulate matter, mitigating slagging, fouling and corrosion are the key initiatives behind these efficient coal technologies. This study focuses on a newly developed fuel additive (Silanite™) based efficient coal combustion technology, which addresses most of the aforementioned key points. Silanite™ a finely milled multi-oxide additive when mixed with the coal without the need to change the boiler installation has proven to increase the boiler efficiency, flame temperature with reduction in corrosion, NOx and particulate matter (dust) emissions. The process has been developed through bench, pilot (100kW) and full scale (233 MWth). The process has been found to have a number of beneficial effects that add up to a viable retrofit to existing power plant as demonstrated on the 233MWth boiler tests (under BS EN 12952-15:2003 standard)
Bai, X., Lu, G., Hossain, M., Szuhánszki, J., Daood, S., Nimmo, W., Yan, Y. and Pourkashanian, M. (2017). Multi-mode Combustion Process Monitoring on a Pulverised Fuel Combustion Test Facility based on Flame Imaging and Random Weight Network Techniques. Fuel [Online] 202:656-664. Available at: https://dx.doi.org/10.1016/j.fuel.2017.03.091.
Combustion systems need to be operated under a range of different conditions to meet fluctuating energy demands. Reliable monitoring of the combustion process is crucial for combustion control and optimisation under such variable conditions. In this paper, a monitoring method for variable combustion conditions is proposed by combining digital imaging, PCA-RWN (Principal Component Analysis and Random Weight Network) techniques. Based on flame images acquired using a digital imaging system, the mean intensity values of RGB (Red, Green, and Blue) image components and texture descriptors computed based on the grey-level co-occurrence matrix are used as the colour and texture features of flame images. These features are treated as the input variables of the proposed PCA-RWN model for multi-mode process monitoring. In the proposed model, the PCA is used to extract the principal component features of input vectors. By establishing the RWN model for an appropriate principal component subspace, the computing load of recognising combustion operation conditions is significantly reduced. In addition, Hotelling’s T2 and SPE (Squared Prediction Error) statistics of the corresponding operation conditions are calculated to identify the abnormalities of the combustion. The proposed approach is evaluated using flame image datasets obtained on a 250 kWth air- and oxy-fuel Combustion Test Facility. Variable operation conditions were achieved by changing the primary air and SA/TA (Secondary Air to Territory Air) splits. The results demonstrate that, for the operation conditions examined, the condition recognition success rate of the proposed PCA-RWN model is over 91%, which outperforms other machine learning classifiers with a reduced training time. The results also show that the abnormal conditions exhibit different oscillation frequencies from the normal conditions, and the T2 and SPE statistics are capable of detecting such abnormalities.
Abbas, F., Yan, Y. and Wang, L. (2020). Mass flow measurement of pneumatically conveyed solids through multi-modal sensing and machine learning. In: 2020 International Instrumentation and Measurement Technology Conference.
Hossain, M., Cugley, J., Lu, G., Smith, D., Kim, I. and Yan, Y. (2018). Investigations into the Impact of Coal Moisture on Burner Performance through Flame Imaging and Spectroscopic Analysis. In: 12th ECCRIA - The European Conference on Fuel and Energy Research and Its Applications. TFERF- The Fuel and Energy Research Forum.
Despite increasing use of renewable energy worldwide, coal remains to be the primary energy resource to meet the increasing demand for electric power in many countries. However, coal-fired power plants have to cope with coals with different properties, including those with high moisture content. It is known that moisture content in coal does not only affect coal handling but also burner performance, and thus combustion efficiency and emission formation process. A study is recently carried out to investigate the impact of moisture content in coal on the burner performance through flame imaging and spectroscopic analysis. Experimental tests were conducted on a 40MWth coal-fired combustion test facility (CTF). A typical pulverised coal was fired in the study. The variation in evaporated coal moisture was replicated by injecting steam into the primary coal flow in the range of 7%-55% (PFM, primary flow moisture) under different operation conditions including variations in furnace load and fuel-to-air ratio. A flame imaging system and a miniature spectrometer were employed to acquire concurrently flame images and spetroscopic data (Fig. 1). The characteristic parameters of the flame such as spreading angle, temperature, oscillation frequency and spectral intensity are computed and their relationship with the operation conditions including PFM and emissions (NOx, CO) are quantified. Fig. 2 illustrates typical flame images under different steam injections. Detailed experimental results and analysis will be presented at the conference.
Hossain, M., Cugley, J., Lu, G., Caesar, S., Cornwell, S., Riley, G. and Yan, Y. (2018). Burner Condition Monitoring based on Flame Imaging and Data Fusion Techniques. In: 12th ECCRIA - The European Conference on Fuel and Energy Research and Its Applications. TFERF- The Fuel and Energy Research Forum.
Rapid growth in electricity generation from intermittent renewables has resulted in increasing demand in conventional fossil-fuel power stations for plant flexibility, load balancing and fuel flexibility. This has led to new challenges in plant monitoring and control, particularly securing combustion stability for optimizing combustion process in terms of furnace safety, fuel efficiency and pollutant emissions. An unstable combustion process can cause many problems including furnace vibration, non-uniform thermal distribution in the furnace, high pollutant emissions and unburnt carbon in the flue gas. The stability of burners should therefore be continuously monitored and maintained for the improved overall performance of the furnace. A study is carried out to investigate the burner stability based on flame imaging and data fusion techniques. Experiments were carried out on a 915 MWth coal-fired power station. A bespoke flame imaging system (Fig. 1) was employed to acquire flame images from 16 individual burners (4 mills each with 4 burners) with a frame rate up to 200 frames per second. The characteristic parameters of the flame, including temperature, non-uniformity, entropy, oscillation frequency and colour characteristics (hue, saturation and intensity), are computed. The relationship between the flame characteristics and burner inputs and flue gas emissions (e.g., NOx) is quantified. Stability index is then introduced as an indicator of the stability of individual burner. Fig. 2 illustrates typical flame images for different burners. Detailed test results and analysis will be presented at the conference.
Liu, J., Wang, T., Yan, Y., Wang, X. and Wang, L. (2018). Investigations into the behaviours of Coriolis flowmeters under air-water two-phase flow conditions on an optimized experimental platform. In: 2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). IEEE. Available at: https://dx.doi.org/10.1109/I2MTC.2018.8409681.
Gas-liquid two-phase flow is commonly encountered in many industrial processes due to production requirement or inevitable gas entrainment from various sources. Accurate liquid phase measurement under two-phase conditions is challenging but important as it is the key factor to reduce cost, improve safety or meet legal requirements. Coriolis flowmeters, owing to their high accuracy in metering single-phase flow, direct mass flow measurement and multivariable sensing nature, are widely used in industry. Recently developed Coriolis flowmeters can work under multiphase conditions, making it possible to achieve accurate multiphase flow measurement through model based error compensation or training based soft computing correction. This paper assesses the behaviours of Coriolis flowmeters under various two-phase conditions for modelling and soft computing algorithm improvement, including previously investigated factors (flowrate, gas volume fraction, flow tube geometry, flow converter, and process pressure) and new factors (flow regimes in terms of bubble size and distribution). Experimental work was conducted on 25 mm and 50 mm bore air-water two-phase flow rigs for liquid mass flowrates between 2500 kg/h and 35000 kg/h with gas volume fraction of 0-60%. With the influence of each factor identified through univariate analysis, comparisons between existing modelling theories and experimental error curves are established. In the meantime, the rig design and control are optimized to provide efficient and automated data acquisition in order to supply ample and high-quality data for the training of soft computing models as well as enhancing the understanding in theoretical modelling.
Wang, T., Wang, L., Yan, Y. and Zhang, S. (2018). Rotational speed measurement using a low-cost imaging device and image processing algorithms. In: IEEE, pp. 1-6. Available at: http://dx.doi.org/10.1109/I2MTC.2018.8409665.
Accurate and reliable measurement of rotational speed is desirable in many industrial processes. A novel method for rotational speed measurement using a low-cost camera and image processing techniques is presented in this paper. Firstly, sequential images are continuously processed using a similarity evaluation method to obtain the periodic similarity level of captured images. Subsequently, the rotational speed is determined from the periodicity of a restructured signal through Chirp-Z transform and parabolic interpolation based auto-correlation, respectively. The measurement principle and system design are presented. The advantages of the proposed measurement system include non-contact measurement, low cost, no markers required and high accuracy. Experimental investigations into the effects of the periodicity detection algorithm, frame rate and image resolution on the accuracy and reliability of the measurement system are conducted on a purpose-built test rig. Experimental results demonstrate that the system with the frame rate of 100 fps yields a measurement error within ±0.6% over a speed range from 100 to 3000 RPM (Revolutions Per Minute). More accurate and reliable speed measurements over a wider speed range are achievable with higher frame rates.
Cugley, J., Lu, G., Yan, y and Searle, I. (2018). Flame monitoring and characterisation through digital imaging and spectrometry. In: IFRF 2018 Conference- Clean, Efficient and Safe Industrial Combustion. Institute of Measurement and Control.
Fossil fuel fired boilers are often required to work under variable operation conditions. The variability in fuel diet and load conditions result in various problems in boiler performances. A methodology based on digital imaging and spectrometric techniques is proposed for flame monitoring and characterisation on utility boilers. The system developed consists of an optical probe/water jacket, a digital camera, a spectrometer covering a spectral range from 200nm to 900nm and an embedded computer with associated application software. Computer algorithms are established to determine flame characteristic parameters, including size, shape, temperature and spectral distributions. The spontaneous emissions of flame radicals (e.g., CH*and C2*) and alkali elements such as Sodium (Na) and Potassium (K) are characterised and their relationships with the combustion inputs (e.g., fuel, air-to-fuel ratio) and pollutant emissions (e.g., NOx) are studied. The methodology proposed are examined on a gas-fired heat recovery boiler under different operation conditions. The results obtained suggest there exist close correlations between flame parameters computed and boiler operation conditions. In particular, flame radicals (CH* and C2*) and their ratio show a close relationship with the air-to-fuel ratio. The spectral intensities of Na (589nm) and K (767nm) also illustrate a strong link to the type of fuel. Current work focuses on quantifying the relationship between the flame parameters and the boiler operation conditions and establishing a computational model for online prediction of emissions from flame characteristic parameters.
Reda, K., Yan, Y. and Wang, L. (2017). A comparative study of various shaped electrostatic sensors for rotational speed measurement. In: IEEE, pp. 1-3. Available at: http://dx.doi.org/10.1109/ICSENS.2017.8234149.
Electrostatic sensors have been successfully used in the field of particle flow measurement due to the advantages of simple structure, robustness and low cost. Recently, advances have been made in developing electrostatic sensing techniques for rotational speed measurement. The geometric shape and size of the electrodes have significant effects on the performance of electrostatic sensors in terms of spatial sensitivity and temporal frequency response. This paper focuses on the theoretical analysis and experimental assessment of strip and butterfly shaped electrodes for rotational speed measurement of a metallic shaft. Spatial sensitivity and filtering effect of the sensors are investigated through mathematical and computational modelling. Analytical and experimental results suggest that the butterfly shaped sensor outperforms the strip sensor in terms of spatial sensitivity, power spectral density and signal bandwidth.
Wang, L., Liu, J., Yan, Y., Wang, X. and Wang, T. (2017). Mass flow measurement of two-phase carbon dioxide using Coriolis flowmeters. In: IEEE International Instrumentation and Measurement Technology Conference (I2MTC 2017). Available at: http://dx.doi.org/10.1109/I2MTC.2017.7969891.
Carbon Capture and Storage (CCS) is considered as an important technology to reduce CO2 emission from electrical power generation and other industrial processes. In the CCS chain, i.e. from capture to storage via transportation, it is essential to realize accurate measurement of CO2 flows for the purpose of accounting and potential leakage detection. However, there are some significant challenges for the current flow metering technologies to achieve the specified 1.5% measurement uncertainty in the EU-ETS (European Union - Emissions Trading Scheme) for all expected flow conditions. Moreover, there are very few CO2 flow test and calibration facilities that can recreate CCS conditions particularly two-phase CO2 flow in pipelines together with accurate measurement standards. As one of the most potential flowmeters that may be used in the CCS chain, Coriolis flowmeters have the advantages of direct measurement of mass flow rate regardless of its state (liquid, gas, gas/liquid two-phase or supercritical) in addition to the measurement of temperature and density of CO2 for the characterization of flow conditions. This paper assesses the performance of Coriolis flowmeters incorporating a soft-computing correction method for gas-liquid two-phase CO2 flow measurement. The correction method includes a pre-trained backpropagation neural network. Experimental work was conducted on a purpose-built 25 mm bore two-phase CO2 flow test rig for liquid mass flowrate between 300 kg/h and 3050 kg/h and gas mass flowrate from 0 to 330 kg/h under the fluid temperature of 19~21 °C and pressure of 54~58 bar. Experimental results suggest that the Coriolis flowmeters with the developed correction method are capable of providing the mass flow rate of gas-liquid CO2 flow with errors mostly within ±2% and ±1.5% on horizontal and vertical pipelines, respectively.
Wang, Y., Wang, L. and Yan, Y. (2017). Rotational speed measurement through digital imaging and image processing. In: IEEE, pp. 1-9. Available at: http://dx.doi.org/10.1109/I2MTC.2017.7969697.
This paper presents a rotational speed measurement system based on a low-cost imaging device with a simple marker on the rotor. Structural similarity and two-dimensional correlation algorithms are deployed to process the images. The measurement principle, structure design and performance assessment of the proposed system are presented and discussed. The effects of different markers, image processing algorithms and illumination conditions on the performance of the measurement system are quantified through a series of experimental tests on a laboratory test rig. Experimental results suggest that the system is capable of providing the maximum relative error of ±1% with normalized standard deviation less than 0.8% over a speed range from 0 to 700 RPM (Revolutions Per Minute).