Hongli Hu

Concentration Measurement of Dilute Pulverized Fuel Flow by Electrical Capacitance Tomography

In order to eliminate the effects of flow regimes on phase concentration measurements, electrical capacitance tomography (ECT) was employed to improve the measurement accuracy. The solid particle distribution in the flow pipelines were characterized by measuring capacitance to obtain flow patterns. An improved offline iteration online reconstruction (OIOR) algorithm was proposed as the image reconstruction for the ECT system and a method of two-dimension maximum entropy threshold image segmentation was used for further image processing. Dynamic experiments were carried out for gas/solid two-phase flows. First, the flow regime was identified by the ECT, and the results were used to trace the fluctuation of the dynamic two-phase flow online. The volume concentrations were then calculated based on the reconstructed results. The experimental results indicate that the average relative error of the concentration measurements was about 1.5% over a pulverized coal volume concentration range 0.96–4.97%.

An ECT Flow Regime Identification Technology for Gas/solid Two-phase Flow Phase Concentration Measurement

In order to increase the sensitivity of the concentration measuring system of gas/solid two-phase flow and make the concentration measuring accuracy not affected by flow patterns, a flow pattern identification system is added for classified processing of measuring results according to the flow patterns on the basis of the original concentration measuring system. Electrical Capacitance Topography (ECT) system that can present the images inside the gas/solid two-phase flow pipelines is developed based on the original hardware system. The radial basis function (RBF) network algorithm is used for image reconstruction and a method of 2-D Maximum Entropy Threshold Image Segmentation with Chaos-Particle Swarm Optimization (CPSO) algorithm is proposed to segment the images collected by an ECT system. Among the system, the 12-electrode capacitance sensor is not only used for concentration measuring but also for ECT measurement. In order to check whether this method is reasonable or not, an experimental platform is built. And the result shows that this flow pattern identification system for the concentration measurement performs efficiently and accurately.

Multisensor Data Fusion Techniques With ELM for Pulverized-Fuel Flow Concentration Measurement in Cofired Power Plant

Due to the phase concentration, measurement of coal/biomass/air three-phase flow is closely associated with the quantity of pollutant discharged and combustion efficiency in the pneumatic conveying system. This paper proposed a multisensor data fusion technique for the online volume concentration measurement of coal/biomass pulverized-fuel flow in cofired power plant. The techniques combine electrostatic sensors with capacitance sensors and incorporates a data fusion technique based on an adaptive wavelet neural network (AWNN), and gradient descent learning algorithm and genetic learning algorithm are used for training of the network parameters. The flow regime is first identified by performing an extreme learning machine on the electrostatic fluctuation signals, thus making the concentration measurement less affected by variations in the flow regimes. Then under the certain identified flow regime, the AWNN data fusion method was applied to determine the phase concentration. An experimental platform was built for phase concentration measurement of pulverized coal (PC)/biomass/air three-phase flow, and the test results confirmed that the technology of the concentration measurement with flow regime identification is better than that of the one without, and the maximum fiducial errors of sawdust and PC are 2.1% and 1.2%.

IDENTIFICATION OF GAS–SOLID TWO-PHASE FLOW REGIMES USING HILBERT–HUANG TRANSFORM AND NEURAL-NETWORK TECHNIQUES

This work presents a new methodology for flow regime identification in a gas–solid two-phase flow system. The approach of identification employs the artificial neural network (ANN) technique, considering the applications with electrostatic sensor as a measuring device and Hilbert–Huang transformation (HHT) as the post-processing method. The electrostatic fluctuation signals detected from an electrostatic sensor are processed using HHT to gain the Hilbert marginal spectrums. Then four characteristic parameters of the marginal spectra are extracted as the input of BP neural network for flow regime identification. They are subband energy (SE), first-order difference of subband energy (DSE), subband energy cepstrum coefficients (SECC), and first-order difference of the subband energy cepstrum coefficients (DSECC). The results show that the characteristic parameters of the Hilbert marginal spectrum of the electrostatic signal can identify the three flow regimes of gas–solid two-phase flow in a horizontal pipe, especially the DSECC.

A self-adapting Landweber algorithm for the inverse problem of electrical capacitance tomography (ECT)

Electrical capacitance tomography (ECT) is considered as one of the tomography techniques with a broad developing prospect owning to the distinct advantages such as low cost, non-invasive and high safety. However, the image reconstruction in ECT is an ill-posed and nonlinear problem. In order to relieve the non-linearity of ECT system, this paper proposed a self-adapting Landweber (SAL) algorithm which use interelectrode capacitances as a prior information to select the optimal sensitive matrix in the database automatically. Typical flow patterns have been examined by simulated data and experimental data. And the results indicate that compared with conventional Landweber algorithm, this method can achieve reconstructed images with better quality, and the artifacts of the reconstructed images are reduced obviously.

Use of double correlation techniques for the improvement of rotation speed measurement based on electrostatic sensors

Electrostatic sensing technology using correlation signal processing offers an approach to the measurement of rotational speed in the automatic control system of large generators and centrifugal machines. In this article, a double autocorrelation method was proposed to improve the accuracy and robustness of the measurement on the designed test rig. An electrostatic sensor was used to obtain signals from the rotor surface. Then, the rotational speed was determined from the period of rotational motion calculated from a double autocorrelation method. At the same time, experiments with sampling rates of 2ksps (kilo samples per second), 5ksps, 10ksps, 20ksps were carried out on a laboratory-scale test rig under a rotational speed range from 400 r min−1 to 4200 r min−1. The results show that the double autocorrelation method improves the accuracy and robustness. The measurement accuracy also improves with the sampling rate-the relative errors using 2ksps, 5ksps, 10ksps, and 20ksps are within 1.5%, 1%, 0.4%, and 0.3% respectively. The linearity of them is 1.47%, 0.61%, 0.28%, 0.17% correspondingly. The experiments also reveal that the measurement error has a tendency to increase with the rotational speed.

Comparison of NOx Emission Reductions with Exclusive SOFA and the Combination of SOFA and CCOFA on Tangentially-fired Boilers

The paper deals with air staging experiments on two full-scale, tangentially fired boilers burning bituminous coal., unit A boiler with separated overfire air (SOFA), unit B boiler with the combination SOFA and close coupled overfire (CCOFA). We compared the NO x emission reductions of the two boilers. The influencing factors of NO x emission including the oxygen levels, the distribution types of pulverized coal and the variations of OFA damper positions were investigated. The NO x emission from unit B boiler was apparently lower than that from unit A boiler (lower 22% or so). The heat efficiency of unit B boiler was higher than that of unit A boiler (higher 0.7% or so). The maldistribution type of pulverized coal was conducive to reduce NO x emission from unit A boiler, but the decrease of NO x emission followed the decrease of boiler heat efficiency. The uniform distribution type of pulverized coal could obtain not only the lowest NO x emission but the highest heat efficiency on unit B boiler. There wasn’t an optimum adjustment of SOFA damper position on unit A boiler, and both NO x emission and boiler heat efficiency decreased with the increase of the opening extent of SOFA. However, there was an optimum matching adjustment of CCOFA and SOFA damper positions in unit B boiler.

A Low Cost, High Resolution Interface Circuit for Differential Capacitance Transducer

Abstract For accurately detecting capacitance changes of the differential‐capacitance transducer, which is widely used in environmental and chemical engineering, a low‐cost interface circuit was developed. The circuit uses a “lock‐in detection” circuit with feedback loop containing a DC amplifier and a modulator to yield such advantages over the others that it reduces the sensitivity to element parameter variations in the “lock‐in detection” circuit, that it decreases the sensitivity to the disturbances within closed‐loop, and that it is immune to parasitic capacitance, resistance, amplitude, and frequency of the excitation voltage source. The circuit has the capability to detect a capacitance change as small as 0.0078% of the total capacitance, up to 80 pF. A capacitance diaphragm pressure transducer and a standard pressure source (Rusca 2465 piston gauge) are connected to the interface to confirm the analysis.

SVM-based multisensor data fusion for phase concentration measurement in biomass-coal co-combustion

In this paper, the electrical method combines the electrostatic sensor and capacitance sensor to measure the phase concentration of pulverized coal/biomass/air three-phase flow through data fusion technology. In order to eliminate the effects of flow regimes and improve the accuracy of the phase concentration measurement, the mel frequency cepstrum coefficient features extracted from electrostatic signals are used to train the Continuous Gaussian Mixture Hidden Markov Model (CGHMM) for flow regime identification. Support Vector Machine (SVM) is introduced to establish the concentration information fusion model under identified flow regimes. The CGHMM models and SVM models are transplanted on digital signal processing (DSP) to realize on-line accurate measurement. The DSP flow regime identification time is 1.4 ms, and the concentration predict time is 164 μs, which can fully meet the real-time requirement. The average absolute value of the relative error of the pulverized coal is about 1.5% and that of the biomass is about 2.2%.

Facile synthesis of ultrathin and perpendicular NiMn2O4 nanosheets on reduced graphene oxide as advanced electrodes for supercapacitors

Ultrathin nickel manganese oxide nanosheets grown vertically on reduced graphene oxide (NiMn2O4 NSs@rGO) were successfully fabricated through a facile co-precipitation and thermal treatment process. Such a unique nanostructure can not only facilitate electron transfer and electrolyte penetration but also withstand the structural damage of the active materials in electrochemical reactions. The resulting product NiMn2O4 NSs@rGO was characterized by X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS) and Brunauer–Emmett–Teller (BET) analysis. When evaluated for its potential as an electrode material in supercapacitors, the as-synthesized NiMn2O4 NSs@rGO nanocomposite exhibits enhanced energy storage performance with high specific capacitance (1243 F g−1 at 3 A g−1) and prominent cycle life (80.8% capacitance retention after 5000 cycles). These results indicate that the ultrathin and perpendicular NiMn2O4 nanosheets with rGO substrates obtained from this green and facile route hold great prospects in high performance supercapacitor applications.