Online continuous measurement of the mass flow rate of pneumatically conveyed solids in a square-shaped pipe is desirable for monitoring and optimizing industrial processes. However, existing techniques using a single type of sensor have limitations in measuring the mass flow rate of solids because of the complexity of the dynamics of solids flow due to the four sharp corners of a square-shaped pipe. This paper proposes a multi-sensor fusion and data-driven modelling-based method to tackle this challenge. A multi-sensor system based on acoustic, capacitive, and electrostatic sensing principles is designed and implemented to obtain the sound pressure level in the flow, volumetric concentration of solids, and solids velocity, respectively. Simultaneously, a range of statistical features is obtained by performing time-domain, frequency-domain, and time-frequency domain analyses on all sensor signals. The statistical features reflecting the variation of the mass flow rate of solids, as well as solids velocity and volume concentration of solids, are then fed into a data-driven model. A data-driven model based on a combined convolutional neural network and long short-term memory (CNN-LSTM) network is established, and its performance is compared with those of the back-propagation artificial neural network, support vector machine, CNN, and LSTM models. Experimental tests were conducted on a laboratory-scale rig on both horizontal and vertical pipelines to train and evaluate the CNN-LSTM model with solids velocity ranging from 11 to 23 m/s and the mass flow rate of solids from 8 to 26 kg/h. The CNN-LSTM model outperforms all other models with a relative error within ±1% under all test conditions.

Accurate determination of particle size distribution is critical to achieving optimal combustion efficiency and minimum pollutant emissions in both biomass and coal fired power plants. This paper presents an instrumentation system for online continuous measurement of particle size distribution based on acoustic emission (AE) method. Impulsive AE signals arising from impacts of particles with a metallic waveguide protruding into the flow carry information about the particle size. The relationship between the particle size and the peak AE voltage has been established through physical modeling of particle impact. A particle sizing algorithm is developed using peak detection techniques. Experimental results obtained with glass beads demonstrate the capability of the system to discriminate particles of different sizes from the recorded AE signals. The system has several appealing features such as online measurement, high sensitivity, simple structure, minimum invasiveness and low cost, which make it well suited for industrial applications.

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.

Online continuous measurement of the cross-sectional velocity distribution of pneumatically conveyed solids in a square-shaped pipe is desirable in monitoring and optimizing circulating fluidized beds, coal-fired power plants and exhaust pipes. Due to the limitation of non-restrictive electrostatic sensors in spatial sensitivity, it is difficult to accurately measure the velocity of particles in large-diameter pipes. In this paper, a novel approach is presented for the measurement of cross-sectional particle velocity distribution in a square-shaped pipe using sensors and Gaussian process regression (GPR). The electrostatic sensor includes twelve pairs of strip-shaped electrodes. Experimental tests were conducted on a laboratory test rig to measure the cross-sectional particle velocities in a vertical square-shaped pipe under various experimental conditions. The GPR model is developed to infer the relationship between the input variables of velocities and the cross-sectional velocity distribution of particles in nine areas of the pipe cross-section and the performance of the built models was compared with other machine learning models. The relative error of velocities predicted under all the experimental conditions is within ±3%. When the training dataset is not comprehensive enough, the performance of the model is negatively affected, and the relative error range is –9% to +15%. With fewer measurement electrodes (input variables), the relative error of the predicted velocities in each area increases slightly, but remains within ±5%. Results obtained suggest that the electrostatic sensor in conjunction with the GPR model is a feasible approach to obtain the cross-sectional velocity distribution of pneumatically conveyed particles in a square-shaped pipe.