Measurement of Flow Characteristics in a Bubbling Fluidized Bed Using Electrostatic Sensor Arrays

Fluidized beds are widely applied in a range of industrial processes. In order to maintain the efficient operation of a fluidized bed, the flow parameters in the bed should be monitored continuously. In this paper, electrostatic sensor arrays are used to measure the flow characteristics in a bubbling fluidized bed. In order to investigate the electrostatic charge distribution and the flow dynamics of solid particles in the dense region, time and frequency domain analysis of the electrostatic signals is conducted. In addition, the correlation velocities and weighted average velocity of Geldart A particles in the dense and transit regions are calculated, and the flow dynamics of Geldart A and D particles in the dense and transit regions are compared. Finally, the influence of liquid antistatic agents on the performance of the electrostatic sensor array is investigated. According to the experimental results, it is proved that the flow characteristics in the dense and transit regions of a bubbling fluidized bed can be measured using electrostatic sensor arrays

Predicting the amount of coke deposition on catalyst through image analysis and soft computing

The amount of coke deposition on catalyst pellets is one of the most important indexes of catalytic property and service life. As a result, it is essential to measure this and analyze the active state of the catalysts during a continuous production process. This paper proposes a new method to predict the amount of coke deposition on catalyst pellets based on image analysis and soft computing. An image acquisition system consisting of a flatbed scanner and an opaque cover is used to obtain catalyst images. After imaging processing and feature extraction, twelve effective features are selected and two best feature sets are determined by the prediction tests. A neural network optimized by a particle swarm optimization algorithm is used to establish the prediction model of the coke amount based on various datasets. The root mean square error of the prediction values are all below 0.021 and the coefficient of determination R 2, for the model, are all above 78.71%. Therefore, a feasible, effective and precise method is demonstrated, which may be applied to realize the real-time measurement of coke deposition based on on-line sampling and fast image analysis

Charge Distribution Reconstruction in a Bubbling Fluidized Bed Using a Wire-Mesh Electrostatic Sensor

The presence of electrostatic charge in a bubbling fluidized bed influences the operation of the bed. In order to maintain an effective operation, the electrostatic charges in different positions of the bed should be monitored. In this paper a wire-mesh electrostatic sensor is introduced to reconstruct the charge distribution in a bubbling fluidized bed. The wire-mesh sensor is fabricated by two mutually perpendicular strands of insulated wires. A Finite Element Model is built to analyze the sensing characteristics of the sensor. The sensitivity distributions of each wire electrode and the whole sensor are obtained from the model, which proves that wire-mesh electrostatic sensor has a higher and more uniform sensitivity distribution than single wire sensors. Experiments were conducted in a gravity drop test rig to validate the reconstruction method. Experimental results show that the charge distribution can be reconstructed when sand particles pass through the cross section of the sensor

Measurement of Charge Distributions in a Bubbling Fluidized Bed Using Wire-Mesh Electrostatic Sensors

In order to maintain safe and efficient operation of a fluidized bed, electrostatic charges in the bed should be monitored continuously. Electrostatic sensors with wire-mesh electrodes are introduced in this paper to measure the charge distribution in the cross section of the fluidized bed. A Finite Element Model is built to investigate the sensing characteristics of the wire-mesh sensors. In comparison with conventional electrostatic sensors, wire-mesh sensors have higher and more uniform sensitivity distribution. Based on the induced charges on the electrodes and the sensitivity distributions of the sensors, the charge distribution in the cross section of the fluidized bed is reconstructed. However, it is difficult to directly measure the induced charges on the electrodes. A charge calibration process is conducted to establish the relationship between the induced charge on the electrode and the electrostatic signal. Experimental studies of charge distribution measurement were conducted on a lab-scale bubbling fluidized bed. The electrostatic signals from the wire-mesh sensors in the dense phase and splash regions of the bed for different fluidization air flow rates were obtained. Based on the results obtained from the charge calibration process, the estimated induced charges on the electrodes are calculated from the Root Mean Square values of the electrostatic signals. The characteristics of the induced charges on the electrodes and the charge distribution in the cross section under different flow conditions are investigated, which proves that wire-mesh electrostatic sensors are able to measure the charge distribution in the bubbling fluidized bed

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 optimise 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 paper, 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%