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

Electrostatic Sensors – Their Principles and Applications

Over the past three decades electrostatic sensors have been proposed, developed and utilised for the continuous monitoring and measurement of a range of industrial processes, mechanical systems and clinical environments. Electrostatic sensors enjoy simplicity in structure, cost-effectiveness and suitability for a wide range of installation conditions. They either provide unique solutions to some measurement challenges or offer more cost-effective options to the more established sensors such as those based on acoustic, capacitive, optical and electromagnetic principles. The established or potential applications of electrostatic sensors appear wide ranging, but the underlining sensing principle and resultant system characteristics are very similar. This paper presents a comprehensive review of the electrostatic sensors and sensing systems that have been developed for the measurement and monitoring of a range of process variables and conditions. These include the flow measurement of pneumatically conveyed solids, measurement of particulate emissions, monitoring of fluidised beds, on-line particle sizing, burner flame monitoring, speed and radial vibration measurement of mechanical systems, and condition monitoring of power transmission belts, mechanical wear, and human activities. The fundamental sensing principles together with the advantages and limitations of electrostatic sensors for a given area of applications are also introduced. The technology readiness level for each area of applications is identified and commented. Trends and future development of electrostatic sensors, their signal conditioning electronics, signal processing methods as well as possible new applications are also discussed

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

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

Mass Flow Rate Measurement of Solids in a Pneumatic Conveying Pipeline in Different Orientations

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

Particle size measurement using electrostatic sensor through spatial filtering method

Particle size measurement is important in powder and particle industries in which the particle size affects the productivity and efficiency of the machine, for example, in coal-fired power plants. An electrostatic sensor detects the electric charge from dry particles moving in a pipeline. Analysis of the detected signal can provide useful information about the particle velocity, mass flow rate, concentration and size. Using electrostatic sensors, previous researches studied particle sizing using magnitude dependent analysis which is a highly conditional method where the results can be affected by other parameters such as particle mass flow rate, velocity and concentration. This research proposes a magnitude independent analysis for particle sizing in the frequency domain called spatial filtering method. The solution was started by modeling and analysis of the charge induced to the ring electrode using finite-element analysis to find the sensitivity of electrode. A mathematical model was provided to compute particle position on the radial axis of the electrode and then a new technique was proposed to extract a single particle size from the calculated particle radial position. To validate the proposed method experimentally, a sensor was designed and five test particles ranging from 4 mm to 14 mm were selected for measurement. The results show a 0.44 mm estimation error between the estimated and expected results. The results also show that the method is promising for the establishment of a reliable and cost-effective solid particle sizing system

Homogenization of the Spatial Sensitivity of Electrostatic Sensors for the Flow Measurement of Pneumatically Conveyed Solids in a Square-Shaped Pipe

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