# Detection of Pneumatic Conveying by Acoustic Emissions
## 1 Introduction  
Pneumatic conveying is widespread in the power, pharmaceutical, metallurgy, food, and various other industrial processes [1]. The capability of online management and measurement of pneumatic conveying is of great significance for monitoring and control of industrial processes. Considering the coal-fired power plant industry as an example, the mass flow rate and particle size of pulverized coal will directly affect combustion inside of the boiler and the carbon content of the resulting ash, significantly impacting operation efficiency and plant economy. Determining pneumatic conveying parameters, including particle size, particle mass flow rate, flow velocity, and humidity, is complicated, and with current technology, it is challenging to establish an accurate model for these conveying characteristics. Over the course of many years, researchers have studied and developed a variety of pneumatic conveying measurement methods. Each method has certain advantages over a specific range, but also possesses notable drawbacks. Among these numerous measurement methods, the most commercially promising techniques include ultrasonic [2; 3], optical [4; 5], electrostatic [6; 7], and capacitance [8] approaches, among others. The ultrasonic method is susceptible to temperature and is regarded as difficult to install. The optical method is mainly subject to the pollution of the sampling window. The electrostatic and capacitive method belongs to the electrical category, however, it is hard for the electrostatic sensor to be electrically shielded from the rest of the environment. The capacitance sensorlacks sufficient sensitivity for dilute flows that contains moisture. During the pneumatic conveying process, particle-particle interaction and particle-wall interaction produce particular acoustic signals, which can be detected by acoustic emission (AE) sensors. Since pneumatic conveying generates these signals, they reveal important characteristics about the conveying process. As an added benefit, acoustic emission measurements are simpler and more convenient for this application. Some researchers investigated the monitoring of particle size by using acoustic emissions method. [PERSON] and [PERSON] [9] originally introduced three important cases to illustrate the principles of the method. Nevertheless, further application of signal processing techniques on the acoustic emission signal had not been performed. [PERSON] et al. [10] applied the approach to research the relationship between AE signal and particle size distribution for limited operating conditions. To this end, they developed a three-step data processing procedure, using wavelet packet decomposition to extract useful features and multivariate data analysis to decrease feature numbers used as the input of the neural network. This led to the development of a neural network to realize particle sizing. For the wavelet packet decomposition process, however, it is necessary to apply multiple adjustments. [PERSON] and [PERSON] [11] experimentally validated the Hertz contact theory in the measurement of particle size distribution, while their experimental results are distinct to practical industrial processes. As a consequence, a more accurate theory is necessary to establish and predict the process. [PERSON] [12] developed an AE-based online particle sizing instrument employing the Hertz theory of contact, and the experimental results were highly consistent with theoretical expectations under limited conditions. However, for increasing solid mass flow rate, the sizing results deviated significantly due to their theoretical assumptions. [PERSON] and [PERSON] et al. [13] used the audio AE signals to monitor particle size in pharmaceutical manufacturing. The investigation demonstrated a predictive relationship between the total power spectral density and wet granule size in a 12 L beaker, with the result corresponding to dry sphere measurement.