title: Acoustic emission data assisted process monitoring.
abstract: Gas-liquid two-phase flows are widely used in the chemical industry. Accurate measurements of flow parameters, such as flow regimes, are the key of operating efficiency. Due to the interface complexity of a two-phase flow, it is very difficult to monitor and distinguish flow regimes on-line and real time. In this paper we propose a cost-effective and computation-efficient acoustic emission (AE) detection system combined with artificial neural network technology to recognize four major patterns in an air-water vertical two-phase flow column. Several crucial AE parameters are explored and validated, and we found that the density of acoustic emission events and ring-down counts are two excellent indicators for the flow pattern recognition problems. Instead of the traditional Fair map, a hit-count map is developed and a multilayer Perceptron neural network is designed as a decision maker to describe an approximate transmission stage of a given two-phase flow system.
authors.affiliation: Intelligent Systems and Control Laboratory, School of Electrical and Computer Engineering, Oklahoma State University, Stillwater 74078, USA.
authors.forename: Gary G
authors.initials: GG
authors.lastname: Yen
authors.forename: Haiming
authors.initials: H
authors.lastname: Lu
mesh_terms: D000162:Acoustics / Q000295:instrumentation*; D016000:Cluster Analysis; D004867:Equipment Design; D005246:Feedback; D005740:Gases; D008422:Materials Testing / Q000379:methods; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D012212:Rheology / Q000295:instrumentation* / Q000379:methods*; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors; D014159:Transducers
chemical_list: D005740:Gases