This paper presents a study on applying machine learning algorithms for the classification of a two-phase flow regime and its internal structures. This research results may be used in adjusting optimal control of air pressure and liquid flow rate to pipeline and process vessels. To achieve this goal the model of an artificial neural network was built and trained using measurement data acquired from a 3D electrical capacitance tomography (ECT) measurement system. Because the set of measurement data collected to build the AI model was insufficient, a novel approach dedicated to data augmentation had to be developed. The main goal of the research was to examine the high adaptability of the artificial neural network (ANN) model in the case of emergency state and measurement system errors. Another goal was to test if it could resist unforeseen problems and correctly predict the flow type or detect these failures. It may help to avoid any pernicious damage and finally to compare its accuracy to the fuzzy classifier based on reconstructed tomography images – authors’ previous work.
W artykule przedstawiono Internetowy Loger eLOG™ do zdalnego monitorowania procesów technologicznych poprzez sieć Internet/Intranet. Urządzenie może być włączane bezpośrednio do tej sieci bez dodatkowych interfejsów, a wizualizacja zbieranych sygnałów oraz konfiguracja i programowanie urządzenia możliwe są poprzez standardową przeglądarkę www.
EN
The paper presents a description of an INTERNET LOGGER eLOG™, designed and manufactured by the Experimental Department of Scientific Equipment and Automation Ltd. It is a miniature, microcomputer remote data concentrator, using real-time operating system, designed for direct connection to the Internet/Intranet network. Visualisation of acquired signals as well as configuration and programming of the device are performed by means of www browser.
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