Monitoring of Measuring Devices Using a Programmable Logic Controller and a Dedicated Desktop Application

Abstract

An appropriate control and measurement system is one of the elements that allows for the safe and effective operation of a technological installation. Such a system may have a diverse structure that corresponds to the expectations and needs of its user. The devices of various manufacturers, including PLC (programmable logic controllers), can be used as a source of measurement data. This enables flexible measurement–control installations, which are adapted to the technological installations, to be built. This paper presents a system, which was created by the authors, for monitoring the temperature, pressure and flow rate of fluids. It uses the operational and IT properties of the PLC and is adapted to an existing installation on the test stand for multiphase spraying processes. In addition, as a part of the research, an application written in Python language, which allows collected data to be displayed, stored and exported, was implemented.

Design and Research of a Field Bus Control System Laboratory for Metal Mining, Beneficiation and Metallurgy

“…The current artificial intelligence deep learning algorithms have been able to perform reliable and interpretable optimization control function blocks, especially intelligent con- The current artificial intelligence deep learning algorithms have been able to perform reliable and interpretable optimization control function blocks, especially intelligent control tools such as multivariable model deep learning predictive control [34][35][36][37][38], fuzzy neural network adaptive control, deep learning optimization soft sensor models, etc. Through these algorithm functional models, the production process control effect can be optimized without significantly increasing investment expenses for other software and hardware costs, promoting the goal of reducing the impact on the production process [39,40], especially the current deep learning optimization soft sensor models, based on deep learning and mathematical optimization algorithms, new prediction architectures, data collection methods, fusion model prediction, feedback correction, deep learning, process mathematical modeling, rolling optimization control, and digital twin real-time precise simulation technology. The online real-time collection, monitoring, control, and visualization of various measurement data, even unmeasurable data, that can accurately predict and simulate the production process is of great significance for optimizing the management of production control.…”