Modern automated process control systems include software complexes for monitoring, dispatching, data processing, and controlling industrial equipment. The correct operation of these systems depends on the predictable and stable deployment of software components, which requires a deterministic approach to configuration management. This paper proposes a mathematical model for software configuration management in APCS, based on difference equations of discrete systems. Numerical modeling was conducted in the Octave environment, confirming the correctness of the proposed model and allowing for the analysis of the impact of control parameters on process dynamics. The obtained results can be used to optimize strategies for automated deployment of software complexes in industrial systems.

Keywords: automated control, software complexes, discrete systems, difference equations, stability, dispatching, monitoring

The article discusses approaches to the systematic analysis of historical data collected from water treatment facilities. By using tools from mathematical statistics, machine learning methods, and visual analysis techniques, the article proposes a formalized approach to assessing the efficiency of water treatment equipment. This approach makes it possible to identify hidden patterns in the data, build robust models of interdependencies, and develop recommendations for optimizing the technological process.

Keywords: water treatment, telemetry data, time series analysis, machine learning, equipment efficiency

This article explores a method for executing data collection systems based on the widely-used industrial protocol, Modbus TCP. In this configuration, the server is a program emulating the Modbus TCP protocol.To implement the client an algorithm and furnished a practical Python programming illustration that employs a lower-level socket library.

Keywords: python, socket, modbus