International Scientific Journals
of Scientific Technical Union of Mechanical Engineering "Industry 4.0"

The integration of engineering support tools is essential for the efficient modeling, simulation, and optimization of complex technical systems. This paper presents a dynamic model of a micro-combined heat and power (mCHP) system, developed to validate the feasibility of integrating various computational environments. The approach leverages modular architectures, enabling seamless data exchange between distinct software platforms, thus supporting both detailed thermodynamic analysis and real-time performance optimization. The flexibility of this approach allows for the inclusion of diverse analytical frameworks, including neural network-based optimization, data-driven control strategies, and alternative programming languages, without being limited to a single computational tool. This adaptability makes the proposed architecture particularly suitable for evolving engineering applications, where rapid prototyping and iterativ e refinement are critical. The study highlights the potential of such integrated environments to enhance the design and operational efficiency of energy systems, providing a scalable foundation for future expansions.

The development of neural algorithms opens new perspectives for the analysis of technological processes. Particularly relevant are strongly nonlinear and complex objects, such as power plants. One of the modern solutions enabling data analysis are convolutional neural networks (CNNs). The research presents the application of CNNs to monitor and optimize combustion processes in biomass boilers. The fuel analyzed was gray straw, which is difficult to control due to the nature of combustion. The proposed technique is based on the processing of temporal data, which represent different stages of the combustion process. The work examined the effectiveness of the model in identifying key operating parameters and detecting the stages of firing from ignition initialization to nominal operation. Analysis of images of parameter curves from the time waveforms makes it possible to capture repeatable relationships that enable faster response to future changes in the conditions of the combustion process. Determining the phase of the process, based on data and trends of selected parameters, allows the control system to react faster, without operator intervention. As a result of the study, the efficiency of process stage change detection by the convolutional network, expressed by means of an error matrix, through the F1-score parameter (harmonic mean between precision and sensitivity) was achieved at a level close to 96%. The proposed solution can be effectively applied to a number of technological processes including those that are part of Industry 4.0 effectively influencing technological transformation..

The article presents an intelligent energy guardian for a polygeneration device. The proposed solution aims to optimize energy usage and minimize wastage by incorporating smart control algorithms that continuously monitor and adjust the energy flow between different subsystems of the device. The energy guardian utilizes machine learning techniques to learn the device’s energy usage patterns and adapt to changing conditions, such as varying energy demands and supply constraints. The article outlines the design and implementation of the energy guardian, and presents experimental results that demonstrate its effectiveness in improving energy efficiency and reducing operational costs. Overall, the intelligent energy guardian offers a promising solution for enhancing the performance of polygeneration devices and promoting sustainable energy use.