With the development of renewable sources of electricity, more and more attention is paid to electricity storage systems and their research. In order to make the research process of various types of batteries automatic, an automatic system for monitoring of the working characteristics and parameters of batteries was created. System management is based on the ATmega328 series microcontrollers. The system control program code and computer application were created using programming languages C and Delphi. The developed system is able to support the automated process of battery testing and to inform the operator about the deviations that have occurred by offering solutions. After the development of this system, a study of the charging and degradation process of four types of batteries was carried out. Four types of lead-based batteries (non-deep cycle battery, deep-cycle battery, GEL battery and AGM battery) were used for the research. Research has shown that GEL-type batteries have the highest number of life cycles, with the number of cycles reaching up to 750. This type of batteries is best suited for storing electricity obtained using renewable energy sources.

This study introduces an innovative smart impact detection system designed for real-time monitoring of shipment status and path integrity. Leveraging the advanced capabilities of the ESPRESSIF ESP32-S3-MINI-1U-N8 microcontroller, which integrates Wi-Fi, a display, a memory card slot, and accelerometers, this detector represents a significant advancement in shipment tracking technology. The device is engineered to continuously measure impact magnitudes in terms of g-force, and records data when predefined impact thresholds are exceeded. These data are then wirelessly transmitted to a remote server, providing users with the ability to track shipment status and path via a dedicated application. The performance testing revealed impact measurements ranging from −0.5 to 2 g, with occasional peaks reaching approximately 4.5 g, demonstrating the system’s sensitivity and reliability in diverse conditions. This smart impact detector not only facilitates continuous monitoring, but also enhances the ability to respond swiftly to potential shipment violations, thus providing a novel solution for ensuring shipment integrity. This research contributes to the field by presenting a comprehensive real-time impact detection system that integrates modern microcontroller technology with effective monitoring capabilities, setting a new benchmark for shipment tracking systems.

This study presents an automated industrial wastewater treatment system based on Siemens programmable logic controller (PLC) that optimizes reagent dosing, aeration, sedimentation, and sludge separation. The system uses accurate pH sensors, dosing pumps, solenoid valves, and a human–machine interface (HMI), and real-time monitoring is provided by a Teltonika TRB255 communication module (<45 sec. response time). As a result, the treatment cycle time was reduced by 31%, reagent consumption by 30%, and operator intervention was reduced from 95 to less than 15 min per day, achieving a pollutant removal efficiency of 89%. A two-layer LSTM architecture developed on the PyTorch platform predicts pH (6.7–7.7), temperature (12–20 °C), and reagent consumption (~9.8 kg/cycle). The model was trained with 240 h of data (64 neurons, learning rate 0.001). The validation loss remained stable, indicating reliable learning. The study confirms that AI-based automation provides greater process stability, meets environmental standards, and promotes sustainable resource use. The scientific novelty of this study is the application of an advanced long short-term memory (LSTM) model to predict wastewater treatment process parameters, allowing for accurate prediction of pH, temperature, flow, and reagent consumption, etc. This provides an opportunity to optimize the process and reduce costs, while ensuring high treatment efficiency and stability. Although there are several publications on the application of artificial intelligence models in the field of industrial wastewater treatment, this is a relatively new field, and there are little data in the scientific literature.

The increasing integration of renewable energy sources increases the variability and uncertainty of power systems, requiring advanced prediction-based control strategies. This paper proposes an integrated AutoML–MPC framework for a hybrid renewable energy system (HRES) combining solar and wind generation, biomass, battery energy storage, and a hydrogen chain (electrolyzer and fuel cell). Short-term load and generation forecasts are made using H2O AutoML models, and the energy flow allocation is optimized using model-based control (MPC) formalized in the form of mixed-integer linear programming (MILP). The objective function minimizes electricity imports from the grid and the associated ${\mathrm{C}\mathrm{O}}_2$ emissions, subject to technological constraints. The results obtained showed a clear distribution of short-term (battery) and long-term (hydrogen) storage functions in time: during periods of excess generation, the electrolyzer operated close to nominal mode, and in the deficit phase, the fuel cell was activated, reducing the need for grid imports. The battery ensured fast short-term balancing, while the hydrogen system compensated for the longer-term energy shortage. The forecast models were characterized by high accuracy ($R^2>0.98$), which allowed for reliable planning of energy flows over the MPC horizon. The proposed methodology allows for effective coordination of storage technologies of different time scales, maximum use of renewable generation and reducing the system’s dependence on the external grid.

Often, solar power parks are built in remote areas where the provider’s internet connection using the GSM network is insufficient. In addition, in order to monitor the parameters of each solar module, a very dense arrangement of IoT components would be required, as a result of which the speed of the external network would not be sufficient. In solving this problem, a hybrid data transmission network (there are no universal hybrid network concepts proposed in the literature for monitoring the operating parameters of a solar power park) was designed and tested for the monitoring system of the working parameters (voltage, current, etc.) of the solar power plant. This network consists of RF modules, RS458 modules communicating with a twisted pair cable, a GSM module, etc. PIC series microcontrollers with original programming code written in the C language were used to ensure the operation of the monitoring system and the hybrid data network. The data of the operating parameters of the solar power plant are stored centrally on a server, in a MySQL database. The central server has the ability to display data not only on the graphical output device, but also to transmit it to mobile devices using the Android operating system. During the testing of this network, observations of the working parameters of the 870 W solar power plant were made. Observations lead to the conclusion that such a network can be fully used in solar power parks as an alternative to the GSM network. Analyzing the data, it was found that the efficiency of bifacial type batteries is about 15–20% higher than that of traditional, silicon-based solar batteries. The voltammetric characteristics of a bifacial type battery, a new silicon battery and a 10-year-old battery basically match, which means that the silicon active material of all types of batteries has a similar alloying degree and the same internal resistance.

This research paper investigates an LED strip lighting system, whose LED lighting controller was developed based on the PIC24FV32KA302 microcontroller and PCA9685 PWM driver. During the study, various parameters of the system were evaluated, including the response time from the length of commands, the dependence of the PWM signal characteristics on the logic values generated by the microcontroller, and the dependence of the LED light flux and power on the percentage of the PWM signal and the power of the power supply channels. The results of the study revealed that the reaction time depended on the length of the sent command, where as the logic values of the microcontroller were changed from 1000 to 4000, the size of the PWM signal changed from 25 to 100%. The use of the I2C communication protocol, which is a master–slave architecture and uses data and synchronization lines, was also found to affect response times. When the percentage of the PWM signal was changed from 10 to 100%, the light flux of the LED strip changed from 100 to 1000 lm. These results reflect the advantages of applying microcontrollers and PWM drivers to LED control systems, emphasizing their flexibility, efficiency, and precise light control.