In order to make plant cultivation as economical and efficient as possible, and to reduce the amount of human errors and labour costs, autonomous systems for growing plants and, in this case, also mushrooms, are increasingly being implemented. In this work, a prototype of an electronic/automatic system was designed and manufactured for the monitoring and control of the parameters of mushroom growing conditions. Appropriate sensors were used to monitor the application parameters such as CO₂ level, temperature and humidity, and the data were sent to the main logic controller using an RS 485 interface-based local data transmission network. Both the main controller and the individual parts of the system were made on the basis of PIC18F25K83 microcontrollers, using the C programming language to create the program code. In order to achieve optimal system operation, a software algorithm was created to ensure fast system operation. During the production, the PCBs of the system were optimized to achieve the smallest possible dimensions and optimal construction and the arrangement of the active electronic elements. For the convenience of the user, a system application was created so that it is possible to monitor information from the environmental sensors and the process of parameter control. This work aimed to show that such autonomous systems based on environmental sensor monitoring are universal and can be applied to a large number of plant species. In addition, the capabilities of the plant cultivation system can be expanded if needed by additionally connecting relevant environmental sensors and environmental parameter regulation units.

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.

In the industrial and sales processes, dosing systems of various constructions, whose operation is based on mechanical vibrations (vibratory feeders), are very often used. These systems face many problems, such as resonant frequency, flow instability of dosed product, instability of mechanical vibration amplitude, etc., because most of them are based on controlling the frequency of the electrical signal of the supply voltage. All these factors negatively affect the durability and reliability of the vibratory feeder systems. During this research, an automatic control system for vibratory feeder was created, whose control process is based on the modification of the sinusoidal signal (partially changing the signal area). In addition, such a way of controlling the vibratory feeder is not discussed in the literature. As the research conducted in this paper has shown, while using sinusoidal signal modification it was possible to achieve a stable flow rate of bulk production (the flow rate varied from 0 to 100 g/s when the frequency of mechanical vibrations changed from 1 to 50 Hz) and a stable amplitude of mechanical oscillations was achieved and equal to 1.5 mm. The control system is based on the microcontroller PIC24FV32KA302 for which the special software was developed. The thyristor BTA16 used for voltage modification of the sinusoidal signal made it possible to ensure the reliable control of the sinusoidal voltage modification process.

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.