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.

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.

This study presents a methodology for preliminary assessment of wastewater pollutant concentrations using RGB color intensity measurements combined with a multi-head neural network implemented in PyTorch. The model was trained on 120 samples from domestic, industrial, and synthetic sources to predict total pollutant concentration and identify the dominant RGB color. Key performance metrics include MAE = 0.012, RMSE = 0.018, MAPE = 2.1%, R² = 0.96 for concentration prediction, and classification accuracy = 0.93 for dominant color identification. Observed color changes, primarily the rapid decrease of the red component, correlate with chemical and organic load, enabling visual differentiation of wastewater types. This approach provides a fast, low-cost, and equipment-free method for preliminary monitoring and has potential for real-time environmental applications. The proposed combination of RGB analysis with a multi-head neural network represents a novel approach for rapid, quantitative, and qualitative wastewater assessment.