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.

Rapid urbanization, the pursuit of a higher quality of life, increasing energy and fuel demands, and remaining dependence on traditional fossil fuels, along with an increasingly pronounced negative impact on the environment, encourage society to seek new, lower-emission solutions. Among them, the biomass-to-biofuels process shows a promising pathway. Biomass, a renewable energy source, can be converted into a variety of biofuels in gas, liquid, or solid forms (e.g., syngas, bio-oil, biochar). There are two primary methods for converting biomass into biofuels: biochemical and thermochemical conversion. The latter is considered more flexible and efficient than biochemical conversion. Thus, this review aims to provide a summary of the most recent results from experimental research on biomass conversion to biofuels using various thermochemical conversion methods. Additionally, this review covers fundamentals and highlights the main evaluation parameters of thermochemical conversion systems, helping readers better understand the results reported by the researchers discussed in this article. This review also briefly addresses prospects and projections for future biofuel production, as well as measures to promote biofuel production.

This study presents a capacitive sensor with a zirconium oxide (ZrO₂) coating for real-time measurement of component concentration in liquid media. The ZrO₂ layer was formed on stainless steel electrodes by magnetron sputtering, and its structural, morphological, and chemical properties were characterized using SEM, EDS, FTIR, and XRD. It was found that increasing coating thickness results in more continuous and highly crystalline layers, while reducing the influence of the substrate on surface properties. The performance of the capacitive sensor was evaluated by analysing the dependence of capacitance on frequency and NaCl concentration. The results show that the thickness of the ZrO₂ layer has a significant influence on sensor sensitivity and measurement stability. A thinner layer (~2 µm) provides higher sensitivity but is more affected by parasitic effects, while thicker layers improve measurement stability at the expense of reduced sensitivity. An optimal trade-off between sensitivity and stability is achieved at a ZrO₂ layer thickness of approximately 4 µm, ensuring sufficient sensitivity and good measurement repeatability. The results indicate that ZrO₂-modified capacitive sensors are a promising technology for monitoring liquid quality, particularly in environmental protection and industrial process control.

Reliable determination of diesel contamination in water remains challenging due to the heterogeneous nature of diesel–water dispersions and the frequency-dependent dielectric response of multiphase systems. This study investigates the performance of capacitive sensors with ZrO₂ coatings of 2, 4, and 6 μm thickness and proposes a physics-informed computational framework for predicting sensor response and estimating diesel content in water. Experimental capacitance measurements were performed at 13 frequencies ranging from 10 Hz to 300 kHz using diesel–water mixtures with different fuel contents. The results revealed a general decrease in capacitance with increasing diesel content and measurement frequency, with the most pronounced concentration-dependent differences observed in the 10²–10⁴ Hz range. The 2 μm coating exhibited the highest sensitivity to diesel content, whereas the 6 μm coating provided improved measurement stability and the highest calibration linearity (R² = 0.733) at an operating frequency of 10 kHz. To support sensor interpretation, a six-dimensional process–structure–property (PSP) model was developed to simulate the relationships between ZrO₂ deposition conditions, coating microstructure, and functional properties. In parallel, a physics-informed neural network (PINN) incorporating monotonicity and high-frequency stabilization constraints was implemented for forward capacitance prediction and inverse diesel content estimation. The PINN achieved a mean absolute error of 0.29 mL in the reported inverse estimation evaluation, demonstrating the potential of physics-informed learning for interpreting nonlinear sensor responses. The complementary PSP and PINN modules establish a preliminary framework for future digital twin development, although direct numerical coupling between them remains to be implemented. The results demonstrate the feasibility of combining ZrO₂-coated capacitive sensing with physics-informed artificial intelligence and provide a foundation for further development of virtual sensors for diesel–water monitoring applications.