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Studies on Cement Pastes Exposed to Water and Solutions of Biological Waste
The paper presents studies on the early stages of biological corrosion of ordinary Portland cements (OPC) subjected to the reactive media from the agricultural industry. For ten months, cement pastes of CEM I type with various chemical compositions were exposed to pig slurry, and water was used as a reference. The phase composition and structure of hydrating cement pastes were characterized by X-ray diffraction (XRD), thermal analysis (DTA/TG/DTG/EGA), and infrared spectroscopy (FT-IR). The mechanical strength of the cement pastes was examined. A 10 to 16% decrease in the mechanical strength of the samples subjected to pig slurry was observed. The results indicated the presence of thaumasite (C3S·CO2·SO3·15H2O) as a biological corrosion product, likely formed by the reaction of cement components with living matter resulting from the presence of bacteria in pig slurry. Apart from thaumasite, portlandite (Ca(OH)2)—the product of hydration—as well as ettringite (C3A·3CaSO4·32H2O) were also observed. The study showed the increase in the calcium carbonate (CaCO3) phase. The occurrence of unreacted phases of cement clinker, i.e., dicalcium silicate (C2S) and tricalcium aluminate (C3A), in the samples was confirmed. The presence of thaumasite phase and the exposure condition-dependent disappearance of CSH phase (calcium silicate hydrate), resulting from the hydration of the cements, were demonstrated.
Management of Chemical Synthesis Processes of Potassium Humate During Coal Beneficiation Waste Processing
The growing accumulation of coal beneficiation waste represents a significant environmental and technological challenge while simultaneously creating opportunities for the resource recovery within circular economy frameworks. This study presents the development and process-oriented evaluation of an environmentally safe technology for converting coal beneficiation waste into potassium humate, with the simultaneous recovery of molybdenum compounds via alkaline extraction. The proposed solution is designed to improve resource efficiency, reduce the volume of waste directed to landfilling, and generate a high value-added product for agricultural and technological applications. The process flow includes preliminary characterization and preparation of the waste, determination of moisture, ash, and organic matter content, and the separation of metal-bearing fractions. Alkaline extraction was carried out using potassium hydroxide under controlled temperature and reaction time conditions, followed by purification and concentration of the humate solution. The process management strategy focuses on optimizing key technological parameters, including alkali concentration, solid-to-liquid ratio, temperature, and reaction time, to maximize humate yield while preserving functional groups responsible for biological activity. Comprehensive physicochemical, thermal, and mineralogical analyses confirmed the stability of the aluminosilicate matrix and the suitability of the material for alkaline processing without adverse structural degradation. Biological tests using oat (Avena sativa) demonstrated that potassium humate derived from coal beneficiation waste exhibits higher growth-stimulating effectiveness than a conventional commercial humate. Economic analysis revealed a strong correlation between humic acid content and added value, confirming the feasibility of transforming coal beneficiation waste from an environmental burden into a valuable secondary resource.
Efficient Management of Asbestos Waste Through Utilization as Mineral Additives in Portland Cement Production
This article presents research on the effectiveness of utilizing asbestos waste, particularly chrysotile asbestos, in the production of Portland cement. The study aimed to evaluate the feasibility of transforming asbestos cement (Eternit) through thermal treatment and its enrichment with mineral additives, enabling its integration into the clinker synthesis process. Differences in the physicochemical properties of types of cement produced from conventional raw materials and those manufactured using asbestos waste were analyzed. The research findings indicate that the presence of asbestos in cementitious materials leads to a significant mass loss of 29.4% due to thermal decomposition. Chemical analysis revealed the presence of aluminum oxide (Al2O3) and iron oxide (Fe2O3) at levels of 4.10% and 3.54%, respectively, suggesting the formation of brownmillerite, a phase typical of cement clinker. Furthermore, compressive strength tests on asbestos-modified cements demonstrated comparable mechanical properties to reference cement (CEM I), indicating their potential applicability in construction. This study provides essential insights into the mineralogical composition of asbestos cement, which is crucial for developing effective methods for its safe disposal. It represents a significant step toward sustainable asbestos waste management and the promotion of innovative solutions in the construction industry.
Smart Resource Management and Energy-Efficient Regimes for Greenhouse Vegetable Production
Greenhouse vegetable production faces significant challenges due to the non-stationary and nonlinear dynamics of the cultivation environment, which demand adaptive and intelligent control strategies. This study presents an intelligent control system for greenhouse complexes based on artificial neural networks and fuzzy logic, optimized using genetic algorithms. The proposed system dynamically adjusts PI controller parameters to maintain optimal microclimatic conditions, including temperature and humidity, enhancing resource efficiency. Comparative analyses demonstrate that the genetic algorithm-based tuning outperforms traditional and fuzzy adaptation methods, achieving superior transient response with reduced overshoot and settling time. Implementation of the intelligent control system results in energy savings of 10–12% compared to conventional stabilization algorithms, while improving decision-making efficiency for electrotechnical subsystems such as heating and ventilation. These findings support the development of resource-efficient cultivation regimes that reduce energy consumption, stabilize agrotechnical parameters, and increase profitability in greenhouse vegetable production. The approach offers a scalable and adaptable solution for modern greenhouse automation under varying environmental conditions.
Convolutional Neural Network Model for Variety Classification and Seed Quality Assessment of Winter Rapeseed
The main objective of this study is to develop an automatic classification model for winter rapeseed varieties, to assess seed maturity and damage based on seed colour using a convolutional neural network (CNN). A CNN with a fixed architecture was built, consisting of an alternating arrangement of five classes Conv2D, MaxPooling2D and Dropout, for which a computational algorithm was developed in the Python 3.9 programming language, creating six models depending on the type of input data. Seeds of three winter rapeseed varieties were used for the research. Each imaged sample was 20.000 g. For each variety, 125 weight groups of 20 samples were prepared, with the weight of damaged or immature seeds increasing by 0.161 g. Each of the 20 samples in each weight group was marked by a different seed distribution. The accuracy of the models’ validation ranged from 80.20 to 85.60%, with an average of 82.50%. Higher accuracy was obtained when classifying mature seed varieties (average of 84.24%) than when classifying the degree of maturity (average of 80.76%). It can be stated that classifying such fine seeds as rapeseed seeds is a complex process, creating major problems and constraints, as there is a distinct distribution of seeds belonging to the same weight groups, which causes the CNN model to treat them as different.
The Energy Footprint in the EU: How CO2 Emission Reductions Drive Sustainable Development
Understanding how sectoral CO2 emissions shape sustainable development outcomes is essential for designing effective energy and economic strategies within the European Union (EU). This study presents a multidimensional analysis of CO2 emissions, the contributions of individual sectors, and their connections to the Sustainable Development Goals (SDGs). Using Bayesian network analysis, the research identifies significant interdependencies between emission reductions and progress in sustainable development, highlighting the complex relationship between energy transition, economic growth, and social justice. The findings show that total CO2 emissions in the EU have decreased since 1990; however, the rate of reduction varies across sectors and member states. The most substantial decreases have been recorded in the energy sector, while industrial processes and agriculture show slower progress. Economic crises, such as the 2008 financial collapse and the COVID-19 pandemic, have led to temporary declines in emissions; however, lasting achievements in sustainability require structural transformations rather than short-term disruptions. The Bayesian model reveals strong connections between emission reductions and progress on clean energy (SDG 7), responsible consumption (SDG 12), and climate action (SDG 13), while also indicating indirect impacts on economic growth (SDG 8) and social equity. This highlights the importance of integrated policymaking to maximise the benefits of sustainable development. This study provides a data-driven foundation for enhancing EU climate strategies, ensuring that emission reductions support environmental goals, economic resilience, and social well-being.
Physicochemical properties of Mn1.45Co1.45Cu0.1O4 spinel coating deposited on the Crofer 22 H ferritic steel and exposed to high-temperature oxidation under thermal cycling conditions
AbstractThe Crofer 22 H ferritic steel substrate was coated with an Mn1.45Co1.45Cu0.1O4 spinel by means of electrophoresis. After high-temperature oxidation under thermal cycling conditions, the physicochemical properties of the obtained system were evaluated. During 48-h cycles that involved heating the samples up to temperatures of either 750 or 800 °C, the oxidation kinetics of both coated and unmodified steel approximately obeyed the parabolic rate law. The unmodified steel was oxidized at a higher rate than the system consisting of the substrate and the coating. In its bulk form, the spinel consisted entirely of the cubic phase and it exhibited high electrical conductivity. The Mn1.45Co1.45Cu0.1O4 coating, on the other hand, was compact and consisted of two phases—the cubic and the tetragonal one—and it was characterized by good adhesion to the metallic substrate. After cyclic oxidation studies conducted for the two investigated temperatures (750 or 800 °C), the coating was determined to provide a considerable improvement in the electrical properties of the Crofer 22 H ferritic steel, as demonstrated by the area-specific resistance values measured for the steel/coating system. The evaporation rate of chromium measured for these samples likewise indicates that the coating is capable of acting as an effective barrier against the formation of volatile compounds of chromium. The Mn1.45Co1.45Cu0.1O4 spinel can therefore be considered a suitable material for a coating on the Crofer 22 H ferritic steel, with intermediate-temperature solid oxide electrolyzer cells as the target application.
Application of a Selected Pseudorandom Number Generator for the Reliability of Farm Tractors
Knowledge of the use-to-failure periods of process equipment, including agricultural vehicles, is essential for the determination of their durability and reliability. Obtaining any empirical data on this issue is difficult and sometimes impossible. Experimental studies are costly and time-consuming. Manufacturers are usually reluctant to share such data, claiming that the information is classified for the sake of their companies. The purpose of this study was to compare empirical data with data generated using adequate statistical tools. The newly generated and very similar in value pseudorandom numbers were obtained by simulations using the Monte Carlo, Latin hypercube sampling and Iman-Conover methods. Reliability function graphs obtained from the generated time-series (use-to-failure periods) with matching Weibull distribution had very similar shape and scale parameters. They were are also comparable to parameters from experimental data extracted from a Polish Zetor agricultural tractor service station. The validation of the applied methods was limited as it was carried out only on the basis of the available data. Analysis of line graphs of cumulative deviations of the values of use-to-failure periods (times-to-fail) generated against empirical times-to-fail indicated that the best method in the studied case was the Monte Carlo method.
Changes in the Phase Composition of Calcium Aluminoferrites Based on the Synthesis Condition and Al2O3/Fe2O3 Molar Ratio
The presented work concerns the study of the changes in the phase composition of calcium aluminoferrites which depend on the synthesis conditions and the selection of the Al2O3/Fe2O3 molar ratio (A/F). The A/F molar ratio extends beyond the limiting composition of C6A2F (6CaO·2Al2O3·Fe2O) towards phases richer in Al2O3. An increase in the A/F ratio above unity favours the formation of other crystalline phases such as C12A7 and C3A, in addition to calcium aluminoferrite. Slow cooling of melts characterised by an A/F ratio below 0.58, results in the formation of a single calcium aluminoferrite phase. Above this ratio, the presence of varying contents of C12A7 and C3A phases was found. The process of rapid cooling of the melts with an A/F molar ratio approaching the value of four favours the formation of a single phase with variable chemical composition. Generally, an increase in the A/F ratio above the value of four generates the formation of a calcium aluminoferrite amorphous phase. The rapidly cooled samples with compositions of C22.19A10.94F and C14.61A6.29F were fully amorphous. Additionally, this study shows that as the A/F molar ratio of the melts decreases, the elemental cell volume of the calcium aluminoferrites decreases.
The Quantification of Operational Reliability of Agricultural Tractors with the Competing Risks Method
Application of continuous wavelet transform and convolutional neural networks for diagnostics of screw wear in wheat extrusion
This study presents a hybrid diagnostic approach combining the Continuous Wavelet Transform (CWT) and Convolutional Neural Networks (CNN) for assessing screw wear in a single-screw extruder operating under controlled conditions. Electrical current signals from the drive motor were analyzed to identify changes associated with the degradation of working components. CWT scalograms were used as time–frequency inputs for a CNN classifier, achieving a classification accuracy of 92.3% in distinguishing between new and worn screw states. Principal Component Analysis (PCA) confirmed clear separability of operating conditions, with the first two components explaining over 99% of the total variance. The results indicate that electrical signals contain diagnostically relevant information and that their combined analysis using CWT and CNN enables automated, non-invasive condition assessment with potential applicability in predictive maintenance systems without additional sensors.
Future-oriented development of agricultural tractor engines: efficiency, modularity and powertrain electrification
The study analyses trends in the development of agricultural tractor engines in the context of technological and environmental transformation between 2015 and 2024, with forecasts up to 2035. Based on catalog data of over 150 tractor models and technical documentation from major manufacturers, changes in displacement, cylinder number, and specific power were evaluated. The aim of this study was to identify and quantitatively assess the key technological shifts in agricultural tractor engine design between 2015 and 2024, and to forecast their development pathways and potential impact on energy efficiency and sustainability up to 2035. The results indicate a continued transition from conventional downsizing to the rightsizing concept, with a simultaneous increase in average engine power by approximately 25% and a 10% reduction in displacement. Modular engine platforms have become dominant, enabling flexible configuration of four- and six-cylinder units and improving design unification. In the high-power segment, a renaissance of large-displacement engines optimized for low-speed efficiency was observed. Hybridization and electrification of powertrains are expected to increase their share to approximately 15% and 8%, respectively, by 2035, leading to a potential 10–20% reduction in fuel consumption and CO₂ emissions. The implementation of Smart Engine Management systems and advanced thermal control strategies contributes to improving thermal efficiency to approximately 43–45%. The obtained results provide a comprehensive overview of current and future engine development trends and may support decision-making processes related to sustainable and resource-efficient agricultural machinery design.
Wear Detection of Extruder Elements Based on Current Signature by Means of a Continuous Wavelet Transform
Assessing the wear of components in a single-screw extruder and its condition during the process is difficult. In this context, wavelet analysis was used to investigate the wear condition of extruder elements, which yielded data on current waveforms obtained from 1 kHz frequency converters. To date, no tests of this type have been conducted on single-screw food extruders, which further emphasizes the relevance of the research undertaken by the authors. Experimental tests have been conducted to verify the hypothesis that it is possible to assess the level of wear of the working elements of an extruder by monitoring the variations in the frequencies on the current spectrum using wavelet analysis tools. The root mean square (RMS) values of the current were compared for two configurations of the working elements of the device, i.e., new and used. Observation of the frequency variations of the current spectrum values using wavelet analysis tools can provide valuable information on the technical condition of the working elements of an industrial extruder. Therefore, they can indicate the need for prompt replacement of friction elements in order to improve the efficiency and performance of the machine.
Convolutional neural network model for the qualitative evaluation of geometric shape of carrot root
The main objective of the study is the development of an automatic carrot root classification model, marked as CR-NET, with the use of a Convolutional Neural Network (CNN). CNN with a constant architecture was built, consistingof an alternating arrangement of five Conv2D, MaxPooling2D and Dropout classes, for which in the Python 3.9 programming language a calculation algorithm was developed. It was found that the classification process of the carrot root images was carried out with an accuracy of 89.06%, meaning that 50 images were misclassified. The highest number of 21 erroneously classified photographs were from the extra class, of which 15 to the first class, thus not resulting in significant loss. However, assuming the number of refuse as the classification basis, the model accuracy greatly increases to 98.69%, as only 6 photographs were erroneously assigned.
System do biologicznej inaktywacji pulweryzacyjnej ścieków bytowych w przyczepach asenizacyjnych
The Effect of Biological Corrosion on the Hydration Processes of Synthetic Tricalcium Aluminate (C3A)
This paper presents a study related to the biological degradation of a tricalcium aluminate (C3A) phase treated with reactive media from the agricultural industry. During one month of setting and hardening, synthetic C3A was subjected to corrosion in corn silage, pig slurry and chicken manure. The hardening process of the C3A phase in water was used as a reference sample. The phase composition and microstructure of the hydrating tricalcium aluminate slurries were characterised by X-ray diffraction (XRD), thermal analysis (DTA/TG/DTG/EGA), scanning microscopy (SEM, EDS) and infrared spectroscopy (FT-IR). In the samples studied, it was observed that the qualitative and quantitative phase composition of the synthetic tricalcium aluminate preparations changed depending on the corrosion exposure conditions. The main crystalline phases formed by the hydration of the examined samples in water as well as in corrosive media were the catoite (Ca3Al2(OH)12) and hydrocalumite (Ca2Al(OH)7·3H2O) phases. Detailed analysis showed the occurrence of secondary crystallisation in hydrating samples and the phases were mainly calcium carbonates (CaCO3) with different crystallite sizes. In the phase composition of the C3A pastes, varying amounts of aluminium hydroxides (Al(OH)3) were also present. The crystalline phases formed as a result of secondary crystallisation represented biological corrosion products, probably resulting from the reaction of hydrates with secondary products resulting from the metabolic processes of anaerobic bacterial respiration (from living matter) associated with the presence of bacteria in the reaction medium. The results obtained contribute towards the development of fast-acting and bio-corrosion-resistant special cements for use in bioenergetics.
Governance Matters: Evidence from Global Analysis on Environmental Sustainable Development Goals
This study explores how governance acts as a critical mediator between key environmental Sustainable Development Goals (SDGs)—SDG 13 (Climate Action), SDG 14 (Life Below Water), and SDG 15 (Life on Land)—and overall sustainability performance. Leveraging global datasets from the UN SDG framework and World Bank Governance Indicators, we construct a composite governance index using Principal Component Analysis (PCA) to capture institutional quality. Through mediation and path analysis, we reveal striking patterns: governance amplifies the positive impact of SDG 15 on the overall SDG Index, underscoring its role in biodiversity and land management. Conversely, governance introduces an adverse indirect effect for SDG 13, highlighting institutional and regulatory gaps that weaken climate policy outcomes. No significant mediation is observed for SDG 14, indicating strong contextual dependencies in marine governance. These findings confirm governance as a pivotal driver—either reinforcing or constraining environmental progress. Strengthening governance frameworks through transparency, accountability, and regulatory quality can accelerate progress toward the SDGs and advance the 2030 Agenda. This study provides empirical evidence on governance as a mediator and deepens understanding of institutional mechanisms shaping sustainability trajectories.
Cement Carbonation Under Fermentation Conditions as a Tool for CO2 Emission Management—Technological, Environmental and Economic Analysis
The aim of this study is an interdisciplinary assessment of the potential of cement pastes to permanently bind carbon dioxide (CO2) under anaerobic digestion conditions, considering technological, microstructural, environmental, and economic aspects. The research focused on three types of Portland cement: CEM I 52.5N, CEM I 42.5R-1, and CEM I 42.5R-2, differing in phase composition and reactivity, which were evaluated in terms of their carbonation potential and resistance to chemically aggressive environments. The cement pastes were prepared with a water-to-cement ratio of 0.5 and subjected to 90-day exposure in two environments: a reference environment (tap water) and a fermentation environment (aqueous suspension of poultry manure simulating biogas reactor conditions). XRD, TG/DTA, SEM/EDS, and mercury intrusion porosimetry were applied to analyze CO2 mineralization, phase changes, and microstructural evolution. XRD results revealed a significant increase in calcite content (e.g., for CEM I 52.5N from 5.9% to 41.1%) and the presence of vaterite (19.3%), indicating intense carbonation under organic conditions. TG/DTA analysis confirmed a reduction in portlandite and C-S-H phases, suggesting their transformation into stable carbonate forms. SEM observations and EDS analysis revealed well-developed calcite crystals and the dominance of Ca, C, and O, confirming effective CO2 binding. In control samples, hydration products predominated without signs of mineralization. The highest sequestration potential was observed for CEM I 52.5N, while cements with higher C3A content (e.g., CEM I 42.5R-2) exhibited lower chemical resistance. The results confirm that carbonation under fermentation conditions may serve as an effective tool for CO2 emission management, contributing to improved durability of construction materials and generating measurable economic benefits in the context of climate policy and the EU ETS. The article highlights the need to integrate CO2 sequestration technologies with emission management systems and life cycle assessment (LCA) of biogas infrastructure, supporting the transition toward a low-carbon economy.
