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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.
Effect of Variable Synthesis Conditions on the Formation of Ye’elimite-Aluminate-Calcium (YAC) Cement and Its Hydration in the Presence of Portland Cement (OPC) and Several Accessory Additives
In the presented study, ye’elimite-aluminate-calcium (YAC) cement was synthesized. Complete synthesis of crystalline phases was achieved at a temperature of 1300 °C, which is 150 °C lower than the temperature standardly used in the processes of obtaining calcium aluminate cements (CAC). The greatest amount of ye’elimite phase (Klein complex), roughly 87% by mass, was acquired utilizing a sulphur ion transporter derived from artificial dihydrate gypsum obtained in the flue gas desulphurization process (variation I). In the case of anhydrite, the amount of synthesized crystalline ye’elimite in the clinker was 67% by weight (variant II). Depending on the synthesis conditions in the clinkers, the quantity of obtained calcium aluminates (C12A7, CA, and CA2) ranged from 20 to 40% by weight. Studies on the hydration process of YAC cement samples showed that the main products are hydrated calcium aluminates and dodecahydrate calcium alumino-sulphate. In sinters of YAC and OPC, no crystalline ettringite was observed. Hydration analysis of Chinese cement revealed the presence of crystalline ettringite and dodecahydrate calcium alumino-sulphate, as well as hydrated calcium silicates of the CSH type accompanied with pseudo-crystalline Al(OH)3. The obtained clinkers from variants I and II constitute a special binder, which, due to its phase composition after hydration, can be used in the construction of reactors for biogas production in eco-energy applications.
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.
Modern Methods of Asbestos Waste Management as Innovative Solutions for Recycling and Sustainable Cement Production
Managing asbestos waste presents a significant challenge due to the widespread industrial use of this material, and the serious health and environmental risks it poses. Despite its unique properties, such as resistance to high temperatures and substantial mechanical strength, asbestos is a material with well-documented toxicity and carcinogenicity. Ensuring the safe removal and disposal of asbestos-containing materials (ACM) is crucial for protecting public health, the environment, and for reducing CO2 emissions resulting from inefficient waste disposal methods. Traditional landfill disposal methods have proven inadequate, while modern approaches—including thermal, chemical, biotechnological, and mechanochemical methods—offer potential benefits but also come with limitations. In particular, thermal techniques that allow for asbestos degradation can significantly reduce environmental impact, while also providing the opportunity to repurpose disposal products into materials useful for cement production. Cement, a key component of concrete, can serve as a sustainable alternative, minimizing CO2 emissions and reducing the need for primary raw materials. This work provides insights into research on asbestos waste management, offering a deeper understanding of key initiatives related to asbestos removal. It presents a comprehensive review of best practices, innovative technologies, and safe asbestos management strategies, with particular emphasis on their impact on sustainable development and CO2 emission reduction. Additionally, it discusses public health hazards related to exposure to asbestos fibers, and worker protection during the asbestos disposal process. As highlighted in the review, one promising method is the currently available thermal degradation of asbestos. This method offers real opportunities for repurposing asbestos disposal products for cement production; thereby reducing CO2 emissions, minimizing waste, and supporting sustainable construction.
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.
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.
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.
Using the Kaplan–Meier Estimator to Assess the Reliability of Agricultural Machinery
Kaplan–Meier analyses can be used in many disciplines, e.g., agricultural engineering. Agricultural machinery and vehicles can be regarded as objects that ‘die’ because, like living creatures, they failed, although after repair they can be used until scrapped. This article presents an example of using the Kaplan–Meier estimator to plot the reliability function curves of five different models of Zetor farm tractors. The research shows that the median operating time for one of the tested models, which is about 200 engine-operating hours, is 20% lower than for the entire population of analyzed Zetor tractors. This means that the quality of the model, which is very popular in Poland, differs significantly from the other models of this manufacturer. The method cannot be validated, due to a lack of similar functions for other brands of tractors. Progressive automation and digitization of agriculture can contribute to improving the reliability of agriculture work. The user can focus on the correct performance of agrotechnical treatments, and modern control systems will signal in real time, about identified or approaching costly failures.
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.
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.
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.
Non-Invasive Electrical Detection of Screw Wear in an Industrial Extrusion System
Reliable detection of mechanical wear is essential for maintaining operational stability and reducing unplanned downtime in industrial extrusion systems. This study investigates non-invasive detection of screw wear using operational electrical measurements acquired from a single-screw industrial extruder. Electrical parameters were recorded under steady-state processing conditions for healthy and worn screw configurations to determine whether measurable differences in electromechanical behavior could support condition assessment. The collected signals were segmented into 1429 labeled samples and evaluated using statistical and time–frequency analyses. Mean electrical parameters were compared between technical states, and independent samples Welch t-tests confirmed statistically significant differences in phase voltage for all monitored phases (p < 0.001). Continuous wavelet transform was applied to capture non-stationary signal characteristics, enabling extraction of energy- and entropy-based descriptors associated with variations in mechanical load. The derived features were subsequently used for automated classification of machine condition. The results revealed consistent increases in phase voltage for the worn screw ranging from 0.50% to 0.61%, indicating a stable shift in the electrical operating characteristics of the drive system. Supervised classification achieved an accuracy of 96.2% (289 of 300 samples correctly classified in the testing subset), demonstrating reliable separability between technical states without the need for additional vibration instrumentation. These findings confirm that operational electrical signals provide diagnostically relevant information for screw wear detection and support scalable implementation of electrical condition monitoring in industrial extrusion systems.
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.
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.
Synthesis and Investigation of the Hydration Degree of CA2 Phase Modified with Boron and Fluorine Compounds
This study investigated the effect of fluoride and boron compound additives on the synthesis and hydration process of calcium aluminate (CA2). The analysis showed that the temperature of the full synthesis of CA2 without mineralizing additives was 1500 °C. However, the addition of fluorine and boron compounds at 1% and 3% significantly reduced the synthesis temperature to a range of 1100–1300 °C. The addition of fluoride compounds did not result in the formation of fluoride compounds from CaO and Al2O3, except for the calcium borate phase (Ca3(BO3)2) under certain conditions. In addition, the cellular parameters of the synthesized calcium aluminate phases were not affected by the use of these additives. Hydration studies showed that fluoride additives accelerate the hydration process, potentially improving mechanical properties, while boron additives slow down the reaction with water. These results highlight the relevance of fluoride and boron additives to the synthesis process and hydration kinetics of calcium aluminate, suggesting the need for further research to optimize their application in practice. TG studies confirmed the presence of convergence with respect to X-ray determinations made. SEM, EDS and elemental concentration maps confirmed the presence of a higher Al/Ca ratio in the samples and also showed the presence of hexagonal and regular hydration products.
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.
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.
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.
