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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.
Correction: Durczak et al. Modern Methods of Asbestos Waste Management as Innovative Solutions for Recycling and Sustainable Cement Production. Sustainability 2024, 16, 8798
The authors would like to make the following corrections to the published paper [...]
The Quantification of Operational Reliability of Agricultural Tractors with the Competing Risks Method
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.
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.
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.
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.
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.
System do biologicznej inaktywacji pulweryzacyjnej ścieków bytowych w przyczepach asenizacyjnych
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.
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.
Study on biodegradable materials from thermoplastic starch with the addition of nuts shell
The paper presents the results of research on film biocomposites made of thermoplastic starch (TPS) and various types of nut shells. The research involved the use of thermally treated nut shells: hazelnuts, pistachios, walnuts and peanuts. TPS biocomposites were produced by the pour method using non-adherent moulds. The obtained samples were used to test the basic physical properties used in testing biodegradable materials. The following parameters were determined: mechanical strength, colour and colour difference, water contact angle, moisture absorption from water and atmospheric air. Images of biocomposite fractures were also taken using a scanning electron microscope (SEM). It was found that the addition of nut shells enabled the production of homogeneous materials and contributed to the improvement of their strength parameters. The research showed that nut shells can be a prospective raw material for the production of innovative biodegradable materials.
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.
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.
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.
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.
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.
