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Biogas Plant Operation: Digestate as the Valuable Product
Fertilization is an essential element in plant cultivation. Supplying the right amounts of nutrients allows plants to grow and develop. Due to the rising price of mineral fertilizers, other fertilizers and soil conditioners are growing in importance. One of these is the digestate produced in agricultural biogas plants. Due to its properties, the digestate can be used directly as a fertilizer. In this case, the effects of application can both change the soil environment and directly affect plant growth. Physical, biological, and thermal transformations can also produce products based on the digestate or its fractions, which can be successfully used for fertilizer purposes. Among other things, this paper discusses the production and use of composts, biocarbon, and/or fertilizer granules from the solid fraction of the digestate. Numerous scientific studies, including the authors’ own research in this article, indicate that digestate can be successfully used as fertilizer, both without processing and with selected methods of treatment. However, further research is needed—especially on the diversity of raw materials used for biogas production and their effects on the composition and performance of the digestate. In addition, research should continue on the processing of digestate into specific products, depending on the needs of soils and plants.
Determination of the Impact of Extruded Soybean Press Cake on Rearing and Health Indices of Piglets
The study was conducted to evaluate the alternative to imported soybean meal—extruded soybean press cake—in feeding weaning pigs. The experiment was carried out with 20 35-day-old weaned pigs weighing about 8.8 kg, divided into 2 groups (10 pigs in each) differing in the main protein source: group I—control, solvent-extraction soybean meal, group II— extruded soybean press cake. The feed mixtures were iso-protein and iso-energetic. After 35 days of feeding, the pigs were slaughtered. The samples of blood, intestinal tissue and digesta, meat, and back fat were collected for analyses. Replacement of soybean meal with extruded soybean press cake in the weaners’ diet had no statistically significant effect on growth performance and feed utilization. Histomorphometry analysis of the intestines did not indicate any harmful changes. The soybean press cake did not affect the sum of volatile fatty acids in the large intestine, but the ammonia concentration was lower in the distal colon (p ≤ 0.05). It was found that the extruded soybean press cake in the feed significantly affected the fatty acid profile (p ≤ 0.05) of meat and back fat, increasing the amount of PUFAs; however, no negative impact on the durability of meat and back fat during storage was noted. In conclusion, the study confirmed the possibility of completely replacing solvent-extraction soybean meal with extruded soybean press cake in the feeding of weaned pigs.
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.
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.
Methods of Handling the Cup Plant (Silphium perfoliatum L.) for Energy Production
The aim of the study was to determine the possibilities of using cup plants (Silphium perfoliatum L.) to generate energy. The energy balances of the combustion and anaerobic digestion were compared. The research showed that cup plants could be used as a raw material for solid fuel and for anaerobic digestion. An energy balance simulation showed that electricity could be generated through the anaerobic digestion of cup plants. The following amounts could be generated in the anaerobic digestion process: 1069 kWhe from 1 Mg of the raw material fragmented with an impact mill, 738.8 kWhe from 1 Mg of the raw material extruded at a temperature of 150 °C, and as much as 850.1 kWhe from 1 Mg of the raw material extruded at 175 °C. The energy balance of the combustion of biofuel in the form of cup plant pellets showed that 858.28 kWht could be generated from 1 Mg of the raw material. The combustion of solid biofuel generated a relatively low amount of heat in comparison with the expected amount of heat from a biogas-powered cogeneration system due to the high energy consumption of the processes of drying and agglomeration of the raw material for the production of pellets.
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.
The Effect of Corn Ensiling Methods on Digestibility and Biogas Yield
This study investigates the impact of different corn silage preparation methods, namely the traditional and Shredlage methods, on digestibility and biogas yield in anaerobic digestion and its nutritional value—the first complex study of its kind. Key parameters of both silage types were analyzed, including chemical composition, fiber content, and elemental makeup. Methane and biogas production were assessed under standardized fermentation conditions. The results showed that the Shredlage method, characterized by more intensive chopping, led to higher biogas and methane yields per unit of organic dry matter compared to traditional silage. This improvement is attributed to enhanced digestibility due to the lower content of neutral detergent fiber (NDF), acid detergent fiber (ADF), and crude fiber in Shredlage. An elemental analysis revealed slight differences in carbon-to-nitrogen (C/N) ratios, with both silages showing values suitable for efficient fermentation. Despite minor variations in mineral content, Shredlage demonstrated greater efficiency in biogas production, particularly for rapid fermentation processes. The findings underscore the importance of silage preparation techniques in optimizing biogas yield and suggest Shredlage as a superior option for enhancing energy recovery in biogas plants. Future work should explore the economic trade-offs and scalability of these methods.
Current State of Development of Demand-Driven Biogas Plants in Poland
Renewable energy sources (RES) are the foundation of the ongoing energy transition in Poland and worldwide. However, increased use of RES has brought several challenges, as most of these sources are dependent on weather conditions. The instability and lack of control over electricity production lead to both overloads and power shortages in transmission and distribution networks. A significant advantage of biogas plants over sources such as photovoltaics or wind turbines is their ability to control electricity generation and align it with actual demand. Biogas produced during fermentation can be temporarily stored in a biogas tank above the digester and later used in an enlarged CHP unit to generate electricity and heat during peak demand periods. While demand-driven biogas plants operate similarly to traditional installations, their development requires navigating regulatory and administrative procedures, particularly those related to the grid connection of the generated electricity. In Poland, it has only recently become possible to obtain grid connection conditions for such installations, following the adoption of the Act of 28 July 2023, which amended the Energy Law and certain other acts. However, the biogas sector still faces challenges, particularly the need for effective incentive mechanisms and the removal of regulatory and economic barriers, especially given its estimated potential of up to 7.4 GW.
Using soap waste from biodiesel production to intensify biogas generation during anaerobic digestion of cow dung
The aim of the work is to increase the yield of biogas and the generation of electricity at biogas plants due to the joint fermentation of cattle manure with the addition of soap stock obtained from soap waste from biodiesel production. To achieve this goal, the following tasks were solved: the yield of biogas from cattle manure was determined with the addition of soap stock for a periodic mode of loading the substrate, taking into account the data obtained, a mathematical model of biogas output for a quasi-continuous mode of loading the substrate into the digester was developed and its adequacy was confirmed. The novelty of the work lies in the fact that according to the data of experimental studies of biogas yield at a periodic loading mode using this model, it is possible to predict the maximum biogas yield for a quasi-continuous mode of loading the digester. The significance of the research results lies in the fact that when soap stock is added to the substrate with a periodic mode of loading the digester, a general increase in the biogas yield without diauxy is observed by about 2 times. The optimal content of soap stock in the substrate for a quasi-continuous mode of loading the digester, at which the biogas yield will be maximum, is 1.32%. When electricity is sold at a feed-in tariff, the payback period of a biogas plant is reduced from 8.7 years to 5.0 years.
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.
Methodology of development of intellectual energy efficient system of control of temperature-humidity regime in industrial heat
Methods of improving the quality of regulation of technological parameters by combining various intelligent control algorithms in one automation system, which helps to reduce energy costs by 10-13%, are analyzed. It has been established that heating and ventilation systems have the highest energy consumption for indoor buildings (on average, more than 4,000 m3 of natural gas and almost 1,000 kWh of electricity are consumed per day for heating and ventilation in an industrial greenhouse. Correlation analysis of links between external disturbances and energy costs that ensure compliance with the technology of plant production, confirmed the hypothesis of conditions of uncertainty in the operation of industrial greenhouses are formed by random disturbances, incomplete information about the biological component, with linear correlation coefficients not exceeding r<0.35. both for forecasting energy costs and for the formation of energy efficient management strategies. Based on the use of fuzzy logic methods and genetic algorithm, models for finding and using optimal parameters of PI controller settings adapted to changes in the operating conditions of the automation system have been developed and studied. This provides better regulation in conditions of uncertainty, the time of regulation, over-regulation is reduced by two to three times. To create an energy-efficient microclimate management system in industrial greenhouses, operating in conditions of uncertainty, a neural network model for predicting the energy consumption of natural gas and electricity has been developed. The input parameters of the neural network forecasting model are: the value of external and internal air temperatures of the greenhouse, the value of relative humidity, the solar radiation absorbed by the greenhouse and the level of carbon dioxide in the greenhouse. The outputs of the forecasting model are the values of natural gas and electricity costs. The structural and functional scheme of the temperature and humidity control automation system in industrial greenhouses has been improved by combining intelligent algorithms for stabilizing the operation of technological equipment at the lower management level and optimizing energy costs by forecasting them at the upper level. The introduction of such a system saves up to 13% on natural gas for heating and up to 10% on electricity.
Research on the Morphology of the Working Surfaces of Contacts Used in Starters in the Agro-Industrial Sector
The operational suitability of electromagnetic starters equipped with experimental contacts has been substantiated within their use in electrical installations of the agro-industrial sector, which may be affected by the environments containing aggressive components. Tests on commutation wear resistance and investigations on arc erosion of the series-produced contact parts of such starters as PML-1100O4, PML-2100O4 (versions A and B; contact material—CpH-90, CpM-0,2 + M1, KMK-A10m, respectively) and PML-1100O4 starter with the experimental copper-based contact parts (Cu + Nb + Zr + Y2O3; Cu + Mo + MoO3 + C + Ni; Cu + Cr + TiB2 + Nb + C + Zr) have been conducted. The influence of energy parameters of a commutated circuit on the value of electro-erosion wear, the morphology of the working surfaces of contacts and contact resistance have been determined. Investigation results have been obtained by conducting a set of tests on electromagnetic starters at the experimental plant that simulates the operating conditions of the AC-3 application category. The impact of the electric arc of alternative current on the arc erosion of silver-based and copper-based contact materials have been determined by using a scanning electron microscope Cambridge Stereoscan S4-10 equipped with an attachment for X-ray spectroscopic analysis, Link System-290 and an X-ray microanalyzer Camebax SX-50 (CAMECA, Gennevilliers, France). A metallographic analysis of the contact surfaces has been conducted, which contributed to the determination of the patterns of erosive destruction of bridging contacts based on Ag and Cu. Evolution of the eroded morphology of contacts and the surface components of electrical contacts under the influence of an arc have been characterized. In addition, contact mass loss and the dependence of contact resistance have been studied. When manufacturing the experimental contacts, it is possible to abandon the use of silver, which is significantly cost saving, and not to use dangerous contact additives that are hazardous to the environment and people’s health.
Biomethane Yield Modeling Based on Neural Network Approximation: RBF Approach
Biogas production plays a key role in the development of renewable energy systems; however, forecasting biomethane yield remains challenging due to the nonlinear nature of anaerobic digestion. The objective of this study was to develop a predictive model based on Radial Basis Function Neural Networks (RBF-NN) to approximate biomethane production using operational data from the Przybroda biogas plant in Poland. Two separate models were constructed: (1) the relationship between process temperature and daily methane production, and (2) the relationship between methane fraction and total biogas flow. Both models were trained using Gaussian activation functions, individually adjusted neuron parameters, and a zero-level correction algorithm. The developed RBF-NN models demonstrated high approximation accuracy. For the temperature-based model, root mean square error (RMSE) decreased from 531 m3 CH4·day−1 to 52 m3 CH4·day−1, while for the methane-fraction model, RMSE decreased from 244 m3 CH4·day−1 to 27 m3 CH4·day−1. The determination coefficients reached R2 = 0.99 for both models. These results confirm that RBF-NN provides an effective and flexible tool for modeling complex nonlinear dependencies in anaerobic digestion, even when only limited datasets are available, and can support real-time monitoring and optimization in biogas plant operations.
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.
Optimization of Straw Particle Size for Enhanced Biogas Production: A Comparative Study of Wheat and Rapeseed Straw
Biogas production from lignocellulosic biomass, such as wheat and rapeseed straw, is an essential strategy for sustainable energy generation. However, the efficiency of anaerobic digestion depends on the physical characteristics of the substrate, particularly the particle size, which influences microbial accessibility and biogas yield. This study aims to optimize straw particle size for enhanced methane production by evaluating different fractionation levels. The straw was processed using a hammer mill and separated into three size fractions (2.4 mm, 1 mm) alongside non-separated and finely ground (2 mm) samples. The chemical composition was analyzed using X-ray fluorescence (XRF), and key parameters such as pH, dry matter (DM), and organic dry matter (ODM) were assessed. The results indicated that rapeseed straw had lower pH (6.05) and DM than wheat straw (7.01). Biogas yield analysis demonstrated that methane production varied with particle size. For rapeseed straw, non-separated samples achieved the highest methane yield (132.87 m3 Mg⁻1), whereas for wheat straw, methane yield decreased with increased fragmentation, with the highest yield observed for non-separated material (206.65 m3 Mg⁻1). The carbon-to-nitrogen (C/N) ratio was highest in rapeseed straw (153.82), potentially limiting microbial activity, while finer fractions had more balanced ratios. These findings highlight the importance of mechanical pretreatment in optimizing biogas production and provide insights into improving the efficiency of straw-based anaerobic digestion systems.