• Energy Research

Abstract The present study extended the Anaerobic Digestion Model No. 1 (ADM1) to simulate co-digestion of silages with pig manure and thin stillage, pig manure and glycerine phase, or thin stillage and glycerine phase under mesophilic conditions. New state variables were added, including glycerol, lactate, valerate and iso-valerate. Furthermore, the model was extended to include both the rapidly and slowly degradable fractions of carbohydrates and proteins. It was found that the model is more sensitive to changes in the constant for slowly degraded carbohydrates than to changes in the constant for rapidly degraded carbohydrates. After parameter calibration with the mixture of silages with pig manure and thin stillage, the ADM1 showed good agreement with measurements of daily biogas and methane production, and of concentrations of individual volatile fatty acids (VFAs) in the effluent. Although, initially, the prediction of biogas production with the silages mixture with thin stillage and glycerine phase was not satisfactory because the calibrated KI_h2_pro value (2.86E-08 kg COD/m3) was too low, adjusting this constant to a value of 9.70E-08 kg COD/m3 enabled this to be predicted with high accuracy.

Abstract The influence of selected geometric bioreactor parameters on the performance of continuous-flow-type low-temperature biogas production from biomass by anaerobic digestion was studied to determine the optimal geometric parameters of the digester. A continuous-mode two-stage bioreactor was applied to produce biogas by anaerobic digestion using model dairy wastewater sludge as substrate. The Monod approach was used to find the optimal diameter of the two cylinder-separated stages of the reactor that maximizes the amount of biogas produced per unit of time. Total biogas production derived from the theoretically optimized reactor in the calculation model was 1.6 times higher than that derived for the experimental bioreactor. The methane fraction in biogas increased from 64.5% to 71.2% after optimization, whereas the carbon dioxide fraction in biogas decreased from 34.5% to 27.8%. The optimization of the intermediate cylinder of the digester significantly increased total biogas production (by up to 160%) in comparison with the output noted before optimization.

Abstract The effect of composting temperature, phase and substrate composition on thermal conductivity of the composted substrate was analyzed. Thermal conductivity was affected by all of the above parameters. Temperature was a significant factor when the difference in the temperature range exceeded 5 °C. Significant differences were observed in substrates where the content of sewage sludge differed by 20% (between 50% and 30%). Thermal conductivity values differed significantly between the phases of the composting process. Thermal conductivity of composted material was determined at 0.37 ± 0.10 in phase I, 0.39 ± 0.072 in phase II, 0.23 ± 0.03 in phase III, and 0.29 ± 0.04 W/mK in phase IV. In the first phase of the process, substrate temperature reached 20 °C, and it was determined at 60 °C, 50 °C and 25 °C in the following stages of composting. At 20 °C, thermal conductivity varied in the range of 0.15–0.37 W/mK.

Abstract Slurry constitutes an important substrate, increasingly often forming part of biogas production in biogas plants due to the significant content of methane in biogas produced from slurry. Slurry fermentation leads also to its deodorisation and significantly affects the sanitation process. Biogas production constitutes a microbiological process, one affected by many parameters, both physical and chemical. The complexity of the processes occurring during slurry fermentation means it is difficult to identify the significant parameters of a process. Therefore, the fermentation model is often defined as a “black box” method. Artificial neural networks (ANN) are becoming more frequently recognised as a tool to analyse processes that do not have a formal mathematical description (e.g. in the form of a structural model). Neural models enable one to conduct a comprehensive analysis of an issue, including in the context of forecasting biogas emissions during the slurry fermentation process. This study aims to develop a neural model that forecasts the level of methane emission during the slurry fermentation process. This study demonstrates that the generated neural predictor constitutes an efficient tool for estimating the amount of methane produced during bovine and porcine slurry fermentation processes.

Abstract The purpose of the present study was to develop a model to describe the heat and mass transfer during the drying of carrot cubes in a spout-fluidized-bed drier. The model took into account the non-homogeneous shrinkage of the material. The Arbitrary Lagrange–Eulerian (ALE) formulation was applied to enter the problem with moving boundaries. Three phases of drying were distinguished according to the behavior of changes in percent local error of estimation: an initial phase of warming up the material – characterized by a low level of error of moisture content prediction, a second phase – characterized by an increase in the error of moisture content prediction and a phase of decreasing error. A simple test of the sensitivity of the model to the changes in heat transfer coefficient was performed in order to improve the ability of the model to predict the changes in moisture content and temperature of dried carrots. The predicted changes in both the moisture content and the temperature of carrot cubes during drying in a spout-fluidized-bed drier indicate that the model can be successfully applied to describe moisture content, temperature and deformation of dried particles in cases when the very high accuracy of moisture content and temperature prediction is not a crucial element of investigation of the drying process.

A mathematical model integrating 11 first-order differential equations describing the dynamics of the aerobic composting process of sewage sludge was proposed. The model incorporates two microbial groups (mesophiles and thermophiles) characterized by different capacities of heat generation. Microbial growth rates, heat and mass transfer and degradation kinetics of the sewage sludge containing straw were modeled over a period of 36days. The coefficients of metabolic heat generation for mesophiles were 4.32×10(6) and 6.93×10(6)J/kg, for winter and summer seasons, respectively. However, for thermophiles, they were comparable for both seasons reaching 10.91×10(6) and 10.51×10(6)J/kg. In the model, significant parameters for microbial growth control were temperature and the content of easily hydrolysable substrate. The proposed model provided a satisfactory fit to experimental data captured for cuboid-shaped bioreactors with forced aeration. Model predictions of specific microbial populations and substrate decomposition were crucial for accurate description and understanding of sewage sludge composting.

This study analyzed the effects of thermohydrolysis on the anaerobic conversion efficiency of lignocellulosic biomass, comparing conventional and microwave heating methods. The research aimed to identify the optimal temperature and duration for biomass pre-treatment to maximize biogas output. Four temperatures (100 °C, 130 °C, 150 °C, and 180 °C) and six durations (10, 15, 20, 25, 30, and 40 min) were tested. The results showed that microwave heating increased biogas production compared to conventional heating at the same temperatures and durations. At 150 °C, microwave heating for 20 min produced 1184 ± 18 NmL/gVS of biogas, which was 16% more than the 1024 ± 25 NmL/gVS achieved through conventional heating. Statistically significant differences in biogas output between microwave and conventional heating were observed at 130 °C, 150 °C, and 180 °C, with the greatest difference recorded between 130 °C and 150 °C: 13% for conventional heating and 18% for microwave heating. Notably, increasing the temperature from 150 °C to 180 °C did not result in a statistically significant rise in biogas production. The energy balance analysis revealed that microwave heating, despite its lower efficiency compared to conventional heating, resulted in higher net energy gains. The most favorable energy balance for microwave heating was observed at 150 °C, with a net gain of 170.8 Wh/kg, while conventional heating at the same temperature achieved a gain of 126.2 Wh/kg. Microwave heating became cost-effective starting from 130 °C, yielding an energy surplus of 18.2 Wh/kg. The maximum energy output from microwave conditioning was 426 Wh/kg at 150 °C, which was 158 Wh/kg higher than conventional heating. These findings suggest that microwave thermohydrolysis, particularly at 150 °C for 20 min, enhances both biogas production and energy efficiency compared to conventional methods. The results highlight the potential of microwave pre-treatment as an effective strategy to boost methane fermentation yields, especially at temperatures above 130 °C.