Browsing by Subject "Bedarfsgerechte Stromproduktion"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Publication
    Auswirkungen der flexiblen Biogasproduktion auf die Effizienz von landwirtschaftlichen Biogasanlagen
    (2020) Kress, Philipp; Jungbluth, Thomas
    In future energy systems based on renewable energies, biogas plants can make a significant contribution to stabilizing the electricity grids. However, this requires demand-flexible and load-driven electricity production, which is only made possible by flexible biogas production with extremely versatile feed management. From the process engineering and process biology point of view, this demand-flexible operation represents a major challenge for the operation of biogas plants. Technically, this demand-flexible biogas production requires a complete utilization of the existing fermenter volume, which in turn requires an optimal mixing of the substrates in the fermenter. Similarly, a continuous high-resolution monitoring of the produced biogas composition is also necessary to detect process disturbances or overloads that begin at an early stage. The objective of this work was to test and optimize new measuring methods for the flow velocity measurement and the mixing quality in the biogas reactor. Furthermore, to achieve a high-resolution gas quality measurement, practical scale tests were conducted. From these results, conclusions about possibilities and limitations of a flexibilisation of the biogas production shall be derived. Stirring is one of the most important processes in biogas production. The power input was intended to generate turbulent flows and thus ensure uniform distribution of nutrients and homogeneous temperatures throughout the reactor and avoid sinking and floating layers. In order to be able to assess and optimize these mixing processes, investigations of flow velocities in the fermenter were carried out using a magnetic-inductive measuring system. Additionally, flow profiles were created as a function of the DM content and the viscosity of the fermentation substrate. At a DM content of 9.45% in the fermenter, the average flow velocity measured was 87.5 cm/s. The DM content and the viscosity of the fermenting substrate were also taken into account. This dropped to 0.96 cm/s with a DM content of 9.95%. For the further description of the mixing quality, spatially dissolved nutrient samples were taken from the entire fermenter to determine the biological parameters. It was proven that the punctual input of the solid biomass via the solid input leads to a locally increased DM content and increased concentrations of organic acids in the vicinity of the input. In contrast to the laboratory tests using the process tomography method, no zone was found in the fermenter at which process disturbances were present. Furthermore, in contrast to laboratory tests, no biologically inactive zones could be detected in the fermenter of the research biogas plant. In further investigations, a photoacoustic sensor with a newly developed measuring system for determining the methane and carbon dioxide concentrations of the biogas was installed, tested and optimized for the first time in a biogas plant in the field. The basic applicability of such a system in biogas plants could be demonstrated. The achieved data density was significantly higher than that of conventional devices with a very high precision of the measured values. Using this innovative measuring technique, a flexible substrate supply and its influence on the product gas quality was subsequently evaluated. Substrates with different degradation behavior and different specific methane yields were fed to the fermenter. The influence of the specific substrate used in biogas production was reflected in the biogas quality. In particular, the relation between the relative change in gas quantity and quality makes it possible to detect process changes at an early stage. The presented studies have created a basis that enables a demand-oriented biogas production: Even with high substrate quantities that are fed to the fermenter, a high mixing quality can be achieved in the fermenter with an appropriate design of the agitators, which also prevents local process overloads. The investigations prove that, despite very low flow rates, there is sufficient nutrient supply for the microorganisms. The newly developed sensors for determining the biogas composition provide measured values with high precision and high temporal resolution, so that possible process disturbances can be detected very early. The investigations contribute to optimizing future demand-oriented electricity production on the basis of demand-flexible feeding in biogas plants. As a result, biogas plants can fulfil an important system service in a renewable energy based grid by decentrally stabilizing the electricity supply.
  • Thumbnail Image
    Publication
    Experimentelle Entwicklung einer modellbasierten prädiktiven Regelung für den flexiblen Betrieb von Biogasanlagen
    (2023) Dittmer, Celina; Lemmer, Andreas
    The transformation of the energy system requires controllable producers due to increasingly decentralised, fluctuating electricity generation from wind turbines and photovoltaics. Biogas plants can make a substantial contribution here by making plant operation more flexible and thus providing electricity as needed. Technical adjustments, such as the expansion of gas storage capacities and CHP output, can compensate for short-term fluctuations. However, in order to be able to shift the potential of electricity generation over longer periods of time, an adapted feed-in strategy is essential. The control of biogas production poses several challenges in practical implementation. First, the conversion of biomass into biogas is a complex process and must be considered individually for each biogas plant. Models developed so far use parameters for all characteristic process phases and influencing variables in order to be able to model anaerobic digestion. In contrast, biogas plants are often only rudimentarily equipped with measurement technology, so that corresponding parameters are not available. In this work, a model-predictive control of biogas plant operation was developed to enable demand-driven electricity generation. The aim was to develop models that are particularly well suited for practical use. Thus, for the first time, a successful application on almost all biogas plants could be possible without or with only minor adaptations to the existing measurement technology. All studies carried out in this thesis are based on a real-world laboratory, the "Unterer Lindenhof". This includes a practical biogas plant as well as an electrical consumption corresponding to that of a village with about 125 inhabitants. In a first step, forecasting models were evaluated to predict the electricity demand of the real-world laboratory over 48 hours in advance. Four models from the field of time series analysis were examined, one TBATS and three different ARIMA models. In an evaluation of 366 forecasts each, all four models performed sufficiently well to provide a set point for biogas plant operation, with average MAPE values of 13-16 %. Further investigations showed that forecasts can also be carried out over a period of up to 14 days without significant losses in forecast quality. In a further step, a model was developed to simulate biogas production. This is also based on time series analysis, or more precisely on a regression model. Thus, it differs significantly from previous developments in this field, which are mostly based on the complex ADM1. It turns out to be very advantageous that the developed simulation model uses as input parameters only historical data of the last four weeks of biogas production and the amount of solid substrates fed in, without considering their composition. The simulation of biogas production over 48 hours in advance is based on correlations resulting from these two data sets. An evaluation of the model over 366 simulations resulted in an average MAPE of 14-18 %. Data from both digesters of the biogas plant were used, which can be considered as independent systems, demonstrating the adaptability of the model. In a third step, the feeding schedule was developed for demand-based biogas production. For each 48 hours in advance, 1500 randomised feeding schedules were calculated. Some constraints were imposed, such as the maximum amount of substrate that is technically possible in the biogas plant. The biogas production expected from the feeding schedules could be calculated using the simulation model. By comparing the simulation with the desired biogas demand profile, the simulation with the least deviations could be determined and the appropriate feeding plan selected and implemented. The entire model predictive control system was used and thoroughly tested in a field trial at the real-world laboratory "Unterer Lindenhof". Over a period of 36 days, an average MAPE of less than 20 % was achieved in comparison between the real biogas production and the desired biogas demand. During the test period, the biogas demand was derived from the predicted electricity demand of the real-world laboratory. The investigations carried out show that the model-predictive control system developed enables demand-oriented electricity generation on full-scale and that, due to the models being very close to practice for the first time, adaptation to almost all biogas plants is possible.