Green hydrogen is central to the energy transition, but its production often requires expensive materials and poses environmental risks due to the perfluorinated substances used in electrolysis. This study introduces a transformative approach to green hydrogen production via chemical looping, utilizing an iron-based oxygen carrier with yttrium-stabilized zirconium oxide (YSZ). A significant innovation is the replacement of Al2O3 with SiO2 as an inert support pellet, enhancing process efficiency and reducing CO2 contamination by minimizing carbon deposition by up to 700%. The major findings include achieving a remarkable hydrogen purity of 99.994% without the need for additional purification methods. The Fe-YSZ oxygen carrier possesses a significantly higher pore volume of 323 mm³/g and pore surface area of 18.3 m²/g, increasing the pore volume in the iron matrix by up to 50%, further improving efficiency. The catalytic system exhibits a unique self-cleaning effect, substantially reducing CO2 contamination. Fe-YSZ-SiO2 demonstrated CO2 contamination levels below 100 ppm, which is particularly noteworthy. This research advances our understanding of chemical looping mechanisms and offers practical, sustainable solutions for green hydrogen production, highlighting the crucial synergy between support pellets and oxygen carriers. These findings underscore the potential of chemical looping hydrogen (CLH) technology for use in efficient and environmentally friendly hydrogen production, contributing to the transition to cleaner energy sources.

Green Carbon Liquids - staged condensation from lab-scale pyrolysis; Green Gas - Green Heat for Industrie from Biogenic Waste; Biohydrogen - Implementation of Dark Fermentation for Industrial Wastewater Treatment; Effects of the climate crisis and pesticide use on fatty acida in the food web; Syngas production from biogenic residues and waste via advanced dual fluidized bed gasification; New developments in gas cleaning for the production of C-based products and fuels via gasification; Advancements in Fischer-Tropsch synthesis using a slurry bubble column reactor; Biofuels - a crucial part of decarbinisation; Speed-Up Algorithms for advanced simulations; Multiscale modeling of metal oxide and biomass conversion for chemical looping processes; Multiscale modeling of metal oxide and biomass conversion for chemical looping processes; Model-Based Control of the Generated Steam Mass Flow in a Fluidized-Bed Waste Incineration Plant; Modular, predictive, optimization-based supervisory control of multi-energy systems; Monitoring of a Renewable Flow Battery; Use cases of optimally planned multi-energy systems with OptEnGrid: hotel resort and renewable energy communities; Optimal Design of Multi-Energy Systems using OptEnGrid; Sustainability assessment: mere obligation or a key to success; 

Reliable experimental data and models are required to better understand and design chemical looping processes with oxygen carrier materials like ilmenite. A dubious variability of suggested kinetics for similar oxygen carrier materials has been presented in the literature. Part I of this work focuses on thermogravimetric analysis (TGA) of gas–solid kinetics and addresses several of its challenges, which are possible reasons behind such deviations. The reduction of synthetic ilmenite (60 mass% Fe2O3 ＋40 mass% TiO2) powder with H2 in a TGA system was investigated for this purpose.
Multiple steps were necessary to overcome mass transfer limitations during the measurements: (i) small sample masses down to 1.6 mg, (ii) high gas flow rates, (iii) a suitable sample carrier and (iv) proper sample dispersion on the sample carrier. Three types of sample carriers (crucible, basket and plate) were tested; the plate showed the best performance overall. It was alarming that an exemplary increase in sample mass from 1.6 to 3 mg, which was still significantly lower than all other studies reviewed, already introduced a noticeable influence of diffusion. Isothermal (650–950 °C, 17–50 vol% H2) and nonisothermal parameter studies were conducted and yielded vastly different isoconversional activation energies. A computational fluid dynamics (CFD) study of the TGA system suggested considerable axial dispersion of H2 influencing the initial conversion period.
These findings help to assess the reliability of kinetic studies and guide towards diffusion-free, kinetic measurements. The results will be used for model development in part II.

Various single particle models to describe the conversion of porous solids with gaseous reactants are available in the literature. It is, therefore, not obvious which models should be selected for specific problems and applications. This work focuses on two popular types of particle models: the volumetric model (VM) and the layer model (LM). Different variations of the layer model were considered: the standard layer model, which is similar to common shrinking core models, and an extended layer model, which solves inherent problems of the shrinking core approach by replacing surface reactions with volumetric reactions. For the first time, all these models were benchmarked together regarding prediction quality and computational cost for relevant applications: gasification and oxidation of biochar, reduction of nickel oxide and oxidation of magnetite. These cases covered a wide range of Thiele moduli and Biot numbers. The volumetric model reliably predicted the conversion for all cases considered. Its computational effort was, however, significantly higher than for the layer models. Suitable reactions kinetics in combination with heat of reaction and pore diameters were integral to prediction accuracy. Char oxidation, having a high Thiele modulus, could be described suitably by the standard layer model and the extended layer model, when it accounted for the residual ash layer. Char gasification and nickel oxide reduction had moderate Thiele moduli, rendering the standard layer model unsuitable in the general case. The extended layer model overcomes these limitations due to its volumetric reaction approach. All layer models showed inferior temperature predictions for biochar gasification and magnetite oxidation owing to their lower spatial resolution compared to the volumetric model. Additional, possible problems for layer models were addressed.

Industry is not only a big primary emitter of CO2 itself, but also a big consumer of electricity with a share of about 43% of electricity consumption in Austria in 2023. Therefore, providing the future electrical grid with storage capacities will be a big opportunity for industrial facilities, as industry has a unique capability of load management due to its diversity compared to households or other services. While major scientific projects have investigated this potential, there is a lack of incentives for industrial plants at various levels to foster the balancing of electricity supply.

In this paper we provide an overview regarding the state of research and the obstacles we need to overcome to close the implementation gap. We expand the state-of-the-art approach of assessing flexibility potentials by not only incorporating technical but also economic and organizational key performance indicators. With the proposed novel assessment strategy, we bridge the gap between top down and bottom-up approaches, aiming to facilitate practical and economically viable demand response measures in energy-intensive industrial processes. Our methodology is demonstrated through a case study based on real life industry application, with results validated against literature and expert discussions.

The increasing share of volatile energy sources as well as variable demands provide challenges for the electrical power grid. To counteract these instabilities a balance between supply and demand needs to be established. In industrial processes, this can be achieved by coordinating the energy production with local storage and demand. Specifically, the optimized scheduling of batch production processes can avoid peak loads. A holistic approach for the control of industrial energy systems and production processes is needed to use this flexibility. This contribution presents an extension to a modular framework for optimization-based, predictive supervisory control of multi-energy systems providing the possibility to incorporate batch production processes, and a first study showing that shifting production processes can result in a more resource- and cost-efficient process.

Absorption heat pumping devices (AHPDs, comprising heat pumps and chillers) can provide heating and cooling in a resource-efficient manner. However, their perceived complexity has limited their widespread application. This contribution shows how mathematical models, systematically capturing this complexity, can be used for model-based control of AHPDs (on the device-level for model-predictive or state feedback control and on the system-level for optimisation-based energy management systems) to facilitate their integration into energy systems, and discusses an exemplary application to solar cooling systems.

The present study examines the applicability of reaction kinetic mechanisms for predicting NOX emissions from biomass furnaces. These mechanisms are essential for numerical optimization of new innovative combustion technologies and therefore must be computationally affordable and provide reasonable accuracy in predicting NOX emissions. The selection of a suitable mechanism from literature is the goal of this work. The numerical investigations carried out utilized chemical reaction kinetic simulations with continuous stirred tank reactor networks. First, the predictions of a detailed benchmark mechanism are compared to experimental data and analyzed with regard to temperature, air-to-fuel equivalence ratio, residence time and producer gas composition. Then, various hybrid and reduced mechanisms are compared with the benchmark mechanism. The investigation showed a good agreement on the trends of NOX emissions from the detailed mechanism and measurements. The detailed mechanism can therefore be employed to find optimal operation windows in terms of temperature, air-to-fuel equivalence ratio and residence time. Benchmarking of the hybrid and reduced mechanisms showed large differences between the mechanisms. In conclusion, only one reduced mechanism is considered suitable for application in a full-scale 3D CFD simulation, which will be investigated in future studies.

Heat is still the largest energy end-use, accounting for about 50% of global final energy consumption in 2022 and contributing to 40% of global carbon dioxide (CO2) emissions. Regarding the heat supply of buildings, district heating plays an important role and is well-established in many countries. However, most of the district heating networks worldwide are still operated with supply temperatures of 70-120°C (medium-high temperature) often produced by caloric power plants. Solar district heating (SDH) systems can be a valuable alternative to decarbonize these systems. How this can be done most efficiently is investigated within the task 68 Efficient Solar District Heating Systems of the International Energy Agency (IEA) from the technology cooperation program – solar heating and cooling (SHC). This contribution presents the latest results of the task regarding comparison of different collector technologies, important digitalization aspects, analysis of available funding schemes and latest efficient SHD installations.

The chemical looping hydrogen (CLH) production process typically uses iron-based oxygen carrier materials and can provide hydrogen with high purity. Chemical looping is particularly attractive when renewable fuels like syngas from biomass gasifiers are used. This work provides a novel assessment of the possible thermodynamic and kinetic limitations for iron-based oxygen carriers in CLH fueled by biomass-based syngas, with a detailed study employing synthetic ilmenite (Fe2O3 + TiO2). Its phase diagram with H2/H2O- or CO/CO2-mixtures was compared to the typical Baur–Glaessner diagram for iron oxides. Thermogravimetric analyses underlined the necessity to consider TiO2 as a chemically active component for this material, in contrast to the common simplification of inert support materials. The validated phase diagram predicted stringent fuel limitations concerning H2O- or CO2-contents. This was confirmed by feeding a real biomass-based syngas, provided by a lab-scale gasifier, to a fixed bed CLH reactor. It was demonstrated for the H2/H2O-system that removing the oxidizing agent from the feed gas helps to overcome these limitations. Kinetic limitations within the thermodynamic boundaries were investigated using a recently published multiscale model for the H2/H2O-system. The influence of the fuel’s reduction potential on reaction rates was explored to formulate simple, kinetic design criteria. A significant retardation of conversion rate in the vicinity of the equilibrium was indicated, effectively narrowing the feasible composition range. Recommendations for the application of biomass-based syngas with iron-based oxygen carrier materials were provided.

This article presents a general representation of the characteristic polynomial of the dynamic matrix for multivariable systems in Luenberger’s canonical forms. The characteristic polynomial is given by means of the determinant of a polynomial matrix of substantially lower order. Therein, the polynomial coefficients of the single elements are the coefficients of the corresponding blocks of the dynamic matrix. The proposed representation of the characteristic polynomial can be helpful for the design of state-feedback controllers and state observers which is demonstrated by a numerical example.

Pore size distribution is a key parameter in the performance of biobased pyrolytic char in novel applications. In industrial-scale production, the size of feedstock particles typically exceeds a few millimeters. For such particle sizes, it is a challenge to tailor the final properties of the char based only on the process conditions (experimental and modeling-wise). Pyrolysis studies of single particles larger than a few millimeters provide data sets useful for modeling and optimization of the process. Part 1 of this research focused on the pyrolysis of single particles of beech wood, secondary cracking, and its effect on the char porous texture. It contains a quantitative assessment of the effects of five conversion temperatures (from 300 to 840 °C) and two particle dimensions (Ø8 × 10 mm and Ø8 × 16 mm) on the composition of the pyrolysis vapors and pore morphology of the char. Results from real-time temperature and mass changes are presented along with release profiles of 15 vapor constituents measured by infrared spectroscopy. Furthermore, characterization of the collected bio-oil (using GC-MS/FID) and the textural hierarchical structured char (through N2 and CO2 adsorption, Hg porosimetry, and scanning electron microscopy (SEM)) was performed. Cracking of vapors above 500 °C was compound-specific. The polyaromatic hydrocarbons (PAHs) yield, between 680 and 840 °C, increased 5 times for 10 mm particles and 9 times for 16 mm ones. Besides temperature, PAH yield was suspected to correlate with particle length and PAHs/soot deposition in the micropores. Results showed that the macropores accounted for over 80% of the total pore volume, regardless of the temperature and particle length. Increasing the particle length by 60% caused a reduction in the specific surface area (ca. 15% at 840 °C) of the resulting char, mainly due to a reduction in microporosity. Based on the findings, the production conditions for a specific char application are suggested. The obtained data will be used in Part 2 of this research, devoted to subsequent CFD modeling of the process.

The prediction of the structural properties of biobased carbonaceous materials of pyrolytic origin (chars) with only base feedstock properties and process conditions still poses a challenge that hinders char tailoring for novel applications. CFD modeling of single biomass particle conversion can help solve this issue since it allows for the quantification of relations between parameters that are difficult to measure. A model for char tailoring must include a validated representation of the structural changes coupled to all other relevant phenomena occurring during conversion. Part 2 of this study focuses on finding the description of the mentioned aspects to achieve the highest precision of prediction of the structural changes in char by a CFD model. The investigation in Part 2 is composed of three cases focused on accurate description and prediction of (1) bulk density and porosity, (2) secondary vapor reactions on yields and soot formation, and (3) permeability, as well as the outflux and conversion of evolved vapors. The experimental results from Part 1 and the literature data were used to find appropriate descriptions of phenomena and assess the accuracy of the model. The model results indicate that for both particle lengths (10 and 16 mm), a high accuracy of prediction of base structural parameters was achieved. The average prediction error for temperatures between 400 and 840 °C of bulk density was 31 ± 15 kg/m3, and the porosity was 1.8 ± 1.1 vol %. The results also show a low error in the prediction of bulk product yields (dry basis) over the mentioned temperature range, which were: for char 2.8 ± 1.1 wt %, for the condensable fraction 6.5 ± 3.3 wt %, and for the pyrolysis gas 4.1 ± 1.9 wt %. The distribution of secondary char formation was found to be nonuniform below 500 °C. The changes in permeability had a minor influence on the vapor outflux but a non-negligible effect on the soot formation, especially at 840 °C. The results indicate a need for further improvement of the primary degradation model to increase the accuracy of the effect of soot formation on the char structure.

Emissions from wood-burning stoves contribute to local air pollution. However, it is difficult to determine the real emissions from such stoves, especially due to unknown user behaviour, which can have a large impact on emissions. In this study, the low-cost emission reduction measure “user training” was evaluated to determine its emission reduction potential on firewood stoves. Two sets of tests were carried out. First, a field measurement campaign was conducted in Styria (Austria) with four wood stoves, where gaseous and particulate emissions were measured before and after a user training on optimised heating behaviour (e.g. ignition mode, fuel properties and placement in the combustion chamber, air supply). Gaseous emissions (carbon monoxide – CO, organic gaseous compounds – OGC) were measured continuously, while particulates were measured in batches, in undiluted and hot as well as in diluted and cooled flue gas in parallel with a specific field measurement setup. In addition, particle filters were analysed to quantify the concentration of the carcinogenic compound benzo(a)pyrene (BaP). Second, user training workshops were conducted. These tests had a simple measurement setup in order to increase the number of tests. Thus, only CO emissions were evaluated.

The results show that real life emissions in the field are high and have a high variability compared to laboratory tests and official type test results. However, user training showed a significant reduction of CO, OGC, TSP and BaP emissions of 42%, 57%, 45% and 76% (median), respectively. In addition, TSPsum (sum of hot and cooled particle emission samples) emissions decreased by 39% (median) after user training. The relative reduction rates of all batches show that the highest emission reduction potential was identified for BaP, with a reduction rate of up to 97%. The results of the workshop tests confirmed the high variability in user behavior and the range for the emission reduction potentials, with a median CO reduction of 41%.

The emission reduction potential of the user training measure is comparable to state-of-the-art technological measures such as electrostatic precipitators and catalysts. However, these measures are costly and require a high level of technical sophistication. User training, on the other hand, is relatively cheap, easy to implement and suitable for all users. Of course, there is some risk that trained end-users will revert to their old habits, leading to higher emissions again. Therefore, regular training may be necessary to maintain the higher level of performance. As we did not assess this aspect in our work, further research would be needed to prove this theory.

A dried dairy processing sludge (sludge from wastewater treatment of an effluent from a milk processing plant) was pyrolysed in a single-particle reactor at different temperatures from 400 °C to 900 °C. NH₃ and HCN were measured online and offline by means of FTIR as well as by cumulative sampling in impinger bottles (in 0.05 M H₂SO₄ and 1 M NaOH, respectively) and analysed by photometric method. NO and NO₂ were measured online using a nitric oxide analyser while N₂O was measured by FTIR. Nitrogen (N) in the sludge and in the remaining char, char-N, was determined. Moreover, tar content in pyrolysis gas was measured and tar-N was determined. The results with respect to N mass balance closure are discussed. The different measurements techniques are compared. For pyrolysis at 520 ℃ and 700 ℃ nitrogen in the gas phase was mainly contained as N₂ (36 % and 40 % respectively), followed by NH₃ (15 % and 18 %), tar-N (10 % and 9 %), HCN (1 % and 3 %), NO (1 %) and NO₂ (0.2 %). The dairy processing sludge has very specific properties with organic-N present predominantly as proteins and a high content of inherent Ca. These characteristics affected the distribution of N. The amount of char-N was higher while the amount of tar-N lower than for sewage sludge from literature, at comparable pyrolysis temperature.

In modern waste management, the energetic utilization of waste is an important key technology. On the one hand, it allows the waste to be disposed of in an environmentally friendly manner and, on the other hand, makes it possible to reduce the use of other controversial energy sources, such as nuclear fission or fossil fuels. However, the efficient and clean incineration of waste is a challenging task due to the strong inhomogeneity of the waste.

Buildings with floor heating or thermally activated building structures offer significant potential for shifting the thermal load and thus reduce peak demand for heating or cooling. This potential can be realised with the help of model predictive control (MPC) methods, provided that sufficiently descriptive mathematical models of the thermal characteristics of the individual thermal zones exist. Creating these by hand is infeasible for larger numbers of zones; instead, they must be identified automatically based on measurement data. In this paper an approach is presented that allows automatically identifying thermal models usable in MPC. The results show that the identified zone models are sufficiently accurate for the use in an MPC, with a mean average error below 1.5K for the prediction of the zone temperatures. The identified zone models are then used in a distributed optimisation scheme that coordinates the individual zones and buildings of a city quarter to best support an energy hub by flattening the overall load profile. In a preliminary simulation study carried out for buildings with floor heating, the operating costs for heating in a winter month were reduced by approximately 9%. Therefore, it can be concluded that the proposed approach has a clear economic benefit.

The Area Automation and Control at BEST - Bioenergy and Sustainable Technologies GmbH focuses on the optimal operation of sustainable biorefinery and renewable energy systems, the optimal interaction of different technologies and systems and the highly automated operation management
by new digital services.

Char originating from biomass can be used as a sustainable carbon additive in the production of polymer compounds with enhanced characteristics.

With respect to the climate objectives Chemical Looping (CL) processes constitute a promising alternative to traditional thermochemical conversion routes. Through the application of solid materials, so-called oxygen carriers (OC), instead of air as oxygen supply, CO2 can be easily separated from the flue gas. By this, biomass can be used for hydrogen production (Chemical Looping Hydrogen, CLH) or it can be burnt without CO2 emissions (Chemical Looping Combustion, CLC).

Absorption heat pumping devices (AHPDs, comprising absorption heat pumps and chillers) are devices that use thermal energy instead of electricity to generate heating and cooling, thereby facilitating the use of waste heat and renewable energy sources such as solar or geothermal energy. Despite this benefit, widespread use of AHPDs is still limited. One reason for this is partly unsatisfactory control performance under varying operating conditions, which can result in poor modulation and part load capability. A promising approach to tackle this issue is using dynamic, model-based control strategies, whose effectiveness, however, strongly depend on the model being used. This paper therefore focuses on the derivation of a viable dynamic model to be used for such model-based control strategies for AHPDs such as state feedback or model-predictive control. The derived model is experimentally validated, showing good modeling accuracy. Its modeling accuracy is also compared to alternative model versions, that contain other heat transfer correlations, as a benchmark. Although the derived model is mathematically simple, it does have the structure of a nonlinear differential–algebraic system of equations. To obtain an even simpler model structure, linearization at an operating point is discussed to derive a model in linear state space representation. The experimental validation shows that the linear model does have slightly worse steady-state accuracy, but that the dynamic accuracy seems to be almost unaffected by the linearization. The presented new modeling approach is considered suitable to be used as a basis for the design of advanced, model-based control strategies, ultimately aiming to improve the modulation and part load capability of AHPDs.

Efficient control of energy systems is an important factor in achieving the CO2-emission goals. District heating (DH) networks are an especially relevant example of such energy systems. State-of-the-art control of small and medium-sized DH networks, however, still mainly relies on simple rule-based control concepts. Handling future challenges such as varying prices and intermittent renewable production is difficult to achieve with such control concepts. Optimization-based energy management systems (EMS) are a promising high-level control approach for the efficient operation of DH networks and complex energy systems in general. An especially interesting challenge arises when DH networks grow, as often the opportunity arises to interconnect them. However, if they operated by different owners, the control task becomes challenging, especially for optimization-based EMS. This is because, in the overall objective function, the cost and revenue for any exchange of energy would cancel out. This thesis presents a solution to this challenge. The main focus of this thesis is on the application of distributed optimization methods for EMS in the context of coupled energy systems, operated by multiple owners, especially interconnected DH networks. The presented methods and ideas are evaluated on a practical application of three DH networks in Austria.  

Annex TS3: Hybrid Energy Networks

The aim of the IEA DHC Annex TS3 „hybrid energy networks" is to promote opportunities and to overcome challenges for district heating and cooling (DHC) networks in an integrated energy system context, focusing on the coupling to the electricity and the gas grid.

Introduction and motivation
A key objective for the operation of biomass boilers is to achieve the highest possible efficiency while emitting the lowest possible pollutant emissions. In order to automate this task, CO-lambda optimization methods have been proposed in literature that ensure that the biomass boiler is operated at the lowest excess air ratio at which no relevant pollutant emissions occur, maximizing efficiency as a result. Since this optimal excess air ratio depends on various external factors, such as fuel properties, CO-lambda optimization methods continuously incorporate new measurements of the excess air ratio and the carbon monoxide content of the flue gas and estimate a new optimal excess air ratio during operation.
While achieving promising results in lab-scale tests, none of the CO-lambda optimization methods presented in literature has yet been able to gain practical acceptance. Either they are not robust enough and provide inaccurate estimates of the optimal excess air ratio or they are too slow and do not allow the optimal excess air ratio to be tracked sufficiently quickly. With the goal of providing a method that is fit for practical application, this publication presents a new modular approach for CO-lambda optimization that determines the optimal excess air ratio robustly and quickly, i.e. in real time.

Method
The new approach for CO-lambda optimization approximates the correlation between the excess air ratio and the carbon monoxide content of the flue gas, the CO-lambda characteristic, with a continuous, algebraic, non-linear model function. For this purpose, it uses a recursive-least-squares algorithm to continuously identify the model function’s parameters that lead to the optimal fit with the measured data, which are the excess air ratio and carbon monoxide content of the flue gas. From these model parameters, the optimal excess air ratio is calculated and defined as a desired value for the biomass boiler’s existing controller. This existing controller then ensures, that the biomass boiler is operated with this desired optimal excess air ratio, thus, maximizing efficiency and decreasing pollutant emissions. As a result, this new approach for CO-lambda optimization is entirely modular and can be applied to any biomass boiler with an existing control strategy capable of accurately adjusting the excess air ratio. For the measurement of the carbon monoxide content of the flue gas, a separate sensor has to be used. For this study the commercially available and proven in-situ exhaust gas sensor “KS1D” provided by the company LAMTEC has been used.

Long-term verification
The new approach for CO-lambda optimization was tested and validated at a biomass boiler with a nominal capacity of 2.5 MW that supplies a local heating network and combusts wood chips with a water content ranging from 30 w.t.% to 50 w.t.%. The long-term validation took place over an entire heating period, i.e. 5 months from November to March, during which the biomass boiler was operated alternately with the new approach for CO-lambda optimization and the standard control strategy, which means a constant desired residual oxygen content. In total the new approach for CO-lambda optimization was active for 1155 operating hours while the standard control strategy was active for 1310 operating hours. Compared to the standard control strategy, the new approach for CO-lambda optimization increased the biomass boiler’s efficiency by 3.8%, decreased total dust emissions by 19.5% and reduced carbon monoxide emissions on average (median) by 200 mg/m³. This demonstrates that the new approach for CO-lambda optimization is not only robust enough to run over a long period of time, it also leads to significant improvements in the biomass boiler’s operation. In addition, following these results, this new approach for CO-lambda optimization has also successfully been implemented and demonstrated at another biomass boiler with a nominal capacity of 1 MW where it has already been active for several months. This contribution presents the new approach to CO-lambda optimization in detail and discusses its technological and economic impact.

The so-called “layer model” or “interface-based model” is a simplified single particle model, originally developed for shorter computation time during computational fluid dynamics (CFD) simulations. A reactive biomass particle is assumed to consist of successive layers, in which drying, pyrolysis and char conversion occur sequentially. The interfaces between these layers are the reaction fronts. The model has already been validated for drying, pyrolysis and char oxidation. Layer models in the literature have commonly employed surface reactions at the reaction front to describe char conversion. In this work, the suitability of this surface reaction concept is assessed when gasifying biochar. It is shown that a particular layer model, already available, which originally employed surface reactions, was unable to adequately describe the mass loss during gasification of a biochar. In order to overcome this incapability, the model was extended to consider volumetric reactions in the char layer. The influence of intraparticle diffusion was considered through an effectiveness factor. The model is easily adaptable for different gas-solid kinetic rate laws, while still allowing for comparably fast solutions of the model equations. The extended model was validated using theoretical calculations and experimental measurements from literature. It was demonstrated that intraparticle diffusion can significantly slow down the biochar gasification process. A general guideline for when to employ volumetric reactions, rather than surface reactions, and when to consider intraparticle diffusion is provided based on the Thiele modulus as the criterion.

Fault-Detection (FD) is essential to ensure the performance of solar thermal systems. However, manually analyzing the system can be time-consuming, error-prone, and requires extensive domain knowledge. On the other hand, existing FD algorithms are often too complicated to set up, limited to specific system layouts, or have only limited fault coverage. Hence, a new FD algorithm called Fault-Detective is presented in this paper, which is purely data-driven and can be applied to a wide range of system layouts with minimal configuration effort. It automatically identifies correlated sensors and models their behavior using Random-Forest-Regression. Faults are then detected by comparing predicted and measured values.

The algorithm is tested using data from three large-scale solar thermal systems to evaluate its applicability and performance. The results are compared to manual fault detection performed by a domain expert. The evaluation shows that Fault-Detective can successfully identify correlated sensors and model their behavior well, resulting in coefficient-of-determination scores between R²=0.91 and R²=1.00. In addition, all faults detected by the domain experts were correctly spotted by Fault-Detective. The algorithm even identified some faults that the experts missed. However, the use of Fault-Detective is limited by the low precision score of 30% when monitoring temperature sensors. The reason for this is a high number of false alarms raised due to anomalies (e.g., consecutive days with bad weather) instead of faults. Nevertheless, the algorithm shows promising results for monitoring the thermal power of the systems, with an average precision score of 91%.

Gas-fermentation is the conversion of gaseous feedstocks (e.g. CO2-rich off gases, CO, H2) into
valuable products such as organic acids and alcohols by microorganisms such as clostridia.
By supplying electrical energy (an alternative source of reducing/oxidizing energy), the fermentation
environment can be further optimized, resulting in products with higher purity, a broader product
spectrum and higher cell densities.

Iron production via blast furnace utilizes coal and coke to reduce iron oxides resulting in high greenhouse gas emissions. This important issue for the iron and steel industry may be mitigated by application of biomass-based reducing agents (bioreducer).

A sustainable energy supply can only be achieved by a flexible, cross-sectoral energy system utilizing the specific advantages of the various renewable technologies. In this workshop possible roles of different technologies will be discussed based on a previous discussion of the users’ needs among the different sectors. In this a special focus should be given on the flexibility provision via the heating sector. By bringing together different users, representing municipal and industrial energy supply, and technological experts from different IEA Technology Collaboration Programmes (TCP) the workshop should support a holistic discussion.

List of presentations: 

• Wien Energie‘s vision of a sustainable energy and ressource supply of Vienna, Teresa Schubert, Wien Energie, Austria
• Digitalization of energy management systems – optimization of internal energy use as an industrial company, Maria Lechner, INNIO Jenbacher, Austria
• Flexible Bioenergy and System Integration, Elina Mäki, VTT Technical Research Centre of Finland, Finland Task Leader – IEA Bioenergy Task 44 Flexible Bioenergy and System Integration
• Use Case: Syngas platform Vienna for utilization of biogenic residues, Matthias Kuba, BEST – Bioenergy and Sustainable Technologies, Austria
• Transformation of District Heating and Cooling Systems towards high share of renewables, Ingo Leusbrock, AEE INTEC, Austria – Lead of Austrian delegation – IEA DHC Annex TS5 Integration of Renewable Energy Sources into existing District Heating and Cooling Systems
• Opportunities offered by long-term heat storages and large-scale solar thermal systems, Viktor Unterberger, BEST – Bioenergy and Sustainable Technologies, Austria Task Manager – IEA SHC Task 68 Efficient Solar District Heating Systems
• Possibilities through digitalization on the example of District Heating and Cooling, Dietrich Schmidt, Fraunhofer Institute for Energy Economics and Energy System Technology IEE, Germany – Operating Agent – IEA DHC Annex TS4 Digitalisation of District Heating and Cooling

List of contributing IEA Tasks:

• IEA Bioenergy Task 44 Flexible Bioenergy and System Integration
• EA DHC Annex TS5 Integration of Renewable Energy Sources into existing District Heating and Cooling Systems
• IEA SHC Task 68 Efficient Solar District Heating Systems
• IEA DHC Annex TS4 Digitalisation of District Heating and Cooling

Future hybrid energy systems require flexible technologies for compensating the volatile nature of most renewable energies. As such, fixed-bed biomass gasifiers are especially relevant as they allow a flexible production of heat, electricity and in a broader sense bio-based products (e.g. biochar). Thus, flexible fixed-bed biomass gasifiers will continuously become more relevant for a sustainable and highly flexible energy and resource system (bioeconomy).

However, due to their current economic dependency on specific feed in tariffs for the produced electricity, they are almost always operated at nominal load, to maximize the electricity production. Thus, their potential for flexibility has not been revealed up to now. Consequently, the currently applied control strategies are typically designed with the focus on steady-state operation. Any operation differing from nominal load typically requires manual interventions of the plant operators to avoid lower efficiencies or operational difficulties. Thus, currently applied control strategies do not allow a fully-automatic and flexible operation of the gasifiers.

To unleash the full potential of the gasifiers’ flexibility, new and more advanced control strategies able to handle varying operating conditions automatically are required. For this reason, this contribution aims for the development of a model-based control strategy, since it allows to explicitly consider all the correlations between the different process variables, and an efficient adaptation of the control strategy to different plants. The development was carried out on the basis of a representative industrial small-scale fixed-bed biomass gasifier operated as combined heat and power plant (CHP) with a nominal capacity of 300 kW_th and 150 kW_el. In this contribution we present the developed method as well as the practical verification of the model-based controller for the industrial small-scale fixed-bed biomass gasification plant.

The practical verification revealed a significant potential for flexibility increase by the new model-based control strategy in comparison to state-of-the-art control strategies. For example, the new controller performs a step-wise load change from 150 kW_el to 100 kW_el (-33%) within less than 2 min without affecting the gasification performance. The new control leads to a much more homogeneous gasification, in particular during partial load operation, and reduces the fluctuation margin of relevant process parameters to less than 1%. This controlled stabilization and homogenization of the gasification at different operating conditions is also a prerequisite for further future flexibilization measures, e.g. the extension of the feedstock variety (fuel flexibility) or increasing product flexibility.

Due to the modular and model-based design, the new control strategy can also be implemented on other fixed-bed gasifiers of the same type without requiring any structural modifications, by solely adjusting the model parameters appropriately. Furthermore, the new control strategy makes only use of sensors and actuators typically already available in state-of-the-art fixed-bed gasification systems. In conclusion, the model-based control strategy to be presented states a very important contribution towards flexible fixed-bed biomass gasification systems.

Buildings account for 30% of the globally consumed final energy and 19% of the indirect emissions, i.e., from the production of electricity and heat. Air-conditioned office buildings have an especially high energy footprint. Retrofitting buildings with predictive control strategies can reduce their energy demand and increase thermal comfort by considering future weather conditions. One challenge lies in the required infrastructure, i.e., sensors and actuators. Another challenge is about satisfying the comfort requirements of the users, getting their feedback and reacting to it. We propose a predictive control strategy, where an optimization-based energy management system (EMS) controls the thermal zones of such office buildings. The approach uses a mathematical model of the building within an optimization problem to predict and shift thermal demand. The individual thermal zones are modelled using a grey-box approach, where the simultaneous state and parameter estimation is handled by an unscented Kalman filter (UKF). This minimizes the needed effort for deployment of the system, as the parameters are learned automatically from historical measurement data. The objective function ensures the users’ comfort based on a comfort model, penalizes unwanted behaviour such as frequent valve position changes, and minimizes the costs for heating and cooling supply. Since the offices are typically shared by multiple users, the internal comfort model is calibrated based on their feedback. Each feedback is viewed as a measurement from the internal comfort model, and an UKF updates the parameters of the model, thus lowering or increasing the temperature setpoint of the zone controller in a robust manner. As a case study, an office building at the “Innovation District Inffeld” is considered. The proposed predictive control strategy, together with the user feedback, is implemented. A central information and communication technology (ICT) handles all communication with the building automation system. We developed a simple web-based feedback system with a five-point Likert scale for user feedback integration. The presented ideas are evaluated based on both a preliminary simulation study and potential evaluation using the building modelling software IDA ICE, and a real-world implementation. A key requirement was to limit the number of new sensors and actuators, thus focusing on how much can be achieved with a retrofit measure with minimal hardware, but intelligent software. The presentation will give, an overview of the developed methods and first results of the real implementation will be given.

Die Erkenntnisse der Arbeitspakete I, II und III bildeten die Grundlage, um Konzepte für
innovative Produktentwicklungen von Systemkomponenten für Microgrids zu entwickeln und
diese fertigzustellen. Dazu erfolgte in Arbeitspaket I die Analyse und Planung der verwendeten
Wärme-, Strom- und Kältetechnologien, welche in AP II installiert und ins lokale System
integriert wurde. Die Datenerfassung, Monitoring und Bewertung des Nutzerverhaltens erfolgte
im Anschluss in AP III. Dadurch wurden die Rahmenbedingungen geschaffen um neue
Produkte und Dienstleistungen zu entwickeln und zu testen.

Absorption heat pumping devices (AHPDs, comprising absorption heat pumps and chillers) use mainly thermal energy instead of electricity as the driving energy to provide resource-efficient heating and cooling when using waste heat or renewable heat sources. Despite this benefit, AHPDs are still not a very common technology due to their complexity. However, better modulation and part-load capability, which can be achieved through advanced control strategies, can simplify the use of AHPDs and help to better integrate them into complex energy systems. Therefore, this paper presents a new, dynamic model-based control approach for single-stage AHPDs that can extend an AHPD’s operating range by employing multi-input-multi-output (MIMO) control methods. The control approach can be used for different AHPD applications and thus control configurations, i.e., different combinations of manipulated and controlled variables, and can also be used for redundantly-actuated configurations with more manipulated than controlled variables. It consists of an observer for the state variables and unknown disturbances, a state feedback controller and, in case of redundantly-actuated configurations, a dynamic control allocation algorithm. The proposed control approach is experimentally validated with a representative AHPD for two different control configurations and compared to two benchmark control approaches – single-input-single-output (SISO) PI control representing the state-of-the-art, and model-predictive control (MPC) as an alternative advanced control concept. The experimental validation shows that the two MIMO control approaches (the proposed state feedback and the MPC approach) allow for a wider operating range and hence better part load capability compared to the SISO PI control approach. While MPC generally results in a comparably high computational effort due to the necessity of continuously solving an optimization problem, the proposed state feedback control approach is mathematically simple enough to be implemented on a conventional programmable logic controller. It is therefore considered a promising new control approach for AHPDs with the ability to extend their operating range and improve their part load capability, which in turn facilitates their implementation and thus the use of sustainable heat sources in heating and cooling systems.

Optimization-based energy management systems (EMS) are a high-level control approach for energy systems like district heating networks. A descriptive model and objective function are required to solve an optimization problem and apply the resulting schedule in a receding horizon fashion. EMS for buildings require a simplified model of each thermal zone, and the objective function includes costs for heating and cooling, virtual costs, and a comfort model. Feedback from users is necessary since thermal comfort varies among individuals.

Absorptionswärmepumpenanlagen (AWPA, beinhaltet Absorptionswärmepumpen und –kältemaschinen), sind Anlagen, die hauptsächlich thermische statt elektrischer Energie nutzen, um Wärme und Kälte zu generieren. Dadurch wird die Nutzung von Abwärme und erneuerbaren Energiequellen wie Solarenergie in Heiz- und Kühlsystemen erleichtert. Trotz dieses Vorteils ist der Einsatz von AWPA nach wie vor stark eingeschränkt. Ein Grund dafür ist das Fehlen von Regelungsstrategien, die eine zufriedenstellende Regelgüte über einen weiten Betriebsbereich, insbesondere unter Teillast, bieten. Deshalb befasst sich diese Arbeit mit der Entwicklung eines neuen, modellbasierten Regelungsansatzes für AWPA, die den Betriebsbereich durch den Einsatz von Mehrgrößen-Regelungsmethoden (multi-input-multi-output (MIMO) Regelungsmethoden) erweitern kann.



Zunächst wird ein geeignetes dynamisches Modell abgeleitet, das im modellbasierten Regelungsansatz verwendet werden soll. Es handelt sich um ein physikalisch basiertes Modell mit modularer Struktur, was eine systematische Anpassung an verschiedene AWPA erleichtert. Um die Anzahl der Zustandsvariablen niedrig zu halten, werden nur diejenigen Masse- und Energiespeicher berücksichtigt, die zu Zeitkonstanten und Totzeiten führen, die für die spätere Regelungsaufgabe relevant sind. Das entwickelte Modell ist mathematisch einfach, hat jedoch die Struktur eines nichtlinearen differential-algebraischen Gleichungssystems. Als solches ist es sehr gut als Simulationsmodell geeignet um verschiedene Regelungsstrategien in der Simulation zu testen, aber es ist zu komplex für viele modellbasierte Regelungsmethoden. Um eine noch einfachere Modellstruktur zu erhalten, wird das Modell an einem Betriebspunkt linearisiert, was auf ein Modell in linearer Zustandsraumdarstellung führt. Die entwickelten nichtlinearen und linearen Modelle werden experimentell validiert und mit zwei alternativen Modellierungsansätzen als Benchmark verglichen. Ein Vergleich zwischen dem abgeleiteten nichtlinearen Modell und den Benchmark-Modellen zeigt eine höhere Genauigkeit für das neue Modell, sowohl stationär als auch dynamisch. Ein Vergleich zwischen dem abgeleiteten nichtlinearen und dem linearisierten Modell zeigt, dass das linearisierte Modell zwar eine etwas schlechtere stationäre Genauigkeit aufweist, die dynamische Genauigkeit jedoch durch die Linearisierung nahezu unbeeinflusst zu sein scheint. Das vorgestellte neue linearisierte AWPA -Modell gilt daher als geeignet, als Grundlage für den Entwurf des modellbasierten Regelansatzes verwendet zu werden.



Als nächstes wird dieses Modell verwendet, um einen neuen modellbasierten Regelungsansatz für AWPA zu entwerfen. Der neue Regelungsansatz kann für verschiedene AWPA-Anwendungen und damit für verschiedene Regelungskonfigurationen verwendet werden, d.h., verschiedene Kombinationen von Stell- und Regelgrößen. Er kann auch für redundante aktuierte Konfigurationen mit mehr Stell- als Regelgrößen verwendet werden, was die Erweiterung des Betriebsbereichs einer AWPA ermöglicht. Der Ansatz besteht aus einem Beobachter für die Zustandsvariablen und unbekannte Störgrößen, einem Zustandsregler und, im Falle von redundant aktuierten Konfigurationen, einem Algorithmus zur dynamischen Stellgrößenverteilung. Der vorgeschlagene Regelungsansatz wird experimentell für zwei verschiedene Regelungskonfigurationen validiert und mit zwei Benchmark-Ansätzen verglichen – einem Eingrößen-PI-Regler (Single-input-single-output (SISO) PI-Regler), der den Stand der Technik repräsentiert, und einem modellprädiktiven Regelungsansatz (model predictive control, MPC) als alternative fortschrittliche Regelungsmethode. Die experimentelle Validierung zeigt, dass die beiden MIMO-Regelungsansätze (der vorgeschlagene Zustandsregler und der MPC-Ansatz) einen erweiterten Betriebsbereich und somit eine bessere Teillastfähigkeit im Vergleich zum SISO-PI-Regler ermöglichen. Während MPC durch die Notwendigkeit zur kontinuierlichen Lösung eines Optimierungsproblems im Allgemeinen eine vergleichsweise hohe Rechenleistung benötigt, ist der vorgeschlagene Zustandsregler-Ansatz mathematisch einfach genug, um auf herkömmlichen speicherprogrammierbaren Steuerungen für AWPA implementiert werden zu können. Er wird daher als vielversprechender neuer Regelungsansatz für AWPA betrachtet, der die Möglichkeit bietet, ihren Betriebsbereich zu erweitern und ihre Teillastfähigkeit zu verbessern, was wiederum eine einfachere Einbindung in moderne Energiesysteme ermöglicht und somit die Nutzung nachhaltiger Wärmequellen für Heizen und Kühlen erleichtert.

Fluidized bed biomass gasification is a complex process whereby gas source terms are released by reactions at the particle level during the movement of fuel particles throughout the reactor. The current study presents for the first time the application of a multi-scale modelling approach for a fluidized bed biomass gasifier of industrial size, coupling a detailed one-dimensional particle model based on the progressive conversion model (PCM) with a commercial CFD software. Results of particle movement and gas source terms are compared with results of an additional simulation employing the simplified uniform conversion model (UCM) which is commonly used in literature. Validation at the particle level showed that the UCM leads to a massive underprediction of the time needed for pyrolysis whereas the PCM is in good agreement with experimental data. This heavily influences the gas sources released during pyrolysis of the biomass particles in the coupled reactor simulations. Volatiles are much more concentrated to the close proximity of the fuel feed when using the UCM whereas the PCM leads to a more homogeneous distribution over the reactor cross-section. The calculation time analysis of the coupled simulations showed that despite the increased complexity, the PCM shows only an increase of 20% in calculation time when compared to the UCM, whereas it is much better suited for these conditions. The coupled multi-scale simulations using the PCM showed the numerical feasibility of the modelling approach for 1,200,000 bed parcels and about 80,000 reacting fuel parcels and furthermore highlighted the importance of a comprehensive description of the particle level.

With the increasing demand for lower emissions and innovative combustion technologies, it is necessary to have a reaction mechanisms that is accurate as well as computationally affordable for geometry and process optimization using computational fluid dynamics (CFD). The objective of this work is to explore the applicability of several reaction mechanisms in predicting NOx emissions from various combustion systems. This work focuses on the selection of suitable mechanisms from literature (see Table 1) in a full scale 3D model for the prediction of NOX especially for furnaces with low oxygen concentration in the fuel bed and enhanced reduction zones.

One of the main tasks for operators of medium- and large-scale biomass boilers is the continuous operational monitoring of these plants in order to assess their performance, detect errors and identify possibilities for operational optimization. However, due to the high complexity of this task, errors are frequently detected too late or not at all, which can lead to even more costly secondary errors. In addition, possibilities for optimization remain unused in many existing plants, resulting in unnecessary pollutant emissions and low efficiencies.
To assist operators in performing this task and to achieve a high level of automation, methods for the automated, model-based monitoring of such plants have been focus of recent research activities. In this contribution, we will discuss the numerous possibilities provided by the application of such methods in a practical context. For this purpose, we present selected results from previous activities, demonstrating how methods for model-based monitoring were applied at combustion plants and used to enable automated error detection and support operational optimization.

Exemplary result 1: We developed a soft-sensor which accurately estimates the non-measurable internal state of heat exchangers and implemented it at a large-scale combustion plant with a nominal capacity of 38.2 MW. This soft-sensor uses a dynamic mathematical model of the heat exchanger in combination with measured data to determine a new estimate for the heat exchanger’s internal state every second. Based on this estimate, the soft-sensor accurately detects fouling and determines the non-measurable flue gas mass flow in real time. The estimated flue gas mass flow was used in a model-based control strategy which resulted in significant improvements of the combustion plant’s operational behaviour and load modulation capabilities. These results are discussed in this contribution.

Exemplary result 2: We developed a method for the real-time estimation of non-measurable fuel properties, i.e. chemical composition, bulk density, lower heating value, in biomass boilers. These estimates were subsequently used in a model-based control strategy and enabled the improvement of the biomass boiler’s fuel flexibility. Results of this estimator achieved for different biomass fuels, e.g. poplar wood chips, corncob grits and standard wood pellets, are discussed in this contribution.
On the basis of these selected results, it will be examined which possibilities arise from the use of methods for model-based monitoring in biomass boilers and also how these results can be extended to other technologies such as biomass gasifiers.

Microgrids can integrate variable renewable energy sources into the energy system by controlling flexible assets locally. However, as the energy system is dynamic, an effective microgrid controller must be able to receive feedback from the system in real-time, plan ahead and take into account the active electricity tariff, to maximize the benefits to the operator. These requirements motivate the use of optimization-based control methods, such as Model Predictive Control to optimally dispatch flexible assets in microgrids. However, the major bottleneck to achieve maximum benefits with these methods is their predictive accuracy. This paper addresses this bottleneck by developing a novel multi-step forecasting method for a Model Predictive Control framework. The presented methods are applied to a real test-bed of a renewable energy community in Austria, where its operational costs and CO2 emissions are benchmarked with those of a rule-based control strategy for Flat, Time-of-Use, Demand Charge and variable energy price tariffs. In addition, the impact of forecast errors and electric battery capacity on energy community operational savings are examined. The key results indicate that the proposed controller can outperform a rule-based dispatch strategy by 24.7% in operational costs and by 8.4% in CO2 emissions through optimal operation of flexibilities if it has perfect foresight. However, if the controller is deployed in a realistic environment, where forecasts for electrical load and PV generation are required, the same savings are reduced to 3.3% for cost and 7.3% for CO2, respectively. In such environments, the proposed controller performs best in highly dynamic tariffs such as Time-of-Use and Real-time pricing rates, achieving real cost savings of up to 6.3%. These results show that the profitability of optimization-based control of microgrids is threatened by forecast errors. This motivates future research on control strategies that compensate for forecast errors in real-world operation and more accurate forecasting methods.

Microgrids generate and store energy for self consumption (electricity, heating, cooling, etc.). Decentralized and renewable generation and storage technologies, as well as energy strategies increase efficiency, resilience, grid stability, independency of imports, sustainability, and climate neutrality.

District heating (DH) networks have the potential for intelligent integration and combination of renewable energy sources, waste heat, thermal energy storage, heat consumers, and coupling with
other sectors. For growing networks in close geographical proximity, often the possibility arises to couple them using bidirectional heat exchangers, possibly unlocking synergies and reducing costs for the consumers. Each DH network may consist of producers, consumers and thermal energy storage (TES) devices. Often, each of the coupled DH networks will be already controlled via low-level controllers. Hence, a high-level control approach is needed, that coordinates the heat exchange between the
networks and takes renewable energy sources and the TES capacities in each network into account. These supervisory controllers are generally referred to as energy management systems (EMS).

To ensure that energy is optimally used on site in local energy grids/microgrids and to achieve cost and/or emission reduction targets, the technologies are controlled by predictive and adaptive microgrid controllers. Based on realtime measurement data as well as load, generation, market and weather forecasts, the optimal deployment plan for the local energy grid is thus calculated using mathematical
optimization algorithms. Synergies of different technologies and sectors (electricity, heating, cooling, mobility, etc.) are taken into account, resulting in high energy efficiency in the system.

Biomassefeuerungen spielen eine Schlüsselrolle in der Energiewende hin zu einem vollständig erneuerbaren Energiesystem. Allerdings müssen sie sich zukünftigen Herausforderungen stellen, um weiterhin relevant zu bleiben. Einerseits müssen Biomassefeuerungen mit dem höchstmöglichen Wirkungsgrad arbeiten, um wirtschaftlich rentabel zu bleiben während sie gleichzeitig eine hohe Lastmodulationsfähigkeit aufweisen müssen, um für eine breitere Palette von Anwendungen eingesetzt werden zu können. Andererseits müssen Biomassefeuerungen immer strengere Grenzwerte für Schadstoffemissionen einhalten und gleichzeitig in der Lage sein, neue und alternative Biomassebrennstoffe mit geringerer Qualität zu verbrennen.

In dieser Arbeit wird eine modellbasierte Regelungsstrategie entwickelt, die es Biomassefeuerungen ermöglicht, all diese Herausforderungen zu meistern. Diese Regelungsstrategie besteht aus drei Teilen, einer Verbrennungsregelung, einem Zustands- und Parameterschätzer und einer Methode zur CO-lambda-Optimierung. Alle drei Teile werden in dieser Arbeit hergeleitet und im Detail diskutiert, insbesondere im Hinblick auf ihre Implementierung an realen Biomassefeuerungen. Darüber hinaus werden alle drei Teile der modellbasierten Regelungsstrategie durch Simulationsstudien sowie durch eine Implementierung in realen Biomassefeuerungen verifiziert.

Als Grundlage für die modellbasierte Regelungsstrategie wird ein mathematisches Modell abgeleitet, welches das dynamische Verhalten der Prozesse in der Biomassefeuerungen einschließlich des Einflusses der Brennstoffeigenschaften beschreibt. Die berücksichtigten Brennstoffeigenschaften sind die Schüttdichte und die chemische Zusammensetzung einschließlich des Wasser- und Aschegehalts sowie der untere Heizwert.

Die Verbrennungsregelung nutz die Stellglieder der Biomassefeuerung um dessen stabilen Betrieb zu gewährleisten und schnelle Laständerungen zu ermöglichen. Diese modellbasierte Regelstrategie berücksichtigt durch ihre Formulierung, die auf dem oben genannten mathematischen Modell basiert, explizit alle relevanten Brennstoffeigenschaften. Dadurch reagiert sie gezielt auf Änderungen dieser Brennstoffeigenschaften und kompensiert direkt deren Einfluss auf den Betrieb der Biomassefeuerung. Gleichzeitig weist sie eine einfache Struktur auf und ist daher leicht zu implementieren und zu warten. Diese modellbasierte Verbrennungsregelung wird sowohl in Simulationsstudien als auch durch Experimente nach einer Implementierung an einer realen Biomassefeuerung verifiziert.

Es wird ein kombinierter Zustands- und Parameterschätzer entwickelt, der gleichzeitig die Brennstoffeigenschaften, die anschließend von der Verbrennungsregelung verwendet werden, und die Zustandsgrößen der Biomassefeuerungen in Echtzeit schätzt. Er basiert auf einem erweiterten Kalman-Filter, der das in dieser Arbeit vorgestellte mathematische Modell verwendet. Diese Methode wird für verschiedene Brennstoffeigenschaften sowohl in Simulationsstudien als auch durch Messdaten aus realen Biomassefeuerungen verifiziert. Die Ergebnisse dieser Verifikation zeigen, dass diese Methode in der Lage ist, die Brennstoffeigenschaften und Zustandsgrößen auch bei Last- oder Brennstoffwechseln genau zu bestimmen.

Um einen Betrieb der Biomassefeuerung mit möglichst hohem Wirkungsgrad und möglichst geringen Schadstoffemissionen zu gewährleisten, wird eine Methode zur CO-lambda-Optimierung entwickelt. Diese Methode verwendet einen erweiterten Kalman-Filter in Kombination mit Messdaten des Sauerstoffgehalts und des CO-Gehalts des Rauchgases zur Bestimmung eines optimalen Luftüberschussverhältnisses für den aktuellen Zustand der Biomassefeuerung. Diese Methode wird an einer realen Biomassefeuerung in einer Langzeitvalidierung über mehrere Monate verifiziert und validiert. Während dieser Langzeitvalidierung führte die Anwendung dieser Methode zur CO-lambda-Optimierung zu einer Wirkungsgradsteigerung von 3,8 %, einer Reduktion der CO-Emissionen um durchschnittlich 200 mg/m³ sowie einer Verringerung der Gesamtstaubemissionen um durchschnittlich 19 %.

Zusammenfassend ermöglicht die in dieser Arbeit vorgestellte modellbasierte Regelungsstrategie es, Biomassefeuerungen mit den geringstmöglichen Schadstoffemissionen und dem höchstmöglichen Wirkungsgrad zu betreiben und dabei ein hohes Maß an Brennstoffflexibilität und Lastmodulationsfähigkeit zu erreichen. Darüber hinaus weist die Regelungsstrategie eine geringe Komplexität auf und ist leicht in realen Biomassefeuerungen zu implementieren und zu warten. Dies ermöglicht den breiten Einsatz dieser Regelungsstrategie an bestehenden und zukünftigen Biomassefeuerungen. Dies unterstützt die weitere Verbreitung von Biomassefeuerungen im Energiesystem, was zur Reduzierung der CO2e-Emissionen beiträgt und auch die verstärkte Nutzung anderer, volatiler erneuerbarer Technologien, wie z.B. solarthermischer Anlagen, ermöglicht.

Both the planning and operation of complex, multi-energy systems increasingly rely on optimization. This optimization requires the use of mathematical models of the system components. The model most often used to describe thermal storage, and especially in the common mixed-integer linear program (MILP) formulation, is a simple integrator model with a linear loss term. This simple model has multiple inherent drawbacks since it cannot be applied to represent the temperature distribution inside of the storage unit. In this article, we present a novel approach based on multiple layers of variable size but fixed temperature. The model is still linear, but can be used to describe the most relevant physical phenomena: heat losses, axial heat transport, and, at least qualitatively, axial heat conduction. As an additional benefit, this model makes it possible to clearly distinguish between heat available at different temperatures and thus suitable for different applications, e.g., space heating or domestic hot water. This comes at the cost of additional binary decision variables used to model the resulting hybrid linear dynamics, requiring the use of state-of-the-art MILP solvers to solve the resulting optimization problems. The advantages of the more detailed model are demonstrated by validating it against a standard model based on partial differential equations and by showing more realistic results for a simple energy optimization problem.

Measuring the producer gas from biomass gasification is very challenging and the use of several methods is required to achieve a complete characterization. Various techniques are available for these measurements, offering very different affordability or time demand requirements and the reliability of these techniques is often unknown. In this work an assessment of commonly employed measuring methods is conducted with a round robin. The main permanent gases, light hydrocarbons, tars, sulfur and nitrogen compounds were measured by several partners employing a producer gas obtained from fluidized bed gasification of wood and miscanthus with steam. Online and offline methods were used for this purpose and their accuracy, repeatability and reproducibility are here discussed. The results demonstrate the reliability of gas chromatography for measuring the main permanent gases, light hydrocarbons, benzene and H2S, validating the obtained results with other methods. An online method could also measure NH3 with a reasonable accuracy, but deviations were present for compounds at even lower concentrations. Regarding tar sampling and analysis, the main source of variability in the results was the analysis of the liquid samples, especially for heavier compounds. The presented work pointed out the need for a complementary use of several techniques to achieve a complete characterization of the producer gas from biomass gasification, and the suitability of certain online techniques as well as their limitations.

Modern buildings with floor heating or thermally activated building structures (TABS) offer a significant potential for shifting the thermal load and thus reduce peak demand for heating or cooling. This potential can be realized with the help of model predictive control (MPC) methods, provided that sufficiently descriptive mathematical models describing the thermal characteristics of the individual thermal zones exist. Creating these by hand or from more detailed simulation models is infeasible for large numbers of zones; instead, they must be identified automatically based on measurement data. We present an approach using only open source tools based on the programming language Julia that allows to robustly identify simple thermal models for heating and cooling usable in MPC optimization. The resulting models are used in a distributed optimization scheme that co-ordinates the individual zones and buildings of a city quarter in order to best support an energy hub.

Modern buildings with floor heating or thermally activated building structures (TABS) offer a significant
potential for shifting the thermal load and thus reduce peak demand for heating or cooling. This potential can be realized with the help of model predictive control (MPC) methods, provided that sufficiently descriptive mathematical models describing the thermal characteristics of the individual thermal zones exist. Creating these by hand or from more detailed simulation models is infeasible for large numbers of zones; instead, they must be identified automatically based on measurement data. We present an approach using only open source tools based on the programming language Julia that allows to robustly identify simple thermal models for heating and cooling usable in MPC optimization. The resulting models are used in a distributed optimization scheme that co-ordinates the individual zones and buildings of a city quarter in order to best support an energy hub.

List of presentations:

Biorefineries

• Learnings from biomass combustion towards future bioenergy applications (M. Schwabl)
• Green Carbon perspectives for regional sourcing and decarbonization (E. Wopienka)
• Bioconversion processes for renewable energy and/or biological carbon capture and utilisation (B. Drosg)
• Second generation biomass gasification: The Syngas Platform Vienna – current status and outlook (M. Kuba)
• Utilization of syngas for the production of fuel and chemicals – recent developments and outlook (G. Weber)

Digital methods, tools and sustainability

• Evaluation of different numerical models for the prediction of NOx emissions of small-scale biomass boilers (M. Eßl)
• Digitalization as the basis for the efficient and flexible operation of renewable energy technologies (M. Gölles)
• Smart Control for Coupled District Heating Networks (V. Kaisermayer)
• Integrated energy solutions for a decentral energy future - challenges and solutions (P. Liedtke)
• Wood-Value-Tool: Techno-economic assessment of the forest-based sector in Austria (M. Fuhrmann)

Der Leitfaden „Energiegemeinschaften im Tourismus“ zeigt, welche Möglichkeiten Energiegemeinschaften für Tourismusbetriebe, ihre Beschäftigten und Menschen, die in Tourismusregionen leben, bieten können und wie eine Energiegemeinschaft ins Leben gerufen werden
kann.

Sector coupling is expected to play a key role in the decarbonization of the energy system by enabling the integration of decentralized renewable energy sources and unlocking hitherto unused synergies between generation, storage and consumption. Within this context, a transition towards hybrid energy networks (HENs), which couple power, heating/cooling and gas grids, is a necessary requirement to implement sector coupling on a large scale. However, this transition poses practical challenges, because the traditional domain-specific approaches struggle to cover all aspects of HENs. Methods and tools for conceptualization, system planning and design as well as system operation support exist for all involved domains, but their adaption or extension beyond the domain they were originally intended for is still a matter of research and development. Therefore, this work presents innovative tools for modeling and simulating HENs. A categorization of these tools is performed based on a clustering of their most relevant features. It is shown that this categorization has a strong correlation with the results of an independently carried out expert review of potential application areas. This good agreement is a strong indicator that the proposed classification categories can successfully capture and characterize the most important features of tools for HENs. Furthermore, it allows to provide a guideline for early adopters to understand which tools and methods best fit the requirements of their specific applications.

Mikro-Netze (Microgrids), ein Unterbereich der Intelligenten Strom/Energie-Netze (Smartgrids),
die sich durch eine enge räumliche Bindung von Energieerzeugungseinheiten und Verbraucher
auszeichnen wird international ein sehr starkes Wachstum zugeschrieben. Microgrids sind kleine,
lokale Energienetze für Strom, Wärme und Kälte, die Haushalte, Betriebe und Gemeinden mit
Energie versorgen. Diese lokalen und regionalen Konzepte der Energieversorgung können in
Zukunft einen wesentlichen Beitrag in Richtung Energieunabhängigkeit und effizientere
Integration von Erneuerbaren in das Energiesystem leisten. Sie können ihren Energiebedarf
selbstständig aus erneuerbaren Energien oder anderen Energieformen decken, etwa Biomasse,
Wärmepumpen, PV, Windräder oder Kraftwärmekopplungen. Diese können nach den
individuellen Zielen der Gemeinden, Haushalte oder der Betriebe gesteuert werden, um
Kostenreduktionen, CO2 Einsparungen oder eine Erhöhung des Unabhängigkeitsgrades zu
realisieren. Sie berechnen den aktuellen und zukünftigen Verbrauch und können Energie im
Bedarfsfall dorthin verlagern, wo sie gerade benötigt wird, oder sie reduzieren den
Energieverbrauch direkt.

Ziel des Tasks ist es, Bioenergielösungen als flexible Ressource in einem dekarbonisierten Energiesystem herauszuarbeiten. Dabei sollen Typen, Qualität und Status von flexibler Bioenergie erhoben sowie Barrieren und Entwicklungsbedarf im Gesamtsystemkontext (Strom-, Wärme- und Transportsektor) identifiziert werden.

Most industrial fixed-bed biomass gasification systems usually operate at steady-state to produce the maximum amount of energy possible although they can principally modulate their loads to compensate for the fluctuations of other volatile renewable energy systems. To unleash their full load modulation capability, their typically traditional control strategies should be improved, their gas residence times affected by typically basic char removal strategies adjusted and any required manual interaction of an operator avoided. In this respect, a new controller for the char handling (accumulation and removal) of the reduction zone in a fixed-bed biomass gasifier of a representative industrial small-scale gasification system is developed and experimentally verified. This new controller consists of a recursive least-squares estimator for the flow resistance of the gasifier representing the amount of char inside and a switching controller for rotating a grate located at its bottom. The experimental verification reveals that only the traditional (pressure-based) controller requires manual adjustment of the thresholds. Moreover, the new controller (flow resistance based) significantly reduces the fluctuation range during partial load and stabilizes the temperature and pressure downstream the gasifier. This provides the basis for enhancing its fuel flexibility too and is an important feature for flexible operation in future.

Die Energiewende in Richtung dezentraler Energieversorgung und der stetige Ausbau
erneuerbarer Energieressourcen erfordert ein angepasstes Energienetz (Strom, Wärme und
Kälte) mit einem flexiblen, ausbau- und integrationsfähigen Regelungssystem, welches
bestehende Energieversorgungsunternehmen (EVU) Systeme komplementiert, Netze
entlastet und die Notwendigkeit des teuren Netzausbaus verringert. Intelligente Mikro-Netze
(Microgrids), ein Bereich der Strom- und Energie-Netze (Smartgrids), erfüllen diese
Anforderungen. Durch Microgrids entstehen lokale Energiemärkte, welche lokale
Ungleichgewichte von den Verbundnetzen fernhalten und somit das Angebot und den
Verbrauch bereits auf lokaler Ebene ausbalancieren (wie z.B. Energiegemeinschaften).
Zusätzlich können die regionale Erzeugung und der Verbrauch von Strom um die Wärme-,
Kälte- und Gas-Seite ergänzt werden. Dies ergibt somit ein ganzheitliches regionales
Energiesystem, welches die gesamte Energieeffizienz erhöht und auch positive Netzeffekte
für den Energieversorger mit sich bringt. Microgrids liefern die Möglichkeit eine 100%ige
dezentrale Energieversorgung zu erreichen.
Gegenstand des Projekts „Microgrid Lab 100%“ war es bestehende und neue
wissenschaftliche Arbeiten und F&E-Ergebnisse zu Microgrids (mathematische &
physikalische Modellierung, modellbasierte Steuerungsmethoden, Regelung mit künstlicher
Intelligenz, Kommunikationsmethoden, Datenerfassung und der Austausch zwischen
Energieversorger, privaten Kunden und Gebäudemanagementsystemen) in einem realen
Umfeld zu evaluieren und auf wissenschaftlicher Ebene weiter zu entwickeln.
Projektinhalte und Projektziele waren die wissenschaftliche Planung und Inbetriebnahme des
Microgrid Forschungslabors, eine Nutzerbefragung, die Entwicklung von Testzyklen und ein
Monitoring, um mit den Ergebnissen die Optimierungsalgorithmen weiterzuentwickeln. Das
über das Projekt hinausgehende Ziel ist die Etablierung des Microgrid Forschungslabors für
verschiedene Wirtschaftszweige, um Planungs-, Steuerungs-, Integrations- und
Kommunikationskonzepte in Echtzeit zu entwickeln und für den Markt zu testen. Die
Involvierung von Industriepartner (u.a. COMET-Partner: EVN AG, Netz NÖ GmbH, Wien
Energie, meo Energie, Wüsterstrom) bereits während der Projektlaufzeit und der Aufbau eines
Kompetenznetzwerkes zu Microgrids, mit Unterstützung des Bau.Energie.Umwelt Cluster und
des Technopolmanagement Wieselburg, trugen zu dieser Zielerreichung wesentlich bei.
Konkret umfasst das geplante Microgrid Forschungslabor das Umfeld des Technologie- und
Forschungszentrum (TFZ) Wieselburg-Land sowie das neue Feuerwehrhaus der
Stadtgemeinde Wieselburg und Gemeinde Wieselburg-Land.

Absorption heat pumping systems (AHPSs, comprising absorption heat pumps and chillers) are devices that mainly use thermal energy instead of electricity to generate heating and cooling. This thermal energy can be provided by, e.g., waste heat or renewable energy sources such as solar energy, which allow AHPSs to contribute to ressource-efficient heating and cooling systems. Despite this benefit, AHPSs are still not a widespread technology. One reason for this is unsatisfactory controllability under varying operating conditions, which results in poor modulation and partial load capability. Emloying model-based control is a promising approach to address this issue, which will be the focus of this  contribution.
First, a viable control-oriented model for AHPSs is developed. It is based on physical correlations to facilitate systematic adaptions to different scales and operating conditions and considers only the most relevant mass and energy stores to keep the model order at a minimum. The resulting model is mathematically simple but still has the structure of a nonlinear differential-algebraic system of equations. This is typical for models of thermo-chemical
processes, but is unfortunately not suitable for many control design methods. Therefore, linearization at an operating point is discussed to derive a model in linear state space representation. Experimental validation results show that the linearized model does have slightly worse steady-state accuracy than the nonlinear model, but that the dynamic accuracy seems to be almost unaffected by the linearization and is considered sufficiently good to be used in control design.
As a next step, the linearized model is used to design model-based control strategies for AHPSs. A special focus is put on redundantly-actuated configurations, i.e. configurations with more manipulated variables than controlled variables, which allows using additional degrees of freedom to extend the operating range of AHPS and hence improve their partial load capability. Two model-based control approaches are discussed: First, a linear model predictive control (MPC) approach is presented - a well-established and generally easy-to-parameterize approach, which, however, often results in high computational effort prohibitive to its implementation on a conventional PLC. Therefore, a second control approach based on state feedback is presented which is mathematically simple enough for implementation on a conventional PLC. It consists of an observer for state variables and unknown disturbances, a state feedback controller and, in case of redundantly-actuated configurations, a dynamic control allocation algorithm. Both approaches are experimentally validated and compared to a state-of-the art control approach based on SISO PI control, showing that the model-based MIMO control approaches allow for a wider operating range and hence better modulation and partial load capability compared to the SISO PI approach. This, in turn, reduces ON/OFF operation of AHPSs and also facilitates their integration into complex energy systems to generate heating and cooling in a ressource-efficient manner.

For many fluidized bed applications, the particle movement inside the reactor is accompanied by reactions at the particle scale. The current study presents for the first time in literature a multi-scale modelling approach coupling a one-dimensional volumetric particle model with the dense discrete phase model (DDPM) of ANSYS Fluent via user defined functions. To validate the developed modelling approach, the current study uses experimental data of pressure drop, temperature and gas composition obtained with a lab-scale bubbling fluidized bed biomass gasifier. Therefore, a particle model developed previously for pyrolysis was modified implementing a heat transfer model valid for fluidized bed conditions as well as kinetics for char gasification taken from literature. The kinetic theory of granular flow is used to describe particle–particle interactions allowing for feasible calculation times at the reactor level whereas an optimized solver is employed to guarantee a fast solution at the particle level. A newly developed initialization routine uses an initial bed of reacting particles at different states of conversion calculated previously with a standalone version of the particle model. This allows to start the simulation at conditions very close to stable operation of the reactor. A coupled multi-scale simulation of over 30 s of process time employing 300.000 inert bed parcels and about 25.000 reacting fuel parcels showed good agreement with experimental data at a feasible calculation time. Furthermore, the developed approach allows for an in-depth analysis of the processes inside the reactor allowing to track individual reacting particles while resolving gradients inside the particle.

Solid oxide fuel cell (SOFC) models used in the past for biomass-to-power plant simulations are limited in their predictability of the carbon deposition risk. In this work, industrial-relevant cell designs were modeled in 2D-CFD considering detailed reaction kinetics which allowed more accurate performance simulations and carbon deposition risk assessments. Via a parametric study, the influence of varying cell operating conditions on the cell performance and carbon deposition risk was quantified when utilizing product gases from steam- and air gasification with varying steam addition. Considering the results from this parameter study and carbon deposition risk assessment, recommendations for promising gasifier-SOFC configurations and cell operating points for stable long-term operation are presented. For smaller-scale biomass-to-power systems, the utilization of product gas from air gasification in anode supported cells with Ni/zirconia-based anode can be recommended, with only moderate steam dilution of the product gas at 750°C cell operating temperature. For larger scales, steam gasification might be meaningful, offering a generally higher electrical efficiency and power output in fuel cells than air gasification. However, a higher risk for carbon deposition could be determined in comparison to air gasification. Hence, a cell temperature of 850°C besides the use of cells with Ni/ceria-based anodes is recommended.

A process demonstration unit for a novel aqueous phase reforming (APR) process was built and scaled up by factor 666. The set-up of this demonstration unit was supported by virtual commissioning using a virtual test bed. By using virtual commissioning, it was possible to speed-up the commissioning and to support stable, reliable and continuous plant operation for 100h.

In various applications in the field of control engineering, the estimation of the state variables of dynamic systems in the presence of unknown inputs plays an important role. Existing methods require the so-called observer matching condition to be satisfied, rely on the boundedness e variables or exhibit an increased observer order of at least twice the plant order. In this article, a novel observer normal form for strongly observable linear time-invariant multivariable systems is proposed. In contrast to classical normal forms, the proposed approach also takes the unknown inputs into account. The proposed observer normal form allows for the straightforward construction of a higher-order sliding mode observer, which ensures global convergence of the estimation error within finite time even in the presence of unknown bounded inputs. Its application is not restricted to systems which satisfy the aforementioned limitations of already existing unknown input observers. The proposed approach can be exploited for the reconstruction of unknown inputs with bounded derivative and robust state-feedback control, which is shown by means of a tutorial example. Numerical simulations confirm the effectiveness of the presented work.

This article presents a new observer design approach for linear time invariant multivariable systems subject to unknown inputs. The design is based on a transformation to the so-called special coordinate basis (SCB). This form reveals important system properties like invertability or the finite and infinite zero structure. Depending on the system's strong observability properties, the SCB allows for a straightforward unknown input observer design utilizing linear or nonlinear observers design techniques. The chosen observer design technique does not only depend on the system properties, but also on the desired convergence behavior of the observer. Hence, the proposed design procedure can be seen as a unifying framework for unknown input observer design.

This paper investigates the optimal planning of microgrids including the hydrogen energy system through mixed-integer linear programming model. A real case study is analyzed by extending the only microgrid lab facility in Austria. The case study considers the hydrogen production via electrolysis, seasonal storage and fueling station for meeting the hydrogen fuel demand of fuel cell vehicles, busses and trucks. The optimization is performed relative to two different reference cases which satisfy the mobility demand by diesel fuel and utility electricity based hydrogen fuel production respectively. The key results indicate that the low emission hydrogen mobility framework is achieved by high share of renewable energy sources and seasonal hydrogen storage in the microgrid. The investment optimization scenarios provide at least 66% and at most 99% carbon emission savings at increased costs of 30% and 100% respectively relative to the costs of the diesel reference case (current situation).

Current barriers to increase the use of bioenergy for different applications are first discussed. Then, recent advances are presented on gasification-based technologies to overcome these barriers that have been reached at TU Graz together with several partners. Gasification-based fuel bed concepts integrated in biomass combustion can significantly reduce emissions for bioheat production. Advances are presented for modern biomass boilers, significantly reducing nitrogen oxides and particle matter emissions as well as increasing the feedstock flexibility; and micro-gasifiers for traditional biomass utilization, significantly reducing the emissions of unburnt products. Gasification-based processes have as well the possibility to score high electrical efficiencies and to synthetize several products as second-generation biofuels. Advances are presented on measures for reducing the presence of contaminants as tars, including the catalytic use of char for tar cracking; and in applications of the producer gas, including gas cleaning and direct coupling with a solid oxide fuel cell to maximize electricity production. © 2021, ETA-Florence Renewable Energies.

The number of large-scale solar thermal installations has increased rapidly in Europe in recent years, with 70 % of these systems operating with flat-plate solar collectors. Since these systems cannot be easily switched on and off but directly depend on the solar radiation, they have to be combined with other technologies or integrated in large energy systems. In order to most efficiently integrate and operate solar systems, it is of great importance to consider their expected energy yield to better schedule heat production, storage and distribution. To do so the availability of accurate forecasting methods for the future solar energy yield are essential. Currently available forecasting methods do not meet three important practical requirements: simple implementation, automatic adaption to seasonal changes and wide applicability. For these reasons, a simple and adaptive forecasting method is presented in this paper, which allows to accurately forecast the solar heat production of flat-plate collector systems considering weather forecasts. The method is based on a modified collector efficiency model where the parameters are continuously redetermined to specifically consider the influence of the time of the day. In order to show the wide applicability the method is extensively tested with measurement data of various flat-plate collector systems covering different applications (below 200 Celsius), sizes and orientations. The results show that the method can forecast the solar yield very accurately with a Mean Absolute Range Normalized Error (MARNE) of about 5 % using real weather forecasts as inputs and outperforms common forecasting methods by being nearly twice as accurate.

Using solid oxide fuel cells in biomass gasification based combined heat and power production is a promising option to increase electrical efficiency of the system. For an economically viable design of gas cleaning units, fuel cell modules and further development of suitable degradation detection methods, information about the behavior of commercially available cell designs during short-term poisoning with H2S can be crucial. This work presents short-term degradation and regeneration analyses of industrial-relevant cell designs with different anode structure and sulfur tolerance fueled with synthetic product gas from wood steam gasification containing 1 to 10 ppmv of H2S at 750°C and 800°C. Full performance regeneration of both cell types was achieved in all operating points. The high H2O content and avoided fuel depletion may have contributed to a lower performance degradation and better regeneration of the cells. A strong influence of the catalytically active anode volume on poisoning and regeneration behavior was quantified, thereby outlining the importance of considering the anode structure besides the sulfur tolerance of the anode material. Hence, cells with less sulfur tolerant anode material but larger anode volume might outperform cells less sensitive to sulfur in the case of an early detection of a gas cleaning malfunction.

Warum ist es sinnvoll, Wärmenetze zu verbinden?

• Erläuterung am Beispiel des Projekts Thermaflex
• Drei Wärmenetze bei Leibnitz in der Steiermark.
• Sind gewachsen und haben die Grenzen ihrer Nachbar-Wärmenetze erreicht.
• Die Wärmenetze werden durch zwei getrennte Eigentümer betrieben.

The current bioenergy uses and conversion technologies as well as future trends for the production of heat, power, fuels and chemicals from biomass are reviewed. The focus is placed in Austria, which is selected due to its high bioenergy utilization, providing 18.4% of the gross energy final consumption in 2017, and its strong industrial and scientific position in the field. The most common bioenergy application in Austria is bioheat with 170 PJ in 2017 mainly obtained from woody biomass combustion, followed by biofuels with 21 PJ and bioelectricity with 17 PJ. Bioheat has a stable market, where Austrian manufacturers of boilers and stoves have a strong position exporting most of their production. Future developments in bioheat production should go in the line of further reducing emissions, increasing feedstock flexibility and coupling with other renewables. For bioelectricity and biofuels, the current framework does not promote the growth of the current main technologies, i.e. combined heat and power (CHP) based on biomass combustion or biogas and first generation biofuels. However, an increase in all bioenergy uses is required to achieve the Austrian plan to be climate neutral in 2040. The current initiatives and future possibilities to achieve this increase are presented and discussed, e.g. mandatory substitution of old oil boilers, production of biomethane and early commercialization of CHP with a high efficiency or demonstration of advanced biofuels production based on gasification.