In the context of SHIP (solar heat for industrial processes) installations, solar preheating appli-cations are becoming increasingly popular for reducing fuel consumption and consequently the carbon dioxide emissions. The poster shows an algorithm, which automatically adapts the
temperature setpoint. 

Cyanobacteria are a large group of prokaryotic microalgae that are able to grow photo-autotrophically by utilizing sunlight and by assimilating carbon dioxide to build new biomass. One of the most interesting among many cyanobacteria cell components is the storage biopolymer polyhydroxybutyrate (PHB), a member of the group of polyhydroxyalkanoates (PHA). Cyanobacteria occur in almost all habitats, ranging from freshwater to saltwater, freely drifting or adhered to solid surfaces or growing in the porewater of soil, they appear in meltwater of glaciers as well as in hot springs and can handle even high salinities and nutrient imbalances. The broad range of habitat conditions makes them interesting for biotechnological production in facilities located in such climate zones with the expectation of using the best adapted organisms in low-tech bioreactors instead of using “universal” strains, which require high technical effort to adapt the production conditions to the organism‘s need. These were the prerequisites for why and how we searched for locally adapted cyanobacteria in different habitats. Our manuscript provides insight to the sites we sampled, how we isolated and enriched, identified (morphology, 16S rDNA), tested (growth, PHB accumulation) and purified (physical and biochemical purification methods) promising PHB-producing cyanobacteria that can be used as robust production strains. Finally, we provide a guideline about how we managed to find potential production strains and prepared others for basic metabolism studies.

Solid oxide fuel cells are a promising alternative to gas engines for combined heat and power production based on biomass gasification. The technical complexity of realizing gasifier – fuel cell couplings has limited the number of experiments conducted in the past. However, results from such experiments are of high importance for the evaluation of tar thresholds and operating conditions ensuring a stable operation of fuel cells. For the first time, it was possible to demonstrate for dozens of hours the operation of solid oxide fuel cells with real product gas from steam gasification with a steam-to-carbon ratio of 2 and a typical tar content for fluidized bed gasification. Four coupling experiments with industrial-relevant cell designs were conducted, demonstrating a stable operation for 30 h without structural degradation of the anodes for cells with nickel/ceria- and nickel/zirconia-based anodes at 800°C and 850°C, if heavy tars were partially removed (2.8–3.7 g·Nm−3 gravimetric tars). Raw gas operation (4.6–4.8 g·Nm−3 gravimetric tars) led to metal dusting effects on nickel contact meshes and nickel/zirconia-based anodes, whereas nickel/ceria-based anodes were less affected. Carbon deposited on the alumina support in all experiments whereby a change from pyrolytic to graphitic structure could be observed when increasing the temperature from 800°C to 850°C, thus significantly reducing the risk for blockages in the flow channels. Moreover, high tar and benzene conversion rates were observed. Concluding, operating temperatures of 850°C and the removal only of heavy tars can enable stable long-term operation with a tar-laden steam gasifier product gas, even without increasing the steam-to-carbon ratio to values exceeding two.

Biochar is attracting attention as an alternative carbon/fuel source to coal in the process industry and energy sector. However, it is prone to self-heating and often leads to spontaneous ignition and thermal runaway during storage, resulting in production loss and health risks. This study investigates biochar self-heating upon its contact with O2 at low temperatures, i.e., 50–300 °C. First, kinetic parameters of O2 adsorption and CO2 release were measured in a thermogravimetric analyzer using biochar produced from a pilot-scale pyrolysis process. Then, specific heat capacity and heat of reactions were measured in a differential scanning calorimeter. Finally, a one-dimensional transient model was developed to simulate self-heating in containers and gain insight into the influences of major parameters. The model showed a good agreement with experimental measurement in a closed metal container. It was observed that char temperature slowly increased from the initial temperature due to heat released during O2 adsorption. Thermal runaway, i.e., self-ignition, was observed in some cases even at the initial biochar temperature of ca. 200 °C. However, if O2 is not permeable through the container materials, the temperature starts decreasing after the consumption of O2 in the container. The simulation model was also applied to examine important factors related to self-heating. The results suggested that self-heating can be somewhat mitigated by decreasing the void fraction, reducing storage volume, and lowering the initial char temperature. This study demonstrated a robust way to estimate the cooling demands required in the biochar production process.

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.

Woody biomass is a raw material and cost factor for a range of industries in Austria. The aim of this article is to assess impacts of price developments on operating costs of particleboard, combined heat and power (CHP) and synthetic natural gas (BioSNG) production. Three price scenarios have been developed for pulpwood, industrial wood chips and forest wood chips for the period 2021 - 2026. Results show that the share of raw material costs on total operating costs ranges between 24 - 64% for particleboard, 45 - 82% for CHP, and 24 - 63% for BioSNG production

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.

Compared to extensive studies on affecting parameters in sulfur removal with ZnO adsorbents from coal gasification syngas, similar studies conducted for biomass gasification syngas (BGS) are quite rare. Thus, considering the BGSs with high water content, this study was performed to investigate the effect of H2O presence in syngas on sulfur removal capacity (SRC) of ZnO adsorbents. Initially, the effect of gas composition and temperature on SRC in binary gas mixture was investigated. While H2O decreased the SRC, as expected, the highest reduction in the capacity occurred in the CO–H2S gas mixture due to observed COS formation. Second, the SRCs and resulting COS formation were compared for synthetic syngas mixtures having different water contents and for different amounts of adsorbents. Finally, the separate and combined effects of temperature and H2O on SRC and COS formation in synthetic syngas were investigated by comparing SRCs of typical syngas under wet and dry conditions. The results showed that increasing the amount of adsorbent and temperature results in higher SRC due to a reduction in COS formation through the reactions of COS with H2 and H2O. This indicates that it is critical to control the residence time of syngas and temperature to reduce COS formation during ZnO adsorption.

A major challenge in the combustion of biomass fuels is the heterogeneity of ash-forming elements, which may cause a wide range of ash-related problems. Understanding the melting tendency of the coarse ash fractions is necessary to mitigate agglomeration and slagging. This work aims to evaluate the melting tendency of the K-Ca-Mg-Si-P-O system by use of thermodynamic equilibrium calculations. The formation of condensed phases were systematically assessed in a combustion atmosphere, varying temperatures, and composition. Compositional ranges were based on fuel ash data extracted from the Phyllis 2 database. The speciation and degree of polymerization of phosphates, silicates, and melts were evaluated and indicated a systematic variation in composition. The melt fraction was predicted as a function of temperature and composition. The melting tendency was modeled for three systems, i.e., a P-dominated, a Si-dominated, and a mixed Si-P system. Four ratios between K2O, CaO, MgO, SiO2, and P2O5 were found to have a large effect on the melting tendency of the ash mixtures: the ratio between network formers (SiO2, P2O5), K2O to total network modifiers, CaO to CaO + MgO, and the ratio of network formers to total ash oxides. This modeling approach showed qualitative agreement with ash-related issues seen in previous lab-scale experiments in bubbling fluidized bed and fixed bed combustion. Practical implications of the results are discussed from the perspective of fuel design with the aim of preventing ash-related problems. This study presents a novel method of applying thermodynamic equilibrium calculations for a broad range of compositions and shows potential for predicting ash-related issues related to the melting of coarse ash fractions.

Acetic acid is an essential industrial building block and can be produced by acetogenic bacteria from molecular hydrogen (H2) and carbon dioxide (CO2). When gasses are supplied as substrates, bioreactor design plays an important role for their availability. Trickle-bed bioreactors (TBs) have an enhanced gas-to-liquid mass transfer and cells remain in the system by forming a biofilm on the carriers. So far, TBs have been investigated extensively for bio-methanation processes, whereas studies for their use in acetic acid production are rare. In this study, we evaluated the reproducibility of two parallel TBs for acetic acid production from H2:CO2 (= 70:30) by a mixed culture with a gas flow rate of 3.8 mL min−1 and a medium flow rate of 10 mL min−1. Additionally, the effect of glucose addition during the starting phase on the resulting products and microbial composition was investigated by setting up a third TB2. Partial medium exchanges to decrease the internal acetic acid concentration (AAC) combined with recycling of withdrawn cells had a positive impact on acetic acid production rates with maxima of around 1 g L−1 d−1 even at high AACs of 19–25 g L−1. Initial glucose addition resulted in the accumulation of unwanted butyric acid up to concentrations of 2.60 ± 0.64 g L−1. The maximum AAC of 40.84 g L−1 was obtained without initial glucose addition. The main families identified in the acetogenic TBs were Peptococcaceae, Ruminococcaceae, Planococcaceae, Enterobacteriaceae, Clostridiaceae, Lachnospiraceae, Dysgonomonadaceae and Tannerellaceae. We conclude that a TB is a viable solution for conversion of H2/CO2 to acetate using an anaerobic enrichment culture.

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.

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)

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.

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%.

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

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.

Firewood stoves are widespread and popular for renewable heat supply in Europe. Several new technological measures have been developed recently that aim at improving the appliance performance in terms of emissions and efficiency. In order to support the trend towards “(nearly) zero-emissions technologies”, the objective of this study was to provide a profound overview of the most relevant technical primary and secondary measures for emission reduction and to analyze their functionality, the relevant framework conditions for their application and their costs. Since user behavior is essential for emission and efficiency performance, the state of knowledge about user behavior is summarized and the latest measures for its optimization are evaluated as non-technical primary measures. Primary and secondary measures were analyzed separately, but also potentially promising combinations of primary and secondary optimization were evaluated using SWOT analysis. The results showed that complementary application of primary and secondary measures will be necessary in order to achieve “(nearly) zero-emission technologies”. The paper is useful for manufacturers and provides them with guidance and recommendations for future developments. They can specifically select appropriate measures for their products and applications not only based on technical aspects, but also with a strong focus on user behavior and user comfort.

Chemical Looping Combustion (CLC) is a highly efficient CO2 separation technology with no direct contact between combustion air and fuel. A metal oxide is used as oxygen carrier (OC) in a dual fluidized bed to generate clean CO2. The use of solid fuels, especially biomass, is the focus of current research, because of the possibility of “negative” CO2-emissions. The OC is a key component, because it must meet special requirements for solid fuels, which are different to gaseous fuels. Most frequently naturals ores or synthetic materials are used as OC. Synthetic OC are characterised by higher reactivity at the expense of higher costs. For this reason, so far not so many experiments have been conducted on a larger scale with synthetic OC on solid CLC. This work deals with the synthetic perovskite C28 and investigating the suitability as oxygen carrier in an 80 kWth pilot plant for chemical looping combustion with biogenic fuels. The experiments show a significantly increased combustion efficiency of 99.6 % compared to natural ores and a major influence of the solid circulation rate on general performance, whereby carbon capture rates up to 98.3 % were reached. Furthermore, the role of the fuel reactor's counter-current flow column and its impact on better gas conversion was investigated. C28 suffered no deactivation or degradation over the experimental time, but first traces of ash layer formation, phase shifting and attrition of fines could be detected. The focus of further research should lie on long-term stability and reactivity for their high impact on the economic scale up of C28.