A drastic reduction in the consumption of fossil resources and efficient use are key factors in limiting the further progression of climate change. Cascading use and recycling of residues in the sense of bioeconomy and circular economy are essential. Thermochemical or microbiological conversion can produce various intermediates and endproducts (e.g. biochar, basic chemicals, bioenergy) from biogenic residues. Implemented decentrally, such concepts can reduce transportation efforts, increase the degree of self-sufficiency with raw materials, increase regional added value creation and close (preferably regional) material and energy cycles.

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.

The Alpine Region is characterized by a high density of biomass processing and conversion plants. Alps4GreenC sets the scene for transnational utilization of biomass residues in biochar-based value chains. The project aims at:

• Researching opportunities for conversion of biomass residues with focus on biochar production.
• Increasing awareness of citizens, plant owners, policy makers and all involved stakeholders.
• Establishing connection and coordination among Austria, Italy and Slovenia.

Anaerobic acidification of pressed sugar beet pulp (PSBP) is a promising strategy for the transition towards a circular economy. In this work, volatile fatty acids were produced by anaerobic acidification of PSBP and subsequently converted to mcl-polyhydroxyalkanoates. The results point to mesophilic acidification as superior to thermophilic one. At the same time, the pH regulated at the value of 6.0 showed a decisive advantage over both the pH of 7.0 and the lack of pH regulation. Furthermore, the conditions with a hydraulic retention time (HRT) of 10 days significantly outperformed those with an HRT of 6 days. The best-performing process (mesophilic, pH controlled at 6, HRT of 10 days) was successfully scaled up to a 250 L reactor, reaching a volatile fatty acid (VFA) concentration of up to 27.8 g L-1. Finally, the produced VFA were investigated as feedstock for mcl-PHA producers, Pseudomonas citronellolis and Pseudomonas putida. Both strains grew and produced PHA successfully, with P. citronellolis reaching a biomass of 15.6 g L-1 with 38% of mcl-PHA, while P. putida grew to 15.2 g L-1 with a polymer content of 31%. This study proves that acidified PSBP is a valuable feedstock for mcl-PHA production and an important approach to developing biorefineries.

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.

Over the last decades the general interest in recycling and upcycling technologies heavily grew and in the agricultural sector, it is not different. Lal estimated already in 2005 that 3,8 billion tons of crop residues alone are produced annually
worldwide.

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.

Aqueous phase reforming (APR) describes the conversion of oxygenated hydrocarbons dissolved in
an aqueous phase to hydrogen and carbon dioxide.

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.

K-feldspar was utilized as bed material for fluidized bed combustion of bark, chicken manure, and their mixture. Bed samples were extracted after 4 and 8 h and the samples were analyzed with scanning electron microscopy to study the impact of P-rich chicken manure on the bed material. The results were compared to fixed bed exposures with different orthophosphates to investigate their influence in detail.

The fresh bed material used for this study exhibited an uneven surface with many cavities which facilitated the deposition and retention of the fuel ash. Utilizing pure chicken manure as fuel led to the formation of Ca- and P-rich particles which accumulated in these cavities. At the same time, larger ash particles were formed which consisted of the elements found in chicken manure ash. The co-combustion of bark and chicken manure led to the interaction of the two ash fractions and the formation of a thicker ash layer, which consisted of elements from both fuel ashes, namely Ca, P, Si, K and S. The layer appeared to be partially molten which could be favorable for the deposition of ash particles and thereby the formation of a mixed Ca/K-phosphate. Fixed bed exposures of the K-feldspar particles with Na3PO4 or K3PO4 caused particle agglomeration which means presence of alkali-phosphates should be limited.

The co-combustion of bark with chicken manure showed promising results both regarding a shift from Ca-phosphates to more bioavailable Ca/K-phosphates and an acceleration in ash layer formation. The formation of an ash layer after only 4 h of exposure with the mixture of bark and chicken manure could be advantageous for catalytic activation of the bed material.

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.

Many processes in environmental biotechnology are working due to the presence of a mix of microbes, with each group playing a specific role, like being responsible for one step of a multistage conversion process. Even in industrial fermentations which have the purpose of producing biomass of one specific microorganism, an accompanying flora of other microbes is almost always present.

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%.

This work presents the first results of a newly commissioned biomass-to-liquid Fischer-Tropsch (FT) pilot plant. A 1 MWth dualfluidized bed (DFB) steam gasifier, a 55 Nm3/h 4-step gas cleaning plant and a 250 kW slurry bubble column FT synthesis reactor (SBCR) form the full process chain.

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).

Simple biorefinery concepts for the production of sustainable carbon products are investigated in the GreenCarbon Lab at the Wieselburg site of BEST. The heart of the GreenCarbon Lab consists of two pyrolysis units: A lab-scale reactor for testing new input materials as well as conducting detailed parameter studies to reveal the correlation of input material, process conditions and products formed, and a pilot-scale to implement and validate knowledge gained in the laboratory environment to
produce specific GreenCarbon products. Also, product batches in larger quantities (approx. 0,1 – 5 tons) can be manufactured for subsequent application tests – e.g. as part of industrial trials at company partners. In addition, equipment for process and product analysis enables a detailed study of the conversion reactions and the characterization of the products obtained.

Bioenergy is the largest source of renewable energy today. It is versatile and can provide heat, power and transport services, and biomass can also serve as a raw material for the production of chemicals and materials. If done responsibly, and wherever it substitutes for fossil fuels, bioenergy provides substantial GHG emission savings, diversifies energy sources, improves energy supply security and provides income through regional biomass supply chains.

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.

A better understanding of end users’ motives for choosing their energy supply system (heating and domestic hot water, cooling and electricity) can support the establishment of favorable conditions for the energy transition. In this research project, a survey was conducted in the Austrian residential sector to identify end users’ interests and decisions for certain energy supply systems as well as motives for the choice. Based on 169 responses to the questionnaire, a statistical analysis was performed to evaluate the influence of gender aspects on interests and decisions. More than 90% of respondents required robust and efficient energy supply systems, which should have the highest technical standards. The environmental performance was also highly valued, whereas financial aspects, including investment costs were considered less important. 79% of men were mainly involved in the decision-making process, whereas only 59% of women were involved and, in most cases, made the decision together with their partner (52%). Identifying these motives and analyzing investment decisions enables the future integration of social and gender aspects into optimization models for individual households or energy communities.

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.

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 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.

One promising technology in the field of residue valorization is the pyrolytic conversion of biomass to biochar. There are a lot of proven technologies for this task, with many of them being quite distinctive. Biochar has a lot of valuable properties and it shows potential to be applicated in many different fields of industry as a green carbon resource. Thus, as the demand for its production rises, more and more people from different fields share interest in the same technologies and the demand for guidance in form of readily available information increases. Two prominent technologies rather similar in appearance are rotary kilns and screw reactors. Both technologies consist of a long, hollow cylinder and both technologies use some form of longitudinal rotation as a means to transport feedstock. In this review, both technologies are described and their biggest differences and similarities are discussed, all under the aspect of biochar production. In total, 21 unique rotary kilns and 58 unique auger reactors were identified. The paper addresses process specific aspects, like heat supply or residence time, but it also gives an overview on current research and general aspects like scale-up considerations. Differences between both technologies were found in all of these aspects, with some of the most pronounced being the bigger maximum capacities and the greater residence time distributions in rotary kiln pyrolysis. Both technologies are viable candidates for producing biochar on a commercial level, however, literature comparing the influence of the reactor type on biochar properties was very scarce. As a future outlook it is recommended to produce data that can be compared on a quantitative level, so a more accurate assessment of each technologies up- and downsides can be made.

Steam gasification in a dual fluidized bed (DFB) reactor has already been developed in the power sector from lab- to commercial-scale for woody biomass as feedstock. A trend towards utilizing feedstock of lower quality, such as low-grade biomass, biogenic residues or waste drives the development of the technology in terms of reactor design, gas cleaning and optimizing operation parameters. Additionally, the need for production of sustainable end products more valuable than electricity and heat leads to the embedding of DFB gasification into complete process chains.

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.

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.

Global hydrogen production is currently still based almost exclusively on fossil resources. A sustainable
hydrogen industry must be based on sustainable, renewable energy sources and resources.

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.

The recovery of phosphorus (P) from sewage sludge ashes has been the focus of recent research due to the initiatives for the use of biogenic resources and resource recovery. This study investigates the ash transformation chemistry of P in sewage sludge ash during the co-gasification with the K-Si- and K-rich agricultural residues wheat straw and sunflower husks, respectively, at temperatures relevant for fluidized bed technology, namely 800 °C and 950 °C. The residual ash was analyzed by ICP­AES, SEM/EDS, and XRD, and the results were compared to results of thermochemical equilibrium calculations. More than 90% of P and K in the fuels were retained in the residual ash fraction, and significant interaction phenomena occurred between the P-rich sewage sludge and the K-rich ash fractions. Around 45–65% of P was incorporated in crystalline K-bearing phosphates, i.e., K-whitlockite and CaKPO4, in the residual ashes with 85–90 wt% agricultural residue in the fuel mixture. In residual ashes of sewage sludge and mixtures with 60–70 wt% agricultural residue, P was mainly found in Ca(Mg,Fe)-whitlockites and AlPO4. Up to about 40% of P was in amorphous or unidentified phases. The results show that gasification provides a potential for the formation of K-bearing phosphates similar to combustion processes.

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)

A better understanding of the underlying motives of consumers considering a new RESS (heating,
cooling and electricity) can contribute to create favorable conditions for an energy transition.
Therefore, the main objectives of this research project are to:
▪ Identify motives of consumers interested in ordeciding for a certain RESS
▪ Assess the impact of gender and intersectingaspects, such as age, income and education on these motives.

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.

The cyanobacterial genus Synechocystis is of particular interest to science and industry because of its efficient phototrophic metabolism, its accumulation of the polymer poly(3-hydroxybutyrate) (PHB) and its ability to withstand or adapt to adverse growing conditions. One such condition is the increased salinity that can be caused by recycled or brackish water used in cultivation. While overall reduced growth is expected in response to salt stress, other metabolic responses relevant to the efficiency of phototrophic production of biomass or PHB (or both) have been experimentally observed in three Synechocystis strains at stepwise increasing salt concentrations. In response to recent reports on metabolic strategies to increase stress tolerance of heterotrophic and phototrophic bacteria, we focused particularly on the stress-induced response of Synechocystis strains in terms of PHB, glycogen and photoactive pigment dynamics. Of the three strains studied, the strain Synechocystis cf. salina CCALA192 proved to be the most tolerant to salt stress. In addition, this strain showed the highest PHB accumulation. All the three strains accumulated more PHB with increasing salinity, to the point where their photosystems were strongly inhibited and they could no longer produce enough energy to synthesize more PHB.

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.

Bed materials and their catalytic activity are two main parameters that affect the performance of the dual fluidized bed (DFB) gasification system in terms of product gas composition and tar levels. Two sources of bed materials were used for the operation of a commercial DFB gasification system in Thailand, using woodchips as a biomass feedstock. One source of the bed materials was the calcined olivine which had been used in the Gussing Plant, Austria, and the other activated bed material was a mixture of fresh Chinese olivine and used Austrian olivine with additives of biomass ash, calcium hydroxide and dolomite. These bed materials were collected and analysed for morphological and chemical composition using a scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS) and X-ray fluorescence spectroscopy (XRF). The product gas was cleaned in a scrubber to remove tars, from which the samples were collected for gravimetric tar analysis. Its composition data was automatically recorded at the operation site before it entered the gas engine. From the SEM, EDS and XRF analyses, calcium-rich layers around the bed materials were observed on the activated bed material. The inner layers of bed materials collected were homogeneous. Biomass ash, which was generally added to the bed materials, had significant calcium and potassium content. These calcium-rich layers of the bed materials, from the calcium hydroxide, biomass ash and dolomite, influenced system performance, which was determined by observing lower tar concentration and higher hydrogen concentration in the product gas.

„Welche Heizung passt zu mir/uns? Vielleicht auch Kühlen oder selber Strom erzeugen?“ – Diese
Fragen stellen sich oft, wenn der Traum von den eigenen vier Wänden näher rückt. Egal ob bei Neubau
oder Sanierung, die Wahl eines passenden Energiebereitstellungssystems (EBS) sollte wohl überlegt
sein. Neben den technologischen Fragen sind es auch persönliche Motive, welche die Entscheidung
der Nutzer*innen beeinflussen.

A novel biomass combustion technology was investigated that operates at a low oxygen content under fixed-bed and double air staging conditions. This technology was used to achieve extremely low NO_X and particle matter emissions in a 30 kW lab-scale reactor, displaying high fuel flexibility and no slagging. In this experimental work, the aim was to minimize inorganic particulate matter emissions, this aim was achieved by enabling the very low release of inorganics such as K from the fixed bed, which operates like a compact updraft gasifier. The elemental composition of the employed fuels, emitted dust particles, and fuel particle samples taken at three different heights within the fixed bed, and the bed temperatures were measured. The main objective in this study was to determine and understand the different processes of inorganic matter release that take place within the compact fixed bed. The results show that 98% and 99.7% of the K could be retained in the fixed bed for wood chips and miscanthus pellets, respectively, thus minimizing the particulate matter emissions. Different processes in the context of K release within the fixed bed could be identified for silica rich/agricultural and calcium rich / woody fuels, respectively and inconsistencies in the literature on these mechanisms could be resolved. In the case of miscanthus pellets, K is retained in silicates, and no accumulation of K, Cl and S occurs in the fixed bed above. In the case of wood chips, on the other hand, there is an unexpected K accumulation in the fixed bed, which is due to the release of K in the hot oxidation zone and the subsequent formation of large amounts of K chlorides and sulfates by condensation in the cooler upper region. Furthermore, for woody fuels, bounding or intercalation of K into the char matrix plays a more important role than the formation of carbonates in avoiding K release from the bed.

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.

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.

Bed material particle layer formation plays a significant role in thermo-chemical conversion of biomass. The interaction between biomass ash and bed material in fluidized bed conversion processes has been described for a variety of different applications and spans from fundamental research of formation mechanisms to effects of this layer formation on long-term operation in industrial-scale. This review describes the current state of the research regarding the mechanisms underlying layer formation and the positive influence of bed material particle layer formation on the operation of thermo-chemical conversion processes. Thus, the main focus lies on its effect on the catalytic activity towards gasification reactions and the impact on oxygen transport in chemical looping combustion. The review focuses on the most commonly investigated bed materials, such as quartz, feldspar or olivine. While the most relevant results for both the underlying mechanisms and the subsequently observed effects on the operation are presented and discussed, knowledge gaps where further research is necessary are identified and described.

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.