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.

Oxygen carrier aided combustion (OCAC) is a novel technology that aims to enhance combustion of heterogenous fuels by replacing the inert bed material with an active oxygen carrier. One of the promising oxygen carriers is natural ilmenite which shows decent oxygen transport capacity and mechanical stability under OCAC operating conditions. However, interactions between ilmenite and woody biomass ash lead to the formation of a calcium-rich ash layer, which affects the ability of the oxygen carrier (OC) to transfer oxygen throughout the boiler and subsequently decreases the combustion efficiency. This paper focuses on the time-dependent morphological and compositional changes in ilmenite bed particles and the consequence effects on the oxygen transport capacity and reactivity of ilmenite. Ilmenite utilized in this study was investigated in a 5 kW bubbling fluidized bed combustion unit, utilizing ash-rich bark pellets as fuel. A negative effect of iron migration on the oxygen transport capacity was observed in ilmenite bed particles after 6 h of operation in the bubbling fluidized bed reactor. The decrease in the oxygen transport capacity of ilmenite was found to correlate with the increased exposure time in the fluidized bed reactor and was caused by the migration and subsequent erosion of Fe from the ilmenite particles. On the other hand, the older bed particles show an increase in reaction rate, presumably due to the catalytic activity of the calcium-enriched outer layer on the bed particle surface.

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.

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



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



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

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

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

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

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

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

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

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

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.

Bioeconomy strategies promote higher shares of biomass products in material and energy sectors. Deploying by-products from sawmills is therefore of major interest. This study aims at analyzing market characteristics and implications of bioeconomy strategies by combining three methods: First, an econometric supply and demand model for sawmill by-products (SBP) was estimated based on data from 2001 to 2020. Second, the model was used to analyze a reference and a bioeconomy scenario. Third, a use case was analyzed dealing with the integration of wood gasification and BioSNG (Synthetic Natural Gas) production into Austrian flows of SBP. The results indicate that SBP supply reacts unit-elastic to sawnwood exports, while both supply and demand respond inelastic to SBP prices. Demand is positively inelastic related to SBP as input in panel and pellet production. In a bioeconomy scenario, long-term supply would exceed demand, resulting in additional SBP to be used for gasification. A 100 MW BioSNG plant converting these SBP could provide 528 MWh BioSNG per year. This is a 11 % share of the Austrian target value of 5 TWh green gas.

In this work, the fate of the ash-forming elements during bubbling fluidized bed combustion and gasification of P-rich sewage sludge (SS) and mixtures with either Si-K-rich wheat straw (WS) or K-Ca-rich sunflower husks (SH) were investigated. The focus of the study was assessing the feasibility of using fuel blends in fluidized bed systems and potential P recovery from the resulting ashes. The used fuels were pure SS and mixtures including 90 wt.% WS (WSS) and 85 wt.% SH (SHS). The analyzed operating conditions were combustion (930–960 °C, λ: 1.2–1.5) and gasification (780–810 °C, λ: 0.4–0.7) in a 5 kW bench-scale reactor. Residual ash and char fractions were collected from different parts of the 5 kW bubbling fluidized bed (bed, cyclone, filter) and analyzed by CHN, SEM/EDS, XRD, and ICP-AES.

The conversion of the fuel mixtures achieved a steady state under the used process conditions except for the combustion of WSS, which led to the formation of large bed agglomerates with the bed material. The morphology of ash samples after combustion showed that SS fuel pellets mostly maintained their integrity during the experiment. In contrast, the ash and char particles from fuel mixtures were fragmented, and larger quantities were found in the cyclone, the filter, or on interior reactor surfaces. The fate of P was dominated by crystalline Ca-dominated whitlockites in all ash fractions, partially including K for the fuel mixtures SHS and WSS. 76–81 % of ingoing P was found in the bed residue after combustion and gasification of the SS-fuel. After conversion of the fuel mixtures SHS and WSS, the share was lower at 22–48 %, with larger shares of P in the entrained fractions (25–34 %). The quantity of identified crystalline compounds was lower after gasification than combustion, likely due to the limited interaction of ash-forming elements in the residual CHN matrix. Altogether, the results show that fuel mixtures of sewage sludge with agricultural residues could expand the fuel feedstock and enable P recovery. This may be used in the fuel and process design of upscaled fluidized bed processes or systems employing both combustion and gasification.

Solar thermal plants have proven to be a successful player in providing heat for district heating networks. However, to ensure the efficient operation of such plants and to achieve optimal coordination with other heat generation units, thorough monitoring, quality control, and system control are required. These tasks strongly depend on accessible and reliable measurement data, which are often unavailable.

Thus, this report focuses on efficient data gathering, storage, distribution, and validation, covering data
management topics- from sensor selection to permanent data storage. The report is mainly targeted at system designers and plant operators, aiming to provide checklists and recommendations on these topics.

The report considers a general solar district heating plant, as depicted by IEA SHC Task 55  – including a collector field, heat storage, and heating center (including a biomass boiler, heat pump, and other auxiliary heating) up to the interface to the district heating network. The topics are described on a summary level of detail while referring the reader to individual articles in case more information is needed. In addition, research groups may use this report to get an overview of data management in the solar-thermal field and identify related work.

The work consists of five sections: The Required Data section lists recommended measurements and discusses meta information required to interpret the data successfully. The Data Gathering section provides recommendations for data logging – e.g., sampling rate, encoding, and formatting. The Data-Distribution section shows proven examples of architectures for collecting and distributing data. Furthermore, the Data Storage section describes what data storage technologies (e.g., CSV files or relational databases) are currently used. The section also discusses the experiences, advantages, and challenges of the respective technologies. Finally, the Data Validation section lists common data-validation procedures that can be applied to solar-thermal data and links to open-source implementations where available.

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

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

First experiments with biogenic residues and a plastic-rich rejects and woody biomass blend were conducted in an advanced 1 MW dual fluidized bed steam gasification demonstration plant at the Syngas Platform Vienna. Wood chips, bark, forest residues, and the plastic-rich rejects and woody biomass blend were tested and the tar composition was analyzed upstream and downstream of the upper gasification reactor, which is designed as a high-temperature column with countercurrent flow of catalytic material. Each feedstock was gasified with olivine as bed material in demonstration scale and is compared to the gasification of softwood pellets with olivine and limestone in pilot scale. A reduction in tar content was observed after countercurrent column for all feedstocks. However, a shift in tar species occurred. While styrene, phenol, and 1H-indene were predominant upstream, naphthalene and polycyclic aromatic hydrocarbons (PAHs) were the prevailing tar species downstream the countercurrent column. Hence, an increase of i.e. anthracene, fluoranthene, and pyrene from the upstream concentration was observed. For pyrene, up to twice the initial concentration was measured. This recombination to PAHs was observed for all feedstocks in demonstration- and pilot-scale. The only exception occurred with limestone as bed material, characterized by a higher catalytic activity in comparison to the typically used olivine. In the perspective of the integrated product gas cleaning, tar with higher temperature of condensation are separated more efficiently in the installed scrubbing unit. Hence, the recombination facilitates an overall decline of tar content after the gas cleaning.