In this paper a stochastic cellular automata model is examined, which has been developed to study a "small" world, where local changes may noticeably alter global characteristics. This is applied to a climate model, where global temperature is determined by an interplay between atmospheric carbon dioxide and carbon stored by plant life. The latter can be released by forest fires, giving rise to significant changes of global conditions within short time.

A cell agglomeration algorithm is proposed to mitigate the computational cost of incorporating detailed chemical kinetics in multi-dimensional Computational Fluid Dynamics (CFD) simulations. Cells that are close in species and energy composition space are agglomerated before calling the reaction integrator, substantially reducing the number of chemistry integrations. The algorithm is generalized and applicable to any reacting flow configuration, and the accuracy is fully controllable. A dynamic hash table is used to efficiently bin cells into high dimensional hyper-cubes in composition space. The method is applied to four different CFD simulations and the speed-up and incurred error are assessed for a range of agglomeration tolerances and table dimensions. The proposed approach exhibits up to an order of magnitude speed-up with a relatively moderate decrease in accuracy.

A one-dimensional model for the thermal conversion of thermally thick biomass particles is developed for the simulation of the fuel bed of biomass grate furnaces. The model can be applied for cylindrical and spherical particles. The particle is divided into four layers corresponding to the main stages of biomass thermal conversion. The energy and mass conservation equations are solved for each layer. The reactions are assigned to the boundaries. The model can predict the intra-particle temperature gradient, the particle mass loss rate as well as the time-dependent variations of particle size and density, as the most essential features of particle thermal conversion. When simulating the fuel bed of a biomass grate furnace, the particle model has to be numerically efficient. By reducing the number of variables and considering the lowest possible number of grid points inside the particle, a reasonable calculation time of less than 1 min for each particle is achieved. Comparisons between the results predicted by the model and by the measurements have been performed for different particle sizes, shapes and moisture contents during the pyrolysis and combustion in a single-particle reactor. The results of the model are in good agreement with experimental data which implies that the simplifications do not impair the model accuracy.

To optimize the combustion of biomass grate furnaces a sensitivity analysis is carried out by means of CFD simulation. The methodical procedure consists of a 3D packed bed biomass combustion model, which describes the most essential characteristics of the thermal conversion of biomass particles, such as the detailed consideration of drying, pyrolysis and char oxidation in parallel processes. Within the sensitivity analysis the following parameters have been investigated: distribution of false air, residence time of fuel on the grate and distribution of recirculated flue gas and primary air below the grate. To evaluate the influence of the varied parameters on the combustion process the focus lied on the position of the thermal conversion of the biomass and the CO at the outlet of the simulation domain. The results of the sensitivity analysis show a shift of the thermal conversion towards the grate end for increased false air as well as for reduced momentum of primary air/recirculated flue gas mixture. An increase of the fuel residence time leads to a shift of the thermal conversion towards the fuel inlet. Consequently a large region of the primary combustion zone is not used due to earlier release of CO inside the fuel bed.

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.

This paper proposes a novel design algorithm for nonlinear state observers for linear time-invariant systems. The approach is based on a well-known family of homogeneous differentiators and can be regarded as a generalization of Ackermann's formula. The method includes the classical Luenberger observer as well as continuous or discontinuous nonlinear observers, which enable finite time convergence. For strongly observable systems with bounded unknown perturbation at the input the approach also involves the design of a robust higher order sliding mode observer. An inequality condition for robustness in terms of the observer gains is presented. The properties of the proposed observer are also utilized in the reconstruction of the unknown perturbation and robust state-feedback control

Detailed reaction schemes and experimental data for the online release of pyrolysis volatiles are required to gain a more fundamental understanding of biomass pyrolysis, which would in turn allow the process to be controlled in a more precise way and the development of more targeted applications. A detailed online characterisation of pyrolysis products has been conducted in single particle experiments with spruce pellets at different temperatures, obtaining a good closure of the elemental mass balances. The yields and online release of CO, CO₂, H₂O, CH₄, other light hydrocarbons and total organic condensable species, as well as char yield and composition, can be predicted with a reasonable accuracy with the application of a single particle model, coupled with a detailed pyrolysis scheme, and a simple one-step scheme for tar cracking. In order to achieve it, improvements have been conducted in the pyrolysis scheme, mainly concerning the release of light hydrocarbons and char yield and composition. Deviations are still present in the different groups in which organic condensable species can be classified.

Energy management systems aiming for an efficient operation of hybrid energy systems with a high share of different renewable energy sources strongly benefit from short-term forecasts for the heat-load. The forecasting methods available in literature are typically tailor-made, complex and non-adaptive. This work condenses these methods to a generally applicable, simple and adaptive forecasting method for the short-term heat load. From a comprehensive literature review as well as the analysis of measurement data from seven different consumers, varying in size and type, the ambient temperature, the time of the day and the day of the week are deduced to be the most dominating factors influencing the heat load. According to these findings, the forecasting method bases on a linear regression model correlating the heat load with the ambient temperature for each hour of the day, additionally differentiating between working days and weekend days. These models are used to predict the future heat load by using forecasts for the ambient temperature from weather service providers. The model parameters are continuously updated by using historical data for the ambient temperature and the heat load, i.e. the forecasting method is adaptive. Additionally, the current prediction error is used to correct the prediction for the near future. Due to their simplicity, all necessary steps of the forecasting method, the update of the model parameters, the prediction based on linear regression models and the correction, can be implemented and computed with little effort. The final evaluation with measurement data from all seven consumers investigated leads to a Mean Absolute Range Normalized Error (MARNE) of 2.9% on average, and proves the general applicability of the forecasting method. In summary, the forecasting method developed is generally applicable, simple and adaptive, making it suitable for the use in energy management systems aiming for an efficient operation of hybrid energy systems.

The syngas mixture produced from biomass (bio-syngas) is characterized by a H2/CO molar ratio of 1.5 in this work, which is different from that of traditional syngas ratio of 2. Therefore a hybrid of winddiesel technology with bio-syngas conversion by Fischer–Tropsch synthesis (WD-FT) on a cobalt based catalyst was investigated, for the first time, using a slurry reactor. The result from feeding this technology is compared with the direct converting biomass derived synthetic gas to fuels via Fischer–Tropsch synthesis (BS-FT). Experiments were performed at different syngas composition (variation of H2/CO ratio), keeping the other parameters (temperature 230 °C; gas flow 5 Nm³/h, pressure 20 bar) constant. Comparison of the WD-FT with the BS-FT synthesis results displayed mass fraction of light hydrocarbons and higher catalytic stability and activity after 500 h. The olefin structures for the different product distributions, obtained from different reactions, are determined by ¹H NMR spectroscopy. Negligible amounts of iso-α-olefins were detected in the product of the WD-FT reaction. In the case of the alpha value, a slight change was observed between 0.93 and 0.92 for the BS-FT and WD-FT reaction.
 

The continuous increase of (volatile) renewable energy production and the coupling of different energy sectors such as heating, cooling and electricity have significantly increased the complexity of urban energy systems. Such multi-energy systems (MES) can be operated more efficiently with the aid of optimization-based energy management systems (EMS). However, most existing EMS are tailor-made for one specific system or class of systems, i.e. are not generally applicable. Furthermore, only limited information on the actual savings potential of the usage of an EMS under realistic conditions is available. Therefore, this paper presents a novel modular modeling approach for an EMS for urban MES, which also enables the modeling of complex system configurations. To assess the actual savings potential of the proposed EMS, a comprehensive case study was carried out. In the course of this the influence of different user behavior, changing climatic conditions and forecast errors on the savings potential was analyzed by comparing it with a conventional control strategy. The results showed that using the proposed EMS in conjunction with supplementary system components (thermal energy storage and battery) an annual cost savings potential of between 3 and 6% could be achieved.

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.

In this paper, the work on the development and test of a basic design tool for the automatic performance of parameter studies for the optimisation of biomass combustion plants is presented. The model consists of parameterisation and optimisation routines linked with an in-house developed empirical packed bed combustion model as well as gas phase CFD models especially adapted for biomass grate furnaces. To test and verify the routine developed, it has been applied to the optimisation of a 180 kW_th pilot-scale grate furnace. The main focus was on the minimisation of CO emissions and the pressure loss by changing the diameter and angle of the secondary air nozzles. The simulation results show that the time of the optimisation process can be reduced considerably by the automatic routine developed and the evaluation of several independent design parameters is possible. This new procedure forms an important milestone towards automatic CFD-based furnace and boiler optimisations in the future. © 2013 Elsevier Ltd.

The Biomass to Liquid (BtL) Fischer-Tropsch (FT) route converts lignocellulosic feedstock to renewable hydrocarbons. This, paper shows a novel production route for biomass-derived synthetic paraffin wax via gasification of lignocellulosic feedstock, Fischer-Tropsch synthesis (FTS) and hydrofining. The Fischer-Tropsch wax was fractionated, refined and analyzed with respect to compliance to commercial standards. The fractioned paraffin waxes were hydrofined using a commercial sulfide NiMo–Al2O3 catalyst and a trickle bed reactor. A parametric variation was performed to optimize the hydrofining process. It was shown that the produced medium-melt paraffin wax could fulfill the requirements for “Paraffinum solidum” defined by the European Pharmacopoeia (Ph. Eur). The high-melt wax fraction showed potential to be used as food packaging additive. Furthermore, the renewable wax was analyzed regarding PAH content and it was shown that the hydrofined wax was quasi-PAH-free.