2 resultados para Anaerobic sequential batch reactor

em Universitätsbibliothek Kassel, Universität Kassel, Germany


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Energy production from biomass and the conservation of ecologically valuable grassland habitats are two important issues of agriculture today. The combination of a bioenergy production, which minimises environmental impacts and competition with food production for land with a conversion of semi-natural grasslands through new utilization alternatives for the biomass, led to the development of the IFBB process. Its basic principle is the separation of biomass into a liquid fraction (press fluid, PF) for the production of electric and thermal energy after anaerobic digestion to biogas and a solid fraction (press cake, PC) for the production of thermal energy through combustion. This study was undertaken to explore mass and energy flows as well as quality aspects of energy carriers within the IFBB process and determine their dependency on biomass-related and technical parameters. Two experiments were conducted, in which biomass from semi-natural grassland was conserved as silage and subjected to a hydrothermal conditioning and a subsequent mechanical dehydration with a screw press. Methane yield of the PF and the untreated silage was determined in anaerobic digestion experiments in batch fermenters at 37°C with a fermentation time of 13-15 and 27-35 days for the PF and the silage, respectively. Concentrations of dry matter (DM), ash, crude protein (CP), crude fibre (CF), ether extract (EE), neutral detergent fibre (NDF), acid detergent fibre (ADF), acid detergent ligning (ADL) and elements (K, Mg, Ca, Cl, N, S, P, C, H, N) were determined in the untreated biomass and the PC. Higher heating value (HHV) and ash softening temperature (AST) were calculated based on elemental concentration. Chemical composition of the PF and mass flows of all plant compounds into the PF were calculated. In the first experiment, biomass from five different semi-natural grassland swards (Arrhenaterion I and II, Caricion fuscae, Filipendulion ulmariae, Polygono-Trisetion) was harvested at one late sampling (19 July or 31 August) and ensiled. Each silage was subjected to three different temperature treatments (5°C, 60°C, 80°C) during hydrothermal conditioning. Based on observed methane yields and HHV as energy output parameters as well as literature-based and observed energy input parameters, energy and green house gas (GHG) balances were calculated for IFBB and two reference conversion processes, whole-crop digestion of untreated silage (WCD) and combustion of hay (CH). In the second experiment, biomass from one single semi-natural grassland sward (Arrhenaterion) was harvested at eight consecutive dates (27/04, 02/05, 09/05, 16/05, 24/05, 31/05, 11/06, 21/06) and ensiled. Each silage was subjected to six different treatments (no hydrothermal conditioning and hydrothermal conditioning at 10°C, 30°C, 50°C, 70°C, 90°C). Energy balance was calculated for IFBB and WCD. Multiple regression models were developed to predict mass flows, concentrations of elements in the PC, concentration of organic compounds in the PF and energy conversion efficiency of the IFBB process from temperature of hydrothermal conditioning as well as NDF and DM concentration in the silage. Results showed a relative reduction of ash and all elements detrimental for combustion in the PC compared to the untreated biomass of 20-90%. Reduction was highest for K and Cl and lowest for N. HHV of PC and untreated biomass were in a comparable range (17.8-19.5 MJ kg-1 DM), but AST of PC was higher (1156-1254°C). Methane yields of PF were higher compared to those of WCD when the biomass was harvested late (end of May and later) and in a comparable range when the biomass was harvested early and ranged from 332 to 458 LN kg-1 VS. Regarding energy and GHG balances, IFBB, with a net energy yield of 11.9-14.1 MWh ha-1, a conversion efficiency of 0.43-0.51, and GHG mitigation of 3.6-4.4 t CO2eq ha-1, performed better than WCD, but worse than CH. WCD produces thermal and electric energy with low efficiency, CH produces only thermal energy with a low quality solid fuel with high efficiency, IFBB produces thermal and electric energy with a solid fuel of high quality with medium efficiency. Regression models were able to predict target parameters with high accuracy (R2=0.70-0.99). The influence of increasing temperature of hydrothermal conditioning was an increase of mass flows, a decrease of element concentrations in the PC and a differing effect on energy conversion efficiency. The influence of increasing NDF concentration of the silage was a differing effect on mass flows, a decrease of element concentrations in the PC and an increase of energy conversion efficiency. The influence of increasing DM concentration of the silage was a decrease of mass flows, an increase of element concentrations in the PC and an increase of energy conversion efficiency. Based on the models an optimised IFBB process would be obtained with a medium temperature of hydrothermal conditioning (50°C), high NDF concentrations in the silage and medium DM concentrations of the silage.

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In dieser Arbeit wird ein Verfahren zum Einsatz neuronaler Netzwerke vorgestellt, das auf iterative Weise Klassifikation und Prognoseschritte mit dem Ziel kombiniert, bessere Ergebnisse der Prognose im Vergleich zu einer einmaligen hintereinander Ausführung dieser Schritte zu erreichen. Dieses Verfahren wird am Beispiel der Prognose der Windstromerzeugung abhängig von der Wettersituation erörtert. Eine Verbesserung wird in diesem Rahmen mit einzelnen Ausreißern erreicht. Verschiedene Aspekte werden in drei Kapiteln diskutiert: In Kapitel 1 werden die verwendeten Daten und ihre elektronische Verarbeitung vorgestellt. Die Daten bestehen zum einen aus Windleistungshochrechnungen für die Bundesrepublik Deutschland der Jahre 2011 und 2012, welche als Transparenzanforderung des Erneuerbaren Energiegesetzes durch die Übertragungsnetzbetreiber publiziert werden müssen. Zum anderen werden Wetterprognosen, die der Deutsche Wetterdienst im Rahmen der Grundversorgung kostenlos bereitstellt, verwendet. Kapitel 2 erläutert zwei aus der Literatur bekannte Verfahren - Online- und Batchalgorithmus - zum Training einer selbstorganisierenden Karte. Aus den dargelegten Verfahrenseigenschaften begründet sich die Wahl des Batchverfahrens für die in Kapitel 3 erläuterte Methode. Das in Kapitel 3 vorgestellte Verfahren hat im modellierten operativen Einsatz den gleichen Ablauf, wie eine Klassifikation mit anschließender klassenspezifischer Prognose. Bei dem Training des Verfahrens wird allerdings iterativ vorgegangen, indem im Anschluss an das Training der klassenspezifischen Prognose ermittelt wird, zu welcher Klasse der Klassifikation ein Eingabedatum gehören sollte, um mit den vorliegenden klassenspezifischen Prognosemodellen die höchste Prognosegüte zu erzielen. Die so gewonnene Einteilung der Eingaben kann genutzt werden, um wiederum eine neue Klassifikationsstufe zu trainieren, deren Klassen eine verbesserte klassenspezifisch Prognose ermöglichen.