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DDC: 630 × Type: Text × Subject: Biogas × WGL Institute: ATB ×

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Now showing 1 - 4 of 4
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    Methanbildungspotenziale verschiedener Pflanzenarten aus Energiefruchtfolgen
    (Darmstadt : KTBL, 2016) Herrmann, Christiane; Plogsties, Vincent; Willms, Matthias; Hengelhaupt, Frank; Eberl, Veronika; Eckner, Jens; Strauß, Christoph; Idler, Christine; Heiermann, Monika
    Das Methanbildungspotenzial ist ein entscheidendes Qualitätsmerkmal von Biomassen bei ihrer Nutzung als Einsatzstoff für die Biogasproduktion. Von 769 unter einheitlichen Bedingungen silierten Erntegütern aus Energiefruchtfolgen wurden mittels Batch-Gärtests in zwei verschiedenen Versuchsanlagen spezifische Methanausbeuten ermittelt. Daraus konnten Richtwerte für mittlere Methanausbeuten je Fruchtart und Fruchtfolgestellung, Schnitt bzw. Trockenmassebereich oder Entwicklungsstadium zur Ernte für 93 verschiedene pflanzliche Biomassen abgeleitet werden. Die Ergebnisse stellen eine umfassende Datengrundlage dar, die in Verbindung mit Biomasseerträgen für die Abschätzung von Methanhektarerträgen zur ökonomischen und ökologischen Bewertung von Energiefruchtfolgen, zur Planung und Auslegung von Biogasanlagen sowie zur Entscheidung hinsichtlich des Anbaus alternativer pflanzlicher Kosubstrate und der Konzeption nachhaltiger Biogasfruchtfolgen genutzt werden können.
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    Prediction of the biogas production using GA and ACO input features selection method for ANN model
    (Amsterdam [u.a.] : Elsevier, 2019) Beltramo, Tanja; Klocke, Michael; Hitzmann, Bernd
    This paper presents a fast and reliable approach to analyze the biogas production process with respect to the biogas production rate. The experimental data used for the developed models included 15 process variables measured at an agricultural biogas plant in Germany. In this context, the concentration of volatile fatty acids, total solids, volatile solids acid detergent fibre, acid detergent lignin, neutral detergent fibre, ammonium nitrogen, hydraulic retention time, and organic loading rate were used. Artificial neural networks (ANN) were established to predict the biogas production rate. An ant colony optimization and genetic algorithms were implemented to perform the variable selection. They identified the significant process variables, reduced the model dimension and improved the prediction capacity of the ANN models. The best prediction of the biogas production rate was obtained with an error of prediction of 6.24% and a coefficient of determination of R2 = 0.9.
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    Comparative advantage of maize- and grass-silage based feedstock for biogas production with respect to greenhouse gas mitigation
    (Basel : MDPI, 2016) Meyer-Aurich, Andreas; Lochmann, Yulia; Klauss, Hilde; Prochnow, Annette
    This paper analyses the comparative advantage of using silage maize or grass as feedstock for anaerobic digestion to biogas from a greenhouse gas (GHG) mitigation point of view, taking into account site-specific yield potentials, management options, and land-use change effects. GHG emissions due to the production of biogas were calculated using a life-cycle assessment approach for three different site conditions with specific yield potentials and adjusted management options. While for the use of silage maize, GHG emissions per energy unit were the same for different yield potentials, and the emissions varied substantially for different grassland systems. Without land-use change effects, silage maize-based biogas had lower GHG emissions per energy unit compared to grass-based biogas. Taking land-use change into account, results in a comparative advantage of biogas production from grass-based feedstock produced on arable land compared to silage maize-based feedstock. However, under current frame conditions, it is quite unrealistic that grass production systems would be established on arable land at larger scale.
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