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End date: 2021 × End date: 2019 × Type: Article × Publisher: Amsterdam [u.a.] : Elsevier × WGL Institute: ATB ×

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    Assessing the organic fraction of municipal solid wastes for the production of lactic acid
    (Amsterdam [u.a.] : Elsevier, 2019) López-Gómez, J. Pablo; Latorre-Sánchez, Marcos; Unger, Peter; Schneider, Roland; Coll Lozano, Caterina; Venus, Joachim
    With an estimated yearly production of about 140 Mt in the EU, conventionally, the organic fraction of municipal solid wastes (OFMSW) has been disposed in landfills with negative environmental effects. Nonetheless, the chemical composition of this residue make it a substrate with great bioconversion potential. In this study, OFMSW from Spanish municipal treatment plants, were evaluated for the production of LA. Samples were identified according to the sorting mechanisms employed for their collection in: (A) separately collected, (B) non-separately collected and (C) separately collected+paper/cardboard. Enzymatic hydrolysis was used to produce hydrolysates A, B and C accordingly. Hydrolysate A showed the highest total sugars and glucose content with values of 70 and 55 g·L−1, respectively. Following the characterisation, a screening showed that growth of B. coagulans was possible in all three hydrolysates. Furthermore, lab scale fermentations showed that LA final concentrations could reach around 60 g·L−1, with yields from total sugars of above 0.60 g·g−1. A technical scale fermentation of the hydrolysate A resulted in a final LA concentration of 60.7 g·L−1, a yield of 0.71 g·g−1 with a productivity of 2.68 g·L−1·h−1. Overall, it was estimated that 0.23 g of LA could be produced from one g of dry OFMSW.
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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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    Dynamics of rural livelihoods and rainfall variability in Northern Ethiopian Highlands
    (Amsterdam [u.a.] : Elsevier, 2019) Adamseged, Muluken E.; Frija, Aymen; Thiel, Andreas
    [No abstract available]