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Publikasjoner

NIBIOs ansatte publiserer flere hundre vitenskapelige artikler og forskningsrapporter hvert år. Her finner du referanser og lenker til publikasjoner og andre forsknings- og formidlingsaktiviteter. Samlingen oppdateres løpende med både nytt og historisk materiale. For mer informasjon om NIBIOs publikasjoner, besøk NIBIOs bibliotek.

2022

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Abstract

Norwegian-grown peas and faba beans are a healthier alternative to meat and dairy products, which are over-consumed in Norway, hence these legumes represent an interesting alternative as food protein source in Norway. However, the environmental impact of these legumes compared to other protein sources has not been studied, in detail. Hence this study, where the environmental impact of this plant protein was analysed and compared to other main protein sources in the Norwegian diet, covers a research gap. The method used was Life Cycle Assessment (LCA) and a large range of impacts was covered. The climate impact for dried grain legumes were 0.55–0.57 kg CO2-eq/kg, The climate impact for dried grain legumes were 0.55–0.57 kg CO2-eq/kg, which is much lower than ruminant meat (19–38 kg CO2-eq/kg), other meat (3.6–4.2 kg CO2-eq/kg), seafood (0.8–22 kg CO2-eq/kg), dairy products (1.2–22 kg CO2-eq/kg products) and cereals (0.66–0.72 kg CO2-eq/kg product). The same trend was found for all impact categories studied. The same pattern was found when comparing the environmental impacts of grain legumes in intermediate and finished products. An evaluation of the nutrient content showed that there is no trade-off between health and environment but the effect of lower protein digestibility and anti-nutritional compounds in legumes remains to be investigated quantitatively. The study indicates that legumes are a more sustainable source of dietary protein than animal protein sources. It is recommended that more research should be done on social and economic sustainability should be done to get at more complete picture of the sustainability of these grain legumes.

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Abstract

The parameters from full-scale biogas plants are highly nonlinear and imbalanced, resulting in low prediction accuracy when using traditional machine learning algorithms. In this study, a hybrid extreme learning machine (ELM) model was proposed to improve prediction accuracy by solving imbalanced data. The results showed that the best ELM model had a good prediction for validation data (R2 = 0.972), and the model was developed into the software (prediction error of 2.15 %). Furthermore, two parameters within a certain range (feed volume (FV) = 23–45 m3 and total volatile fatty acids of anaerobic digestion (TVFAAD) = 1750–3000 mg/L) were identified as the most important characteristics that positively affected biogas production. This study combines machine learning with data-balancing techniques and optimization algorithms to achieve accurate predictions of plant biogas production at various loads.