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NIBIOs employees contribute to several hundred scientific articles and research reports every year. You can browse or search in our collection which contains references and links to these publications as well as other research and dissemination activities. The collection is continously updated with new and historical material.



This report shows results from an experiment where it was investigated whether a powder of freeze-dried microalgae (Phaeodactylum tricornutum) had a biostimulating effect on the growth and content of nutrients and antioxidants in basil (Ocimum basilicum). The effect of the microalgae powder was tested as a supplement to either mineral fertilizer or a commercial organic fertilizer. We found no significant effect on the yield of applied microalgae powder, but there was a tendency for a higher yield with added microalgae powder for the treatment with organic fertiliser. This may be due to additional nitrogen supply with the microalgae powder. With mineral fertiliser, there was the opposite tendency, highest yield without microalgae powder. The only statistically significant effect of the microalgae powder was an increase in the concentration of boron for the treatment with organic fertiliser. This was probably an effect of a significant additional supply of boron with the microalgae biomass. There was a tendency for an increased concentration of copper with the addition of microalgae powder with both mineral and organic fertiliser, although the additional copper supply with the microalgae powder was small. With organic fertiliser, there was also a tendency towards increased phosphorus and potassium concentrations with the addition of microalgae powder. This could be a biostimulating effect as the additional phosphorus and potassium supply with the microalgae powder was small, but as mentioned, the effect was not statistically significant. We found no significant differences between the treatments for total antioxidant content.

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Industrial-scale garage dry fermentation systems are extremely nonlinear, and traditional machine learning algorithms have low prediction accuracy. Therefore, this study presents a novel intelligent system that employs two automated machine learning (AutoML) algorithms (AutoGluon and H2O) for biogas performance prediction and Shapley additive explanation (SHAP) for interpretable analysis, along with multiobjective particle swarm optimization (MOPSO) for early warning guidance of industrial-scale garage dry fermentation. The stacked ensemble models generated by AutoGluon have the highest prediction accuracy for digester and percolate tank biogas performances. Based on the interpretable analysis, the optimal parameter combinations for the digester and percolate tank were determined in order to maximize biogas production and CH4 content. The optimal conditions for the digester involve maintaining a temperature range of 35–38 °C, implementing a daily spray time of approximately 10 min and a pressure of 1000 Pa, and utilizing a feedstock with high total solids content. Additionally, the percolate tank should be maintained at a temperature range of 35–38 °C, with a liquid level of 1500 mm, a pH range of 8.0–8.1, and a total inorganic carbon concentration greater than 13.8 g/L. The software developed based on the intelligent system was successfully validated in production for prediction and early warning, and MOPSO-recommended guidance was provided. In conclusion, the novel intelligent system described in this study could accurately predict biogas performance in industrial-scale garage dry fermentation and guide operating condition optimization, paving the way for the next generation of intelligent industrial systems.