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Survey of microbes in industrial-scale second-generation bioethanol production for better process knowledge and operation

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Survey of microbes in industrial-scale second-generation bioethanol production for better process knowledge and operation

Abstract

The microbes present in bioethanol production processes have been previously studied in laboratory-scale experiments, but there is a lack of information on full-scale industrial processes. In this study, the microbial communities of three industrial bioethanol production processes were characterized using several methods. The samples originated from second-generation bioethanol plants that produce fuel ethanol from biowaste, food industry side streams, or sawdust. Amplicon sequencing targeting bacteria, archaea, and fungi was used to explore the microbes present in biofuel production and anaerobic digestion of wastewater and sludge. Biofilm-forming lactic acid bacteria and wild yeasts were identified in fermentation samples of a full-scale plant that uses biowaste as feedstock. During the 20-month monitoring period, the anaerobic digester adapted to the bioethanol process waste with a shift in methanogen profile indicating acclimatization to high concentrations of ammonia. Amplicon sequencing does not specifically target living microbes. The same is true for indirect parameters, such as low pH, metabolites, or genes of lactic acid bacteria. Since rapid identification of living microbes would be indispensable for process management, a commercial method was tested that detects them by measuring the rRNA of selected microbial groups. Small-scale testing indicated that the method gives results comparable with plate counts and microscopic counting, especially for bacterial quantification. The applicability of the method was verified in an industrial bioethanol plant, inspecting the clean-in-place process quality and detecting viability during yeast separation. The results supported it as a fast and promising tool for monitoring microbes throughout industrial bioethanol processes.

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