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Clustering and modelling of rheological parameters for anaerobic digestion materials (ADMs) and its application for feed pump selection

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, , Citation Yang Yang and Hongguang Zhu 2020 IOP Conf. Ser.: Earth Environ. Sci. 467 012053 DOI 10.1088/1755-1315/467/1/012053

1755-1315/467/1/012053

Abstract

Anaerobic digestion technology is a promising technology for renewable energy and environmental protection. Rheological properties of anaerobic digestion materials (ADMs) are the essential parameters for transporting and mixing system design. Anaerobic slurry with high total solid(TS) is a Non-Newton fluid fit for power law model, its apparent viscosity is a function of consistency coefficient K and non-dimensional rheology index n. At present, studies on rheological parameters of ADMs mostly focus on specific single raw material, and the TS content has the greatest influence on rheological parameters. By comparing the rheological properties of different ADMs in literatures, it was found that the large difference among the rheological parameters comes from not only different types of ADMs (due to different components), but also the same type (due to different ways of pretreatment), which makes it difficult for the selection of rheological parameter model in biogas design. In this work, 20 different ADMs were clustered into 5 types by statistical method and then their rheological parameters were conducted. The five types of ADMs respectively are: low fiber content slurry, high fiber content slurry, straw manure mixture, straw suspension and digested sludge. The rheological parameter models of the five types can be written as K as an exponential function of TS, and n as a linear function of TS, which the range of TS is 4%-10%. Furthermore, the rheological parameter models were applied to the selection of feed pump of a 6000m3 biogas plant in Funan county of Anhui province, China. In this paper, the values of K and n of 20 ADMs were calculated when TS equals to 4%, 6%, 8% and 10%, and the optimal classification results were obtained by comparing the three results between hierarchical clustering method and K-Means clustering method.

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10.1088/1755-1315/467/1/012053