Using Big Data in Industrial Milk Powder Process Systems

Abstract This work looks at the application of big data in the milk powder processing industry, where the focus is on improving product quality and preventing off-specification product. This results in an increased focus on the quality attributes, which may be infrequently measured, and have low repeatability, thus resulting in challenges to the veracity of the data. Combined with the low frequency of failures, the data set is also ‘unbalanced’, making it difficult to analyse using normal black-box big data techniques. However, these can be addressed using tailored algorithms for elucidating the effects of specific parts of the process on the quality attributes of interest, and by using techniques such as bootstrapping with up- and down-sampling of data to address the issue of an unbalanced dataset.