Abstract: The present disclosure relates to a combined treatment method for surface modification of fumed silica, which comprises the following steps: (1) two sets of modification devices are used to jointly treat fumed silica; the fumed silica is modified with a modifier in the reaction furnace of each set of modification devices to obtain two groups of modified fumed silica and exhaust gas respectively; (2) the exhaust gas obtained in step (1) is separated respectively to obtain unreacted modifier and by-products, and the obtained by-products are input into the reaction furnace of the other set of modification devices as reaction assistants to participate in the modification reaction; and the obtained unreacted modifiers are returned to the reaction furnace of the original modification device for repeated use.

Abstract: Embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for training a machine learning model comprising a hidden module and an output module and configured for identifying one of a plurality of original labels for an input. In accordance with one embodiment, a method is provided that includes generating sufficiently-labeled data comprising example-pairs each associated with a sufficient label. The sufficient label of an example-pair indicates whether a first and a second input example have the same original label. The method further includes training the hidden module using the sufficiently-labeled data, and subsequently, training the output module using a plurality of input examples each having an original label. The plurality of input examples may be a plurality of fully-labeled data. The method further includes automatically providing the resulting trained machine learning model for use in prediction tasks.