Abstract: Systems and methods are disclosed herein for proactive intervention in an industrial process involving a defoamer composition. Learning models are iteratively trained using historical input data sets comprising directly measured variables from the industrial process and correlations between combinations of the input data and respective process states. For a current input data set associated with the industrial process, and responsive to detected process attributes, an intervention event is determined with respect to at least a first defoamer component in a current composition. Models are utilized to predict, responsive to the intervention event, changes in target values within a specified range of target values for other components in the current composition and/or for directly measured variables as corresponding with a specified process state. Automatic control is performed for respective actuators to produce a new defoamer composition, and as needed to other actuators for the directly measured variables.

Abstract: Defoamer compositions are described and the use of defoamer compositions in paper and pulp mills is described, especially for use at the brown stock washing steps in a pulp mill. The defoamer composition comprises: a) about 20 wt % to about 90 wt % of at least one organophosphorous component (e.g., compound or polymer, n-tributoxyethyl phosphate); b) about 1 wt % to about 10 wt % of at least one silane treated silica; c) about 0.5 wt % to about 10 wt % of at least one surfactant that is anionic or non-ionic; and d) optionally, water or an aqueous liquid.

Abstract: Defoamer compositions are described and the use of defoamer compositions in paper and pulp mills is described, especially for use at the brown stock washing steps in a pulp mill.