Stabilization of industrial processes with time series machine learning
This addresses stabilization issues in industrial processes, but it appears incremental as it builds on existing neural network methods.
The paper tackled the problem of stabilizing industrial time series processes by applying a machine learning pipeline with two neural networks, achieving a 3x improvement in temperature control stability compared to ordinary solvers.
The stabilization of time series processes is a crucial problem that is ubiquitous in various industrial fields. The application of machine learning to its solution can have a decisive impact, improving both the quality of the resulting stabilization with less computational resources required. In this work, we present a simple pipeline consisting of two neural networks: the oracle predictor and the optimizer, proposing a substitution of the point-wise values optimization to the problem of the neural network training, which successfully improves stability in terms of the temperature control by about 3 times compared to ordinary solvers.