Uncertainty-Aware Data-driven Tobacco Loosening and Conditioning Process Moisture Prediction and Control Optimization

Tobacco loosening and conditioning process is one of the most important processes in cigarette production, the quality of which affects the final product quality largely. The control of water proportion added in this process is human experience-centric and inconsistent without intensive human tuning. This paper proposes an uncertainty-aware outlet moisture prediction and optimization approach to provide updated water proportion to the PID controller for consistently desirable quality performance and stability. In this research, quantile regression is applied to describe the confidence of prediction, and Monte Carlo simulation is induced in the online optimization to deal with the uncertainty. The approach has been applied to a cigarette production line and the resulted performance showed significant improvement compared with the results of human tuning.