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The loosening and conditioning process is at the beginning of a tobacco manufacturing line, and thus the quality of the following processes depends on the accuracy and stability of it. This work investigated on improving output moisture content stability by collecting and analyzing process data and then applying advanced control technologies to the process. Specifically, multi-dimensional process data was collected and analyzed using correlation and regression methods. A generalized predictive control (GPC) algorithm was then developed and applied to optimize the control of water flow rate in real time. Results of 133 batches of production under the new GPC method showed that CPK of output material moisture content increased by more than 50% after applying the new control method.