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Data-driven approach of scheduling the ratio of waste paper and pulp properties prediction

The study aimed to realize the automatic production scheduling of waste paper in paper mills. Based on the quantities of the field data (mixing ratios of waste paper and pulp properties) stored in a paper mill, the methods of back propagation neural networks (BP-NN) and support vector machine (SVM), the all data set and the average data set, were firstly applied to develop the prediction models of the pulp properties; and then the genetic algorithm was used to search for the suitable scheduling solutions. The simulation results revealed that: demonstrating its acceptable prediction accuracy and fast training time, the prediction model with SVM method and the average data set was good enough to be used in the genetic algorithm to search for the optimal scheduling ratios of waste paper, the obtained optimal scheduling solution not only had the lowest purchase cost of waste paper, but also met the requirements of the mill: specified types of waste paper, specified ratio of some kind of waste paper and the required pulp brightness.