Optimal scheduling ratio of recycling waste paper with NSGAII based on deinked-pulp properties prediction

The recycling of waste paper has been an effective way to achieve the environmental-friendly growth of papermaking industry. Focusing on the mixed-pulping process which has been generally employed, to ensure the required properties of the deinking pulp (DIP) and minimize the purchase cost of waste paper, an intelligent model scheduling the mixing ratio of waste paper was developed in the study. Primarily, driven by the field data of mixing ratio of waste paper and DIP properties, the prediction models of DIP properties were developed by SVM, GA-SVM, and BP-NN algorithms. Subsequently, based on the best prediction model, the scheduling model for the mixing ratio of waste paper was set up by NSGAII algorithm. The experimental results showed that, based on the obtained best BP-NN prediction model of DIP properties, the developed NSGAII scheduling model for the ratio of waste paper could achieve excellent scheduling results under the rigorous constraints and multi-objective. Compared with the previous study, the developed prediction and scheduling models in this study reduced the purchase cost of waste paper by 2.16%, and improved the acceptable proportion of DIP property from 56.07% to 100%.