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MLP-based nonlinear modelling for energy saving in forming section of paper machines

Due to the increasing cost of energy and the demand of reducing the environmental footprints, energy saving is becoming an important subject in the industry operation. To realize the energy consumption optimization of papermaking, the energy model should be established while the product quality and process model also need to be constructed, which are taken as the constraints for optimization. This paper describes the identification of a forming section of paper machines with Multilayer Perception (MLP) Neural Networks. The process model, product quality model and energy consumption model are established for the energy saving in papermaking. The real industrial step tests are performed and the data are used to model training and validation. The models are validated by means of mean-squared error (MSE), fit measure and Akaike's Final Prediction Error (FPE). The results show the effectiveness of the established models, which are suitable for the next work of energy optimization.