ABSTRACT

Data from two thermo-mechanical pulp mills are collected to simulate the refining process using deep learning. A multilayer perceptron neural network is utilized for pattern recognition of the refining variables. Results show the impressive capability of artificial intelligence methods in refining energy simulation so that the correlation coefficient of 98% is accessible. A comprehensive parametric study has been made to investigate the effect of refining disturbance variables, plate gap and dilution water on refining energy simulation. The generated model reveals the non-linear hidden pattern between refining variables, which can be used for optimal refining control strategy. Considering the disturbance variables’ effect in refining energy simulation, model accuracy could increase by 15%. Removing the plate gape from predictive variables reduces the simulation determination coefficient by up to 25% in both mills, while the mentioned value for removing dilution water is 9–17% in mill 1 and about 35% in mill 2.

摘要

收集来自两个热机械制浆厂的数据，以使用深度学习模拟精炼过程。多层感知器神经网络用于细化变量的模式识别。结果表明，人工智能方法在完善能源模拟方面的能力令人印象深刻，相关系数可达 98%。已经进行了全面的参数研究，以研究精炼扰动变量、板间隙和稀释水对精炼能量模拟的影响。生成的模型揭示了精炼变量之间的非线性隐藏模式，可用于优化精炼控制策略。考虑到扰动变量对精炼能源模拟的影响，模型精度可提高15%。