SOM based analysis of pulping process data

Simula, Olli; Alhoniemi, Esa

doi:10.1007/bfb0100524

Cited by 5 publications

(1 citation statement)

References 8 publications

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“…However, their research could be improved by performing a parametric study and evaluating the effect of the different refining manipulated variables on the refining energy simulation. Similar studies regarding the application of the machine learning approach in chemical pulping could be found in Simula and Alhoniemi (2006), Ciesielski and Olejnik (2014), Musavi et al (1995), Sainlez and Heyen (2013).…”

Section: Introductionsupporting

confidence: 69%

Advanced energy-saving optimization strategy in thermo-mechanical pulping by machine learning approach

Talebjedi

Laukkanen

Holmberg

et al. 2022

Nordic Pulp &Amp; Paper Research Journal

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Thermo-mechanical Pulping (TMP) is one of the most energy-intensive industries where most of the electrical energy is consumed in the refining process. This paper proposes the energy-saving refining optimization strategy by integrating the machine learning algorithm and heuristic optimization method. First, refining specific energy consumption (RSEC) and pulp quality identification models are developed using Artificial Neural Networks. In the second step, the developed identification models are incorporated with the Genetic algorithm to minimize the total refining specific energy consumption while maintaining the same pulp quality. Simulation results prove that a deep multilayer perceptron neural network is a powerful tool for creating refining energy and quality identification models with the model correlation coefficients of 0.97, 0.94, 0.92, and 0.67 for the first-stage RSEC, second-stage RSEC, final pulp fiber length, and freeness prediction, respectively. Findings confirm that the average total RSEC reduction of 14 % is achievable by utilizing the proposed optimization method.

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Section: Introductionsupporting

confidence: 69%