A1 Journal article (refereed), original research

Energy Modeling of a Refiner in Thermo-Mechanical Pulping Process Using ANFIS Method

Open Access publication

Publication Details
Authors: Talebjedi Behnam, Khosravi Ali, Laukkanen Timo, Holmberg Henrik, Vakkilainen Esa, Syri Sanna
Publisher: MDPI
Publication year: 2020
Language: English
Related Journal or Series Information: Energies
Volume number: 13
Issue number: 19
ISSN: 1996-1073
JUFO-Level of this publication: 1
Open Access: Open Access publication


In the pulping industry, thermo-mechanical pulping (TMP) as a subdivision of the refiner-based mechanical pulping is one of the most energy-intensive processes where the core of the process is attributed to the refining process. In this study, to simulate the refining unit of the TMP process under different operational states, the idea of machine learning algorithms is employed. Complicated processes and prediction problems could be simulated and solved by utilizing artificial intelligence methods inspired by the pattern of brain learning. In this research, six evolutionary optimization algorithms are employed to be joined with the adaptive neuro-fuzzy inference system (ANFIS) to increase the refining simulation accuracy. The applied optimization algorithms are particle swarm optimization algorithm (PSO), differential evolution (DE), biogeography-based optimization algorithm (BBO), genetic algorithm (GA), ant colony (ACO), and teaching learning-based optimization algorithm (TLBO). The simulation predictor variables are site ambient temperature, refining dilution water, refining plate gap, and chip transfer screw speed, while the model outputs are refining motor load and generated steam. Findings confirm the superiority of the PSO algorithm concerning model performance comparing to the other evolutionary algorithms for optimizing ANFIS method parameters, which are utilized for simulating a refiner unit in the TMP process.

Research Areas

Last updated on 2020-13-10 at 10:35