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

Behnam Talebjedi,Esa Vakkilainen,Sanna Syri,Ali Khosravi,Timo Laukkanen,Henrik Holmberg

doi:10.3390/en13195113

Abstract

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.

Highlights

Due to rising energy prices, environmental concerns, and carbon taxes, energy is becoming one of the critical considerations for large energy consumers [1]
The findings show an impressive ability of numerical analysis to investigate the torque and pressure of the refiner discs and calculating refining motor load
Study, to to identify identify the the model model for for predicting predicting motor motor load load and and steam steam generation generation with with the the available data sets, four main effective refining variables are taken into account as input parameters of available data sets, four main effective refining variables are taken into account as input parameters the model

Summary

Introduction

Due to rising energy prices, environmental concerns, and carbon taxes, energy is becoming one of the critical considerations for large energy consumers [1]. The pulp and paper industry is in the fourth place of the most extensive industrial energy consumer worldwide. The pulp and paper industry contributes to approximately 6% and 2% percent of final industrial energy consumption and carbon dioxide emissions, respectively. Thermo-mechanical pulping is of the extreme energy-intensive processes with a low energy efficiency close to 10 to 15% [6]. An increase in the electricity price and carbon emission taxes, along with environmental concerns, have committed the pulp mills to increase the energy efficiency to keep their market share and profitability [9,10,11].

Objectives

Methods

Discussion

Conclusion