Machine Learning-Based Energy System Model for Tissue Paper Machines

Huanhuan Zhang,Jigeng Li,Mengna Hong

doi:10.3390/pr9040655

Abstract

With the global energy crisis and environmental pollution intensifying, tissue papermaking enterprises urgently need to save energy. The energy consumption model is essential for the energy saving of tissue paper machines. The energy consumption of tissue paper machine is very complicated, and the workload and difficulty of using the mechanism model to establish the energy consumption model of tissue paper machine are very large. Therefore, this article aims to build an empirical energy consumption model for tissue paper machines. The energy consumption of this model includes electricity consumption and steam consumption. Since the process parameters have a great influence on the energy consumption of the tissue paper machines, this study uses three methods: linear regression, artificial neural network and extreme gradient boosting tree to establish the relationship between process parameters and power consumption, and process parameters and steam consumption. Then, the best power consumption model and the best steam consumption model are selected from the models established by linear regression, artificial neural network and the extreme gradient boosting tree. Further, they are combined into the energy consumption model of the tissue paper machine. Finally, the models established by the three methods are evaluated. The experimental results show that using the empirical model for tissue paper machine energy consumption modeling is feasible. The result also indicates that the power consumption model and steam consumption model established by the extreme gradient boosting tree are better than the models established by linear regression and artificial neural network. The experimental results show that the power consumption model and steam consumption model established by the extreme gradient boosting tree are better than the models established by linear regression and artificial neural network. The mean absolute percentage error of the electricity consumption model and the steam consumption model built by the extreme gradient boosting tree is approximately 2.72 and 1.87, respectively. The root mean square errors of these two models are about 4.74 and 0.03, respectively. The result also indicates that using the empirical model for tissue paper machine energy consumption modeling is feasible, and the extreme gradient boosting tree is an efficient method for modeling energy consumption of tissue paper machines.

Highlights

This paper studies the empirical energy consumption modeling of tissue paper machines, and the energy consumption model consists of the electricity consumption model and the steam consumption model
The root mean square error (RMSE) and mean absolute percentage error (MAPE) of these three electricity consumption models are shown in Table 6, while the RMSE and MAPE of the three steam consumption models are listed in
The RMSE and MAPE of these three electricity consumption models are shown in Table 6, while the RMSE and MAPE of the three steam consumption models are listed in to the7.predicted values allcan thebemodels, means modeling consumption and

Summary

Introduction

Industry is one of the largest energy consumption end-use sectors, and its energy consumption was 237.3 quadrillion Btu (British thermal unit, A British thermal unit (Btu) is a measure of the heat content of fuels or energy sources. It is the quantity of heat required to raise the temperature of one pound of liquid water by 1 degree Fahrenheit at the temperature that water has its greatest density (approximately 39 degrees Fahrenheit).) in. The worldwide energy-related carbon dioxide emission was 33.9018 billion metric tons in 2015 and will reach 42.7714 billion metric tons in 2050

Objectives

Results

Conclusion