PAPERmaking! FROM THE PUBLISHERS OF PAPER TECHNOLOGY Volume 7, Number 1, 2021
handsheets were measured by standard test methods and the results were modeled using Design Expert.10 software and response surface method (RSM). The results and obtained models showed that addition of these additives did not make a significant difference on brightness of the hand sheet paper compared with the control sample. However, the addition of these additives increased the dry and wet tensile and tear strengths, which in the optimal addition level the increasing levels for mentioned properties were 145%, 35% and 70%, respectively, compared with the control sample. The increasing rate of tensile and tear resistance were higher than increasing rate obtained by conventional dry strength agents, such as cationic starch. “ Machine Learning-Based Energy System Model for Tissue Paper Machines ” , Huanhuan Zhang, Jigeng Li & Mengna Hong, Processes 2021, 9(4), 655, OPEN ACCESS, DOI https://doi.org/10.3390/pr9040655. 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 WASTE TREATMENT “ Pulp and paper mill wastes: utilizations and prospects for high value-added biomaterials ”, Adane Haile, Gemeda Gebino Gelebo, Tamrat Tesfaye, Wassie Mengie, Million Ayele Mebrate, Amare Abuhay & Derseh Yilie Limeneh, Bioresources and Bioprocessing , 8, Article 35 (2021). A wide variety of biomass is available all around the world. Most of the biomass exists as a by-product from manufacturing industries. Pulp and paper mills contribute to a higher amount of these biomasses mostly discarded in the landfills creating an environmental burden. Biomasses from other sources have been used to produce different kinds and grades of biomaterials
Technical Abstracts
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