Valorization of mixed office waste as macro-, micro-, and nano-sized particles in recycled paper containerboards for enhanced performance and improved environmental perception

2022 ◽  
Vol 180 ◽  
pp. 106125
Author(s):  
Lisandra Chacon ◽  
Nathalie Lavoine ◽  
Richard A. Venditti
1992 ◽  
Vol 266 ◽  
Author(s):  
Roger M. Rowell ◽  
Sandra Harrison

AbstractFor most products, processing recycled paper back into paper will require de-inking and removal of coatings and fillers. Paper to paper recycling will also require sorting the paper waste stream to insure that the stock used contains the type of fiber needed. These problems can be eliminated if the waste paper product stream is used to make fiber based composites. Mixed office waste, packaging materials, paperboard, newspaper, and many other types of paper fiber can be used directly for the production of thermosetting or thermoplastic fiber based composites. High levels of clay used in magazine paper can be tolerated in these composites. This paper will review research in making fiber based composites from several waste paper streams and the properties of these products.


TAPPI Journal ◽  
2014 ◽  
Vol 13 (11) ◽  
pp. 37-43 ◽  
Author(s):  
LIISA KOTANEN ◽  
MIKA KÖRKKÖ ◽  
ARI ÄMMÄLÄ ◽  
JOUKO NIINIMÄKI

The use of recovered paper as a raw material for paper production is by far the most economical and ecological strategy for the disposal of waste paper. However, paper production from recovered paper furnish generates a great amount of residues, and the higher the demand requirements for the end product, the higher the amount of rejected material. The reason for this is that the selectivity of the deinking process is limited; therefore, some valuable components are also lost in reject streams. The rejection of usable components affects the economics of recycled paper production. As the cost of waste disposal continues to increase, this issue is becoming more and more severe. This paper summarizes the current state of the resource efficiency in recycled pulp production and provides information on the volumes of rejected streams and the usable material within them. Various means to use these reject streams are also discussed, including the main findings of a recent thesis by the main author. This review summarizes current internal and external use of reject streams generated in the deinking operations.


MRS Advances ◽  
2020 ◽  
Vol 5 (52-53) ◽  
pp. 2669-2678
Author(s):  
Jeovani González P. ◽  
Ramiro Escudero G

AbstractDeinking of recycled office (MOW) paper was carried out by using a flotation column and adding separately sodium hydroxide, and the enzyme Cellulase Thricodema Sp., as defibrillators.The de-inked cellulose fibers were characterized according to the standards of the paper industry, to compare the efficiency of the deinking of each chemical reagent used to hydrolyze the fibers and defibrillate them.The computational simulation of the molecular coupling between the enzyme and cellulose was performed, to establish the enzyme-cellulose molecular complex and then to identify the principal amino-acids of endo-β-1,4-D-glucanase in this molecular link, which are responsible for the hydrolysis of the cellulose.Experimental results show the feasibility to replace sodium hydroxide with the enzyme Cellulase Thricodema Sp., by obtaining deinked cellulose with similar optical and physical properties.The use of the enzyme instead of sodium hydroxide avoids the contamination of the residual water; in addition to that, the column is operated more easily, taking into consideration that the pH of the system goes from alkaline to neutral.


2012 ◽  
Vol 11 (4) ◽  
pp. 829-839 ◽  
Author(s):  
Siti Rozaimah Sheikh Abdullah ◽  
Mohd Hafizuddin Muhamad ◽  
Abu Bakar Mohamad ◽  
Rakmi Abdul Rahman ◽  
Abdul Amir Hasan Kadhum

Author(s):  
Peter Rez

Timber has the lowest embodied energy of any of the construction materials. Paper production from trees requires much more energy. There is some energy saving in recycling, as recycled paper substitutes for pulp derived from wood chips. Growing crops for food also requires energy. The energy required for plants to grow comes from the sun, but there are additional energy inputs from fertiliser and farm machinery to speed up the growth process and vastly improve crop yields. If grains are used as animal feed, then the energy inputs are much larger than the dietary energy output—the larger the animal and the longer it is fattened up before slaughter, the more inefficient the process. The use of crops to make fuel for electrical power generation or for processing into liquid fuels is horribly inefficient. The problem is simple—the plants do not grow fast enough!


2021 ◽  
Vol 13 (6) ◽  
pp. 1064
Author(s):  
Zhangjing Wang ◽  
Xianhan Miao ◽  
Zhen Huang ◽  
Haoran Luo

The development of autonomous vehicles and unmanned aerial vehicles has led to a current research focus on improving the environmental perception of automation equipment. The unmanned platform detects its surroundings and then makes a decision based on environmental information. The major challenge of environmental perception is to detect and classify objects precisely; thus, it is necessary to perform fusion of different heterogeneous data to achieve complementary advantages. In this paper, a robust object detection and classification algorithm based on millimeter-wave (MMW) radar and camera fusion is proposed. The corresponding regions of interest (ROIs) are accurately calculated from the approximate position of the target detected by radar and cameras. A joint classification network is used to extract micro-Doppler features from the time-frequency spectrum and texture features from images in the ROIs. A fusion dataset between radar and camera is established using a fusion data acquisition platform and includes intersections, highways, roads, and playgrounds in schools during the day and at night. The traditional radar signal algorithm, the Faster R-CNN model and our proposed fusion network model, called RCF-Faster R-CNN, are evaluated in this dataset. The experimental results indicate that the mAP(mean Average Precision) of our network is up to 89.42% more accurate than the traditional radar signal algorithm and up to 32.76% higher than Faster R-CNN, especially in the environment of low light and strong electromagnetic clutter.


2021 ◽  
Vol 1950 (1) ◽  
pp. 012040
Author(s):  
R.V Shynu ◽  
K.G Santhosh Kumar ◽  
R.D Sambath

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