Technical field

The invention relates to a method for manufacturing recycled cellulose based fibers. The invention further relates to use of a simulation process for a manufacturing process of recycled cellulose based fibers.

Background

Nowadays, recyclability and use of less virgin raw materials is valued in production. Usage of recycled fibers decreases the amount of virgin pulp needed in paper or board manufacturing process.

Recycled cellulose based fibers may be manufactured from recovered papers and boards. The recovered papers and boards may include different kinds of magazines, newsprints, boards etc. The recovered raw material comprising cellulose-based fibers may be pulped and de-inked in order to obtain the recycled fibers. The recycled fibers may be bleached to obtain recycled papers having improved whiteness.

However, there is still a need for an improved method to obtain recycled cellulose based fibers.

Summary

This invention relates to a method for manufacturing recycled cellulose based fibers. The invention may further relate to sorting, logistics and warehousing of recovered papers and/or boards before the manufacturing of recycled cellulose based fibers.

Aspects of the invention are characterized by what is stated in the independent claims. Various embodiments of the invention are disclosed in the dependent claims. Recycled fibers may have many different raw material sources such as magazines, white office papers, boards etc., hence, quality of the recycled fibers may vary. Thus, the recycled fibers might decrease quality of papers or boards comprising said recycled cellulose based fibers.

For paper mills using the recycled fibers, the quality of the pulp is important. Thanks to the novel solution, it may be possible to use recycled cellulose based fibers with many kinds of paper and board grades in an optimized way.

Moreover, it may be possible to control the deinking process, such as determine the amount of needed chemicals, according to raw materials applied into the process.

Thus, thanks to the novel solution, it may be possible to adjust at least one step of a deinking process in order to improve quality of the manufactured cellulose based recycled fibers.

Still further, thanks to the novel solution, it may be possible to select raw materials for the manufacturing process of recycled cellulose based fibers in order to obtain a predetermined quality for said recycled fibers.

The method for manufacturing recycled cellulose based fibers may comprise the following steps:

- analyzing and describing a composition of recovered raw material comprising papers and/or boards by use of at least 4 product groups, preferably at least 6 product groups, each product group defining a type of the raw material,

- determining product groups available for a deinking process,

- predicting quality achievable at the deinking process based, at least, on at least one quality parameter, and the product groups available for the deinking process, thereby determining a predicted level of said at least one quality parameter,

- applying a predetermined share of each product to the deinking process based on the prediction, wherein a gravimetric share of each product group may be used to predict a level of said at least one quality parameter,

Wherein the deinking process comprises at least the steps of: - pulping the applied materials in water-based solution in order to obtain pulped material, and

- removing at least some impurities from the pulped material in a flotation step and/or a screening step, thereby obtaining the recycled cellulose based fibers.

The method may further comprise:

- predicting quality of the recovered raw material based, at least, on said at least one quality parameter.

The method may further comprise

- predicting the quality by simulation in which the predetermined share of each product group is used.

The method may further comprise the following step:

- adjusting at least one process step of the deinking process based on

- the applied materials, and

- the predicted level of said at least one quality parameter.

Thanks to the predicted level of said at least one quality parameter, it may be possible to achieve a lower process and quality variation compared to a process adjustment based on the measured quality deviation at the deinking process.

Further, the method may comprise the following step:

- applying one or more than one chemical to at least one process step of the deinking process, wherein a dosage of the one or more than one chemical is based at least on the predicted level of said at least one quality parameter.

Therefore, it may be possible to obtain decreased consumption of chemicals and, hence, decreased cost of chemicals compared to a process adjustment based on the measured quality deviation at the deinking process.

In addition or alternatively, the method may comprise the following steps: - measuring at least one quality parameter from the obtained recycled cellulose based fibers,

- checking the reliability of prediction by comparing said at least one predicted quality parameter with said at least one measured quality parameter, and

- optionally, calibrating the prediction based on a difference between the at least one measured quality parameter and the at least one predicted quality parameter.

A deviation between predicted and measured parameters may e.g. result from changes in the share printing methods or composition of printing inks. Therefore, at too high deviation, the parameters in the prediction model may be updated.

The novel solution may comprise a use of equal to or more than 4 product groups, preferably equal to or more than 6 product groups, more preferably equal to or more than 9 product groups, and most preferably equal to or more than 16 product groups.

By increasing the number of product groups, it is possible to improve the reliability of the prediction. However, if too many product groups are used, the analyzing and describing step may take too much time. Thus, in an embodiment, the novel solution may comprise a use of equal to or less than 25 product groups, more preferably equal to or less than 21 product groups, and most preferably equal to or less than 19 product groups.

In an embodiment, the following product groups are used in the method:

- at least one product group for White Boards, and/or at least one product group for Woodfree Coated Papers (WFC), and/or at least one product group for Woodfree Uncoated Papers (WFC),

- at least one product group for Grey and/or Brown Boards,

- at least one product group for Light Weight Coated Papers (LWC), and/or at least one product group for Supercalanderd Papers (SC), and

- at least one product group for Newsprint Papers.

In an embodiment, the following product groups are used in the method: at least one product group for Grey Boards and/or Brown Boards, at least one product group for White Woodfree Papers and White Boards, at least one product group for Light Weight Coated Papers (LWC) and/or Supercalanderd Papers (SC), and - at least one product group for Newsprint Papers.

In an embodiment, the following product groups are used in the method: at least one product group for White Boards, at least one product group for Grey Boards and/or Brown Boards, - at least one product group for Woodfree Coated Papers (WFC) and/or

Woodfree Uncoated Papers (WFU), at least one product group for Light Weight Coated Papers (LWC) and/or

Supercalanderd Papers (SC), and at least one product group for Newsprint Papers.

Each of the product groups may be composed of a range of paper products which will show similar values of predetermined quality parameters e.g. after pulping and/or flotation in the deinking process. The use of a too low number of different product groups may result in a too wide variation of quality within product groups, resulting in unreliable prediction results. The product groups may be defined in a way that paper products for recycling can be allocated to them by use of visual inspection.

The at least one quality parameter may comprise at least one quality parameter selected from the following group:

- at least one optical quality parameter,

- deinking process yield,

- mineral filler content,

- fiber content,

- ink elimination value,

- dark fiber content, and

- macrostickies.

In recycled fibers, mineral filler content strongly effects the strength. The predicted quality may be used to evaluate if a recovered paper composition is suitable for the production of defined paper products. In this application, the terms “optical quality parameter” and “optical parameter” refer to brightness, luminosity Y, and shade L,a,b. Thus, the at least one optical quality parameter can comprise at least one of:

- brightness,

- luminosity Y, and

- shade L,a,b. The method may further comprise: using Kubelka-Munk model for predicting at least one optical quality parameter.

In recycled fibers, the optical parameters brightness and luminosity may be key to reach the visual quality of final paper products. The predicted quality may be used to evaluate if a recovered paper composition is suitable for the production of defined paper products.

In printing paper products, visual impression and/or mechanical properties (such as a strength) can be key quality characteristics.

In an embodiment, the deinking process comprises at least the process steps of pulping, - flotation, optionally, screening, optionally, at least 1 bleaching step, such as from 1 to 3 bleaching steps.

The method can comprise the following step: - predicting the at least one quality parameter for at least two deinking process steps.

The method may comprise, for example, the following step: predicting at least one quality parameter after the pulping and before the flotation. The method may comprise, for example, the following step: predicting at least one quality parameter after the flotation and before the bleaching.

The method may comprise, for example, the following step: predicting at least one quality parameter after at least one bleaching step, for example for a first bleaching step, for a second bleaching step, or for a third bleaching step.

In an embodiment, the method may comprise: determining a quality of the recovered raw material based on the predicted level of said at least one quality parameter.

The method may comprise the following step before the raw material is applied to the deinking process: determining a quality of at least 2 supplies of the recovered raw material based on at least one predicted quality parameter.

Alternatively or in addition, the method may comprise:

- determining a needed minimum value of at least one quality parameter for the recycled cellulose based fibers,

- determining which product groups are suitable for the deinking process in order to obtain said needed minimum value, and

- calculating the share of each product group needed to obtain the said at least one minimum value.

In the method, a quality can be predicted by simulation in which the predetermined share of each product group is used.

The novel solution may comprise a use of a simulation process in a sorting plant to generate a sorted paper mixture with specified quality to be applied into a pulping stage of a manufacturing process of recycled cellulose based fibers. Alternatively or in addition, the novel solution may comprise a use of a simulation process to specify a quality of a recovered raw material to be used in a manufacturing process of recycled cellulose based fibers.

Alternatively or in addition, the novel solution may comprise a use of a simulation process to optimize a feed of materials to be applied into a pulping stage of a manufacturing process of recycled cellulose based fibers.

Due to the novel solution, it may be possible to obtain a paper, which comprises recycled cellulose based fibers and has suitable properties for its purpose. Thus, it may be possible to use recycled cellulose based fibers for new products and/or to obtain high quality products comprising recycled cellulose based fibers in a more replicable and reliable way. The usage of recycled fibers may be an environmentally friendly way to manufacture papers and boards.

Further, thanks to the novel solution, it may be possible to use a simulation process for deinking process optimization. Further, it may be possible to gain quality information of single supplies or of grouped supplies. Still further, it may be possible to determine how much raw material from each product groups should be used to obtain predetermined quality for the recycled cellulose based fibers.

The novel solution may be used, for example, for

- predicting quality level of recycled cellulose based fibers based on the raw materials,

- improving deinking line process optimization, and/or

- gaining quality information of single supplies or of grouped supplies.

Due to the novel solution, the recycling of papers and boards may be used to help to minimize solid waste and to maximize the reuse of raw materials. In addition, the novel solution may help to minimize water pollution and air pollution.

In an embodiment, value of recovered raw materials can be evaluated based on the predicted quality. The resulting quality of grade mixtures can be modelled, which may allow to evaluate the suitability of a planned or used mix, to select towards an average target quality, and/or towards reduced costs.

Thanks to the novel solution, deinkability and recyclability levels of recovered papers and boards may be predicted. The novel solution may be used for optimization of the warehousing, e.g., by quality specific feed to storage space and or creation of quality homogeneous mixtures in the warehouse, and optimization of the recycling processes by using forward control. Still further, the novel solution may be used to decrease costs with same quality level and/or improve quality level with the same costs.

Brief description of the drawings In the following, the invention will be illustrated by drawings in which

Figs 1-2 show some example methods for manufacturing recycled fibers, and

Figs 3a-b show examples of brightness levels for some example product groups.

The drawings are schematic.

Detailed description

The following reference numbers are used in this application:

10 analyzing and describing a composition,

20 predicting quality achievable at a deinking process, 30 applying predetermined share of each product group to a deinking process,

110 pulping step of the deinking process, 120 flotation and/or washing step(s) of the deinking process, 130 screening step of the deinking process, 140 thickening step of the deinking process.

START0 recovered raw material, REJECT reject from the recovered raw material, GROUP1 first product group, GROUP2 second product group, GROUP3 third product group, GROUP4 fourth product group, RCF recycled cellulose based fibers, WFC Woodfree Coated Paper, WFU Woodfree Uncoated Paper, LWC Light Weight Coated Paper, and SC Supercalandered Paper.

The term “recovered raw material” STARTO refers to recovered papers and boards, such as magazines, white and grey boards, etc., to be used in order to obtain recycled cellulose based fibers. The term “recovered raw material” refers to all types of unsorted or sorted post-consumer or pre-consumer paper and board products. The recovered raw material STARTO may be divided into groups according to type of the raw material, such as newsprint, white board, etc. by using several product groups GROUP1, GROUP2, GROUP3. The terms “recycled fibers” and “recycled cellulose based fibers” both refer to recycled cellulose based pulp, which may be reused as a raw material in another product. The recycled cellulose based fibers RCF may be obtained from the recovered raw material STARTO. The term “board liner” refers to a board with a white top.

Unless otherwise stated, the following standards (valid in 2020) refer to methods which may be used in obtaining stated values of parameters representing paper or pulp quality: An example of a recycling process

The process whereof recycled cellulose based fibers RCF may be obtained, may be called as a recycling process. The recycling process may comprise several mechanical steps for removal of unsuitable materials.

The recycling process may include the following main steps:

- collection of papers and boards after use in e.g. households or byproducts from production processes, - sorting and/or allocation of trading goods,

- logistics and warehousing, and

- processing of the recovered raw material to recycled fibers.

This application may relate to the last 3 steps of the recycling process, wherein the recovered raw material is stored for use and then processed into recycled fibers. The application may particularly relate to the last step, i.e. , processing of the recovered raw material to recycled fibers. The processing of the recovered raw material to recycled fibers typically includes a deinking process. Some main properties of the fibers, such as a lignin content, cannot be substantially changed during the deinking process. Therefore, the type and quality of the recovered raw materials STARTO need to be suitable for the product to be manufactured from the recycled fibers RCF in order to obtain a needed paper and board product quality. Typically, it is not possible to produce all paper grades from all types of recovered raw materials.

Typically, paper mills categorize the recovered raw materials according to the EN 643 standard. It is also possible to use quality control tests of the supplied raw materials. Said quality control tests may include the manual removal from non-paper components from the recovered papers, pulping, cleaning of pulped stock from non-paper components and upgrading of the cleaned stock by chemical additions, flotation, and bleaching, if necessary. Thus, quality control tests are typically quite time-consuming processes. An example of a dein king process

The recovered raw material STARTO can be repulped in a water-based solution to release at least some of the ink and/or other unwanted particles from the fibres to the water-based solution. This process for removal of ink from a slurry of defibrated recovered paper can be called as deinking.

During the deinking process, the recovered raw material can be mixed with chemicals and heated and mechanically sheared, usually called dispersing, to form recycled cellulose based fibers.

In accordance with an embodiment and referring to Figs 1-2, the deinking process may have at least a dilution step, which may be called as a pulping step 110. The deinking process may further have at least one screening step 130 to remove impurities. In addition, the deinking process may comprise at least one flotation step 120. The flotation step 120 may be used to remove at least some of the released particles, such as inks, from water-based solution. The deinking process may further comprise at least one thickening step 140. The deinking process may further comprise bleaching step(s).

Thus, the method for manufacturing recycled cellulose based fibers RCF may comprise the following steps: pulping 110 at least part of the recovered raw material STARTO in water- based solution in order to obtain pulped material, - removing at least some impurities from the pulped material by using a flotation step 120, and/or a screening step 130, and/or a cleaning step, and removing at least some water from the material in order to obtain recycled cellulose based fibers RCF.

The deinking process for manufacturing recycled cellulose based fibers RCF may comprises at least some of the following steps in the following order, more preferably all the following steps 1 to 5:

1. pulping, 2. first screening and/or cleaning,

3. flotation and/or washing, 4. second screening and/or cleaning, and

5. thickening and/or washing.

The pulping may be implemented with a pulper. The pulper may be used to disintegrate the recovered raw material into small pieces in a water-based solution. The recovered raw material needed to obtain the recycled cellulose based fibers can be fed, preferably with chemicals, to the pulper.

In an embodiment, the consistency of the pulp may be e.g. in a range between 7 and 25 % in the pulper and, for example, approximately 2.5% before pumping to the first tank after pulping.

The pulped raw material may be screened in a screening section. In an embodiment, the pulp is diluted to a 0.6 - 4 % consistency for the screening. The screening section may comprise several screening steps. The fine- screening step may use, for example, consistencies between 0.6 - 1 .5%. The screening section may comprise, for example, primary and secondary screens, preferably primary, secondary, tertiary, and quaternary screens.

If the recovered raw materials comprise ink, the ink of the recovered raw materials can be released from the fibers to the water-based solution, after which the ink can be at least partly removed. The ink may be removed, at least partly, in a flotation step 120. Thus, the deinking process can comprise a flotation step 120 with the main task to remove printing ink particles from the recycled fibers. The flotation step 120 may further remove some other particles, such as mineral fillers, some fibers, as well as some adhesives or silicone particles. Flotation is the mostly used deinking process. Alternatively, a wash deinking step may be used instead of the flotation step.

The flotation step may be implemented with flotation cells. Air is typically injected into the water-based solution comprising the cellulose-based fibers and, thus, e.g. ink particles may attach themselves to air bubbles and, hence, rise to a surface. Therefore, at least some of the ink particles can be removed. A process temperature may be, for example, between 40 and 60°C, and consistency during the flotation step may be in a range between 0.4% and 2.5%, such as in a range between 0.6% and 1.8%. The pulp may be screened and/or cleaned by using, for example, centrifugal cleaner(s) and/or pressure screen(s). Screens may be used, for example, to remove small impurities. Screening can be implemented e.g. with at least one pressure screen.

In an embodiment, after the flotation step and at least one screening step, the pulp may be thickened, and/or the pulp may be washed. At least some of the screen(s) and/or cleaners may be situated after the flotation step.

The pulp may be thickened, for example, by using at least one of the following: disc filter, drum filter, gravity thickener, - gap washer thickener, wire press, screw press, and roll press. For example, at least one disc filter may be used for the thickening step because it may be the least expensive device for the purpose.

At least one screw press and/or at least one drum press may be used to reach higher pulp consistency.

Furthermore, the deinking process may comprise, for example, dispersing step e.g. between the fine screening step and the post-flotation step.

Not only the above-mentioned process steps, but also other steps may be included to the deinking process, such as a post-flotation and a final thickening step(s).

Still further, bleaching may be used to improve the brightness and luminosity of the recycled cellulose based fibers RCF. Thus, the recycled cellulose based fibers may be bleached. The bleaching of the recycled cellulose based fibers may be implemented after one or more flotation steps. Preferably, at least a first bleaching step and a second bleaching step are used. In an embodiment wherein a high brightness level is needed for the obtained RCF, the deinking process may comprise two or three bleaching steps.

The main chemical for the bleaching step(s) may be selected e.g. from the following list:

Hydrogen Peroxide, - Sodium Dithionite, and

Aminoiminomethanesulfinic acid.

Conventionally, only a very rough estimation about the quality of the recycled fibers to be obtained from the process may have been obtained, and even this has been required a person highly skilled in the art having an expert level of knowledge. The information about quality of the recycled cellulose based fibers has been limited, and there has not been any kind of quality prediction for recycled cellulose based fibers from mixtures of recovered paper supplies in use.

An example of recovered raw materials for a deinking process

The recovered raw material STARTO, wherefrom the recycled cellulose based fibers RCF can be obtained via the deinking process, typically comprises several different papers and/or board grades.

The recovered raw material STARTO comprises, at least, papers and/or boards comprising cellulose-based fibers. The cellulose-based fibers may originate from wooden material. The cellulose- based fibers can originate, e.g., from softwood trees, such as spruce, pine, fir, larch, douglas-fir and/or hemlock, or from hardwood trees, such as birch, aspen, poplar, alder, eucalyptus and/or acacia, or from a mixture of softwoods and hardwoods. Wood species differ from each other in their mechanical properties and chemical compositions. In addition, the cellulose-based fibers may comprise non-wooden material. Hardwood has different properties compared to softwood, and non-wood material has different properties compared to wood material. Still further, the recovered raw material may comprise recycled fibers, which may comprise e.g. different fiber length distribution compared to material consisting of virgin pulp. Typically, recycled fibers are shorter than e.g. virgin chemical softwood pulp fibers.

Depending on the processing method, the cellulose-based fibers may have been obtained via mechanical methods, chemical methods, or by using a semi chemical method, wherein a combination of mechanical and chemical methods is used. Mechanical pulp has different properties compared to chemical pulp.

The recovered raw material STARTO may further comprise e.g. mineral fillers and/or coating layers. Therefore, the recovered raw material STARTO may also comprise different kind of fillers and coatings. Mineral fillers can comprise, for example, clay, calcined clay, kaolin, natural ground calcium carbonate, precipitated calcium carbonate, talc, calcium sulphate, and/or titanium dioxide. Still further, the recovered raw material may comprise adhesives etc.

Moreover, papers and boards are available in different colors. Therefore, the recovered raw material STARTO may contain papers and boards in different colors. In addition, the recovered raw material STARTO may comprise printing colors, i.e. , inks.

Due to the variety of the recovered raw material STARTO, the quality of recycled fibers may also greatly vary depending on the raw materials.

Some examples of product groups

The novel solution can be based on a use of at least 4 product groups, which each product group defines a type of the raw material, such as a paper or board grade.

Thus, the recovered raw material may be divided into groups by using said product groups. By using the product groups, it may be possible to obtain some predictivity for the recycled cellulose based fibers. Conventionally, the focus of the incoming quality inspection has mostly been on evaluating the unwanted materials, which are only a small fraction of the recovered raw materials. Typically, quality inspection has been done, at least mostly, by visual inspection. Conventionally, the main composition in e.g. the product types like newspapers and magazines are not analyzed in detail, but the minimal requirement has been a splitting the recovered raw material into accepted papers (including papers and boards), and unwanted materials.

The Paper for Recycling grades can be defined according to EN 643 standard. The focus of said standard is on the description of the wanted and unwanted content of the grades but it does not give profound quality statement of the recycled fibers during or after the deinking process. The EN643 merely defines what the different grades of paper for recycling must and must not contain as well as defining prohibited materials and unwanted materials, including tolerance levels by grade for unwanted materials.

In an embodiment, the raw material may be accepted or rejected based on the visual inspection. The unwanted materials may be defined as follows: Non-paper components,

Paper and board not according to grade description,

Paper and board detrimental to production, and Paper not suitable for deinking.

In an embodiment, when a load of recovered raw material entries to a sorting plant of a mill, there is first an entry inspection. The entry inspection may comprise a visual inspection (manually and/or automatically) wherein one or more than one of the followings are determined:

- baling condition,

- grade characterization, and

- unwanted materials.

Further, the entry inspection may comprise, for example, a measurement of a moisture content.

The load may be accepted or rejected based on the visual inspection. The visual inspection can be made

- manually, - automatically, or

- partly automatically and partly manually.

The recovered raw materials STARTO may comprise different paper and board grades on the market. Thus, in order to predict quality of the recycled fibers RCF obtained from the recovered raw materials STARTO, the recovered raw material STARTO can be analyzed and divided into groups by using predetermined product groups. As discussed, there is a very wide range of printed and converted paper products on the market. Further, papers can be printed with printing inks with very different composition in e.g. coldest offset, heatset offset, sheets offset, inkjet, liquid toner, or dry toner printing. Still further, inks can penetrate into the paper, they can be dried in an oven, or crosslinked. Additionally, printed products can be varnished by using dispersion varnish or UV-varnish methods. Moreover, different glues can be applied to the papers and boards.

Surprisingly, it was found out that the wide range of final paper products can be grouped into product groups in order to predict quality of the recycled fibers RCF obtained from the recovered raw materials.

In order to divide the recovered raw material into groups, the recovered raw material STARTO can be analyzed. Analysis of the recovered raw material STARTO may be used to distinguish different raw materials from each other. The analysis to distinguish different raw materials from each other may be done manually and/or, at least partly, automatically.

The novel solution comprises a use of 4 or more product groups, preferably 6 or more product groups. As discussed, in addition to the product groups, at least some of the recovered material may belong to unwanted materials i.e. , the reject.

The recovered raw material STARTO may be divided by taking a representative sample of a load and determine the product group for the sample. In this embodiment, preferably, at least 2 bales or 1 truck with loose raw material can be chosen for the representative sample. The product group(s) of the recovered raw material may be determined manually. In this embodiment, a person skilled in the art uses his/her expert skills to determine the product group(s) of the recovered raw material STARTO. Visual inspection may comprise a visual evaluation of all loads by a person skilled in the art.

In an embodiment, the recovered raw material is allocated to the product groups automatically, or at least partly automatically. For example, a high- resolution camera may be used to acquire an image of a sample. The properties of the paper product, such as fibers, coatings, and inks, in the sample may be determined from the image. The product groups may be distinguished by a comparison method, wherein result of the sample is compared against known reference samples. In an embodiment, at least one sensor is used to determine one or more properties from the sample, such as an estimation of a moisture content of the sample.

The recovered raw material may be divided or allocated, at least, by using the following product groups: - group 1 : white board and white woodfree paper,

- group 2: non-white board and colored paper,

- group 3: magazines and advertisement products comprising light weight coated paper (LWC) and supercalandered paper (SC), and

- group 4: newspaper.

The white woodfree paper may further be divided by using, at least, the following product groups:

- Woodfree Coated Paper (WFC), and

- Woodfree Uncoated Paper (WFU).

The non-white board may further be divided by using, at least, the following product groups:

- grey board,

- grey board with a white top, - brown board, and

- brown board with a white top. Magazines are illustrated publications. Magazines may comprise uncoated and coated papers. Newspapers may comprise newsprint paper, telephone books and similar types of printed products.

The magazines and newsprint papers may comprise the following non advertisement product groups:

- Light Weight Coated Paper (LWC),

- SC-A Paper (SC-A), - SC-B Paper (SC-B),

- Improved Newspaper,

- Standard Newspaper, with mostly black printing,

- Standard Newspaper with high share of color printing, and

- Colored paper.

Further, magazines and newsprint papers may comprise the following advertisement product groups:

- Light Weight Coated Paper (LWC) of advertisement products,

- Supercalanderd Paper (SC) of advertisement products, and - Newsprint Paper (News) of advertisement products.

In an embodiment, light weight coated paper LWC may be divided by using at least the following product groups:

- Light Weight Coated Paper (LWC) magazines, and - Light Weight Coated Paper (LWC) of advertisement products.

Further, Supercalanderd Paper (SC) may be divided by using at least the following product groups:

- SC-A Paper - SC-B Paper, and

- Supercalanderd Paper (SC) of advertisement products.

Further, the newspapers may be divided at least into the following product groups: - Improved Newspaper,

- Standard Newspaper, with mostly black printing, - Standard Newspaper with high share of color printing, and

- Newsprint Paper (News) of advertisement products.

In an embodiment, the recovered raw material may be divided, at least, by using the following product groups:

Group 1: White Board,

Group 2: Grey Board,

Group 3: Grey Board with a white top,

Group 4: Brown Board,

Group 5: Brown Board with a white top,

Group 6: Woodfree Coated Paper (WFC),

Group 7: Woodfree Uncoated Paper (WFC),

Group 8: Light Weight Coated Paper (LWC) which may further be divided into

Group 8.1 LWC magazines, and Group 8.2 LWC advertisement products,

Group 9: Supercalanderd Paper (SC), which may be further divided into Group 9.1 : SC-A Paper (SC-A), and Group 9.2. SC-B Paper (SC-B),

Group 9.3: SC of advertisement products,

Group 10: Newsprint Paper, which may be further divided into Group 10.1: Improved Newspaper,

Group 10.2: Standard Newspaper with mostly black printing, and Group 10.3: Standard Newspaper with high share of color printing, Group 10.4 Newspaper of advertisement products, and Group 11 : Colored paper.

In an advantageous embodiment, the recovered raw material may be divided, at least, by using the following product groups s:

Group 1: News,

Group 2: Magazines,

Group 3: Advertisements,

Group 4: White Papers,

Group 5: White Board,

Group 6: Grey Board, and Group 7: Brown Board, and Group 8: Colored Papers.

Any and each of the above mentioned product group may be further divided into other product groups, for example, as disclosed in this application.

In an advantageous embodiment, the method comprises analyzing and describing a composition of recovered raw material comprising papers and/or boards by use of the following product groups, each product group defining a type of the raw material - Group 1: Woodfree Coated Paper (WFC),

- Group 2: Woodfree Uncoated Paper (WFU),

- Group 3: LWC Advertising Paper,

- Group 4: LWC magazines,

- Group 5: White Board, - Group 6: SC-A Products,

- Group 7: SC Advertising Paper,

- Group 8: SC-B Products,

- Group 9: News Improved,

- Group 10: News Advertising, - Group 11 : News Standard,

- Group 12: News color print,

- Group 13: Grey Board,

- Group 14: Grey board with a white top,

- Group 15: Brown board with a white top, - Group 16: Brown board, and

- Group 17: Colored Paper.

The recovered raw material STARTO may comprise materials from some or all of the used product groups.

Advantageously, the method may comprise a use of at least 6 product groups. Preferably, the solution comprises a use of at least 8 product groups, more preferably at least 9 product groups, such as in a range between 6 and 21 product groups, or in a range between 10 and 18 product groups. Target of the lower number of product groups is mainly the reduction of the workload at the recovered paper income quality control testing at the mills. However, this typically results in lower precision of the prediction of quality of the recycled fibers to be obtained.

A reduction of the total number of the product groups increases the variation of resulting quality parameters. Particularly with a number of the product groups lower than 4, the results vary too much for being useful for a simulation. For deinked pulp for use in graphical paper grades, the minimum of 17 product groups was found to be the most suitable number of product groups.

An automated visual inspection system, such as an NIR hyperspectral imaging (near infra-red) inspection, may be used to detect the composition of paper for recycling, and to calculate the share of selected product groups. This may improve efficiency of allocation to product groups if compared with manual inspection. The combination of the automated detection of the share of product groups and the simulation of quality parameters may give very fast information about the value and potential of mixtures of paper for recycling.

The prediction of quality parameter(s) of recycled fibers made from the recovered raw material STARTO, may be based on the gravimetric share of specified product groups.

Quality parameters of the recycled cellulose based fibers

For production of graphical paper grades, the optical quality (such as brightness, luminosity Y, shade L, a, b) of the recycled fibers may be essential. Another quality criterion may be a low content of sticky materials in the recovered raw material, because recycled fibers comprising stickies may create runnability and quality problems during paper production.

The novel solution may be used to predict quality parameters of recycled cellulose based fibers RCF.

The predicted quality may describe deinkability and recyclability of the recovered raw materials. In an embodiment, a feed of recycled fibers, virgin fiber, fillers, and dye to a paper or board machine is determined based on the quality of the recycled cellulose based fibers.

The method may comprise one or more than one of the following quality parameters: at least one optical quality parameter, deinking process yield, mineral filler content and/or fiber content, ink elimination value, dark fiber content, at least one strength parameter, and macrostickies.

Suitable optical properties, i.e. , brightness and/or luminosity, may be essential for production of e.g. printing paper grades, tissue, and some board grades. Therefore, most advantageously, a predicted level of said at least one optical quality parameter may be determined.

Most preferably, said at least one optical parameter is predicted, at least, for the obtained recycled cellulose based fibers RCF.

In this application the term “strength parameter” may refer to any strength parameter measurable from the obtained pulp. In an advantageous embodiment, the strength parameter comprises at least one of tensile strength and tear strength.

The shade may be measured on the CIELAB model. CIE L ^* a ^* b ^* (CIELAB) is a color space specified by the International Commission on Illumination (French Commission Internationale de I'eclairage, hence its CIE initialism). It describes the colors visible to the human eye. L ^* is for lightness. The red/green opponent colors are represented along the a ^* axis, with green at negative a ^* values and red at positive a ^* values. The yellow/blue opponent colors are represented along the b ^* axis, with blue at negative b ^* values and yellow at positive b ^* values. A skilled person knows the CIELAB model. A real (measured) value may be determined for each quality parameter, for each product group. Advantageously, at least one optical quality parameter, preferably selected from brightness and luminosity, is determined for each product group.

Further, for example, a mineral filler content may be determined for each product group used in the method.

Visual impression of final paper products is strongly linked to brightness and luminosity, and strength is linked to mineral filler content.

The determined/measured values may be used in order to create a simulation model suitable for prediction. Thus, real values of selected quality parameters may be determined for each product group used in the method.

An example wherein real brightness values are shown for each selected product group is shown in Figs 3a and 3b.

Preferably, selected quality parameter(s) is/are measured/determined at least for the obtained recycled cellulose based fibers.

In an advantageous embodiment, said selected quality parameter(s), for each product group, is/are further determined at least directly after pulping step. Thus, it may be possible to adjust the deinking process based on the predicted quality value(s).

The selected quality parameters may comprise at least one quality parameter selected from the following list:

Deinking process yield,

Fiber content,

Dark fiber content,

Macrostickies,

Ink elimination value,

Dirt spec content, and

Strength parameter, such as tensile strength and/or tear strength. In an embodiment, at least one, such as at least one strength parameter, more preferably at least two of the above-mentioned quality parameters is used in the method. Level of said quality parameter(s) for each product group can be determined, at least, for the obtained recycled cellulose based fibers RCF.

Thanks to the measured/determined values of quality parameter(s) for each product group, it may be possible to predict a quality level of a mixture comprising several product groups.

In an embodiment, values of quality parameter(s) for each product group are further determined/measured for an unbleached deinked pulp. Most preferably, values of quality parameter(s) for each product group are determined/measured for

- a pulping step,

- unbleached recycled fibers, and

- bleached recycled fibers.

Thanks to the novel solution wherein quality values may be determined for each product group used in the method, the novel method may be used to predict quality parameters,) of a mixture comprising several different kinds of raw materials. This prediction may be used to determine amounts of each product groups to be applied to the deinking process. Thus, it is possible to obtain improved recycled fibers quality, and/or reduce manufacturing costs of the recycling fibers.

Further, it is possible to obtain an improved controllability of the deinking process so that one or more process steps can be controlled to obtain improved quality for the recycled fibers.

Still further, it may be possible to decide which kind of raw materials (product groups) are needed to obtain predetermined quality of the recycled fibers.

In order to predict a level for a selected quality parameter, such as a brightness, the level of the selected quality parameter can be determined for each product group to be used. Thus, first, the product groups to be used may be selected, after which values of selected quality parameter(s) may be determined for each product group by measuring said quality parameter(s) from samples of every single product groups to be used. Thus, it is possible to determine real level for the selected quality parameter(s) for all selected product groups.

An example illustrating brightness level for 16 selected product groups is shown in Figs 3a and 3b. Said Figures 3a and 3b show an example, wherein brightness potential level is determined for each predetermined product groups after pulping (Fig. 3a) and after flotation (Fig. 3b). The determined brightness potential levels can be used to predict a brightness level to be achievable at a deinking process, when a predetermined share of each product group is applied to the deinking process.

Similarly, any other selected quality parameter may be predicted by first measuring values of the selected quality parameter(s) for each product group, and then use the determined quality values to predict a quality achievable at a deinking process when a predetermined share of each product group is applied to a deinking process.

An example of analyzing tools

A system according to the invention may comprise an analyzing unit comprising a power unit, a processor for processing data, a data storage comprising at least one memory component, optionally, transmitting means, such as a component for transmitting and receiving data, and a display.

In an embodiment, a cloud service unit may be a part of the system.

The processor used for processing data can be, for example, a processor of a mobile device, a tablet, or a personal computer (such as a laptop computer). Preferably, the processor is the processor of the personal computer. The processor may be configured to analyze the amount of materials needed from each product groups in order to obtain predetermined quality level of the recycled cellulose based fibers. The analyzing process may be done, e.g. in the computer, or in a cloud service unit.

The prediction of the quality in which the predetermined share of each product group GROUP1 , GROUP2, GROUP3, GROUP4 is used, can be implemented by using a simulation model.

An application may be used to collect data, analyze the data, and to create a feedback based on the analysis. The feedback may be used to refine the simulation model.

In an embodiment, the recovered raw material STARTO is divided by using product groups, and the amount of each product group available for the deinking process is stored in to the at least one memory component. Further, the information of achievable quality level of each product group may be stored in to the at least one memory component.

The novel solution uses product groups to predict a quality achievable at the deinking process.

The novel solution may comprise analyzing means predicting quality of recycled cellulose based fibers based on the product groups. Thanks to the novel solution, it is possible to obtain deinkability and recyclability prediction of the recovered raw materials.

As discussed, the analyzing means may use a simulation model, which can be used to predict at least one quality parameter of the recycled cellulose based fibers based on the product groups used in the deinking process.

Thus, analyzing means may be used for predicting quality of the recycled fibers, based on the amount of each product groups fed to the deinking process.

Therefore, the method may comprise the following steps: - analyzing the composition of a mixture based on amounts of each product groups fed to the deinking process, and

- predicting a level of at least one quality parameter of the recycled cellulose based fibers to be obtained based on the analysis.

In an embodiment, the step of predicting a level of at least one quality parameter is based on a use of Kubelka-Munk model. Thus, the Kubelka- Munk model may be used for prediction of at least one quality parameter.

The Kubelka-Munk model may use, e.g., light scattering and light absorption parameters for calculation of e.g. brightness of a pulp mix from minimum two pulps of known quality. Optical parameters may not be predicted for pulp mixtures with a direct use of e.g. brightness data of the single pulps. Therefore, Kubelka-Munk model may be used in order to predict quality of the recycled cellulose based fibers based on the product groups. The Kubelka-Munk model may be used for prediction of quality parameters for mixtures of product groups.

The simulation model may combine the determined parameters for each product group for combined quality profile simulation. Thus, it may be possible to predict quality parameters of recycled cellulose based fibers based on the amount of product groups in the raw material mix.

Each of the product groups may be composed of a range of paper products which will show similar quality parameters after pulping and flotation in the deinking process. The use of a too low number of different product groups may result in a too wide variation of quality in product group, resulting in unreliable prediction results.

The product groups may be defined in a way that paper products for recycling can be allocated to them by using visual inspection. This procedure can be very valuable for selection of recovered raw material supply. Laboratory testing of samples may not be needed anymore, because it can be substituted by determination of the weight share of product groups. As discussed, the recovered raw materials may comprise rejected materials. The rejected materials may not be a part of any simulation model, because the rejected material is mostly removed (e.g. in the pulping stage) before the deinking process.

The recovered raw material STARTO can be analyzed and described by using at least 4 product groups, each product group defining a type of the raw material. However, each load of recovered raw material may not comprise all product groups. The recovered raw material STARTO may comprise materials from only some, or several product groups.

In an embodiment, the method may comprise:

- optionally, removing a reject from recovered raw material,

- grouping the recovered raw material into product groups, preferably by using equal to or more than 4 product groups, more preferably equal to or more than 9 product groups, and most preferably equal to or more than 16 product groups, and

- predicting a level of at least one quality parameter for at least one deinking process step, preferably by using a simulation model, such as the Kubelka- Munk model.

Further, the method may comprise the following step:

- adjusting at least one process step of the deinking process based on the prediction.

The adjustment may be performed automatically, for example, under the control of a processor. Alternatively, or in addition, the adjustment may be performed based on commands in a User Interface.

Further, the method may comprise the following step:

- measuring a level of at least one quality parameter from the obtained recycled cellulose based fibers in order to calibrate the simulation model. Example steps of a method for manufacturing recycled cellulose based fibers

In an embodiment, the method for manufacturing recycled cellulose based fibers may comprise the following steps:

- selecting a needed minimum level of at least one quality parameter,

- determining product groups available for the deinking process, and

- determining amount of each product group needed to obtain the said minimum level.

In an embodiment, the method comprises:

- determining amount of materials in each product group available for deinking process,

- determining amounts of materials needed from each product group to obtain a needed minimum level of a selected quality parameter,

- applying the determined amounts of materials into the deinking process, and

- optionally, adjusting at least one process step of the deinking process based on the analysis.

In an embodiment, at least some of data relating to product groups are stored into the at least one memory component. Thus, the step of determining product groups available for the deinking process may comprise reading and/or analyzing the data stored to the at least one memory component.

As discussed, in an embodiment, the recovered raw material STARTO can be grouped by using 4 or more product groups. Thus, in an embodiment, a simulation model for predicting quality of the recycled fibers may use said product groups in order to be able to predict a level of at least one quality parameter of the recycled cellulose based fibers.

In an advantageous example, the model comprises equal to or more than 10 product groups, and the simulation model for predicting quality of the recycled fibers may use said product groups. However, it should be understood, that even if the simulation model comprises a use of several product groups, the applied material may comprise materials only from some product groups of the model.

Therefore, the simulation model preferably uses at least 6 product groups for determining the expected quality of the manufactured recycled cellulose based fibers. In most cases, the simulation model should use at least 9 product groups to maintain good predictability of the process. The predictability of the process will improve if the simulation model uses at least 10 product groups, more preferably equal to or more than 13 product groups, and most preferably equal to or more than 17 product groups.

The smaller total number of product groups enables faster sorting of the coming recovered raw material. Thus, the sorting step may be done more efficiently if the coming recovered raw material is not divided into more than 17 product groups. Therefore, for improving efficiency of the process, the model may comprise a use of equal to or less than 18 product groups, and more preferably equal to or less than 17 product groups in order to decrease the workload of the testing and sorting of the coming raw materials. However, for quality prediction, equal to or more than 15 product groups may be an advantageous embodiment for the simulation model.

Thus, the method may comprise the following steps:

- predicting an expected quality of recycled fibers after and/or before at least one of the following process steps:

- Pulping,

- Flotation, and

- First bleaching,

- Second bleaching, and

- Third bleaching, and

- applying materials based on the prediction.

The recovered raw material may be sorted into product groups automatically and/or manually. As discussed, the recovered raw material may be sorted e.g. by using automated measuring system, which may comprise e.g. NIR and/or at least one camera together with analyzing tools.

In an embodiment, the method for manufacturing recycled cellulose based fibers comprises the following steps:

- selecting at least one quality parameter from the following list:

- brightness R457,

- Luminosity Y,

- shade (L, a, b),

- opacity,

- ash, and

- fiber content,

- determining a needed minimum value for each selected quality parameter,

- analyzing the amount of each product group needed to obtain the determined minimum value(s),

- applying material from the product groups according to the analyzing in order to obtain the minimum value(s),

- pulping the applied material, and

- removing at least some impurities from the pulped material in a flotation step, a screening step and/or a cleaning step, thereby obtaining the recycled cellulose based fibers.

The method may further comprise e.g. a step of removing non-paper components.

Still further, the method may comprise

- measuring value(s) of at least one quality parameter from the obtained recycled cellulose based fibers, and

- refining a simulation model used for the prediction by comparing the predicted parameter(s) and the measured value(s) of the at least one quality parameter.

Thus, deviation between the predicted and real data can be used for improving the quality of the simulation model. Teaching the analyzing unit Thanks to the invention, levels of selected quality parameters may be predicted for different raw material mixtures so that it may be possible to obtain recycled fibers having a predetermined quality. The quality parameters and their minimum level can be selected based on a quality needed for a product. For each product group, parameters used e.g. for a simulation process, may be created, for example based on the laboratory recycling processing results.

For example, for graphical paper grades, the optical parameters (including e.g. brightness, luminosity Y, and/or shade L,a,b), the content of mineral fillers (e.g. ash), and e.g. sticky materials content may be suitable examples of the quality parameters.

Thus, in an example, the selected quality parameters comprise at least brightness, luminosity, shade, ash content, and, optionally, sticky material content.

Further, process related parameters, such as yield, may be analyzed, and predicted. Further, other parameters may be selected, in addition or alternatively, e.g. for other final products.

The prediction model (such as a simulation model) for some parameters, e.g., ash or sticky materials content, may be done by using linear calculations. In an embodiment, optical parameters are modelled by using Kubelka-Munk model.

For mono-fraction paper group samples, e.g. a coldset printed newspaper, laboratory recycling processing with optimized methods including e.g. pulping, deinking, oxidative bleaching with hydrogen peroxide, reductive bleaching with Na-dithionite or Na-formamidinesulfinate is common and can deliver trustworthy results for selected quality parameters These laboratory processes can be used also for paper product mixtures of known composition for refining the simulation model.

In an embodiment, parameters for simulation are created for selected product groups after at least two processing steps. This may be done by taking and analyzing several samples after the selected recycling processing steps. Further, the created data can be used to predict the expected pulp quality at or after the different steps of the recycling mills. Suitable process steps for the prediction may be, e.g., after a pulper, after a flotation step, and after bleaching steps. The quality after pulping can describe the material composition quality in the feed, the quality after the first flotation can be strongly linked to the optical quality potential of the material composition, and the quality after bleaching is usually the final recycled pulp quality.

Embodiments for usage of the recycled cellulose based fibers

The recycled cellulose based fibers may be suitable for use as a raw material for a paper. The recycled fibers may be suitable for use as a raw material for a board. Further, the recycled fibers may be suitable for use e.g., as a raw material for a composite comprising cellulose-based fibers and plastic.

In an embodiment, the recycled cellulose based fibers are used for a determined grade of paper comprising at least 30 dry wt.% of said recycled fibers, calculated from the total weight of the cellulose-based fibers.

Recycling process typically reduces fiber length of cellulose, which may e.g., decrease the tear strength of a paper formed from the recycled fibers. Flowever, fibers having shorter average fiber length and increased surface area may be obtained, which may enable formation of a dense and smooth paper surface.

The quality of the recycled fibers may be critical to obtain predetermined properties for the product. Thanks to the novel solution, it may be easier to obtain recycled cellulose based fibers having suitable properties for each product. Further, the usage of the recycled fibers may reduce the needed amount of the virgin fibers. Furthermore, the usage of the recycled fibers may reduce water pollution and/or air pollution.

With the novel process, it may be possible to produce a product with a specific quality target, e.g., a brightness or ash range after pulping, or a specified final brightness after flotation and bleaching.

The novel solution may be used for recovered paper quality profile simulation in recovered paper sorting lines for production of specific qualities, for quality specifications, and for quality certificates.

The novel solution may be used to characterize the recyclability of paper and board packages as well as of graphic print products. Thanks to the novel solution, it may be possible to decrease costs of the raw materials while obtaining required quality. Further, it may be possible to obtain improved quality for the recycled cellulose based fibers, hence, recycled cellulose based fibers may be used with new products.

EXPERIMENTAL TESTS Example 1

First experimental test used 17 product groups. The selected product groups are shown in Table 1. Table 1: Shares of 17 Product Groups

The gravimetric share of the paper product groups was determined by hand sorting and weighing all product groups before the predetermined share of each product group (as shown in Table 1) was applied to a deinking process. Further, a quality achievable at a deinking process was predicted for both deliveries, for the quality parameters shown in Table 2.

Table 2. Predicted quality data for RCP deliveries The predicted parameters shown in Table 2 were used to determine the suitability for a given use for the obtained recycled cellulose based fibers. Example 2

Second experimental test used 17 product groups. The selected product groups are shown in Table 3.

Table 3 Shares of 17 Product Groups The gravimetric share of the paper product groups was determined by hand sorting and weighing all product groups before the predetermined share of each product group (shown in Table 3) was applied to a deinking process.

Further, a quality achievable at a deinking process was predicted for both deliveries, for the quality parameters shown in Table 4. Further, the same quality parameters were validated by measuring the real values from the process. These predicted and validated values are shown in Table 4. Table 4 : Predicted and validated quality data. As can be seen, the accuracy of the prediction was very good, proofing that the predicted parameters can be used as such for many purposes as described in the application. Example 3

Third experimental test used 8 product groups. The selected product groups are shown in Table 5. Table 5 Shares of 8 Product Groups The gravimetric share of the paper product groups was determined by hand sorting and weighing all product groups before the predetermined share of each product group (shown in Table 5) was applied to a deinking process.

Further, a quality achievable at a deinking process was predicted for the quality parameters shown in Table 6.

Table 6 predicted quality data The predicted parameters shown in Table 6 were used to determine the end use for the obtained recycled cellulose based fibers.

Example 4

Fourth experimental test used 4 product groups. The selected product groups are shown in Table 7.

Table 7 Shares of 4 Product Groups

The gravimetric share of the paper product groups was determined by hand sorting and weighing all product groups before the predetermined share of each product group (shown in Table 7) was applied to a deinking process. Further, a quality achievable at a deinking process was predicted for the quality parameters shown in Table 8. Table 8 predicted quality data The predicted parameters shown in Table 8 were used to determine the end use for the obtained recycled cellulose based fibers. The simulation process gave precisely the needed quality parameters for samples, therefore no laboratory processing like laboratory pulping or flotation was anymore required. The invention has been described with the aid of illustrations and examples. The methods or any product obtained by the methods are not limited solely to the above presented embodiments but may be modified within the scope of the appended claims.