Paper Machine Research Articles

A new technique for the measurement of show-through mottle of fine paper

Mottling within print-through and show-through is caused by the variability of the local optical properties of the sheet. This mottling is visually disturbing and a mark of poor paper quality. The ability to predict print-through mottle of printed paper by measuring show-through mottle on the unprinted sheet would be a valuable asset for paper machine control. We examined the relationship between print-through mottle and show-through mottle. We worked with nine samples of 60 lb. uncoated fine paper (90 g/m2), from various North American paper companies, that were printed on an offset press, 400K (400% Black), on both sides. A show-through mottle instrumental determination technique was developed using an existing Fast Fourier Transform-based algorithm. The nine samples examined were ranked similarly by the visual evaluation of print-through mottle and by the instrumental determination of show-through mottle. We thus established that show-through on the unprinted sheet can be used as a reliable predictor of print-through, therefore saving time and money for papermakers. We also found a significant two-sidedness in show-through for some of the samples.

Machine Learning Algorithms for MR Brian Image Classification

Effects of Visual and Auditory Response Times in Males and Females by using Tactile and Mouse on Web Based EnvironmentClassification of MR brain images accurately is the crucial part in the medical imaging research since brain is a complex structure which needs multiple convergence approaches to classify. In this paper machine learning algorithms like SVM, HMM were employed to classify the brain images with the use of spatial features. Super pixel image segmentation approach is applied to segment the image into distinct super pixel region for which the features are calculated.

Machine Learning Based Decision-Making Model to Determine Size of Micro Nano Bubble

Machine learning has been widely used for large data processing with varied scope of application aspects. In this paper machine learning is used to determine the size of air bubbles that can be generated in an optimal condition of various parameters such as gas flow rate, water temperature and operating pressure of the system. Air bubbles have significant role to play when it comes to water treatment. Bubbles having significance in volume are proportionally valued when it comes to extent of treatment. The research concludes with a conceptual model influenced by machine learning approach that can estimate best combination of the parameter that are feasible for generation of most efficient generation.

Investigation of rolling contact between metal and rubber-covered cylinders governing the paper compaction process

With the goal of partial or complete replacement of plastic components with paper-based products, 3D paper structures have seen a growing interest in industrial applications: from packaging to more complex daily life objects. However, the extensibility of paper has become a key issue within this context and is the main factor determining the formability of these products and determining the depth of the achievable shapes. The most effective way to increase paper’s stretch potential is by subjecting the moist paper web to a compaction process, which can be achieved through an extensible unit that is located in the drying section of a paper machine, where the network experiences in-plane compression in the machine direction (MD), under out-of-plane lateral constraints. The objective of this work is to clarify the mechanisms governing the compression process and to evaluate the influence of various parameters on the final material properties to optimize the industrial production of extensible paper. The system operation has been simulated realistically and shows that paper experiences a compressive state passing through the nip, with plastic strains in MD direction being of the same order of the applied speed difference, which was expected experimentally in optimal conditions of compaction. Starting from an initial reference case, a sensitivity study has been performed to identify and address the following factors that may affect the compression state: (a) friction coefficients; (b) indentation level; (c) speed difference; and (d) rubber properties. The analysis of the numerical results gives an insight into the mechanisms governing the compaction operations and allows a better comprehension of the features controlling the process outcome.

Stability Assessment of Rubble Mound Breakwaters Using Extreme Learning Machine Models

The stability number of a breakwater can determine the armor unit’s weight, which is an important parameter in the breakwater design process. In this paper, a novel and simple machine learning approach is proposed to evaluate the stability of rubble-mound breakwaters by using Extreme Learning Machine (ELM) models. The data-driven stability assessment models were built based on a small size of training samples with a simple establishment procedure. By comparing them with other approaches, the simulation results showed that the proposed models had good assessment performances. The least user intervention and the good generalization ability could be seen as the advantages of using the stability assessment models.

Cationic polyacrylamide induced flocculation and turbulent dewatering of microalgae on a Britt Dynamic Drainage Jar

Dewatering is one of the major constraints to the large scale production of microalgae biofuels, with many drawbacks in currently deployed technologies. Using an approach similar to papermaking, cationic polyacrylamide (CPAM) based flocculants can stabilize flocs of microalgae under turbulent conditions to achieve effective dewatering of dilute suspensions in high speed filtration systems. The effects of flocculant dosage, stirrer speed (i.e. turbulence) and pH on filtration retention was investigated using a Britt Dynamic Drainage Jar (BDDJ) which simulates a commercial paper machine. The development of a stable floc system that withstood turbulence was achieved through the flocculant’s dual effects of charge neutralization and bridging of the microalgae cells. Retention of microalgae on a 76 µm screen improved from 7% when no flocculant was used to 94% at a flocculant dosage of 10 mg/L of CPAM, stirrer speed of 1200 rpm and pH of 6.5 as optimum conditions. The most significant effects on microalgae retention were that of flocculant dosage; followed by the combined interaction between stirrer speed and flocculant dosage; and that of pH. A key finding is that high retention was obtained under moderately turbulent conditions. This study shows that the paper dewatering technique will be potentially applicable for microalgae preconcentration in the production of biofuels, and further pilot scale studies are recommended.

Inquiry of Personal Intelligence Of Adolescence and Early Adulthood College Students of Tamilnadu using Id3 Algorithm

Educational Data Mining is an Emerging research domain which focus on extracting knowledge from educational databases to promote the learning environment. Educational psychology helps to understand the differences in learning process of a person from cognitive and behavioral perspective. This research aims to predict the personal intelligence of adolescence and early adulthood who are studying under graduate and post graduate courses in educational institution. Adolescence plays a very import role in the development of personality to new dimensions in human life. The Adolescence physical, mental, social, moral and spiritual outlooks undergo revolutionary changes. Many teachers and parents fail to asses these changes and they do not like to slacken their control over them. Psychologist has stressed to properly channelize the behavior of adolescence and give them adequate education. In this paper machine learning technique with decision tree induction algorithm was used to analyze the personal intelligence of college students. To construct the decision tree, Entropy and Information Gain are used as attribute selection measures in ID3 algorithm. Applying data mining techniques in the field of Educational Psychology is a new method and the rules generated from the decision tree helps to identify the personal intelligence of the students. This result helps the Educators to improve the learning environment better for Adolescence.

Enhancement of processability, surface, and mechanical properties of paper based on rice straw pulp using biopolymers for packaging applications

Two biopolymers, chitosan and oxidized starch, were used as wet-end additives to improve the strength properties of the paper because of their biodegradable and non-hazardous qualities. The present study reports the improvement in surface and strength properties of packaging-grade paper made with rice straw pulp using biopolymers, chitosan, oxidized starch, and surface sizing added at the wet end of the paper machine. Use of chitosan at all doses from 0.5 to 10 kg/ton enhanced important surface and strength properties of paper. The breaking length, tear index, burst index, ring crush strength, stretch, tensile energy absorption index, and Taber stiffness of the paper with 10 kg/ton of chitosan as a wet-end additive showed 22%, 14%, 20%, 59%, 16%, 44%, and 48% improvement, respectively, in comparison to control, (i.e, without its addition). The Cobb60 was also reduced by 45%, showing better resistance to water in comparison to rice straw paper alone. The effects of chitosan added at the wet end on the paper surface were investigated using Fourier transform infrared spectroscopy (FTIR). The use of 10 kg/ton of chitosan at the wet end reduced the color and total suspended solids in the back water of the papermaking system by 55% and 51%, respectively. Further enhancement in the surface and strength properties of paper was observed following surface sizing with oxidized starch.

Epigenetically Upregulated MicroRNA-602 Is Involved in a Negative Feedback Loop with FOXK2 in Esophageal Squamous Cell Carcinoma.

MicroRNA is an endogenous, small RNA controlling multiple target genes and playing roles in various tumorigenesis processes. In this study, our results revealed that miR-602 expression levels in tumor tissues and preoperative serum from esophageal squamous cell carcinoma (ESCC) patients were higher than those in non-tumorous tissues and healthy volunteers. miR-602 overexpression was closely related to lymph node metastasis and TNM stages and correlated short overall, and it acted as an independent prognostic marker of ESCC. The methylation status of the miR-602 gene indicated more frequent hypomethylation of the CpG sites located upstream of the miR-602 gene in the ESCC tissues than in the adjacent normal tissues, and the methylation status of miR-602 correlated inversely with its expression levels. Subsequently, miR-602 overexpression promoted ESCC proliferation and metastasis and regulated cell cycles invitro and invivo. Mechanistically, a dual-luciferase experiment validated that Fork head box (FOX)K2 (FOXK2) was a direct target of miR-602. More importantly, systemic delivery of formulated miR-602 antagomir could reduce tumor growth and increased FOXK2 protein expression in nude mice. This work provides novel insight into the molecular pathogenesis of ESCC.

Patent Activity as an Indicator Determining the Vector of Development of the World Economy

Analysis of the change in the number of annually filed patent applications is a new method for studying the patterns of economic development. The article considers two trends. Firstly, the growth of patent applications is decreasing in the whole world with the exception of China since the 1980s excluding the short – term interval of 1997–2002, after 2002 does not exceed 1.5% per year. In China, the annual 20% increase in the number of patent applications over 25 years led to an increase in its share in the patent applications of the world by more than 40%. At the same time, the distribution of patent applications in various areas in China almost completely correlates with the global vector of scientific and technological development. Secondly: there was a change in the nature of the movement in the direction of diversification of the global scientific and technological development, as indicated by the dynamics of the Herfindahl – Hirschman index. At least from 1980 to the end of the 20th century, the monopolization index had been decreasing. The change occurred in 1997–2002, and since 2002 the monopolization index has been growing. There is an increase in the share of patent applications in the areas of Computer technology, Digital communication, Electrical equipment apparatus, Energy, Measurement. At the same time, the share of both wellformed areas, the share of which is above 2–3% (these are Telecommunications, Textile and paper machines, Audiovisual technology, Machine tools, Organic fine chemistry, Chemical engineering), and those at the initial stage (basic communication, Micro-structural and nano-technology) are decreasing. The analysis of the dynamics Revealed Technological Advantage performed. For the leading economies of the world, directions of technological development have been revealed, along which the most successful development takes place. It is shown that the directions in which the share of patent applications in Russia is ahead of its average world level, namely Food chemistry, Micro-structural and nanotechnology, Analysis of biological materials, have had a negative dynamics in the world over the past five years. Therefore, the adjustment of the domestic vector of scientific and technological development and the transfer of efforts to the development of computer technology, medical technology, civil engineering, computer technology is required.

Processing-Structure-Property Correlation Understanding of Microfibrillated Cellulose Based Dimensional Structures for Ferric Ions Removal

In this research article, wood based microfibrillated cellulose (MFC) was studied to gain a better understanding of the process of dependent network formation. Networking potential and obtained properties of the produced dimensional structures could be controlled using opted processing routes. The fabricated dimensional structure, using freeze-drying (FD) is a highly open and porous network (98% porosity) compared to slightly tight, dense and less porous network produced after pressing at 200kN (96% porosity), followed by vacuum-filtered (VF) networks (33% porosity). The porosity (17%) was further decreased when the casting (CS) method was used, further producing a highly dense and compressed network. High water flux (180.8 ± 11 L/m2h) of pressed freeze-dried (PFD) followed by vacuum-filtered (VF) (11.4 ± 1.9 L/m2h) and casting CS (0.7 ± 0.01 L/m2h) were calculated using device. Furthermore, increased water flux (1.4 fold) of Experimental Paper Machine (XPM) based structures was reported in comparison with CS structures. Pore-sized distribution and surface area were measured using Hg porosimetry; they showed an average pore size of 16.5 μm for FD, followed by PFD (8.2 μm) structures. A 27-fold decrease in average pore-size was observed for CS structure in comparison with the FD structures. Highest tensile strength (87 ± 21 MPa) was recorded for CS structures, indicating a more highly compacted network formation compared to VF (82 ± 19 MPa) and PFD (1.6 ± 0.06 MPa). Furthermore, an attempt was made to upscale the VF structures using traditional paper making approach on XMP. Improved tensile strength (73 ± 11 MPa) in machine produced structures is due to alignment of fibers towards machine direction compared to cross directional (43 ± 9 MPa) fractured structures as shown in our Scanning Electron Microscopy (SEM) analysis. Surface functionalization of MFC using enzyme (hexokinase) was performed to increase the adsorption efficiency towards ferric ions removal. All fabricated structures were further evaluated for Fe(iii) removal and it was summarized that charge densities of functional groups, produced ζ-potential and networking potential were dominating influential factors for adsorption fluctuation of ferric ions.

Poorly controlled diabetes during pregnancy and lactation activates the Foxo1 pathway and causes glucose intolerance in adult offspring

Exposure to maternal diabetes during pregnancy results in diabetes in offspring, but its underlying mechanisms are unclear. Here, we investigated the phenotype and molecular defects of the offspring of poorly controlled diabetic female mice generated by streptozotocin (STZ) administration. Offspring was exposed to maternal diabetes during pregnancy and lactation. The body weight of STZ offspring was lower than that of control offspring at birth and in adulthood, and glucose tolerance was impaired in adult STZ offspring. Interestingly, the phenotype was more pronounced in male offspring. We next investigated the morphology of islets and expression of β cell-related genes, but no significant changes were observed. However, transcriptome analysis of the liver revealed activation of the fork head box protein O1 (Foxo1) pathway in STZ male offspring. Notably, two key gluconeogenesis enzyme genes, glucose 6 phosphatase catalytic subunit (G6pc) and phosphoenolpyruvate carboxykinase 1 (Pck1), were upregulated. Consistent with this finding, phosphorylation of Foxo1 was decreased in the liver of STZ male offspring. These changes were not obvious in female offspring. The activation of Foxo1 and gluconeogenesis in the liver may have contributed to the impaired glucose tolerance of STZ male offspring.

Large data set analysis to determine refiner plate total cost of ownership

Efficient process data storage and its subsequent analysis are vital to the profitable operation of a paper mill. An evaluation of refiner plate performance was conducted for an integrated paper mill. Trials with two new plate patterns were compared with the incumbent pattern; the goal was to increase refiner plate life. Plate life increased with both new design patterns, and the mill selected one of the two new choices. However, additional impacts to paper machine performance and mill maintenance costs were not considered in the selection decision. A large data set analysis tool was used to identify unrealized savings of 1.5% with reduced fiber usage (basis weight) on the paper machine’s most prevalent grade. The large data analysis indicated that this unrealized benefit with the alternate plate design was not attainable with the chosen plate design.

The evolution of reel statistical methods

Multiple statistical methods for calculating the variance partition analysis (VPA) of reel data have existed for decades. In the paper industry, VPA is also commonly known as reel statistics. VPA commonly consists of total variance (TOT) that is then divided into three components: cross direction (CD), machine direction (MD), and residual (RES). A common mathematical procedure is referred to as ANOVA (analysis of variance). TAPPI Standard Test Method T 545 “Cross-machine grammage profile measurement (gravimetric method)” addresses paper testing and includes the ANOVA equations that have also been used to analyze scanning data. In the 1990s, TAPPI published TIP 1101-01 “Calculation and partitioning of variance using paper machine scanning sensor measurements,” which contained simple formulas that were easy to implement and could be used by a novice to generate statistics on a spreadsheet. All involved quality control system (QCS) suppliers agreed to support this common method in their QCS. TIP 1101 was recently revised, and this paper concerns the analysis of data collected from a scanning sensor in a QCS and the creation of a common method for the calculation of reel statistics by TAPPI’s Process Control Division.

Radial basis neural tree model for improving waste recovery process in a paper industry

AbstractIn this article, we propose a novel hybridization of regression trees (RTs) and radial basis function networks, namely, radial basis neural tree model, for waste recovery process (WRP) improvement in a paper industry. As a by‐product of the paper manufacturing process, a lot of waste along with valuable fibers and fillers come out from the paper machine. The WRP involves separating the unwanted materials from the valuable ones so that the recovered fibers and fillers can be further reused in the production process. This job is done by fiber‐filler recovery equipment (FFRE). The efficiency of FFRE depends on several crucial process parameters, and monitoring them is a difficult proposition. The proposed model can be useful to find the essential parameters from the set of available data and to perform prediction task to improve WRP efficiency. An idea of parameter optimization along with regularity conditions for the universal consistency of the proposed model is given. The proposed model has the advantages of easy interpretability and excellent performance when applied to the FFRE efficiency improvement problem. Improved waste recovery will help the industry to become environmentally friendly with less ecological damage apart from being cost‐effective.

A blower for high temperature fumes in paper machine

The article describes the project, strength calculations and construction optimization of a fan development, and its application in the blower, which was used for research of experimental, micro-jet blowing system, that regulates the moisture profile of the paper web. The blower was designed to compress to positive gauge pressure of 0.4 bar of fumes at a temperature of up to 500 °C with capacity of 1 m3/s. To meet these requirements, the blower was realized as an aggregate of four identical radial fans connected in series. Achieving the assumed pressure ratio for a single fan required the development of a specific rotor design and the use of steel with very good strength parameters at high operating temperatures. The fan was made in four copies and assembled into a prototype blower system for blowing fumes, which was successfully used in an experimental blower system for adjusting the cross profile of the web's moisture content in a paper machine.

Turinga propozycja testu dla „papierowej maszyny szachowej” z 1948 roku jako prototyp dla testu Turinga

The aim of this paper is to present the idea which served as a prototype and probably a test field for the idea of the well-known Turing test. This idea is the ‘paper machine’ (an algorithm) for playing chess and the proposal to test its abilities in confrontation with a human chess player. I will describe the details of this proposal and discuss it in the light of the Turing test setting.

A hybrid regression model for water quality prediction

In this work, we propose a hybrid regression model to solve a specific problem faced by a modern paper manufacturing company. Boiler inlet water quality is a major concern for the paper machine. If water treatment plant can not produce water of desired quality, then it results in poor health of the boiler water tube and consequently affects the quality of the paper. This variation is due to several crucial process parameters. We build a hybrid regression model based on regression tree and support vector regression for boiler water quality prediction and show its excellent performance as compared to other state-of-the-art.

Paper Machine Breakdown Reduction by FMEA and Preventive Maintenance Improvement: A Case Study

FMEA is a widely selected tool for the application of machinery breakdown prevention. This research adopted FMEA technique to reduce and prevent the breakdown of the important equipment of a large commercial paper machine that has capacity of 200,000 tonnes per annum located in the Northeast area of Thailand. This research accomplished the improvement of preventive maintenance in other machines for rapid implementation and improved machine inspections. After implementation, the downtime of paper machine belonging to maintenance department responsibility reduced significantly 5 hours per month by average, machine availability, OEE, and MTBF increased 0.46%, 0.51%, and 10 hours respectively. The machine downtime has been on the downward trend.

Long-term Product Rating Prediction Based on Users' Short-term Multiple Ratings

Ratings and product reviews could be considered as one of the main features determining the quality of a product in online store systems, especially in deciding whether to place a product as part of an online store's inventory. online vendors are often attracted by product reviews and ratings. However, when the average product rating observed based on a small number of user ratings, the decision maker may not be certain about choosing that product, even if it has a fairly high rate. Long-term rating predictions would help online vendors to identify products and advertise their websites by choosing potential ones. In this paper machine learning approach utilizing linear regression model is used to predict long-term product rate. The model evaluated using the Datasheet of the Amazon Online Store website,1996 to 2014. Keywords : Rating, Long-term Prediction, Machine Learning Algorithm, Linear Regression. DOI : 10.7176/JIEA/9-4-04 Publication date :June 30 th 2019