Paper Machine Research Articles

Possibilities of Reducing the Heat Energy Consumption in a Tissue Paper Machine—Case Study

The article presents studies on the impact of the significant process parameters of a paper machine with a Yankee cylinder on its production capacity and heat energy consumption for drying the paper web. Parameters such as machine speed, web moisture content before and after pressing, parameters of steam supplied to the cylinder and parameters of hot air flowing from the nozzles of the hood were analyzed. The study’s results were used to optimize production to improve the energy efficiency and performance of the machine. In order to use the possible methods of improving the production capacity and heat energy consumption, the parameters of the production process were measured and the basic indicators characterizing the operation of the machine were calculated in the Yankee cylinder–dryer hood system. The correct functioning of the machine components and the possibility of their modernization were also analyzed. Technological and construction changes introduced based on the research results made it possible to increase the production capacity by 10% and to reduce the consumption of heat energy per 1 ton of produced paper by 16.3%. The article presents a description of changes in the technology of paper production and modernization of the tissue machine made in the years 2013–2022.

FOXM1 promotes TGF-β2-induced injury of human lens epithelial cells by up regulating VEGFA expression.

To explore whether Fork head box protein M1 (FOXM1) is involved in TGF-β2-induced injury of human lens epithelial cells and its related mechanism. Human lens epithelium samples from cataract patients and healthy controls were collected. A cellular epithelial injury model was established by treating HLE-B3 cells with TGF-β2. QPCR, immunoblot assays were performed to detect the levels of FOXM1 in human cataract samples and the lens epithelial injury cell model. FOXM1 siRNA and pcDNA3.1-FOXM1 plasmids were transfected into the cells to knockdown and overexpress FOXM1, respectively. MTT and wound closure and transwell assays were performed to analyze cell proliferation and migration in HLE-B3 cells. Immunoblot assays were also conducted to detect the effects of FOXM1 on EMT, VEGFA and MAPK/ERK signaling. We found high expression of FOXM1 in lens tissues of cataract patients. Silencing of FOXM1 in TGF-β2-induced HLE-B3 cells suppressed cell proliferation, migration, and the EMT process. Mechanistically, we found that downregulation of FOXM1 inhibited the VEGFA/MAPK signaling pathway in TGF-β2-induced HLE-B3 cells. FOXM1 promoted TGF-β2-induced injury of human lens epithelial cells (hLECs) by promoting VEGFA expression. FOXM1 could be a potential drug target for the treatment of ocular diseases.

Progression of vacuum level in successive vacuum suction boxes in a paper machine – Impact on dewatering efficiency and energy demand – A laboratory study

Producing tissue paper is an energy-demanding process; a significant amount of energy is expended when removing water by vacuum, mechanical pressing, and thermal drying. Because the water is most energy-demanding to remove in drying, making the preceding step of vacuum dewatering more efficient would benefit the whole process. This article focuses on developing a laboratory-scale method for verifying the nature of diminishing returns of water removal and investigating efficiency strategies of the vacuum dewatering. The theoretical concept of successive vacuum suction boxes with progressing vacuum levels was tested at the laboratory scale in order to show quantifiable results of the previously solely theoretical concept. The results confirmed that vacuum dewatering can be improved by adding progressively higher vacuum levels and that such a practice can benefit both outgoing dryness levels and expended vacuum pump energy. To truly examine the power of progression of vacuum levels, rewetting can be included in the calculations, by using an approximate value collected from pilot or full-scale measurements. For any new fiber mix, pulp type, vacuum level setup, basis weight, etc. the vacuum levels, rewetting, and dwell times need to be tuned to that specific case.

Identification and validation of the microRNAs and hub genes for pancreatic ductal adenocarcinoma by an integrated bioinformatic analysis.

In the progression of pancreatic ductal adenocarcinoma (PDAC), aberrant micro RNAs (miRNAs) expression plays a crucial role. This study sought to identify and validate the key miRNAs and potential target genes involved in PDAC. A bioinformatic analysis was conducted to determine their potential use as biomarkers and therapeutic targets. Gene profiling data sets (GSE41372 and GSE32688) were retrieved from the Gene Expression Omnibus database. Differentially expressed miRNAs (DEMs) with a P value <0.05, and |fold change| >2 was identified. The prognostic value of the DEMs was accessed using the online server Kaplan-Meier plotter. Further, gene ontology terms and Kyoto Encyclopedia of Genes and Genomes pathway analyses were performed using DAVID 6.7. The protein-protein interaction analyses were conducted with STRING, and miRNA-hub gene networks were constructed using Cytoscape software. The PDAC cells were transfected with miRNA inhibitors or mimics. Cell Counting Kit-8 assays and terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) staining were used to examine cell proliferation and apoptosis, respectively. Wound-healing assays were performed to evaluate cell migration. Three DEMs (hsa-miR-21-5p, hsa-miR-135b-5p, and hsa-miR-222-3p) were identified. High expression levels of hsa-miR-21-5p, hsa-miR-135b-5p, or hsa-miR-222-3p predicted poor overall survival in PDAC patients. The pathway analysis revealed that the predicted target genes of the DEMs were closely related to several signaling pathways (including 'pathways in cancer', 'miRNAs in cancer', 'platinum drug resistance', 'lipid and atherosclerosis', and 'MAPK signaling pathway'). The MYC proto-oncogene (MYC), phosphate and tensin homolog gene (PTEN), poly(ADP-ribose) polymerase 1 (PARP1), von Hippel-Lindau (VHL), and fork head box p3 (FOXP3) were identified as potential target genes. The inhibition of hsa-miR-21-5p, hsa-miR-135b-5p, or hsa-miR-222-3p expression decreased cell proliferation. The overexpression of hsa-miR-21-5p, hsa-miR-135b-5p, or hsa-miR-222-3p facilitated PDAC cell migration. This study constructed the miRNA-hub gene network, which provides novel insights into the PDAC progression. Although further research is required, our results offer clues for new potential prognostic markers and therapeutic targets of PDAC.

Impact of protocatechuic acid on alleviation of pulmonary damage induced by cyclophosphamide targeting peroxisome proliferator activator receptor, silent information regulator type-1, and fork head box protein in rats

Cyclophosphamide (CP) is a chemotherapeutic agent that causes pulmonary damage by generating free radicals and pro-inflammatory cytokines. Pulmonary damage has a high mortality rate due to the severe inflammation and edema occurred in lung. PPARγ/Sirt 1 signaling has been shown to be cytoprotective effect against cellular inflammatory stress and oxidative injury. Protocatechuic acid (PCA) is a potent Sirt1 activator and exhibits antioxidant as well as anti-inflammatory properties. The current study aims to investigate the therapeutic impacts of PCA against CP-induced pulmonary damage in rats. Rats were assigned randomly into 4 experimental groups. The control group was injected with a single i.p injection of saline. CP group was injected with a single i.p injection of CP (200 mg/kg). PCA groups were administered orally with PCA (50 and 100 mg/kg; p.o.) once daily for 10 consecutive days after CP injection. PCA treatment resulted in a significant decrease in the protein levels of MDA, a marker of lipid peroxidation, NO and MPO along with a significant increase in GSH and catalase protein levels. Moreover, PCA downregulated anti-inflammatory markers as IL-17, NF-κB, IKBKB, COX-2, TNF-α, and PKC and upregulated cytoprotective defenses as PPARγ, and SIRT1. In addition, PCA administration ameliorated FoxO-1 elevation, increased Nrf2 gene expression, and reduced air alveoli emphysema, bronchiolar epithelium hyperplasia and inflammatory cell infiltration induced by CP. PCA might represent a promising adjuvant to prevent pulmonary damage in patients receiving CP due to its antioxidant and anti-inflammatory effects with cytoprotective defenses.

Comparative Study of Linear Variable Flux Reluctance Machine with Linear Wound Field Flux Reversal Machine

As members of doubly salient magnetless linear machines, linear variable flux reluctance (LVFR) and wound field flux reversal (LWFFR) machines inherit the merits of conventional magnetless linear machines such as low cost, high flux adjustment capability and high reliability. Furthermore, like linear switched reluctance machine, they have a very simple and compact long secondary, which are very attractive for long stroke applications. However, low force capability is their major defect. To solve this issue, new LVFR and LWFFR machine topologies were proposed in recent work, while lacking studies on their force improvement mechanism and further force evaluation. In this paper, LVFR and LWFFR machines with improved force performance are comparatively studied with the emphasis on their force capabilities. The operation principle of the two machines is analyzed based on magnetic field harmonics produced by flux modulation. Contributions of air-gap flux density harmonic components to no-load back electromagnetic forces of the two machines are analyzed and the average force equation is derived. Moreover, force capabilities of the both machines are investigated by means of the time-stepping finite-element analysis to verify the theoretical analysis.

Mechanistic aspects of nanocellulose–cationic starch–colloidal silica systems for papermaking

Optimization of a chemical additive program for a paper machine can require attention to both colloidal charges and kinetic effects. This work considered an additive program with two negatively charged substances (nanofibrillated cellulose [NFC] and colloidal silica) and two positively charged items (cationic starch and cationic acrylamide copolymer retention aid). Results were shown to depend on charge interactions; however, that clearly was not the whole story. Some findings related to cationic demand, dewatering, fine-particle retention, and flocculation among fibers were best explained in terms of at least partly irreversible complexation interactions between the charged entities. Adjustments in ratios between oppositely charged additives, their sequences of addition, and effects of hydrodynamic shear levels all affected the results. In general, the most promising results were obtained at a cationic starch level of 0.25% to 0.5% based on sheet solids in systems where the cationic starch was used as a pretreatment for NFC.

A Study on Laboratory Type Paper Machine Using Nano Fibrillated Cellulose from Recycled Old Corrugated Containerboard as Bio Additive in Board Production

Raw material, energy, water, and additive cost are challenges for today’s board manufacturing and new sustainable solutions are needed to produce paper products with an favorable environmental footprint.&#x0D; A laboratory Fourdrinier paper machine study manufactured a board product with a targeted basis weight of 80 g/m² without and with the addition of ground calcium carbonate at a targeted filler level of 10%. Nano fibrillated cellulose produced from recycled old corrugated containerboard with a Valley Beater at a Canadian Standard Freeness level of 40 ml was added at 4% based on oven dry basis weight. Results revealed an increased ash and fine retention as well as an increased burst Index, short span compression strength, and tear index for the base paper as well as with and without ground calcium carbonate addition.&#x0D;

Machine learning classification of non-Markovian noise disturbing quantum dynamics

In this paper machine learning and artificial neural network models are proposed for the classification of external noise sources affecting a given quantum dynamics. For this purpose, we train and then validate support vector machine, multi-layer perceptron and recurrent neural network models with different complexity and accuracy, to solve supervised binary classification problems. As a result, we demonstrate the high efficacy of such tools in classifying noisy quantum dynamics using simulated data sets from different realizations of the quantum system dynamics. In addition, we show that for a successful classification one just needs to measure, in a sequence of discrete time instants, the probabilities that the analysed quantum system is in one of the allowed positions or energy configurations. Albeit the training of machine learning models is here performed on synthetic data, our approach is expected to find application in experimental schemes, as e.g. for the noise benchmarking of noisy intermediate-scale quantum devices.

Overexpression of FoxM1 optimizes the therapeutic effect of bone marrow mesenchymal stem cells on acute respiratory distress syndrome

BackgroundInjury of alveolar epithelial cells and capillary endothelial cells is crucial in the pathogenesis of acute lung injury/acute respiratory distress syndrome (ALI/ARDS). Mesenchymal stem cells (MSCs) are a promising cell source for ALI/ARDS treatment. Overexpression of Fork head box protein M1 (FoxM1) facilitates MSC differentiation into alveolar type II (AT II) cells in vitro. Moreover, FoxM1 has been shown to repair the endothelial barrier. Therefore, this study explored whether overexpression of FoxM1 promotes the therapeutic effect of bone marrow-derived MSCs (BMSCs) on ARDS by differentiation of BMSCs into AT II cells or a paracrine mechanism.MethodsA septic ALI model was established in mice by intraperitoneal administration of lipopolysaccharide. The protective effect of BMSCs-FoxM1 on ALI was explored by detecting pathological variations in the lung, total protein concentration in bronchoalveolar lavage fluid (BALF), wet/dry (W/D) lung weight ratio, oxidative stress levels, cytokine levels, and retention of BMSCs in the lung. In addition, we assessed whether FoxM1 overexpression promoted the therapeutic effect of BMSCs on ALI/ARDS by differentiating into AT II cells using SPC−/− mice. Furthermore, the protective effect of BMSCs-FoxM1 on lipopolysaccharide-induced endothelial cell (EC) injury was explored by detecting EC proliferation, apoptosis, scratch wounds, tube formation, permeability, and oxidative stress, and analyzing whether the Wnt/β-catenin pathway contributes to the regulatory mechanism in vitro using a pathway inhibitor.ResultsCompared with BMSCs-Vector, treatment with BMSCs-FoxM1 significantly decreased the W/D lung weight ratio, total BALF protein level, lung injury score, oxidative stress, and cytokine levels. With the detected track of BMSCs-FoxM1, we observed a low residency rate and short duration of residency in the lung. Notably, SPC was not expressed in SPC−/− mice injected with BMSCs-FoxM1. Furthermore, BMSCs-FoxM1 enhanced EC proliferation, migration, and tube formation; inhibited EC apoptosis and inflammation; and maintained vascular integrity through activation of the Wnt/β-catenin pathway, which was partially reversed by XAV-939.ConclusionOverexpression of FoxM1 enhanced the therapeutic effect of BMSCs on ARDS, possibly through a paracrine mechanism rather than by promoting BMSC differentiation into AT II cells in vivo, and prevented LPS-induced EC barrier disruption partially through activating the Wnt/β-catenin signaling pathway in vitro.

Optimization of the control paper production system of using the extremum seeking control method

Currently the pulp and paper industry is one of the fastest growing sectors of the economy in Russia and other countries. An important aspect of the further development of this industry is associated with an increase in the efficiency of paper production and the quality of finished products with an economical and rational use of raw materials, fuel and energy and other material resources. This is facilitated by the use of modern means of automation and process control. It was experimentally established that there is an extreme dependence between the ratio of the speeds of the paper pulp and the paper machine and the standard deviation of the linear density of the paper web, which determines the degree of non-uniformity of the paper gap. The use of a noise-resistant algorithm for extreme control of an inertial object based on the combination of synchronous accumulation and synchronous detection methods with subsequent search for the maximum amplitude of the second harmonic of the filtered signal, aimed at reducing the root-mean-square deviation of the mass of a square meter of paper web, is considered. It is proposed to simultaneously stabilize the mass flow rate of paper pulp and the coordinate of the fall of the paper pulp jet onto the conveyor mesh, which made it possible to increase the efficiency of paper production.

Using novel DNA methods to achieve higher process efficiency and performance

Uncontrolled microbiological activity is a challenge for recycled fiber (RCF) mills as it can have negative effects on production and end-product quality. The microbes that exist in these systems have been largely unknown, and the strategies employed to control microbiology have been non-specific. Understanding the specific microbial groups present in RCF mills, their properties, and where they exist, as well as having the ability to accurately measure the true troublemakers, are key to targeted control of the bad actors. In this study, we present the results of a global survey of over 40 RCF paper machines. The same RCF-specific problem-causing bacterial groups were found on different continents, including large densities of newly identified bacteria in paper processes. Those can degrade cellulose and starch, produce acids and odorous substances, and have a significant impact on fiber strength and additive consumption. We also demonstrate how modern DNA tools can quantify the impact of biocidal countermeasures against the actual troublemakers, including bacteria found to degrade cellulose during RCF pulp storage, which may be linked to a negative impact on end-product strength. These novel DNA tools give producers updated biocide program key performance indicators (KPIs) and actionable information to more effectively design and adjust microbiological control to achieve higher process efficiency and performance.

A Hybrid Approach for Android Malicious Software Classification

Android operating systems have grown in popularity and are currently being increasingly used on smartphone devices. Because of the quickly expanding quantity of Android malware and the potential safeguard of vast amounts of data kept on Android devices, the identification and categorization of Android malware is regarded as a big data challenge. Researchers started using deep learning (DL) techniques for malware detection, according to the most recent state-of-the-art studies. Nevertheless, researchers and practitioners face challenges such as the choice of DL architecture, extracting and processing features and evaluating results. In this paper, several traditional and hybrid machine learning (ML) models were developed and analyzed in detail to classify Android malware. First, some state-of-the-art DL model has been developed like multi-layer perceptron (MLP), deep neural network (DNN), recurrent neural network (RNN), long short-term memory (LSTM), convolutional neural network (CNN) and later several hybrid models have been developed by combining DNNs with support vector machine (SVM). Second, we have focused on maximizing achievement by fine-tuning a variety of configurations to guarantee the best possible combination of the hyper-parameters and attain the maximum statistical metric value. Finally, we have accomplished performance analysis between these hybrid models and a model based on VGG-16-CNN-1D along with SVM has been proposed to enhance the accuracy and efficacy of large-scale Android malware detection which outperforms all the other developed models using Drebin Dataset. Among the DL methods, DNN achieved the highest F1 score 99.07%. After doing a comparison between hybrid models, it was found that DNN-SVM achieved 99.12% while VGG16-CNN-1D-SVM achieved 98.56% precision, 98.78% recall and 99.20% F1 score, respectively.

Latest Paper Machine Clothing Development for Tissue and Toilet Papers

Latest Paper Machine Clothing Development for Tissue and Toilet Papers

Heart Disease Prediction Using Machine Learning

Machine Learning (ML), which is one of the most prominent applications of Artificial Intelligence, is doing wonders in the research field of study. In this paper machine learning is used in detecting if a person has a heart disease or not. A lot of people suffer from cardiovascular diseases (CVDs), which even cost people their lives all around the world. Machine learning can be used to detect whether a person is suffering from a cardiovascular disease by considering certain attributes like chest pain, cholesterol level, age of the person and some other attributes. Classification algorithms based on supervised learning which is a type of machine learning can make diagnoses of cardiovascular diseases easy. The diagnosis and prognosis of cardiovascular disease are crucial medical tasks to ensure correct classification, which helps cardiologists provide proper treatment to the patient. Machine learning applications in the medical niche have increased as they can recognize patterns from data. Using machine learning to classify cardiovascular disease occurrence can help diagnosticians reduce misdiagnosis. This research develops a model that can correctly predict cardiovascular diseases to reduce the fatality caused by cardiovascular diseases. This paper proposes a method of k-modes clustering with Huang starting that can improve classification accuracy. Models such as random forest (RF), decision tree classifier (DT), multilayer perceptron (MP), and XGBoost (XGB) are used.

Heart Disease Diagnosis and Prediction using Multi Linear Regression

The correct prediction of heart disease can prevent life threats, and incorrect prediction can prove to be fatal at the same time. In this paper machine learning algorithm is applied to compare the results and analysis of primary dataset. The dataset consists of 46 attributes among these Information gain is used to select 24 features for performing the analysis. Various promising results are achieved and are validated using accuracy and confusion matrix. The dataset consists of some irrelevant features which are handled and data are also normalized for getting better results. Using machine learning approach, 77.78% accuracy was obtained. Multiple linear regressions are used to construct and validate the prediction system. Our experimental result shows that multiple linear regressions are suitable for modelling and predicting cholesterol.

Robust Cross Directional Controller Design for Paper Machine Spatial Distributed System

Robust Cross Directional Controller Design for Paper Machine Spatial Distributed System

Thermodynamic Performance Analysis of New Paper Machine Air Hood Heat Recovery System

In this paper, a new heat recovery system of hot air hood of paper machine is proposed, the thermodynamic model of the system is established, and the effect of different thermal parameters on the heat recovery efficiency of the system is studied. The results indicate that both the recovered energy and the proportion of exhaust air passing through the latent heat exchanger attenuate as fresh air temperature increase and fresh air moisture content decrease. The system heat recovery increasing with rising of exhaust temperature. Exhaust temperature and exhaust relative humidity have different effects on sensible and latent heat recovery. The maximum value of proportion of exhaust air passing through latent heat exchanger appears when the exhaust relative humidity is 35.8%. Overall, the fresh air temperature and the exhaust relative humidity have great influence on the heat recovery efficiency.

Identification and characterization of sticky contaminants in multiple recycled paper grades

Organic sticky contaminants represent one of the biggest technical challenges in the paper recycling process. These contaminants reduce paper strength, cause plugging of wires and felts, and stick to or deposit on machine parts affecting the runnability of the paper machine. The removal of these sticky contaminants is difficult to achieve along the process due to the heterogeneous nature of these organic contaminants. In this study, the nature of sticky contaminants in multiple wastepaper grades was analyzed and characterized using screening, solvent extraction with subsequent analysis such as FT-IR, GC-MS, and SEM. The content of stickies in wastepaper varies among different paper grades and their recovery methods. The wastepaper from curb side collection contains nearly 4 times the amount of stickies than most of the wastepaper grades collected from a local paper recycling mill. Polyvinyl acetate polymers, styrene butadiene rubber (SBR), paraffin waxes and polyamines are the major components in the extracted sticky contaminants. In addition, these stickies deposit heavily on fibers surface leading to high negative charge in fibers suspension ranging between 2 and 4.5 µeq/L. It is expected that these findings will assist addressing the knowledge gaps in understanding the nature of stickies and their behaviors, and to eventually develop highly efficient technologies for contamination removal in the paper recycling process.

Algorithm of coordinated control of an open-type headbox

The control system of an open-type headbox has a high complexity, which is associated with the need not only to stabilize the parameters of the object, but also to carry out coordinated control of it in the case of automatic transfer of a paper machine from one type of product to another for a minimum time.&#x0D; A mathematical model of an open-type headbox as an object of mass level control has been developed. In the model, the output variable of the control object is the level of mass in the injection chamber, and the control action is the mass flow rate supplied to the injection chamber. The model takes into account the disturbing action - the area of the discharge slot. On the basis of the developed model, two options for constructing an algorithm for controlling the pressure box during its transfer from one mode to another are considered. The structure of the algorithm for coordinated control of the pressure box is developed. An illustrative example of application of the developed algorithm is given. The calculations of the optimal transient process in the object confirm the effectiveness of the developed control algorithm. The algorithm of coordinated control is applied to the standard pressure box YANO-180. The development of the algorithm for transferring the pressure box from the working (0.485 m) to the maximum (0.575 m) height of the mass level in the pressure box for 180 seconds is demonstrated. The calculation results show that the coordinated control algorithm provides the transfer of the mass level to the maximum height for a given time.&#x0D; The algorithm developed on the basis of the obtained mathematical model allows to control the level of mass in such a complex object as an open-type headbox when transferring a paper machine from one mode of operation to another for the time specified by the coordinated control system.