Modelling of ring yarn unevenness by soft computing approach

Majumdar, Abhijit; Ciocoiu, Mihai; Blaga, Mirela

doi:10.1007/s12221-008-0034-0

Cited by 25 publications

(12 citation statements)

References 17 publications

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“…This is attributed to the higher unevenness of finer yarns as compared to their coarser counterpart. Majumdar et al (2008) also reported the use of the ANFIS for the prediction of ring spun yarn unevenness using the cotton fibre properties measured by the advanced fibre information system (AFIS) and yarn count as inputs. ANN and linear regression models were also developed for the comparative evaluation of prediction performance.…”

Section: © Woodhead Publishing Limited 2011mentioning

confidence: 99%

“…During roller drafting, short fibres float in between the front and back roller nip and their velocity is totally uncertain. Thus, the short fibres generate 7.5 Effect of fibre length and short fibre content on ring yarn unevenness (source: Majumdar et al, 2008). drafting waves and increase the unevenness of the fibre strand. Figure 7.6 shows the impact of yarn count and short fibre content on yarn unevenness, keeping fibre length constant at mid-level (0.93 inch).…”

Section: Prediction Performance Of Different Modelsmentioning

confidence: 99%

“…These linguistic rules are in agreement with the established perceptions of spinning technology. Figure 7.7 also shows 7.6 Effect of yarn count and short fibre content on ring yarn unevenness (source: Majumdar et al, 2008 Majumdar et al, 2008).…”

Section: Prediction Performance Of Different Modelsmentioning

confidence: 99%

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Adaptive neuro-fuzzy systems in yarn modelling

Majumdar

2011

Soft Computing in Textile Engineering

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Section: © Woodhead Publishing Limited 2011mentioning

confidence: 99%

Section: Prediction Performance Of Different Modelsmentioning

confidence: 99%

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Adaptive neuro-fuzzy systems in yarn modelling

Majumdar

2011

Soft Computing in Textile Engineering

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“…Analyses had proved that artificial neural network models improved the prediction performance with regards to regression models. Majumdar et al 3 demonstrated the application of two soft computing approaches namely artificial neural network and neural-fuzzy system to forecast the unevenness of ring spun yarns. Ghosh et al 4 presented a support vector machine regression approach to forecast the properties of cotton yarns produced on ring and rotor spinning technologies from the fiber properties measured by HVI and AFIS.…”

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confidence: 99%

Evaluation of yarn evenness in fabric based on image processing

Liu

Jiang

Pan

et al. 2011

Textile Research Journal

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The traditional approach to unevenness characterization of yarn is based on the CV (i.e. coefficient of variation) of mass between defined portions of yarn measured with the USTER evenness tester. In fact, yarn with the same parameters has different evenness in fabric, and the evenness of yarn in fabric is different from the source yarn, which is caused by the producing process and parameters of the fabric. However, there is no good method to describe the appearance of yarn in fabric. The paper focuses mainly on a novel method known as yarn evenness in fabric (YEF). The method processes the image of the fabric and is divided into four steps. The first step is the acquisition of the relative image from the sample of woven fabric. The second step is the pretreatment of the image and segmentation of the warp and weft from the fabric based on fast Fourier transform and inverse fast Fourier transform. The third step is to separate the single yarn from the warp or weft sets by the gray unidirectional mean method. In the fourth step, the average relative thickness was selected for characterizing yarn. SD data of thickness that we will analyze in future is also a method. Experimental results on virtual and physical woven fabric showed that the method mentioned can obtain the fine information of yarn from fabric in details. The method of YEF was programmed by Matlab software. Computational burdens are about 11.4 seconds on average, for one meter of warp and weft yarn samples. The program could be valuable when applied to the practical industry.

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“…One or more hidden layers are located between input and output layers. The number of hidden layers and number of hidden layer's neurons vary, depending on the complexity of problem and training dataset' quality and size (Majumdar et al, 2008). A small number of neurons in hidden layers may lead the network to fall into a local minimum, in which case the network does not have sufficient time to learn the dataset's feature.…”

Section: Introductionmentioning

confidence: 99%

Storage Capacity Estimation of Commercial Scale Injection and Storage of CO2 in the Jacksonburg-Stringtown Oil Field, West Virginia

Zhi¹

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Geological capture, utilization and storage (CCUS) of carbon dioxide (CO2) in depleted oil and gas reservoirs is one method to reduce greenhouse gas emissions with enhanced oil recovery (EOR) and extending the life of the field. Therefore CCUS coupled with EOR is considered to be an economic approach to demonstration of commercial-scale injection and storage of anthropogenic CO2. Several critical issues should be taken into account prior to injecting large volumes of CO2, such as storage capacity, project duration and long-term containment. Reservoir characterization and 3D geological modeling are the best way to estimate the theoretical CO2 storage capacity in mature oil fields. The Jacksonburg-Stringtown field, located in northwestern West Virginia, has produced over 22 million barrels of oil (MMBO) since 1895. The sandstone of the Late Devonian Gordon Stray is the primary reservoir. The Upper Devonian fluvial sandstone reservoirs in Jacksonburg-Stringtown oil field, which has produced over 22 million barrels of oil since 1895, are an ideal candidate for CO2 sequestration coupled with EOR. Supercritical depth (>2500 ft.), minimum miscible pressure (941 psi), favorable API gravity (46.5°) and good water flood response are indicators that facilitate CO2-EOR operations. Moreover, Jacksonburg-Stringtown oil field is adjacent to a large concentration of CO2 sources located along the Ohio River that could potentially supply enough CO2 for sequestration and EOR without constructing new pipeline facilities. Permeability evaluation is a critical parameter to understand the subsurface fluid flow and reservoir management for primary and enhanced hydrocarbon recovery and efficient carbon storage. In this study, a rapid, robust and cost-effective artificial neural network (ANN) model is constructed to predict permeability using the model's strong ability to recognize the possible interrelationships between input and output variables. Two commonly available conventional well logs, gamma ray and bulk density, and three logs derived variables, the slope of GR, the slope of bulk density and Vsh were selected as input parameters and permeability was selected as desired output parameter to train and test an artificial neural network. The results indicate that the ANN model can be applied effectively in permeability prediction. Porosity is another fundamental property that characterizes the storage capability of fluid and gas bearing formations in a reservoir. In this study, a support vector machine (SVM) with mixed kernels function (MKF) is utilized to construct the relationship between limited conventional well log suites and sparse core data. The input parameters for SVM model consist of core porosity values and the same log suite as ANN's input parameters, and porosity is the desired output. Compared with results from the SVM model with a single kernel function, mixed kernel function based SVM model provide more accurate porosity prediction values. Base on the well log analysis, four reservoir subunits within a marine-dominate...

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Modelling of ring yarn unevenness by soft computing approach

Cited by 25 publications

References 17 publications

Adaptive neuro-fuzzy systems in yarn modelling

Adaptive neuro-fuzzy systems in yarn modelling

Evaluation of yarn evenness in fabric based on image processing

Storage Capacity Estimation of Commercial Scale Injection and Storage of CO2 in the Jacksonburg-Stringtown Oil Field, West Virginia

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