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"Fiber damage and moisture content during ginning (adapted from Mayfield et al., 1994). Fiber Moisture Content Fiber damage Poor cleaning, Lower bale value
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Ginning practices affect both economic returns to cotton producers and quality of fiber produced for textile mills and, ultimately, consumers. Because of the shift from a primarily domestic to an export market for U.S. cotton and the loss of textile market share to synthetic fibers, production of high-quality cotton is critical to maintaining the c...
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Context 1
... in an improvement of approximately one leaf grade over a decrease in moisture content of 3 to 4 percentage points (Anthony, 1990(Anthony, , 1996Boykin, 2005). Selecting the amount of drying needed is a compromise between efficient cleaning and operation with increased drying, and preserving fiber length and conserving fuel with less drying (Fig. 3). Drying cotton to a fiber moisture content of 5 to 6% might be necessary to attain the leaf grades that maximize bale value, although fiber quality might suffer; length, uniformity, and strength could be reduced slightly, whereas neps and short fiber could increase. Ginning and lint cleaning cotton at moisture levels lower than 5% ...
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... Management in the Gin. The ginner's compromise shown in Fig. 3 is important to keep in mind, particularly when drying seed ...
Context 3
... in an improvement of approximately one leaf grade over a decrease in moisture content of 3 to 4 percentage points (Anthony, 1990(Anthony, , 1996Boykin, 2005). Selecting the amount of drying needed is a compromise between efficient cleaning and operation with increased drying, and preserving fiber length and conserving fuel with less drying (Fig. 3). Drying cotton to a fiber moisture content of 5 to 6% might be necessary to attain the leaf grades that maximize bale value, although fiber quality might suffer; length, uniformity, and strength could be reduced slightly, whereas neps and short fiber could increase. Ginning and lint cleaning cotton at moisture levels lower than 5% ...
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The immediate customers of cotton gins are the producers; however, the ultimate customers are textile mills and consumers. The ginner has the challenging task to satisfy both producers and the textile industry. Classing and grading systems are intended to assign an economic value to the bales that relates to textile mill demands and the quality of...
Citations
... Many factors, including genetics, management practices, and environment, affect the quality of cotton from planting to harvesting (Raper et al., 2019). However, regardless of the quality of cotton delivered to the gins, moisture content (MC) is a factor that affects the overall lint output quality because it has significant influences on the efficiencies of many cotton ginning unit operations (Valco et al., 2004;Hardin IV et al., 2018;Byler and Anthony, 1992;Anthony et al., 1995;Laird and Barker, 1996). Thus, commercial gins often have one or more integral dryers and moisture restoration systems to optimize the seed cotton and lint moisture contents preginning (separating seed and fiber) and post-ginning, respectively (see Figure 3 of Adeleke, 2023). ...
Cotton moisture content is a factor that affects many unit operations in ginning and, thus, the overall lint quality obtainable from any initial seed cotton quality. Therefore, it is always a good practice to control cotton moisture using one or more dryer(s) or a moisture restoration system during cotton ginning. The effectiveness of cotton moisture control depends on the capability of the dryer controller, which is conventionally a Proportional-Integral-Derivative (PID) controller implemented in cascade control architecture. However, the PID controller is sluggish, even in cascade control implementation that should be faster, necessitating only constant temperature reference for gin dryers and leading to energy inefficiency or investments in expensive moisture restoration systems at more than one location. Hence, an improved gin dryer controller is imperative for achieving higher-quality lint and energy efficiency. We propose a sliding mode control (SMC) algorithm, which is fast-acting and robust to disturbances and parameter uncertainties, for gin dryer temperature control as an alternative to the PID controller. Here, we present the algorithm synthesis, system identification, simulation, and validation studies for the proposed SMC on the physical dryer system at Texas A&M microgin laboratory. The performance of the SMC is graphically and numerically compared to that of the PID controller to make an informed judgment on the control approach with a better performance.
In the primary processing technology, the cotton is dried and cleaned before separating the fiber from the seed. The low content of impurities in the cotton serves for the optimal course of subsequent processes. It also reduces the content of impurities and defects in the produced fiber which improves its quality. Many factors affect the cleaning efficiency of the cotton ginning process. Based on the analysis of the literature, it was determined that there are opportunities to improve cotton ginning equipment. There are a number of shortcomings in the supply of cotton to cleaning equipment, prompting research to address it. This article presents the results of theoretical and practical research conducted to determine the effect of the width of the supplier on the clogging of the cotton in it, the density and speed of the cotton, and the cleaning efficiency of the equipment. Theoretical studies have shown that when the width of the supplier is greater than 201.4 mm, it does not become clogged with moving cotton. According to the results of a practical study, it was found that the speed of cotton in the feeder varies from 0.100 m / s to 0.037 m / s when the width of the feeder is changed from 150 mm to 400 mm. However, when it was less than 250 mm, it was found that there were cases of cotton blockage in the supplier. When the width of the supplier was 250 mm, the cleaning efficiency of the cleaning equipment was optimized and congestion was eliminated. Based on theoretical and practical research, it is recommended that the width of the supplier is 250 mm as the optimal value.
Cotton requires multiple processing steps to convert the raw agricultural products into finished textiles. Genetic and environmental factors, crop management decisions, and processing practices interact to affect optimal end use, product quality, and process efficiency. Currently, only limited data sharing occurs between sectors of the cotton industry, primarily the official USDA classing data used to determine the value of cotton bales. Increasing digitization could improve productivity, sustainability, and competitiveness with synthetic fibers. Current research has focused on utilizing RFID technology incorporated in a recently introduced harvest system for logistics and associating cotton fiber quality with field locations. Gins and textile mills use some connected sensors; however, their use is primarily limited to remote monitoring and diagnostics.
In the future cotton industry, a much larger number of connected devices and sensors can provide information on the production and processing history of raw materials. Developments in agricultural robotics will offer a platform for measuring yield and quality on a site-specific basis in the field. Additional networked sensors and devices at gins and textile mills will provide additional information on product quality and process efficiency. Networking these connected devices will allow for the development of advanced analytics for optimizing logistics and processing industry-wide. Several challenges must be addressed to successfully implement IoT devices in the cotton industry. Improved rural broadband access and more suitable wireless networking protocols for field sensors are needed, although recently introduced technology may offer potential solutions. The cotton industry needs to develop appropriate data standards and data sharing policies. Integrating these data sources creates a new management paradigm, but research will be needed to optimally use this data.
Within-sample variation in cotton fiber length is important when explaining variation in yarn quality. However, typical High Volume Instrument (HVI) length parameters, the Upper Half Mean Length (UHML) and Uniformity Index (UI), do not characterize the total within-sample variation in fiber length. HVI fiber length measurements are based on the fibrogram principle where the HVI generates a curve called a fibrogram and reports the UHML and UI. Our results, based on 19,628 commercial bales, reveal that the typical HVI length measurements do not characterize unique types of length variation. Fibrograms from a subset of 538 commercial samples suggest that the fibrograms capture additional within-sample variation in fiber length that is not being currently reported. Two additional sets of samples were then used to evaluate the importance of this additional length variation. Partial Least Square Regression models and leave-one-out cross-validation reveal that the HVI fibrogram explains yarn quality better than current HVI length parameters and is comparable with the Advanced Fiber Information System (AFIS) length distribution by number. The validation results show that the models built with the HVI fibrogram are better than models with the current HVI length parameters and at least as good as the AFIS length distribution by number when predicting yarn quality. Fiber length variation captured by the whole fibrogram could provide a new tool to breeders for selecting breeding lines and spinners for purchasing cotton bales.
