Location: Cotton Ginning Research
Project Number: 6066-41440-009-002-S
Project Type: Non-Assistance Cooperative Agreement
Start Date: Sep 1, 2018
End Date: Aug 31, 2023
Objective:
Objective 1. Develop technologies to prevent plastic contamination of U.S. cotton bales.
Sub-objective 1.1. Evaluate unmanned aerial vehicle (UAV) remote sensing as a means to map plastic contamination in a cotton field prior to harvest.
Sub-objective 1.2. Develop a camera system and image-analysis techniques for identifying contaminants at the module feeder of a gin.
Objective 2. Develop gin process models for improving ginning efficiency and quality
Sub-objective 2.1. Determine relative seed cotton velocity and drag force over a range of operating parameters.
Sub-objective 2.2. Develop appropriate models, including drag force, heat transfer, mass transfer, and turbulence, of the seed cotton-air system for numerical simulation.
Sub-objective 2.3. Simulate operation of a seed cotton drying system and conduct testing with actual system to validate model.
Approach:
Areas between 20 and 50 acres within three cotton fields in various locations will be used as test fields. Shortly before harvest, plastic bags of various colors will be attached at multiple GPS (Global Positioning System)-identified positions to plants in these areas. A unmanned aerial vehicle (UAV) flight of each area will be conducted with a typical multispectral camera including green, red, and near-infrared bands of energy sensitivity, and with pixel resolution of 8 cm or better. Image stitching software will be used to create an orthomosaic of each area. Positions of the plastic bags will be manually noted on the image mosaics within geographical information system (GIS) software. Image analysis routines will be developed to automatically identify the bags from the multispectral image data. Automated identifications of the bags will be compared to the manually noted bag positions.
A new imaging system will be developed for employment at the module feeder of a gin. Considerations will involve cost, distance between module feeder and gin control office, camera type and exposure variability, lighting requirements, and flexibility to conform to variations in design among gins. Two imaging systems will be developed. One will be employed in the lab gin at Texas A&M, and the other will be employed at a commercial gin. Research will be done (i) to identify proper system settings to enable still images to be collected, and (ii) to develop image-processing algorithms to detect the presence of contaminants on the dispersing cylinders of the module feeder. At the lab gin at Texas A&M, module-wrap plastic will be introduced into the mini module feeder to ensure the system operates as designed.
To model moisture and heat transfer in a seed cotton drying system, the velocity of the seed cotton relative to the air must be determined. Seed cotton (supplied by USDA-ARS) will be conveyed in the pneumatic conveying system at the USDA-ARS CGRU under varying process parameters. Particle tracking velocimetry or particle imaging velocimetry will be used to determine seed cotton and air velocity. The resulting drag force can be calculated and the drag coefficient modeled as a function of relevant process parameters.
Modeling of the seed cotton-air system will be done using suitable software. The system will incorporate a drag force model developed from testing and heat and moisture transfer models derived from past research. Appropriate closure models (likely large eddy simulation or detached eddy simulation) will be included.
The seed cotton drying system in the commercial-scale gin at the USDA-ARS CGRU will be simulated. Dimensions or drawings of system components will be provided by USDA-ARS to Texas A&M. Data will be collected while processing cotton in the commercial-scale gin under varied operating parameters to validate the models. Measurements of seed cotton moisture, air temperature, and velocity will be compared to values predicted by simulation. Based on testing results, models may need to be corrected and re-validated. A sensitivity analysis of the final model to the varied operating parameters will be conducted.
