Research On The Key Technologies Of Intelligent Cotton Production Process

There's a giant gap between China's cotton processing and that of the advanced countries of the world. Currently, a single pattern is employed to seed cotton ginning regardless of moisture regain and various impurity rates; or operators adjust manually by rules of thumb, lack of both on-line detection and intelligent control, which makes cotton processing still in an extensive form. Because of the poor techniques in cotton processing, different grades of seed cotton are ginned on a mixed-level, affecting lint quality, and leading to a great waste of resources and economic loss.There are a variety of factors influencing cotton quality in cotton processing, and control process is complex. In view of insufficient automation and intellectualization of cotton processing, the dissertation carries out research into on-line detection and control techniques of moisture regain, color feature withdrawing, automatic classification of impurities, the laws of influence of processing equipment on the parameters of cotton traits, numerical control design of key equipment, lint quality evaluation approach, and process intelligent optimization. The main research contents of the dissertation are as follows:Because moisture regain is a key factor of cotton processing and secure storage, research on on-line detection and control technology of moisture regain is conducted. On-line detection method based on relative humidity was proposed. Relation model among temperature, relative humidity and moisture regain was established by improved BP neural network. In order to realize accurate control over moisture regain in processing, fuzzy PID control model of seed cotton drying was built, guaranteeing moisture regain suitable for cotton processing. According to cotton bale storage theories, the fuzzy PID control model of lint humidification was set up to reach accurate control over lint humidity.Cotton images reflect color features and impurity information of cotton. A program of extracting cotton images on-line based on color CCD cameras was launched. Through analysis of cotton color features reflectance Rd and yellowness +b were calculated in XYZ space, laying foundations for grading cotton quality. Each piece of cotton processing equipment focused differently in terms of cotton cleaning, so based on the impurity color and shape feature a classified statistics approach of cotton impurity was adopted. For impurity color features, vector median filtering was applied to color images in color space HIS firstly, and then through improved fuzzy C-means image segmentation was conducted. Improved fuzzy C-means adaptively adjusted initial clustering centers and clustering numbers, which drew on both the experience of the cotton images and the adaptive reasoning function effectively. In the iterative process, the fuzzy C-means clustering algorithm adopted improved Euclidean distance to measure color differences between sample points and cluster centers, which was consistent with the pattern that the impact of color saturation on vision varies with I. The shape features of cotton impurities were withdrawn by the area, roundness, complexity, rectangle degree and elongation of impurity graphic region. Based on color and shape features BP neural network model of cotton impurity identification was built to obtain the types of cotton impurities and the respective amount of different types, which laid foundations for accurate optimization of process route.Cleaning mechanism of key processing equipment in cotton processing was studied, and BP neural network model for cleaning was established due to complex non-linear mapping relations between cotton trait parameters and process parameters. At the stage of seed cotton cleaning, BP models were set up respectively in inclined seed cotton cleaners, stripper and stick cleaners, inclined and recovery seed cotton cleaners and saw gin stands. The influence on the parameters of seed cotton traits exercised by the rotational speed of barbed nail rollers, the clearance between barbed nail rollers and lattice grates, the rotational speed of defecation rollers and that of sawtooth rollers and the yield of seed cotton was analyzed. The orthogonal experimental method was used to verify the correctness of each model. Due to the structural defects of inclined seed cotton cleaners, numerical control design scheme of automatic adjustment of the clearance between barbed nail rollers and lattice grates was brought forward.At the lint cleaning stage, BP neural network models were respectively established in saw lint cleaners, flow-through air lint cleaners. Besides, the influence on the parameters of lint traits exerted by the rotational speed of saw type rollers, the clearance between grid bars and saw type rollers, the numbers of grid bars, the slit width and the yield of lint was analyzed. The orthogonal experimental method was employed to verify the correctness of each model. A program of automatic control of the numbers of grid bars in saw lint cleaners and that of automatic adjustment of the clearance between saw type rollers and grid bars were put forward.To achieve intelligent optimization of cotton processing technology, parameter optimization strategies based on BP-GA were proposed. A method of controlling the parameters of cotton traits of series equipment was established based on BP model which provided the genetic algorithm with parametric variables space. Cotton processing optimization is multi-objective, multi-variable and non-linear, which can be converted to single-targeted and multi-variable optimization based on maximizing revenue by the linear weighted sum, providing the fitness evaluation function for the genetic algorithm. Cotton quality, which highly depends on its grade, is a primary parameter of evaluation function. BP model of grade evaluation was set up through cotton fiber maturity, color characteristics and rolling quality. As to genetic algorithm, the genome-based mixed-real-coded method was proposed, making multi-layer genetic manipulation simplified. In order to meet the requirements of processing technology, according to the constraint condition that there is no flotation in ginned cotton after ginning, the penalty function was used to associate this constraint condition with the fitness evaluation function. An improved genetic algorithm was brought forward based on the ideas of similarity comparison, fitness sorting, optimal retention strategies and small-scale competition. The algorithm had strong global search ability and fast convergence speed.On-line detection techniques and adaptive optimization strategies of parameters are proposed in the dissertation. The method can optimize automatically processing programs based on moisture regain and trait parameters of seed cotton, and also determine the parameters of each piece of equipment. The method provides a solution to fine processing and helps to realize "adjusting according to cotton traits".