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Project Title: Real Time Analysis And Control of Batch Processes

Project Number: C95-S4

PI(s): R. McGregor (Leader NCSU), K. R. Beck, G. K. F. Lee, C. B. Smith, W J. Jasper Graduate Students: M. S. Arora, C. E. Bright, R. P. Joshi, M. R. Lefeber Wallace, J. T. Merritt. Industrial Collaborator: W. Hunter (Cotton Inc.).

Goals:

The overall objective of this project is to optimize batch- process control by monitoring dyebaths, and by understanding and controlling the state of the dyeing process, both in the laboratory and commercial dyeing machines. Specific objectives are to:

1. Educate graduate students and advance dyeing knowledge and technology. 2. Develop neural network/fuzzy logic control for dyebath monitoring and colorant formulation. 3. Develop control systems and strategies for closed loop control via dosing, fuzzy logic and neural networks. 4. Comparatively evaluate control strategies and the absolute levels of control possible. 5. Develop useful theoretical kinetic and thermodynamic models and generic process models.

Abstract:

This report describes our (Dye Applications Research Group, DARG) recent progress in real-time monitoring, modeling, and controlling batch dyeing processes. The flow injection analysis (FIA) system was modified to allow injection of a small volume of dyebath into a high performance liquid chromatograph (HPLC). This FIAIHPLC system is being used to study four different procedures for exhaust dyeing cotton with reactive . Data are reported for isothermal dyeings at different temperatures. Preliminary work to develop a spectrophotometric method for monitoring exhaustion of vat dyes, especially indigo, is reported. Calibration models developed from indigo powder and indigo paste under a nitrogen atmosphere indicate significant differences in the purity of the dye in these two forms. Once developed, these models will be incorporated into a FIA system for monitoring indigo in a dye range. A neural network shade matching program has been developed and is briefly described. The dye-concentration prediction of the neural network model was shown to be more accurate in predicting the actual experimental dye-concentrations than conventional linear models. Closed-loop control of acid dyeing is reported. The DARG direct dyebath monitoring system was modified to allow computerized control of dosing of acid and base for pH control, individual dye concentrations, and total sorption of each dye during the dyeing process.

Relevance to NTC Goals:

1. This project has developed innovative and improved closed-loop dyeing processes based upon modern dyeing theory and modem control strategies and methods. 2. Our development of a new portable dyebath monitoring and control system will both enhance our research capabilities, and facilitate the transfer of our approach to industry. 3. The strong contacts already developed with industrial companies have been formalized through the formation of an industrial Dye Applications Research Consortium (DARC), which will transfer our results to commerce.

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4. Education and training, both of graduate students and industry personnel, will be continued to ensure the future competitiveness of the industry. 5. There is a unique collaboration between textile chemists, mechanical and aerospace engineers, and textile engineers at the heart of this project, which may serve as a model for the industry.

Technical Approach:

Key questions:

1. Can the consistency of batch-dyeing be improved by closed-loop control? 2. What are the best modeling, control, and monitoring procedures and strategies? 3. Can these strategies be adapted for specific commercial dyeing machinery and processes?

The barriers:

1. The limitations of the best existing theoretical models of the physical chemistry of dyeing. 2. The multiplicity of parameters which can influence a dyeing process, and the difficulty of monitoring and evaluating them all. 3. The proprietary natures and complex compositions of commercial dye preparations. 4. The complexity of the mass transfer processes characteristic of specific batch dyeing machinery. 5. The length of time required to develop absorbance-to-concentration models for any multi-dye system.

Current state of the art:

1. Our NTC work to date represents, to the best of our knowledge, the state of the art in closed-loop-adaptive, and fuzzy-logic-based control methods for dyeing processes, and in the application of both FIA and spectral transmittance measurements for dyebath monitoring. This viewpoint is shared by the U. S. industry. We are also at the leading edge with our new developments in the use of neural nets for color matching. 2. In general, pre-developed and “pre-optimized” dyeing procedures are used without closed-loop feedback control. These are compromise procedures, for a family of dyes and a general class of fiber for example, and are not truly optimal. 3. Dyebath monitoring for dye exhaustion, if used at all, is generally based on relatively primitive methods which yield insufficient information. 4. Dosing of dyes and chemicals for process control is common, but closed-loop control of dosing appears not to be used. Dosing profiles are pre-set (See # 1 above). 5. At least one group in England has developed a computer model of the package dyeing process, which is stated to produce information on the instantaneous distribution of dye in the fibers, and in the yams and packages. The mathematics involved has not been published to our knowledge, but it is most probably a simplified model.

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Industry Outreach:

Several companies, including Milliken, Dystar, Cotton Incorporated, Datacolor International, Glen Raven, Guilford Mills, Liberty Fabrics, Ovelacq (Belgian Yarn Dyeing Company), Gaston County Dyeing Machine Co. continue to closely follow the progress of our research. We have been approached by an instrument manufacturer to consider production of our flow injection analysis system and are currently doing a dyeing project for a major fiber producer. In the past three years DARG graduates have been hired by Monsanto (now Solutia), Crompton and Knowles, Burlington Industries, Dystar, and companies specializing in control software and hardware. We expect to continue as a source of highly educated personnel for our industry. We have an excellent group of graduate students who will complete their programs in the next few years, and can help us to train their successors before they leave us. These valuable contacts and interactions will increase as this research continues, through the formation of the DARC.

Current Progress:

Presented here is progress in various continuing areas of research and in one new area (indigo dyeing).

Optimization of Reactive Dyeing Processes

Since flow injection analysis for dyebath analysis was developed by our group earlier in this project, we have continued to develop FL4 as a monitoring tool. FIA has been used to monitor reactive dyeing of cotton, direct dyeing of cotton, cationic dyeing of acrylic, and disperse dyeing of polyester and acetate. To our knowledge, our ability to monitor disperse dyeings is unique. Our FIA systems have been used to monitor dyeings on a pilot scale package dyeing machine, a laboratory beaker dyeing machine, a laboratory package dyeing machine, and a pilot scale jet dyeing machine. In the dyeing of cotton with reactive dyes, monitoring exhaustion of individual dyes has been possible since the development of our FIA system. The spectral data obtained by FIA do not contain enough information to determine the degree of hydrolysis of reactive dyes during the dyeing process. Information about hydrolysis can, however, be obtained from analysis by high performance liquid chromatography (HPLC). We have successfully merged the exhaustion monitoring capabilities of FL4 with the separation abilities of HPLC into a system that reports exhaustion data once per minute and hydrolysis data once per five minutes. This FWHPLC system is currently being used to compare the efficiencies of four different reactive dyeing processes (all-in, constant temperature, traditional, and high-temperature) involving a heterobifunctional blue dye and a heterobifunctional yellow dye. In order to monitor the hydrolysis of these dyes, an isocratic HPLC method was developed to separate the various reactive and hydrolyzed forms of the dyes. Samples of partially hydrolyzed dyes were taken to Sequa Chemicals in Chester, SC and separated by HPLC. The eluent was deposited on a rotating germanium disk, and an IR spectrum was obtained at several different points along the deposition path to match up with the chromatogram peaks. The FTIR data, in conjunction with supporting knowledge from the dyeing process and the basic principles of HPLC separation, tentatively identified the chromatogram peaks as occurring in the following order: dihydrolyzed dye, monohydroxytriazine/ P-sulfatoethyl sulfone, monochlorotriazine/ hydroxyethyl sulfone, monochlorotriazine/ P-sulfatoethyl sulfone (original dye), monohydroxytriazine/ vinyl sulfone, monochlorotriazine/ vinyl sulfone. With this information, the disappearance, production, and conversion of each form of the dyes can be followed continuously during the dyeing procedure.

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Isothermal dyeings at five different temperatures (100, 120, 140, 160, and 180 OF) were conducted in order to determine the best dyeing temperature to be used for each of the other three procedures. A dyeing temperature of 140 “F was selected as best because it yielded the highest exhaustion in a reasonable amount of time without producing an excess of dihydrolyzed dye before the dyeing period was completed. In Figure 1, the overall exhaustion vs. time profile, as determined from FIA data, for the isothermal dyeings at 140°F and 180°F are shown. The change in concentration of SA add dye add NaOH add end of dyeing dihydrolyzed dye at * + these two temperatures with time is presented 80 in Figure 2. Each of the four dyeing procedures - traditional, all-in, high temperature and isothermal- has been performed and monitored at least once 0 using the FIA-HPLC system. Repeat 0 20 40 60 80 100 120 140 160 -r:-. \ dyeings and analysis I-14OF--18OFI of the data are being done.

Figure 1. Exhaustion data for blue dye

u2 80 a 2 60 73 3h 40 .- z 8 20 0 0 20 40 60 80 100 120 140 160 Time (min)

4-140F -~w180F

Figure 2. Hydrolysis data for blue dye

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Real-Time Analysis of Indigo Dyebaths

Currently, a method to spectrophotometrically determine the concentration of indigo in its reduced state is in development. Due to the instability of a standard indigo dyebath in the presence of oxygen, a system that allows characterization of reduced indigo in an inert atmosphere has been constructed. The system includes a manifold to supply a nitrogen blanket. A stock dyebath is reduced and hydrolyzed in a multi-necked flask from which pH and temperature may be monitored. Dilutions to 100 mL in volumetric flasks, also under nitrogen, are made via hypodermic needles purged of oxygen. These dilutions are measured spectrophotometrically by means of a flow-through cell. This system has been useful in characterizing the dyebath by developing a calibration curve with minimal error. The initial system as developed with a CCD spectrophotometer using a separate source and fiber-optics was not reliable at the specific wavelength range at which reduced indigo must be measured. An instrument which has dual beam capabilities and a strong signal at 406 nanometers has been purchased. Once this spectrophotometer has been used to successfully characterize indigo, it will be incorporated into the existing FIA system. Several calibration models have been developed on a Cary UVNis Spectrophotometer. These were more difficult due to the risk of exposing the reduced dyebath to oxygen. Using this spectrophotometer, a comparison between commercial powder indigo and commercial paste indigo was made. The paste indigo exhibited a stronger absorptivity coefficient at the same concentration. Diluents in the powder may be responsible for the discrepancy. A pure powder indigo has been ordered to use as a concentration reference. More conclusions may be drawn once this number is known. Another method for determining indigo concentration in the dyebath is potentiometric titration. A system which incorporates an automatic titrator is being developed verify indigo concentrations measured spectrophotometrically. The reducing agent, sodium hydrosulfite, and the indigo are titratable in the same potentiometric experiment. The reducing agent is one key factor in color yield and overall dyeability of the cotton substrate. Monitoring this, pH, and indigo concentration may provide the best insight into how an indigo dyebath behaves on a continuous range.

Color Matching Using Neural Networks

The process of color matching is highly nonlinear. The sources of these nonlinearities are many, including dye-dye interactions, dye-fiber interactions, and the human response to color. In this research effort, a neural network model was constructed using 3 1 input neurons, 15 hidden layer neurons, and three output neurons which are fully connected. Each of the hidden and output layer neurons are based upon a sigmoid activation function. In order to test the accuracy of the neural network model in comparison to a linear model, the neural network model was trained on 150 experimental samples to 250,000 epochs. The dye-concentration prediction of the neural network model was shown to be more accurate in predicting the actual experimental dye- concentrations than conventional linear models.

There are a number of reasons why a color match prediction will not give the correct recipe. These include:

Errors in the uresentation. The way a sample is measured by the spectrophotometer can influence the measured color. To minimize this problem, a special fixture (see Figure 3) was designed and fabricated so that the yam sample is always presented to the instrument in the same manner. Also, when yam is wound on a board, the tension in the yam affects

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the measured color. For example, if one side is under tension and it other side is slack, color differences of 1.6 AE or greater have been recorded.

Errors in the calibration set. When producing a calibration set, the dye recipe gives the percent on weight of goods of the dye in the solution, not on the cloth. Differences in exhaustion profiles due to dyeing conditions will affect color matching predictions. We tried to minimize this effect by doing redyeings of the same recipe and checking for consistency.

Errors in the model. The non-linear mappings of reflectance or K/S values to dye concentration introduce errors in the conventional color match prediction. A neural-network produces a non-linear mapping of K/S values to concentrations.

The neural network model was compared to the DATAMATCH 2.2 software produced by DATACOLOR by dyeing the recipe predictions. When these samples were compared to the original samples, the color difference of the neural network predicted samples was equivalent to the DATAMATCH predicted samples. Further, the absolute error between the actual recipes and the recipe predictions of the neural network model improved from a training set of 76 to 150 samples.

Figure 3. Color spectrophotomerer and sample presentation device

MID-LAYER NODES The neural network algorithm developed in this research

Log-Sigmoid Neurons project is an improvement over 31 classical neural network I N methods in that the approach Reflectance L incorporates adaptive learning Data T 3 OUTPUT NODES rates with an innovative Input N Dye stabilizing algorithm. This 0 D Concentration results in a reduction in training E output s time, compared to classical neural network approaches. Currently, the neural network can be trained to predict the concentrations of three dyes in a Fully Connected color sample to an average AE of Figure 4. Schematic diagram of color matching neural network

1.46; the approach can be extended to predict additional dyes. Further, the neural network algorithms are supported by a graphical user interface, a non-relational database, an interface to a spectrometer, and other software support modules. A graph of typical experimental test results is shown in Figure 4.

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Dimensionless Models for the Equilibrium Sorption of Acid Dyes by Polyamides

The earlier work of McGregor & Iijima (1, 2) on ion sorption equilibria between polyamides and aqueous solutions has been extended to describe the behavior of polysulfonated acid dyes (3),and of mixtures applied from finite dyebaths (4). This approach can permit compact descriptions of the end states of ionic dyeing processes, and provide estimates of possible final dyebath exhaustion levels and individual dye “blocking effects” in dyeing with acid dye mixtures, provided that appropriate calibration dyeing data are available.

Control of the Dyeing of Nylon by Dye and Chemical Metering

Closed-loop control of the dosing of dyes and chemicals has been developed as a new practical approach to situations in which theoretical data for the dyeing system are not available, and in which commercial dyes of unknown composition must be used. This new approach permits, for example, an on-tone build-up of shade in the dyeing of polyamide fibers with a binary mixture of monosulfonated acid dyes. Computerized dosing pumps are used to control the pH, the dyebath concentrations of the individual dyes, and the total sorption of each dye by the fabric during the dyeing process. This real-time, closed-loop adaptive control produced good reproducibility and uniform shade build-up in a laboratory dyeing machine. It was also possible, for example, to reuse a dyebath which contained residual dyes and chemicals from a previous dyeing. A patent application has been filed for this invention. Figures 5,6 and 7 show the results for a closed-loop dosing experiment in which a residual dyebath containing two dyes was reused. The amount of each dye taken up by the substrate at various l-----l time intervals was determined by Om005 measuring the solution concentration of the dyebath in real-time, using the DARG direct dyebath monitoring Blue system. Yellow This required the calculation of the total amount of each dye present in the initial dyebath, and in both the dyebath and the fabric during the dosing process. An optimum scheme was developed for dosing the dyes 0 20 40 60 80 100 120 under the particular conditions of the TlME(min) dyeing experiments (5).

Figure 5. Change in Solution Concentrations of CI Acid Blue 25 and CI Acid Yellow 49 with Time for Control Dyeing

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- Blue ----.Yellow

0 0 20 40 60 80 100 120 TIME(min)

Figure 6. Change in Fabric Concentrations of CI Acid Blue 25 and CI Acid Yellow 49 with Time for Control Dyeing

0 20 40 60 80 100 120 TIME(min)

Figure 7: Change in the Ratio of the Fabric Concentrations of CI Acid Blue 2.5 and CI Acid Yellow 49 with Time for Control Dyeing

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References:

1). McGregor, R., and Iijima, T., “Dimensionless Groups for the Description of Sorption Equilibria in Dyeing with Anionic Dyes”, I: Inorganic Co-Ion Exclusion”, J. Appl. Polym. Sci. 41,2969-2782( 1990).

2). McGregor, R., and Iijima, T., “Dimensionless Groups for the Sorption of Dye and Other Ions by Polymers, II: Hydrochloric Acid, C.I. Acid Blue 25, and Polyamides with an Excess of Basic Groups”, J. Appl. Polym. Sci. 45, 101 l-1021 (1992).

3). Arora, MS., and McGregor, R., A Dimensionless Model for the Equilibrium Sorption of Mixtures of Acid Dyes by Polyamides from Finite Dyebaths, Textile Research Journal, 67(6), 389-396 (1997).

4). McGregor, R., Arora, M. S., and Jasper, W. J., Control of the Dyeing of Nylon by Dye and Chemical Metering, Textile Research Journal, 67(8), 609-616 (1997).

5). Arora, M.S., and McGregor, R., Dimensionless Groups for the Equilibrium Sorption of Polysulfonated Acid Dyes By Polyamides, Textile Research Journal, @ (7), 385-391 (1995).

6). McGregor, R., Arora, M. S., and Jasper, W. J., Closed-Loop Textile Dyeing Process Utilizing Real-Time Metered Dosing of Dyes and Chemicals, U.S. Patent Application.

7). Snyder, W., Berkstresser, G., Smith, C. B., Beck, K. R., McGregor, R., and Jasper, W. J., The Correlation of Optical and Kinetic Deviations from Ideality in Fiber Mixtures, Textile Research Journal, 67(8), 57 l-579 (1997).

8). Van Delden, T., Beck, Keith R., and Smith, C. B., Flow Injection Analysis of Disperse Dyeing of Acetate and Polyester, Book of Papers, AATCC International Conference and Exhibition, Sept. (1996) and Textile Chemist and Colorist, In Press.

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