Research Into Control Methods Of Dyeing Uptake Rate For Batch Dyeing Process Based On Supervisory Control

As an important variety of natural fiber, the development of cotton fiber still exists manyproblems, such as intensive energy consumption, water consumption, wastewater discharge inthe process of reactive dyeing, which is against our basic national policy of energy conservationand emissions reduction. Therefore the factors of the cotton fabric dyeing process and therelationship between the dyeing rate model have been combined. Finally, the dyeing process canbe controlled by model prediction and online feedback technology, which can reduce the costs,improve the production efficiency, conserve energy and reduce emission.In the paper, dyeing rate of batch dyeing process based on supervision and control wasdeveloped, which was based on batch dyeing process control at home and abroad, and which wasconsisted of online and off-line supervisory control. Our goal was to obtain the real-timeparameter values, and the real-time dyeing rate can be received by soft measurement model,which was compared with preinstall dyeing rate. Then error and the error rate can be obtained,which was used as the input of the online monitoring controller. Then online monitoringcontroller can get the desired temperature based on the current conditions. Sliding predictivecontrol algorithm was realized by the online controller, by which the temperature of theexperimental platform can be controlled by adjustment on-off time of the heating rods of thereaction kettle. Then the vat rose (declined) to given temperature according to certain slope.Finally, it came true that the dyeing rate of the dyeing process was accuracy control.The first step is to establish the gray BP neural network model, and design three singlefactor experiments. The relations were developed between temperature, salt concentration andbath ratio and the dyeing rate of the single factor grey model, on which three parameters changedat the same time. The obtained dyeing rate can serve as the teacher signal of training neuralnetworks, and network obtained by training can serve as soft measurement model of the controlsystem. The second step is to design online and offline supervision controller, Fuzzy control algorithm was applied to online monitoring controller, which made the error and the error ratebetween the real-time dyeing rate and the given dyeing rate translate into the desired temperature,which served as the input of sliding predictive controller. Taguchi method was applied to off-linemonitoring controller. The temperature is the largest impact by analysis of the experimental data,so the temperature was selected as the controlling factor. The third step is to design and set upthe experiment platform of the batch dyeing processes, whose function is to verify thecorrectness of our designed supervisory control system.In the thesis the experiment platform of batch dyeing which verified the practical of thecontrol system designed in the paper was designed and developed based on supervisory controlsystem. The dyeing rate of the dyeing process can be controlled, in which the dyeing rate curvecan be tracked and set effectively. As shown in the result, the control method can control thedyeing rate of the batch dyeing process in time, which can prove that the control method ofdyeing rate of the batch dyeing process owns strong robustness, real-time and accuracy.