Bleaching of cotton fabric with hydrogen peroxide (H 2 O 2 ) is conventionally carried out under high temperature and strong alkaline conditions, for which the pad-steam process can be employed to present the benefits in lowering water and chemical consumptions. A notable drawback observed with the pad-steam process for bleaching of cotton fabric with H 2 O 2 is that, although the cotton fabric is bleached to an acceptable whiteness, it is required to dwell in steam for almost an hour, causing tremendous energy consumption as well as extensive damage to the cotton fibers. It has been found that an activated peroxide system can be formed with the addition of N-[4-(triethylammoniomethyl)benzoyl]caprolactam chloride (TBCC) to H 2 O 2 solution to generate 4-(triethylammoniomethyl)perbenzoic acid (TPA), allowing the establishment of a rapid pad-steam process. Sodium citrate was selected as the desired alkali for use in the rapid pad-steam process to neutralize 4-(triethylammoniomethyl)benzoic acid (TBA) released from the TBCCactivated peroxide system. By shortening the steaming time to 4 min or less, the rapid pad-steam process afforded enormous advantages over the conventional pad-steam process in saving energy and increasing production efficiency, and also, the bleached cotton fabric underwent no apparent chemical damage and met the requirements for post-dyeing.
In this paper, the five-leg inverter-dual permanent magnet synchronous motor system is taken as the research object. To solve the problem of large steady-state speed fluctuation when applying the traditional model predictive control strategy, based on the mathematical models of the five-leg inverter and the dual permanent magnet synchronous motor, the causes of the fluctuation are analyzed, and then an improved model predictive control algorithm with error feedback is proposed. The algorithm makes full use of the high-speed operation ability of modern digital processor, and introduces the prediction value of error feedback correction model, so that the rolling optimization is not only based on the model, but also makes full use of the actual value of the feedback state variables. At the same time, the second-order Euler discrete method is used to further improve the model progress. Simulation and experimental results show that the model predictive control strategy with error feedback can effectively improve the steady-state speed control performance of the system while maintaining the dynamic performance of the original system and the decoupling control effect of the two motors.
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