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A neuro-fuzzy decoupling approach for real-time drying room control in meat manufacturing

机译:一种神经模糊解耦方法,用于肉类生产中的实时干燥室控制

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This paper proposes a method to resolve a strong coupling between temperature and relative humidity in drying room control systems. The coupling issue not only affects the control accuracy but also causes system instability, unnecessary adjustment, and extra energy consumption. A real-time temperature and relative humidity decoupling control method is reported in this paper. An improved aspirated psychrometer was used as a relative humidity measurement unit, which achieved relative humidity measurement accuracy of ±0.7%. The decoupling approach was developed based on a large real-time drying room, whose physical size was 22 × 15 × 3.5 m. A decoupling approach was developed using the adaptive neuro-fuzzy inference system for the control of temperature and relative humidity in the drying room. The simulated result shows that after the decoupling treatment, the relative humidity fluctuation was reduced from ±2.5% to ±0.6%.
机译:本文提出了一种解决干燥室控制系统中温度与相对湿度之间强耦合的方法。耦合问题不仅会影响控制精度,还会导致系统不稳定,不必要的调整和额外的能耗。本文报道了一种实时的温度和相对湿度解耦控制方法。使用改进的吸气干湿计作为相对湿度测量单元,其相对湿度测量精度达到±0.7%。去耦方法是基于大型实时干燥室开发的,该干燥室的物理尺寸为22×15×3.5 m。使用自适应神经模糊推理系统开发了一种去耦方法,用于控制干燥室的温度和相对湿度。模拟结果表明,经过解耦处理后,相对湿度波动从±2.5%降低至±0.6%。

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