CN103610227B - Cut tobacco dryer head and tail section process variable optimizing control method - Google Patents

Cut tobacco dryer head and tail section process variable optimizing control method Download PDF

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CN103610227B
CN103610227B CN201310659839.1A CN201310659839A CN103610227B CN 103610227 B CN103610227 B CN 103610227B CN 201310659839 A CN201310659839 A CN 201310659839A CN 103610227 B CN103610227 B CN 103610227B
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彭辉
顾云峰
王丹
刘明月
李立
阮文杰
魏吉敏
肖玉娇
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Central South University
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Abstract

本发明公开了一种烘丝机头尾段工艺变量优化控制方法,依据烘丝过程头尾段筒温、风温、排潮风门等工艺变量的历史数据,采用三次函数作为径向基函数的Cubic-RBF-ARX模型对烘丝动态特性进行建模;所建模型具有自调节能力,能反映不同模式下的入口流量以及入口水分的变化对出口水分的影响,可根据头尾段不同模式的入口流量及入口水分的变化来预测未来出口水分的变化情况;根据所建模型对各工艺变量进行优化设定,可使头尾段叶丝出口水分的控制达到较好的效果。本发明方法综合考虑了来料量与各输入变量间的动态特性,可以更有效地克服来料流量和水分变化对烘丝过程头尾段的影响,适用于不同模式下叶丝入口流量与入口水分时的头尾段控制。

The invention discloses a process variable optimization control method at the head and tail sections of a silk drying machine. According to the historical data of process variables such as cylinder temperature, wind temperature, and damp damper at the head and tail sections of the silk drying process, a cubic function is used as the radial basis function. The Cubic-RBF-ARX model is used to model the dynamic characteristics of silk drying; the built model has the ability of self-regulation, and can reflect the influence of inlet flow and inlet moisture changes on outlet moisture under different modes, and can be adjusted according to the different modes of the head and tail sections. The change of inlet flow and inlet moisture is used to predict the change of outlet moisture in the future; according to the established model, each process variable is optimized and set, so that the control of the outlet moisture of the head and tail sections can achieve better results. The method of the present invention comprehensively considers the dynamic characteristics between the amount of incoming material and each input variable, can more effectively overcome the influence of incoming material flow and moisture changes on the head and tail sections of the drying process, and is suitable for the inlet flow and inlet flow of shredded silk in different modes. Head and tail section control when wet.

Description

一种烘丝机头尾段工艺变量优化控制方法A Method for Optimal Control of Process Variables in the Head and Tail Sections of a Silk Drying Machine

技术领域technical field

本发明涉及烘丝机头尾段工艺变量优化控制方法。The invention relates to a process variable optimization control method at the head and tail sections of a silk drying machine.

背景技术Background technique

烘丝过程是香烟制丝生产中最重要的一道加工工序,它主要是通过对叶丝进行加热干燥,降低叶丝的含水率,使烘烤后叶丝的含水率、温度均匀一致,并控制在一定的数值范围内,以满足生产工艺要求。烘丝的工艺流程主要分为预热、干头、中间以及干尾过程四个部分。在干头阶段,叶丝入口流量不断增加,但无叶丝出口水分的检测值,难以进行反馈控制,容易造成干头阶段出口水分控制品质差、干料多;在干尾阶段,由于叶丝入口流量骤然减少,而烘丝筒具有较大热容,筒壁内部温度难以按规定的速率下降等问题,也容易造成干尾阶段出口水分控制性能低且干料多。因此,“干头干尾”是目前烘丝过程出口水分控制的难点所在。The drying process is the most important process in the production of cigarette shreds. It mainly reduces the moisture content of the shredded leaves by heating and drying the shredded leaves, so that the moisture content and temperature of the shredded leaves after baking are uniform, and controlled. Within a certain range of values to meet the requirements of the production process. The process of drying silk is mainly divided into four parts: preheating, dry head, middle and dry end. In the dry head stage, the inlet flow rate of the shredded leaves is continuously increasing, but there is no detection value of the outlet moisture of the shredded leaves, so it is difficult to carry out feedback control, which will easily cause poor quality of water control at the outlet of the dried head stage and more dry materials; The sudden decrease of the inlet flow rate and the large heat capacity of the drying drum make it difficult for the internal temperature of the drum wall to drop at a specified rate, which may also easily lead to low moisture control performance and a lot of dry material at the outlet during the dry end stage. Therefore, "dry head and dry tail" is the difficulty of outlet moisture control in the current drying process.

现有的干头干尾过程控制方法主要有:The existing control methods for dry start and dry end process mainly include:

(1)利用进入和输出烘丝机的物料和干燥介质作为热质平衡对象建立数学模型,结合前馈PID调节筒温的控制方式。但前馈数学模型仅考虑了进料的含水率和流量,并没有考虑热风温度等其他对出口水分有重要影响的因素,不能完全反应真实过程,造成头尾段烘丝机出口水分波动大,需要操作人员进行人工干预,对于头尾段不同模式下的、具有不同入口流量和入口水分的来料难以获得满意的控制效果。(1) Use the materials entering and exiting the drying machine and the drying medium as heat and mass balance objects to establish a mathematical model, and combine the control method of feedforward PID to adjust the cylinder temperature. However, the feed-forward mathematical model only considers the moisture content and flow rate of the feed material, and does not consider other factors that have an important impact on the outlet moisture, such as hot air temperature, and cannot fully reflect the real process, resulting in large fluctuations in the outlet moisture of the head and tail silk dryers. Operators are required to intervene manually, and it is difficult to obtain a satisfactory control effect for incoming materials with different inlet flow rates and inlet moisture in different modes at the head and tail sections.

(2)在上述前馈控制的基础上,在头尾段增加蒸喷加湿装置对头尾料施加蒸汽水来提高头尾料的含水率,以降低干料量。但此方法仅对叶丝表层进行加湿,仅提高了叶丝表层湿度,仍然会造成烟丝内在质量的降低,且增加了出口水分控制的难度与稳定性。(2) On the basis of the above-mentioned feed-forward control, a steam spray humidifier is added to the head and tail sections to apply steam water to the head and tail materials to increase the moisture content of the head and tail materials to reduce the amount of dry materials. However, this method only humidifies the surface of the shredded leaf and only increases the humidity of the surface of the shredded leaf, which still causes a reduction in the internal quality of the shredded tobacco and increases the difficulty and stability of outlet moisture control.

(3)通过多次试验、寻求最佳头尾阶段热风温度值和调整排潮阀门开度等工艺参数来减少干料量。此方法缺乏自调节能力,无法保证对于不同模式下具有不同入口流量和入口水分的来料时,该组工艺参数均为最优设定值;(3) Reduce the amount of dry material through multiple tests, seek the best hot air temperature value at the head and tail stages, and adjust technological parameters such as the opening of the tide valve. This method lacks the ability of self-regulation, and cannot guarantee that the process parameters of this group are optimal setting values for incoming materials with different inlet flow rates and inlet moisture in different modes;

(4)在PID控制策略的基础上,将模糊控制的思想应用到烘丝机水分控制中。(4) On the basis of the PID control strategy, the idea of fuzzy control is applied to the moisture control of the silk drying machine.

仅仅用单纯的二维模糊控制器来解决烘丝过程出口水分的控制问题仍然无法获得最优的工艺参数设定值,而且对于不同模式下的入口流量与入口水分的变化,还需对模糊控制规则表进行调整,这对工业生产带来不便。Only using a simple two-dimensional fuzzy controller to solve the control problem of outlet moisture in the drying process still cannot obtain the optimal process parameter setting value, and for the changes of inlet flow and inlet moisture in different modes, fuzzy control is still required. The rule table is adjusted, which brings inconvenience to industrial production.

发明内容Contents of the invention

本发明所要解决的技术问题是,针对现有技术不足,提供一种烘丝机头尾段工艺变量优化控制方法,使干头阶段叶丝出口水分尽可能快地上升、并快速到达稳定状态,使干尾阶段叶丝出口水分尽可能缓慢地下降,从而有效地减少头尾段的干料量,提高烘丝过程的控制性能;更有效地克服来料流量和水分变化对烘丝过程头尾段的影响,避免人工整定输入工艺变量参数的不便。The technical problem to be solved by the present invention is to provide a process variable optimization control method at the head and tail section of the silk drying machine in view of the deficiencies in the prior art, so that the moisture at the outlet of the shredded silk in the drying stage can rise as fast as possible and reach a stable state quickly. Make the moisture at the outlet of shredded leaves drop as slowly as possible in the drying and tailing stage, thereby effectively reducing the amount of dry material in the head and tail section and improving the control performance of the drying process; more effectively overcome the impact of incoming material flow and moisture changes on the beginning and tail of the drying process The influence of the segment, avoiding the inconvenience of manually setting and inputting process variable parameters.

为解决上述技术问题,本发明所采用的技术方案是:一种烘丝机头尾段工艺变量优化控制方法,该方法为:In order to solve the above-mentioned technical problems, the technical solution adopted in the present invention is: a method for optimizing the process variables in the head and tail sections of the silk drying machine, the method is:

1)根据烘丝机的运行流程,建立烘丝过程中叶丝入口流量、入口水分、筒温、风温、排潮风门、出口水分的时序关系,同时根据烘丝过程干头阶段无叶丝出口水分检测值、干尾阶段无叶丝入口流量与入口水分检测值的特点,采用三次函数作为径向基函数的Cubic-RBF-ARX模型,分别建立烘丝过程干头阶段与干尾阶段的Cubic-RBF-ARX模型;1) According to the operation process of the silk drying machine, establish the time sequence relationship of the inlet flow of the shredded silk, the inlet moisture, the cylinder temperature, the air temperature, the damp damper, and the outlet moisture in the drying process, and at the same time, according to the drying process, there is no outlet of the shredded silk at the end of the drying stage. According to the characteristics of the moisture detection value, the inlet flow rate and the inlet moisture detection value of the dry-end stage, the cubic function is used as the Cubic-RBF-ARX model of the radial basis function, and the Cubic-RBF-ARX model of the dry-end stage and the dry-end stage of the drying process are respectively established. - RBF-ARX model;

2)根据烘丝机头尾段的历史运行数据,采用结构化非线性参数优化方法分别优化烘丝过程干头阶段与干尾阶段的Cubic-RBF-ARX模型;2) According to the historical operation data of the head and tail section of the drying machine, the Cubic-RBF-ARX model of the dry head stage and the dry end stage of the silk drying process were respectively optimized by using the structured nonlinear parameter optimization method;

3)依据优化的烘丝过程干头阶段与干尾阶段的Cubic-RBF-ARX模型,采用双S型函数描述干头阶段的排潮风门、风温、筒温的最优输入曲线;采用阶跃函数描述干头阶段的入口流量的最优输入曲线;采用指数函数描述干尾阶段排潮风门、风温、筒温和筒体电机频率的最优输入曲线;3) According to the optimized Cubic-RBF-ARX model of the dry end stage and dry end stage of the silk drying process, the double S-type function is used to describe the optimal input curve of the moisture exhaust damper, air temperature and cylinder temperature in the dry end stage; The jump function describes the optimal input curve of the inlet flow in the dry head stage; the exponential function is used to describe the optimal input curve of the tidal discharge damper, air temperature, barrel temperature and barrel motor frequency in the dry end stage;

4)采用列维布格奈奎尔特方法,通过使优化的干头阶段与干尾阶段的Cubic-RBF-ARX模型计算出的出口水分预测值与出口水分设定值的误差最小,寻找出烘丝过程干头阶段与干尾阶段的最优输入曲线的参数,以适应来料情况的变化,减少干尾阶段的干料量。4) Using the Levi Burger-Nyquilt method, by minimizing the error between the predicted value of outlet moisture and the set value of outlet moisture calculated by the Cubic-RBF-ARX model in the optimized dry head stage and dry tail stage, find out the The parameters of the optimal input curve in the dry head stage and the dry end stage of the drying process are used to adapt to changes in incoming materials and reduce the amount of dry materials in the dry end stage.

所述步骤1)中,烘丝机干头阶段Cubic-RBF-ARX模型为:In the step 1), the Cubic-RBF-ARX model of the drying stage of the silk dryer is:

其中:in:

其中,yH(tH)表示烘丝机干头阶段Cubic-RBF-ARX模型的出口水分;分别表示干头阶段Cubic-RBF-ARX模型的排潮风门开度、风温、筒温、入口流量及入口水分;XH(tH-1)为入口流量和入口水分的状态变量;npH,nqH,dH和mH均表示干头阶段Cubic-RBF-ARX模型的阶次;分别为干头阶段Cubic-RBF-ARX模型输出项与输入项的RBF神经网络的中心; 为干头阶段Cubic-RBF-ARX模型的标量权系数;||·||F表示矩阵的Frobenius范数;ξH(tH)是干头阶段Cubic-RBF-ARX模型的建模误差,为高斯白噪声;T0 H为烘丝机干头阶段Cubic-RBF-ARX模型建模采样时间,T1为从有入口流量检测值到有入口水分检测值的时间,T2为从有入口水分检测值到有出口水分检测值的时间,T3为从有入口水分检测值到烘丝筒入口的时间,T4为叶丝在烘丝筒烘干的时间。Among them, y H (t H ) represents the outlet moisture of the Cubic-RBF-ARX model in the drying stage of the silk dryer; represent the tidal damper opening, wind temperature, cylinder temperature, inlet flow and inlet moisture of the Cubic-RBF-ARX model in the dry head stage; X H (t H -1) is the state variable of inlet flow and inlet moisture; np H , nq H , d H and m H all represent the order of the Cubic-RBF-ARX model at the dry head stage; Respectively, the center of the RBF neural network of the Cubic-RBF-ARX model output and input items in the dry head stage; is the scalar weight coefficient of the Cubic-RBF-ARX model in the dry head stage; ||·|| F represents the Frobenius norm of the matrix; ξ H (t H ) is the modeling error of the Cubic-RBF-ARX model in the dry head stage, Gaussian white noise; T 0 H is the sampling time of the Cubic-RBF-ARX model modeling in the drying stage of the silk dryer, T 1 is the time from the detection value of the inlet flow to the detection value of the inlet moisture, T 2 is the time from the inlet moisture The time from the detection value to the detection value of the outlet moisture, T3 is the time from the detection value of the inlet moisture to the entrance of the drying drum, and T4 is the drying time of the shredded leaves in the drying drum.

所述步骤1)中,烘丝机干尾阶段Cubic-RBF-ARX模型为:In the step 1), the Cubic-RBF-ARX model of the drying end stage of the silk dryer is:

其中:in:

其中,yT(tT)表示烘丝机干尾阶段Cubic-RBF-ARX模型的出口水分;分别表示干尾阶段Cubic-RBF-ARX模型的筒温、热风风温、排潮风门开度、入口流量、入口水分及筒体电机频率;XT(tT-1)为热风风温和筒体电机频率的状态变量;npT,nqT,dT和mT均表示干尾阶段Cubic-RBF-ARX模型的阶次; 分别为干尾阶段Cubic-RBF-ARX模型输出项与输入项的RBF神经网络的中心; 为干尾阶段Cubic-RBF-ARX模型的标量权系数;ξT(tT)是干尾阶段Cubic-RBF-ARX模型建模误差,为高斯白噪声;T0 T为烘丝机干尾阶段Cubic-RBF-ARX模型建模采样时间。Among them, y T (t T ) represents the outlet moisture of the Cubic-RBF-ARX model at the dry end stage of the silk dryer; Respectively represent the barrel temperature, hot air temperature, tide damper opening, inlet flow, inlet moisture and barrel motor frequency of the Cubic-RBF-ARX model in the dry end stage; X T (t T -1) is the hot air temperature and barrel The state variable of the motor frequency; np T , nq T , d T and m T all represent the order of the Cubic-RBF-ARX model in the dry tail stage; are the centers of the RBF neural network of the output and input items of the Cubic-RBF-ARX model in the dry tail stage; is the scalar weight coefficient of the Cubic-RBF-ARX model at the dry end stage; ξ T (t T ) is the modeling error of the Cubic-RBF-ARX model at the dry end stage, which is Gaussian white noise; T 0 T is the dry end stage of the silk dryer The Cubic-RBF-ARX model models sampling time.

所述步骤2)中,烘丝机干头阶段Cubic-RBF-ARX模型优化如下:In the step 2), the optimization of the Cubic-RBF-ARX model in the drying stage of the silk dryer is as follows:

(( θθ ^^ NN Hh ,, θθ ^^ LL Hh )) == argarg minmin θθ NN Hh ,, θθ LL Hh ΣΣ tt ohoh == 11 NN Hh (( ythe y ‾‾ Hh (( tt ohoh )) -- ythe y ^^ Hh (( tt ohoh )) )) 22

其中,是烘丝机干头阶段出口水分的实际值,是在实际输入作用下,由烘丝机干头阶段Cubic-RBF-ARX模型计算出的出口水分的预测值; θ ^ L H = { ω 0 H , 0 , ω i H , 0 y H , ω n , j H , 0 u H , ω k H H , 0 , ω i H , k H y H , ω j H , k H u H | i H = 1 , . . . , np H ; j H = 1 , . . . , nq H ; k H = 1 , . . . , m H } 为烘丝机干头阶段Cubic-RBF-ARX模型的线性参数;为烘丝机干头阶段Cubic-RBF-ARX模型的非线性参数;NH为烘丝机干头阶段Cubic-RBF-ARX模型建模数据长度。in, is the actual value of the outlet moisture in the drying stage of the drying machine, is the predicted value of the outlet moisture calculated by the Cubic-RBF-ARX model at the drying stage of the silk dryer under the actual input; θ ^ L h = { ω 0 h , 0 , ω i h , 0 the y h , ω no , j h , 0 u h , ω k h h , 0 , ω i h , k h the y h , ω j h , k h u h | i h = 1 , . . . , np h ; j h = 1 , . . . , nq h ; k h = 1 , . . . , m h } is the linear parameter of the Cubic-RBF-ARX model in the drying stage of the silk dryer; is the nonlinear parameter of the Cubic-RBF-ARX model in the drying stage of the silk dryer; N H is the modeling data length of the Cubic-RBF-ARX model in the drying stage of the silk drying machine.

烘丝机干尾阶段Cubic-RBF-ARX模型优化如下:The optimization of the Cubic-RBF-ARX model in the dry end stage of the silk dryer is as follows:

(( θθ ^^ NN TT ,, θθ ^^ LL TT )) == argarg minmin θθ NN TT ,, θθ LL TT ΣΣ tt otot == 11 NN TT (( ythe y ‾‾ TT (( tt otot )) -- ythe y ^^ TT (( tt otot )) )) 22

其中,是烘丝机干尾过程中出口水分的实际值;是在实际输入作用下,由烘丝机干尾阶段Cubic-RBF-ARX模型计算出的出口水分的预测值; θ L T = { ω 0 T , 0 , ω i T , 0 y T , ω n , j T , 0 u T , ω k T T , 0 , ω i T , k T y T , ω j T , k T u T | i T = 1 , . . . , np T ; j T = 1 , . . . , nq T ; k T = 1 , . . . , m T } 为烘丝机干尾阶段Cubic-RBF-ARX模型的线性参数,为烘丝机干尾阶段Cubic-RBF-ARX模型的非线性参数;NT为烘丝机干头阶段Cubic-RBF-ARX模型建模数据长度。in, is the actual value of the outlet moisture in the drying process of the silk drying machine; is the predicted value of the outlet moisture calculated by the Cubic-RBF-ARX model at the dry end stage of the silk dryer under the actual input; θ L T = { ω 0 T , 0 , ω i T , 0 the y T , ω no , j T , 0 u T , ω k T T , 0 , ω i T , k T the y T , ω j T , k T u T | i T = 1 , . . . , np T ; j T = 1 , . . . , nq T ; k T = 1 , . . . , m T } is the linear parameter of the Cubic-RBF-ARX model in the dry end stage of the silk dryer, N T is the nonlinear parameter of the Cubic-RBF-ARX model in the dry end stage of the silk dryer; NT is the modeling data length of the Cubic-RBF-ARX model in the dry end stage of the silk dryer.

所述步骤3)中:In step 3):

用于描述烘丝机干头阶段排潮风门、风温、筒温的最优输入曲线的双S型函数表达式为:The expression of the double S-type function used to describe the optimal input curves of the damper, wind temperature and cylinder temperature in the drying stage of the silk dryer is:

Uu scsc (( tt sthe s )) == λλ 11 11 ++ ee tt sthe s -- λλ 22 λλ 33 ++ λλ 44 ++ λλ 55 11 ++ ee tt sthe s -- λλ 66 λλ 77

其中,ts为输入的时间,单位为s;λ145分别为双S型函数的起点、转折点及终点值;λ26分别为双S型函数的两条对称轴中心位置;λ37分别为双S型函数上升或下降的速度;λ37大于0时表示S型函数上升,λ37小于0时表示S型函数下降;c=1,2,3,Us1(ts)是排潮风门的设定值;Us2(ts)是风温的设定值;Us3(ts)是筒温的设定值。Among them, t s is the input time, the unit is s; λ 1 , λ 4 , λ 5 are the starting point, turning point and end point of the double S-shaped function respectively; λ 2 , λ 6 are the two symmetrical axis center position; λ 3 and λ 7 are the rising or falling speeds of the double S-shaped function respectively; when λ 3 and λ 7 are greater than 0, it means that the S-shaped function is rising, and when λ 3 and λ 7 are less than 0, it means that the S-shaped function is falling; c =1,2,3, U s1 (t s ) is the setting value of the damper; U s2 (t s ) is the setting value of the air temperature; U s3 (t s ) is the setting value of the cylinder temperature.

用于描述烘丝机干头阶段入口流量的最优输入曲线的阶跃函数表达式为:The expression of the step function used to describe the optimal input curve of the inlet flow in the drying stage of the drying machine is:

Uu TT (( tt TT )) == κκ 11 tt TT κκ 22 tt TT ∈∈ [[ 11 ,, κκ 22 ]] κκ 11 tt TT ∈∈ [[ κκ 22 ++ 11 ,, κκ 33 ]] ;;

其中,tT为输入的时间,单位为s;κ123分别为阶跃函数的上升速度、上升时间与终值。Among them, t T is the input time, the unit is s; κ 1 , κ 2 , κ 3 are the rising speed, rising time and final value of the step function respectively.

所述步骤4)中,烘丝机干头阶段Cubic-RBF-ARX模型计算出的出口水分预测值为: y ‾ H ( t a ) = f ( U s 1 ( t a ) , U s 2 ( t a ) , U s 3 ( t a ) , U T ( t a ) ) , 通过将烘丝机干头阶段各工艺变量的优化设定曲线代入所构建的干头阶段Cubic-RBF-ARX的输入变量中得到。通过使干头阶段Cubic-RBF-ARX模型计算出的出口水分预测值与出口水分设定值yset(ta)的误差eH(ta)最小,即采用列维布格奈奎尔特方法求解优化问题寻找出干头阶段排潮风门、风温、筒温的输入曲线的参数λx和入口流量输入曲线的参数κ123;其中,x=1,2,…,7;g=1,2,3;M是干头阶段持续的时间。In the step 4), the predicted value of the outlet moisture calculated by the Cubic-RBF-ARX model in the drying stage of the silk dryer for: the y ‾ h ( t a ) = f ( u the s 1 ( t a ) , u the s 2 ( t a ) , u the s 3 ( t a ) , u T ( t a ) ) , By substituting the optimized setting curve of each process variable in the dry head stage of the silk dryer into the input variables of Cubic-RBF-ARX in the dry head stage get in. Predicted value of outlet moisture calculated by making dry head stage Cubic-RBF-ARX model The error e H (t a ) with the outlet moisture setting value y set (t a ) is the smallest, that is, the Levi Burger-Nyquilt method is used to solve the optimization problem Find the parameter λ x of the input curve of the tidal discharge damper, wind temperature and cylinder temperature in the dry head stage, and the parameters κ 1 , κ 2 , κ 3 of the input curve of the inlet flow; among them, x=1,2,...,7; g =1,2,3; M is the duration of the dry head stage.

所述步骤3)中,用于描述干尾阶段排潮风门、风温、筒温和筒体电机频率的最优输入曲线的指数函数的表达式为:In the step 3), the expression of the exponential function used to describe the optimal input curve of the damper, wind temperature, cylinder temperature and cylinder motor frequency in the dry end stage is:

Uu zpzp (( tt zz )) == αα pp 11 ×× (( αα pp 22 )) tt zz ++ αα pp 33 pp == 1,2,3,41,2,3,4 ;;

式中Uz1(tz)、Uz2(tz)、Uz3(tz)、Uz4(tz)分别表示干尾阶段排潮风门、风温、筒温和筒体电机频率的最优输入曲线。In the formula, U z1 (t z ), U z2 (t z ), U z3 (t z ), and U z4 (t z ) represent the optimal frequency of the damper, wind temperature, cylinder temperature, and cylinder motor frequency in the dry end stage, respectively. Enter the curve.

所述步骤4)中,干尾阶段的Cubic-RBF-ARX模型计算出的出口水分预测值为: y ‾ T ( t b ) = f ( U z 1 ( t b ) , U z 2 ( t b ) , U z 3 ( t b ) , U z 4 ( t b ) ) , 通过将烘丝机干尾阶段各工艺变量的优化设定曲线代入所构建的干尾阶段Cubic-RBF-ARX模型的输入变量中得到;通过使干尾阶段的Cubic-RBF-ARX模型计算出的出口水分预测值与出口水分设定值y'set(tb)的误差eT(tb)最小,即采用列维布格奈奎尔特方法求解优化问题寻找出干尾阶段排潮风门、风温、筒温和筒体电机频率最优输入曲线的参数αpg;其中,g=1,2,3;M'是干尾阶段持续时间。In the step 4), the predicted value of outlet moisture calculated by the Cubic-RBF-ARX model in the dry tail stage for: the y ‾ T ( t b ) = f ( u z 1 ( t b ) , u z 2 ( t b ) , u z 3 ( t b ) , u z 4 ( t b ) ) , By substituting the optimized setting curves of each process variable in the dry end stage of the silk dryer into the input variables of the constructed Cubic-RBF-ARX model in the dry end stage Obtained in; the predicted value of the outlet moisture calculated by the Cubic-RBF-ARX model of the dry tail stage The error e T (t b ) with the outlet moisture set value y' set (t b ) is the smallest, that is, the Levi Burger-Nyquilt method is used to solve the optimization problem Find the parameter α pg for the optimal input curve of the damper, air temperature, cylinder temperature and cylinder motor frequency in the dry end stage; where, g=1,2,3; M' is the duration of the dry end stage.

与现有技术相比,本发明所具有的有益效果为:本发明方法可使干头阶段叶丝出口水分尽可能快地上升、并快速到达稳定状态,可使干尾阶段叶丝出口水分尽可能缓慢地下降,从而有效地减少头尾段的干料量,提高烘丝过程的控制性能,具有较大的经济价值;本发明方法综合考虑了来料量与各输入变量间的动态特性,可以更有效地克服来料流量和水分变化对烘丝过程头尾段的影响,适用于不同模式下叶丝入口流量与入口水分时的头尾段控制;本发明方法基于辨识的模型优化出最优的输入设定曲线,避免了人工整定输入工艺变量参数的不便。Compared with the prior art, the present invention has the beneficial effects that: the method of the present invention can make the outlet moisture of shredded leaves in the first stage of drying rise as fast as possible and reach a steady state quickly, and can make the outlet moisture of shredded leaves in the tail stage of drying as fast as possible. It may decrease slowly, thereby effectively reducing the amount of dry material in the head and tail sections, improving the control performance of the silk drying process, and having greater economic value; the method of the present invention comprehensively considers the dynamic characteristics between the amount of incoming material and each input variable, It can more effectively overcome the impact of incoming material flow and moisture changes on the head and tail sections of the drying process, and is suitable for the head and tail section control of the shred inlet flow and inlet moisture in different modes; the method of the present invention optimizes the most based on the identification model. Excellent input setting curve avoids the inconvenience of manually setting input process variable parameters.

附图说明Description of drawings

图1为烘丝机工艺过程示意图。Figure 1 is a schematic diagram of the process of the silk drying machine.

具体实施方式Detailed ways

烘丝机工艺过程如图1所示。叶丝进入烘丝工序之前,首先检测叶丝的入口流量u4和入口水分u5。经过T3时间,叶丝到达烘丝机入口处。叶丝在烘丝筒烘干时,系统会定时采样筒体的排潮风门开度u1、风温u2、筒温u3等工艺变量参数值。烘干过程持续T4时间,烘干后的叶丝从烘丝筒出口倒出,并在出口处测量叶丝出口水分值y。从有入口流量检测值到有出口水分检测值需经历一段较长时间,例如某烘丝生产线大约需340s。另外,烘丝机的输入/输出变量间也具有较大的时滞。The process of the wire drying machine is shown in Figure 1. Before the shredded leaf enters the drying process, the inlet flow rate u 4 and the inlet moisture u 5 of the shredded leaf are detected first. After T3 time, the shredded leaves arrive at the entrance of the silk dryer. When the shredded leaves are drying in the silk drying cylinder, the system will regularly sample the process variable parameter values such as the opening of the damp damper u 1 , the air temperature u 2 , and the cylinder temperature u 3 of the cylinder. The drying process lasts for T4 time, and the dried shredded leaves are poured out from the outlet of the drying drum, and the outlet moisture value y of the shredded leaves is measured at the outlet. It takes a long time from the detection value of the inlet flow to the detection value of the outlet moisture, for example, it takes about 340s for a silk drying production line. In addition, there is a large time lag between the input/output variables of the silk dryer.

当检测到有入口流量时,表明烘丝过程开始运行。运行初期烘丝过程有叶丝入口流量与入口水分检测值,没有叶丝出口水分检测值,此时烘丝过程干头阶段开始。根据烘丝过程干头阶段的特性,建立Cubic-RBF-ARX模型结构:When an inlet flow is detected, it indicates that the drying process is running. In the initial stage of operation, the silk drying process has the detection value of the inlet flow and inlet moisture of the shredded silk, but there is no detection value of the outlet moisture of the shredded silk. At this time, the drying stage of the shredded shredded process starts. According to the characteristics of the drying stage of the silk drying process, the Cubic-RBF-ARX model structure is established:

其中:in:

其中,yH(tH)表示烘丝机干头阶段Cubic-RBF-ARX模型的出口水分;分别表示干头阶段Cubic-RBF-ARX模型的排潮风门开度、风温、筒温、入口流量及入口水分;XH(tH-1)为入口流量和入口水分的状态变量;npH,nqH,dH和mH均表示干头阶段Cubic-RBF-ARX模型的阶次;分别为干头阶段Cubic-RBF-ARX模型输出项与输入项的RBF神经网络的中心; 为干头阶段Cubic-RBF-ARX模型的标量权系数;||·||F表示矩阵的Frobenius范数;ξH(tH)是干头阶段Cubic-RBF-ARX模型的建模误差,为高斯白噪声;T0 H为烘丝机干头阶段Cubic-RBF-ARX模型建模采样时间,T1为从有入口流量检测值到有入口水分检测值的时间,T2为从有入口水分检测值到有出口水分检测值的时间,T3为从有入口水分检测值到烘丝筒入口的时间,T4为叶丝在烘丝筒烘干的时间。Among them, y H (t H ) represents the outlet moisture of the Cubic-RBF-ARX model in the drying stage of the silk dryer; represent the tidal damper opening, wind temperature, cylinder temperature, inlet flow and inlet moisture of the Cubic-RBF-ARX model in the dry head stage; X H (t H -1) is the state variable of inlet flow and inlet moisture; np H , nq H , d H and m H all represent the order of the Cubic-RBF-ARX model at the dry head stage; Respectively, the center of the RBF neural network of the Cubic-RBF-ARX model output and input items in the dry head stage; is the scalar weight coefficient of the Cubic-RBF-ARX model in the dry head stage; ||·|| F represents the Frobenius norm of the matrix; ξ H (t H ) is the modeling error of the Cubic-RBF-ARX model in the dry head stage, Gaussian white noise; T 0 H is the sampling time of the Cubic-RBF-ARX model modeling in the drying stage of the silk dryer, T 1 is the time from the detection value of the inlet flow to the detection value of the inlet moisture, and T 2 is the time from the inlet moisture The time from the detection value to the detection value of the outlet moisture, T3 is the time from the detection value of the inlet moisture to the entrance of the drying drum, and T4 is the drying time of the shredded leaves in the drying drum.

当入口流量由正常值变为0时,标志着干尾过程的开始,当出口水分下降到3%时,标志着烘丝机整个烘丝过程的结束。干尾过程中无入口流量检测值,但有出口水分检测值。根据烘丝机干尾过程段的特性,建立如下的Cubic-RBF-ARX模型:When the inlet flow rate changes from the normal value to 0, it marks the beginning of the drying process, and when the outlet moisture drops to 3%, it marks the end of the whole drying process of the silk dryer. There is no inlet flow detection value during the dry tail process, but there is an outlet moisture detection value. According to the characteristics of the dry end process of the silk dryer, the following Cubic-RBF-ARX model is established:

其中:in:

其中,yT(tT)表示烘丝机干尾阶段Cubic-RBF-ARX模型的出口水分;分别表示干尾阶段Cubic-RBF-ARX模型的筒温、热风风温、排潮风门开度、入口流量、入口水分及筒体电机频率;XT(tT-1)为热风风温和筒体电机频率的状态变量;npT,nqT,dT和mT均表示干尾阶段Cubic-RBF-ARX模型的阶次; 分别为干尾阶段Cubic-RBF-ARX模型输出项与输入项的RBF神经网络的中心; 为干尾阶段Cubic-RBF-ARX模型的标量权系数;ξT(tT)是干尾阶段Cubic-RBF-ARX模型建模误差,为高斯白噪声;T0 T为烘丝机干尾阶段Cubic-RBF-ARX模型建模采样时间。Among them, y T (t T ) represents the outlet moisture of the Cubic-RBF-ARX model at the dry end stage of the silk dryer; Respectively represent the barrel temperature, hot air temperature, tide damper opening, inlet flow, inlet moisture and barrel motor frequency of the Cubic-RBF-ARX model in the dry end stage; X T (t T -1) is the hot air temperature and barrel The state variable of the motor frequency; np T , nq T , d T and m T all represent the order of the Cubic-RBF-ARX model in the dry tail stage; are the centers of the RBF neural network of the output and input items of the Cubic-RBF-ARX model in the dry tail stage; is the scalar weight coefficient of the Cubic-RBF-ARX model at the dry end stage; ξ T (t T ) is the modeling error of the Cubic-RBF-ARX model at the dry end stage, which is Gaussian white noise; T 0 T is the dry end stage of the silk dryer The Cubic-RBF-ARX model models sampling time.

本发明采用结构化非线性参数优化方法(SNPOM)方法对模型进行估计。为了使得上面所构造的Cubic-RBF-ARX模型能够描述烘丝过程头尾段的全局动态特性,我们首先采用SNPOM方法来优化模型的、一步预测误差最小情形下的参数,并以此参数作为长期预测优化目标下的模型参数初始值。然后,采用列维布格奈奎尔特方法(LMM)来进行长期预测性能最优的模型参数的优化。The present invention uses a structured nonlinear parameter optimization method (SNPOM) method to estimate the model. In order to enable the Cubic-RBF-ARX model constructed above to describe the global dynamic characteristics of the head and tail sections of the drying process, we first use the SNPOM method to optimize the parameters of the model in the case of the minimum one-step prediction error, and use this parameter as the long-term Predict the initial values of the model parameters under the optimization objective. Then, the Levi Burger-Nyquilt method (LMM) is used to optimize the model parameters with the best long-term forecast performance.

烘丝机干头阶段Cubic-RBF-ARX模型(1)的参数优化问题如下:The parameter optimization problem of the Cubic-RBF-ARX model (1) in the drying stage of the silk dryer is as follows:

(( θθ ^^ NN Hh ,, θθ ^^ LL Hh )) == argarg minmin θθ NN Hh ,, θθ LL Hh ΣΣ tt ohoh == 11 NN Hh (( ythe y ‾‾ Hh (( tt ohoh )) -- ythe y ^^ Hh (( tt ohoh )) )) 22 -- -- -- (( 55 ))

其中,是烘丝机干头阶段出口水分的实际值,是在实际输入作用下,由烘丝机干头阶段Cubic-RBF-ARX模型计算出的出口水分的预测值; θ ^ L H = { ω 0 H , 0 , ω i H , 0 y H , ω n , j H , 0 u H , ω k H H , 0 , ω i H , k H y H , ω j H , k H u H | i H = 1 , . . . , np H ; j H = 1 , . . . , nq H ; k H = 1 , . . . , m H } 为烘丝机干头阶段Cubic-RBF-ARX模型的线性参数;为烘丝机干头阶段Cubic-RBF-ARX模型的非线性参数;NH为烘丝机干头阶段Cubic-RBF-ARX模型建模数据长度。in, is the actual value of the outlet moisture in the drying stage of the drying machine, is the predicted value of the outlet moisture calculated by the Cubic-RBF-ARX model at the drying stage of the silk dryer under the actual input; θ ^ L h = { ω 0 h , 0 , ω i h , 0 the y h , ω no , j h , 0 u h , ω k h h , 0 , ω i h , k h the y h , ω j h , k h u h | i h = 1 , . . . , np h ; j h = 1 , . . . , nq h ; k h = 1 , . . . , m h } is the linear parameter of the Cubic-RBF-ARX model in the drying stage of the silk dryer; is the nonlinear parameter of the Cubic-RBF-ARX model in the drying stage of the silk dryer; N H is the modeling data length of the Cubic-RBF-ARX model in the drying stage of the silk drying machine.

烘丝机干尾阶段Cubic-RBF-ARX模型(3)的参数优化问题如下:The parameter optimization problem of the Cubic-RBF-ARX model (3) in the dry end stage of the silk dryer is as follows:

(( θθ ^^ NN TT ,, θθ ^^ LL TT )) == argarg minmin θθ NN TT ,, θθ LL TT ΣΣ tt otot == 11 NN TT (( ythe y ‾‾ TT (( tt otot )) -- ythe y ^^ TT (( tt otot )) )) 22 -- -- -- (( 66 ))

其中,是烘丝机干尾过程中出口水分的实际值;是在实际输入作用下,由烘丝机干尾阶段Cubic-RBF-ARX模型计算出的出口水分的预测值; θ L T = { ω 0 T , 0 , ω i T , 0 y T , ω n , j T , 0 u T , ω k T T , 0 , ω i T , k T y T , ω j T , k T u T | i T = 1 , . . . , np T ; j T = 1 , . . . , nq T ; k T = 1 , . . . , m T } 为烘丝机干尾阶段Cubic-RBF-ARX模型的线性参数,为烘丝机干尾阶段Cubic-RBF-ARX模型的非线性参数;NT为烘丝机干头阶段Cubic-RBF-ARX模型建模数据长度。in, is the actual value of the outlet moisture in the drying process of the silk drying machine; is the predicted value of the outlet moisture calculated by the Cubic-RBF-ARX model at the dry end stage of the silk dryer under the actual input; θ L T = { ω 0 T , 0 , ω i T , 0 the y T , ω no , j T , 0 u T , ω k T T , 0 , ω i T , k T the y T , ω j T , k T u T | i T = 1 , . . . , np T ; j T = 1 , . . . , nq T ; k T = 1 , . . . , m T } is the linear parameter of the Cubic-RBF-ARX model in the dry end stage of the silk dryer, N T is the nonlinear parameter of the Cubic-RBF-ARX model in the dry end stage of the silk dryer; NT is the modeling data length of the Cubic-RBF-ARX model in the dry end stage of the silk dryer.

依据估计出的烘丝过程干头阶段Cubic-RBF-ARX模型来设计各工艺变量的最优输入曲线,以适应来料情况的变化,尽量减少干头阶段的干料量。本发明采用双S型函数来描述干头阶段排潮风门、风温、筒温的最优输入曲线,采用阶跃型函数来描述入口流量的最优输入曲线。According to the estimated Cubic-RBF-ARX model in the drying stage of the drying process, the optimal input curve of each process variable is designed to adapt to the change of the incoming material and minimize the amount of dry material in the drying stage. The present invention uses a double S-shaped function to describe the optimal input curve of the damper, air temperature, and cylinder temperature in the dry head stage, and uses a step-type function to describe the optimal input curve of the inlet flow.

双S型曲线公式如下:The double S-curve formula is as follows:

Uu scsc (( tt sthe s )) == λλ 11 11 ++ ee tt sthe s -- λλ 22 λλ 33 ++ λλ 44 ++ λλ 55 11 ++ ee tt sthe s -- λλ 66 λλ 77 -- -- -- (( 77 ))

其中,ts为输入的时间,单位为s;λ145分别为双S型函数的起点、转折点及终点值;λ26分别为双S型函数的两条对称轴中心位置;λ37分别为双S型函数上升或下降的速度;λ37大于0时表示S型函数上升,λ37小于0时表示S型函数下降;c=1,2,3,Us1(ts)是排潮风门的设定值;Us2(ts)是风温的设定值;Us3(ts)是筒温的设定值。Among them, t s is the input time, the unit is s; λ 1 , λ 4 , λ 5 are the starting point, turning point and end point of the double S-shaped function respectively; λ 2 , λ 6 are the two symmetrical axis center position; λ 3 and λ 7 are the rising or falling speeds of the double S-shaped function respectively; when λ 3 and λ 7 are greater than 0, it means that the S-shaped function is rising, and when λ 3 and λ 7 are less than 0, it means that the S-shaped function is falling; c =1,2,3, U s1 (t s ) is the setting value of the damper; U s2 (t s ) is the setting value of the air temperature; U s3 (t s ) is the setting value of the cylinder temperature.

描述入口流量输入曲线的阶跃型函数公式如下:The step function formula describing the inlet flow input curve is as follows:

Uu TT (( tt TT )) == κκ 11 tt TT κκ 22 tt TT ∈∈ [[ 11 ,, κκ 22 ]] κκ 11 tt TT ∈∈ [[ κκ 22 ++ 11 ,, κκ 33 ]] -- -- -- (( 88 ))

其中,tT为输入的时间,单位为s;κ123分别为阶跃函数的上升速度、上升时间与终值。Among them, t T is the input time, the unit is s; κ 1 , κ 2 , κ 3 are the rising speed, rising time and final value of the step function respectively.

将各工艺变量的优化设定曲线(7-8)代入所构建的Cubic-RBF-ARX模型(1)的输入变量中,可得到干头阶段出口水分的预测值 Substitute the optimal setting curve (7-8) of each process variable into the input variable of the constructed Cubic-RBF-ARX model (1) , the predicted value of outlet moisture in the dry head stage can be obtained

ythe y ‾‾ Hh (( tt aa )) == ff (( Uu sthe s 11 (( tt aa )) ,, Uu sthe s 22 (( tt aa )) ,, Uu sthe s 33 (( tt aa )) ,, Uu TT (( tt aa )) )) -- -- -- (( 99 ))

采用列维布格奈奎尔特(Levenberg-Marquardt Method,LMM)方法,通过使模型计算出的出口水分预测值与出口水分设定值的误差最小,寻找出干头阶段排潮风门、风温、筒温最优输入曲线的λi(i=1,2,…,7)参数和入口流量最优输入曲线的κj(j=1,2,3)参数。干头阶段出口水分设定值与基于干头动态模型预测值(9)之间的误差为:Using the Levenberg-Marquardt Method (LMM) method, by minimizing the error between the predicted value of the outlet moisture calculated by the model and the set value of the outlet moisture, find out the tide discharge damper and wind temperature at the dry end stage. , λ i (i=1,2,...,7) parameters of the optimal input curve of cylinder temperature and κ j (j=1,2,3) parameters of the optimal input curve of inlet flow. The error between the set value of outlet moisture in the dry head stage and the predicted value based on the dry head dynamic model (9) is:

ee Hh (( tt aa )) == ythe y setset (( tt aa )) -- ythe y ‾‾ Hh (( tt aa )) -- -- -- (( 1010 ))

yset(ta)是出口水分设定值。y set (t a ) is the outlet moisture setting value.

干头阶段工艺变量最优设定的优化问题如下:The optimization problem of the optimal setting of process variables in the dry head stage is as follows:

minmin λλ xx ,, κκ gg JJ == ΣΣ tt aa == 11 Mm ee Hh 22 (( tt aa )) -- -- -- (( 1111 ))

M是干头阶段持续时间。通过求解上述优化问题可得到最优设定曲线的参数值,从而设计出烘丝机干头阶段各个工艺变量的最优输入曲线。M is the duration of the dry head phase. By solving the above optimization problems, the parameter values of the optimal setting curve can be obtained, so as to design the optimal input curve of each process variable in the drying stage of the silk drying machine.

依据估计出的烘丝过程干尾阶段Cubic-RBF-ARX模型来设计各工艺变量的最优输入曲线,以适应来料情况的变化,尽量减少干尾阶段的干料量。采用指数型函数来描述干尾阶段排潮风门、风温、筒温和筒体电机频率的最优输入曲线,该指数型曲线公式如下所示:According to the estimated Cubic-RBF-ARX model in the dry end stage of the drying process, the optimal input curve of each process variable is designed to adapt to the change of incoming materials and minimize the amount of dry material in the dry end stage. An exponential function is used to describe the optimal input curve of the damper, air temperature, cylinder temperature and cylinder motor frequency in the dry end stage. The formula of the exponential curve is as follows:

Uu zpzp (( tt zz )) == αα pp 11 ×× (( αα pp 22 )) tt zz ++ αα pp 33 pp == 1,2,3,41,2,3,4 -- -- -- (( 1212 ))

式中Uz1(tz)、Uz2(tz)、Uz3(tz)、Uz4(tz)分别表示干尾阶段排潮风门、风温、筒温和筒体电机频率的最优输入曲线。将各工艺变量的优化设定曲线(12)代入所构建的Cubic-RBF-ARX模型(3)的输入变量中,可得到干尾阶段出口水分的预测值:In the formula, U z1 (t z ), U z2 (t z ), U z3 (t z ), and U z4 (t z ) represent the optimal frequency of the damper, wind temperature, cylinder temperature, and cylinder motor frequency in the dry end stage, respectively. Enter the curve. Substitute the optimized setting curve (12) of each process variable into the input variable of the constructed Cubic-RBF-ARX model (3) , the predicted value of outlet moisture in the dry tail stage can be obtained:

ythe y ‾‾ TT (( tt bb )) == ff (( Uu zz 11 (( tt bb )) ,, Uu zz 22 (( tt bb )) ,, Uu zz 33 (( tt bb )) ,, Uu zz 44 (( tt bb )) )) -- -- -- (( 1313 ))

采用列维布格奈奎尔特(LMM)方法,通过使模型计算出的出口水分预测值与出口水分设定值的误差最小,寻找出干尾阶段排潮风门、风温、筒温和筒体电机频率最优输入曲线的αpg;其中,g=1,2,3。干尾阶段出口水分设定值与基于干尾动态模型预测值(13)之间的误差为:Using the Levi Burger Nyquilt (LMM) method, by minimizing the error between the predicted value of the outlet moisture calculated by the model and the set value of the outlet moisture, find out the damper, wind temperature, cylinder temperature and cylinder temperature in the dry end stage α pg of the optimal input curve of motor frequency; where, g=1,2,3. The error between the set value of outlet moisture in the dry tail stage and the predicted value based on the dry tail dynamic model (13) is:

ee TT (( tt bb )) == ythe y ′′ setset (( tt bb )) -- ythe y ‾‾ TT (( tt bb )) -- -- -- (( 1414 ))

yset(t)是出口水分设定值。y set (t) is the set value of outlet moisture.

干尾阶段工艺变量最优设定的优化问题如下:The optimization problem of the optimal setting of process variables in the dry tail stage is as follows:

minmin αα pgpg JJ ′′ == ΣΣ kk == 11 Mm ′′ ee TT 22 (( tt bb )) -- -- -- (( 1515 ))

M是干尾阶段持续时间。通过求解上述优化问题可得到最优设定曲线的参数值,从而设计出烘丝机干尾阶段各个工艺变量的最优输入曲线。M is the duration of the dry tail phase. By solving the above optimization problems, the parameter values of the optimal setting curve can be obtained, so as to design the optimal input curve of each process variable in the drying stage of the silk drying machine.

Claims (3)

1.一种烘丝机头尾段工艺变量优化控制方法,其特征在于,该方法为:1. A process variable optimization control method at the head and tail section of a silk drying machine, characterized in that, the method is: 1)根据烘丝机的运行流程,建立烘丝过程中叶丝入口流量、入口水分、筒温、风温、排潮风门、出口水分的时序关系,同时根据烘丝过程干头阶段无叶丝出口水分检测值、干尾阶段无叶丝入口流量与入口水分检测值的特点,采用三次函数作为径向基函数的Cubic-RBF-ARX模型,分别建立烘丝过程干头阶段与干尾阶段的Cubic-RBF-ARX模型;1) According to the operation flow of the silk drying machine, establish the time sequence relationship of the inlet flow of shredded shreds, inlet moisture, cylinder temperature, wind temperature, moisture discharge damper, and outlet moisture in the shredded shredded drying process, and at the same time, according to the drying process, there is no shredded shredded outlet at the end of the drying stage. According to the characteristics of the moisture detection value, the inlet flow rate and the inlet moisture detection value of the dry-end stage, the cubic function is used as the Cubic-RBF-ARX model of the radial basis function, and the Cubic-RBF-ARX model of the dry-end stage and the dry-end stage of the drying process are respectively established. - RBF-ARX model; 2)根据烘丝机头尾段的历史运行数据,采用结构化非线性参数优化方法分别优化烘丝过程干头阶段与干尾阶段的Cubic-RBF-ARX模型;2) According to the historical operation data of the head and tail sections of the silk drying machine, the Cubic-RBF-ARX models of the dry head stage and the dry end stage of the silk drying process were respectively optimized by using the structured nonlinear parameter optimization method; 3)依据优化的烘丝过程干头阶段与干尾阶段的Cubic-RBF-ARX模型,采用双S型函数描述干头阶段的排潮风门、风温、筒温的最优输入曲线;采用阶跃函数描述干头阶段的入口流量的最优输入曲线;采用指数函数描述干尾阶段排潮风门、风温、筒温和筒体电机频率的最优输入曲线;3) According to the optimized Cubic-RBF-ARX model of the dry end stage and the end stage of the drying process, the double S-shaped function is used to describe the optimal input curve of the damper, air temperature, and cylinder temperature in the dry end stage; The jump function describes the optimal input curve of the inlet flow in the dry head stage; the exponential function is used to describe the optimal input curve of the tidal discharge damper, air temperature, barrel temperature and barrel motor frequency in the dry end stage; 4)采用列维布格奈奎尔特方法,通过使优化的干头阶段与干尾阶段的Cubic-RBF-ARX模型计算出的出口水分预测值与出口水分设定值的误差最小,寻找出烘丝过程干头阶段与干尾阶段的最优输入曲线的参数,以适应来料情况的变化,减少干尾阶段的干料量;4) Using the Levi Burger-Nyquilt method, by minimizing the error between the predicted value of outlet moisture and the set value of outlet moisture calculated by the Cubic-RBF-ARX model in the optimized dry head stage and dry tail stage, to find out The parameters of the optimal input curve in the dry head stage and the dry end stage of the drying process to adapt to the change of incoming materials and reduce the amount of dry materials in the dry end stage; 所述步骤1)中,烘丝机干头阶段Cubic-RBF-ARX模型为:In the step 1), the Cubic-RBF-ARX model of the dry head stage of the silk dryer is: 其中:in: 其中,yH(tH)表示烘丝机干头阶段Cubic-RBF-ARX模型的出口水分;分别表示干头阶段Cubic-RBF-ARX模型的排潮风门开度、风温、筒温、入口流量及入口水分;XH(tH-1)为入口流量和入口水分的状态变量;npH,nqH,dH和mH均表示干头阶段Cubic-RBF-ARX模型的阶次;分别为干头阶段Cubic-RBF-ARX模型输出项与输入项的RBF神经网络的中心;为干头阶段Cubic-RBF-ARX模型的标量权系数;||·||F表示矩阵的Frobenius范数;ξH(tH)是干头阶段Cubic-RBF-ARX模型的建模误差,为高斯白噪声;T0 H为烘丝机干头阶段Cubic-RBF-ARX模型建模采样时间,T1为从有入口流量检测值到有入口水分检测值的时间,T2为从有入口水分检测值到有出口水分检测值的时间,T3为从有入口水分检测值到烘丝筒入口的时间,T4为叶丝在烘丝筒烘干的时间;Among them, y H (t H ) represents the outlet moisture of the Cubic-RBF-ARX model in the drying stage of the silk dryer; represent the tidal damper opening, wind temperature, cylinder temperature, inlet flow and inlet moisture of the Cubic-RBF-ARX model in the dry head stage; X H (t H -1) is the state variable of inlet flow and inlet moisture; np H , nq H , d H and m H all represent the order of the Cubic-RBF-ARX model at the dry head stage; Respectively, the center of the RBF neural network of the Cubic-RBF-ARX model output and input items in the dry head stage; is the scalar weight coefficient of the Cubic-RBF-ARX model in the dry head stage; ||·|| F represents the Frobenius norm of the matrix; ξ H (t H ) is the modeling error of the Cubic-RBF-ARX model in the dry head stage, Gaussian white noise; T 0 H is the sampling time of the Cubic-RBF-ARX model modeling in the drying stage of the silk dryer, T 1 is the time from the detection value of the inlet flow to the detection value of the inlet moisture, and T 2 is the time from the inlet moisture The time from the detection value to the detection value of the outlet moisture, T3 is the time from the detection value of the inlet moisture to the entrance of the drying drum, and T4 is the drying time of the shredded leaves in the drying drum; 所述步骤1)中,烘丝机干尾阶段Cubic-RBF-ARX模型为:In said step 1), the Cubic-RBF-ARX model of the drying end stage of the silk dryer is: 其中:in: 其中,yT(tT)表示烘丝机干尾阶段Cubic-RBF-ARX模型的出口水分;分别表示干尾阶段Cubic-RBF-ARX模型的筒温、热风风温、排潮风门开度、入口流量、入口水分及筒体电机频率;XT(tT-1)为热风风温和筒体电机频率的状态变量;npT,nqT,dT和mT均表示干尾阶段Cubic-RBF-ARX模型的阶次;分别为干尾阶段Cubic-RBF-ARX模型输出项与输入项的RBF神经网络的中心;为干尾阶段Cubic-RBF-ARX模型的标量权系数;ξT(tT)是干尾阶段Cubic-RBF-ARX模型建模误差,为高斯白噪声;T0 T为烘丝机干尾阶段Cubic-RBF-ARX模型建模采样时间;Among them, y T (t T ) represents the outlet moisture of the Cubic-RBF-ARX model at the dry end stage of the silk dryer; Respectively represent the barrel temperature, hot air temperature, tide damper opening, inlet flow, inlet moisture and barrel motor frequency of the Cubic-RBF-ARX model in the dry end stage; X T (t T -1) is the hot air temperature and barrel The state variable of the motor frequency; np T , nq T , d T and m T all represent the order of the Cubic-RBF-ARX model in the dry tail stage; are the centers of the RBF neural network of the output and input items of the Cubic-RBF-ARX model in the dry tail stage; is the scalar weight coefficient of the Cubic-RBF-ARX model at the dry end stage; ξ T (t T ) is the modeling error of the Cubic-RBF-ARX model at the dry end stage, which is Gaussian white noise; T 0 T is the dry end stage of the silk dryer Cubic-RBF-ARX model modeling sampling time; 所述步骤2)中,烘丝机干头阶段Cubic-RBF-ARX模型优化如下:In said step 2), the Cubic-RBF-ARX model optimization of the dry head stage of the silk dryer is as follows: (( θθ ^^ NN Hh ,, θθ ^^ LL Hh )) == argarg minmin θθ NN Hh ,, θθ LL Hh ΣΣ tt ohoh == 11 NN Hh (( ythe y ‾‾ Hh (( tt ohoh )) -- ythe y ^^ Hh (( tt ohoh )) )) 22 其中,是烘丝机干头阶段出口水分的实际值,是在实际输入作用下,由烘丝机干头阶段Cubic-RBF-ARX模型计算出的出口水分的预测值; θ ^ L H = { ω 0 H , 0 , ω i H , 0 y H , ω n , j H , 0 u H , ω k H H , 0 , ω i H , k H y H , ω j H , k H u H | i H = 1 , . . . , np H ; j H = 1 , . . . , nq H ; k H = 1 , . . . , m H } 为烘丝机干头阶段Cubic-RBF-ARX模型的线性参数;为烘丝机干头阶段Cubic-RBF-ARX模型的非线性参数;NH为烘丝机干头阶段Cubic-RBF-ARX模型建模数据长度;in, is the actual value of the outlet moisture in the drying stage of the drying machine, is the predicted value of the outlet moisture calculated by the Cubic-RBF-ARX model at the drying stage of the silk dryer under the actual input; θ ^ L h = { ω 0 h , 0 , ω i h , 0 the y h , ω no , j h , 0 u h , ω k h h , 0 , ω i h , k h the y h , ω j h , k h u h | i h = 1 , . . . , np h ; j h = 1 , . . . , nq h ; k h = 1 , . . . , m h } is the linear parameter of the Cubic-RBF-ARX model in the drying stage of the silk dryer; is the nonlinear parameter of the Cubic-RBF-ARX model in the drying stage of the silk dryer; N H is the modeling data length of the Cubic-RBF-ARX model in the drying stage of the silk drying machine; 烘丝机干尾阶段Cubic-RBF-ARX模型优化如下:The optimization of the Cubic-RBF-ARX model in the dry end stage of the silk dryer is as follows: (( θθ ^^ NN TT ,, θθ ^^ LL TT )) == argarg minmin θθ NN TT ,, θθ LL TT ΣΣ tt otot == 11 NN TT (( ythe y ‾‾ TT (( tt otot )) -- ythe y ^^ TT (( tt otot )) )) 22 其中,是烘丝机干尾过程中出口水分的实际值;是在实际输入作用下,由烘丝机干尾阶段Cubic-RBF-ARX模型计算出的出口水分的预测值; θ L T = { ω 0 T , 0 , ω i T , 0 y T , ω n , j T , 0 u T , ω k T T , 0 , ω i T , k T y T , ω j T , k T u T | i T = 1 , . . . , np T ; j T = 1 , . . . , nq T ; k T = 1 , . . . , m T } 为烘丝机干尾阶段Cubic-RBF-ARX模型的线性参数,为烘丝机干尾阶段Cubic-RBF-ARX模型的非线性参数,NT为烘丝机干头阶段Cubic-RBF-ARX模型建模数据长度;in, is the actual value of the outlet moisture in the drying process of the silk drying machine; is the predicted value of the outlet moisture calculated by the Cubic-RBF-ARX model at the dry end stage of the silk dryer under the actual input; θ L T = { ω 0 T , 0 , ω i T , 0 the y T , ω no , j T , 0 u T , ω k T T , 0 , ω i T , k T the y T , ω j T , k T u T | i T = 1 , . . . , np T ; j T = 1 , . . . , nq T ; k T = 1 , . . . , m T } is the linear parameter of the Cubic-RBF-ARX model in the dry end stage of the silk dryer, is the nonlinear parameter of the Cubic-RBF-ARX model in the drying end stage of the silk drying machine, and NT is the modeling data length of the Cubic-RBF-ARX model in the drying head stage of the silk drying machine; 所述步骤3)中:In the step 3): 用于描述烘丝机干头阶段排潮风门、风温、筒温的最优输入曲线的双S型函数表达式为:The expression of the double S-type function used to describe the optimal input curves of the damper, wind temperature and cylinder temperature in the drying stage of the silk dryer is: Uu scsc (( tt sthe s )) == λλ 11 11 ++ ee tt sthe s -- λλ 22 λλ 33 ++ λλ 44 ++ λλ 55 11 ++ ee tt sthe s -- λλ 66 λλ 77 其中,ts为输入的时间,单位为s;λ145分别为双S型函数的起点、转折点及终点值;λ26分别为双S型函数的两条对称轴中心位置;λ37分别为双S型函数上升或下降的速度;λ37大于0时表示S型函数上升,λ37小于0时表示S型函数下降;c=1,2,3,Us1(ts)是排潮风门的设定值;Us2(ts)是风温的设定值;Us3(ts)是筒温的设定值;Among them, t s is the input time, the unit is s; λ 1 , λ 4 , λ 5 are the starting point, turning point and end point of the double S-shaped function respectively; λ 2 , λ 6 are the two symmetrical axis center position; λ 3 and λ 7 are the rising or falling speeds of the double S-shaped function respectively; when λ 3 and λ 7 are greater than 0, it means that the S-shaped function is rising, and when λ 3 and λ 7 are less than 0, it means that the S-shaped function is falling; c =1,2,3, U s1 (t s ) is the setting value of the damper; U s2 (t s ) is the setting value of the air temperature; U s3 (t s ) is the setting value of the cylinder temperature; 用于描述烘丝机干头阶段入口流量的最优输入曲线的阶跃函数表达式为:The expression of the step function used to describe the optimal input curve of the inlet flow in the drying stage of the drying machine is: Uu TT (( tt TT )) == κκ 11 tt TT κκ 22 tt TT ∈∈ [[ 11 ,, κκ 22 ]] κκ 11 tt TT ∈∈ [[ κκ 22 ++ 11 ,, κκ 33 ]] ;; 其中,tT为输入的时间,单位为s;κ123分别为阶跃函数的上升速度、上升时间与终值;Among them, t T is the input time, the unit is s; κ 1 , κ 2 , κ 3 are the rising speed, rising time and final value of the step function respectively; 所述步骤3)中,用于描述干尾阶段排潮风门、风温、筒温和筒体电机频率的最优输入曲线的指数函数的表达式为:In the step 3), the expression of the exponential function used to describe the optimal input curve of the damper, wind temperature, cylinder temperature and cylinder motor frequency in the dry end stage is: Uu zpzp (( tt zz )) == αα pp 11 ×× (( αα pp 22 )) tt zz ++ αα pp 33 ;; p=1,2,3,4p=1,2,3,4 式中Uz1(tz)、Uz2(tz)、Uz3(tz)、Uz4(tz)分别表示干尾阶段排潮风门、风温、筒温和筒体电机频率的最优输入曲线。In the formula, U z1 (t z ), U z2 (t z ), U z3 (t z ), and U z4 (t z ) represent the optimal frequency of the damper, wind temperature, cylinder temperature, and cylinder motor frequency in the dry end stage, respectively. Enter the curve. 2.根据权利要求1所述的烘丝机头尾段工艺变量优化控制方法,其特征在于,所述步骤4)中,将烘丝机干头阶段优化设定曲线Usc(ts)、UT(tT)代入所述烘丝机干头阶段Cubic-RBF-ARX模型的输入变量中,得到烘丝机干头阶段Cubic-RBF-ARX模型计算出的出口水分预测值通过使干头阶段Cubic-RBF-ARX模型计算出的出口水分预测值与出口水分设定值yset(ta)的误差eH(ta)最小,即采用列维布格奈奎尔特方法求解优化问题寻找出干头阶段排潮风门、风温、筒温的输入曲线的参数λx和入口流量输入曲线的参数κ123;其中,x=1,2,…,7;g=1,2,3;M是干头阶段持续的时间。2. The process variable optimization control method at the head and tail section of the silk drying machine according to claim 1, characterized in that, in the step 4), the optimal setting curve U sc (t s ) of the drying head stage of the silk drying machine, U T (t T ) is substituted into the input variable of the Cubic-RBF-ARX model in the dry head stage of the silk dryer In the process, the predicted value of outlet moisture calculated by the Cubic-RBF-ARX model in the drying stage of the silk dryer is obtained Predicted value of outlet moisture calculated by making dry head stage Cubic-RBF-ARX model The error e H (t a ) with the outlet moisture setting value y set (t a ) is the smallest, that is, the Levi Burger-Nyquilt method is used to solve the optimization problem Find the parameter λ x of the input curve of the tide discharge damper, air temperature and cylinder temperature in the dry head stage, and the parameters κ 1 , κ 2 , κ 3 of the input curve of the inlet flow; where, x=1,2,...,7; g =1,2,3; M is the duration of the dry head stage. 3.根据权利要求1所述的烘丝机头尾段工艺变量优化控制方法,其特征在于,所述步骤4)中,将烘丝机干尾阶段优化设定曲线Uzp(tz)代入所述烘丝机干尾阶段Cubic-RBF-ARX模型的输入变量中,得到烘丝机干尾阶段的Cubic-RBF-ARX模型计算出的出口水分预测值通过使干尾阶段的Cubic-RBF-ARX模型计算出的出口水分预测值与出口水分设定值y'set(tb)的误差eT(tb)最小,即采用列维布格奈奎尔特方法求解优化问题寻找出干尾阶段排潮风门、风温、筒温和筒体电机频率最优输入曲线的参数αpg;其中,g=1,2,3;M'是干尾阶段持续时间。3. The process variable optimization control method at the head and tail section of the silk drying machine according to claim 1, characterized in that, in the step 4), the optimal setting curve U zp (t z ) for the dry tail stage of the silk drying machine is substituted into The input variables of the Cubic-RBF-ARX model in the dry end stage of the silk dryer In the process, the predicted value of outlet moisture calculated by the Cubic-RBF-ARX model at the dry end stage of the silk dryer is obtained Predicted value of outlet moisture calculated by making dry tail stage Cubic-RBF-ARX model The error e T (t b ) with the outlet moisture set value y' set (t b ) is the smallest, that is, the Levi Burger-Nyquilt method is used to solve the optimization problem Find the parameter α pg for the optimal input curve of the damper, wind temperature, cylinder temperature and cylinder motor frequency in the dry end stage; where, g=1,2,3; M' is the duration of the dry end stage.
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