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 CN 201310659839 CN201310659839A CN103610227B CN 103610227 B CN103610227 B CN 103610227B CN 201310659839 CN201310659839 CN 201310659839 CN 201310659839 A CN201310659839 A CN 201310659839A CN 103610227 B CN103610227 B CN 103610227B
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dry
rbf
cubic
drying
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CN103610227A (en )
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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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中南大学
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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。 The present invention discloses a head end section Drying process variable optimization method of controlling the temperature history data Drying cylinder head and tail segment basis, air temperature, moisture exhaust damper of the process variables and the like used as a cubic function of the radial basis function Cubic-RBF-ARX model for modeling the dynamic characteristics drying; the model having a self-adjusting ability, can reflect the influence of the inlet flow in different modes and the change of the water outlet of the inlet water, the head and tail sections may be different according to the mode change the water inlet flow and an inlet to predict future changes in the water outlet; optimized for each set of process variables according to the model, can control the water outlet head and tail cut tobacco segment achieve better results. 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 method of the present invention to consider the quantity and the dynamic characteristics among the input variables, can be more effectively overcome the incoming water flow and change on Drying head and tail sections for the inlet and the inlet flow cut tobacco in different modes when moisture control head and tail sections.

Description

一种烘丝机头尾段工艺变量优化控制方法 Drying the head end section one kind of optimal control process variables

技术领域 FIELD

[0001] 本发明涉及烘丝机头尾段工艺变量优化控制方法。 [0001] The present invention relates to a head end section Drying optimal control process variables.

背景技术 Background technique

[0002] 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。 [0002] Drying process cigarette silk production is the most important one processing step, it is mainly carried out by heating the dried cut tobacco, cut tobacco to reduce the moisture content, a moisture content of cut tobacco after baking, uniform temperature and controlled within a certain range of values, in order to meet the production process. 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。 Drying is divided into the preheating process, dry head, middle and tail dry process four portions. 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。 In the dry first stage, cut tobacco inlet flow is increasing, but the non-detection value cut tobacco outlet of water, it is difficult to perform feedback control, is likely to cause dry head phase difference between the outlet water quality control, dry feed more; in the dry end of the stage, since the cut tobacco inlet flow suddenly decreased and drying cartridge having a large heat capacity, the internal temperature of the cylinder wall is difficult to fall at the rate specified problems are likely to cause low moisture management properties of the dry end of the outlet stage and multiple dry material. 因此,"干头干尾"是目前烘丝过程出口水分控制的难点所在。 Thus, "dry dry head-tail" is cut tobacco drying process where the outlet moisture control difficulties.

[0003] 现有的干头干尾过程控制方法主要有: [0003] Dry the head end of the conventional dry process control methods are:

[0004] (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。 [0004] (1) the use of materials and drying medium into and output Drying machine as heat and mass balance Object mathematical model combined feedforward PID control barrel temperature regulation. 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。 But the feed-forward mathematical model only takes into account the water flow rate and feed, and did not take into account other factors such as hot air temperature has an important influence on the export of water, can not fully reflect the true process, causing a large head and tail section Drying outlet moisture fluctuations, It requires operator manual intervention, the head and tail sections for the different modes having different inlet flow and the inlet of the incoming water is difficult to obtain a satisfactory control effect.

[0005] (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。 [0005] (2) Based on the above-described feedforward control, an increase in the head and tail section of the vapor discharge humidifying device craniocaudal applying steam feed water to raise the moisture content of the material end to end, in order to reduce the quantity dry. 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。 However, this method only cut tobacco humidify the surface, only to improve the silk leaf surface humidity, will still result in lower intrinsic quality of tobacco, and increasing the difficulty and stability of export control moisture.

[0006] (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。 [0006] (3) through several tests, to seek the best process parameters craniocaudal phase adjustment value and the temperature of hot air moisture exhaust valve opening degree is reduced quantity dry. 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; When this method is the lack of self-regulation, is not guaranteed for the different modes having different incoming water inlet and an inlet flow rate, the optimal set of process parameters are set values;

[0007] (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。 [0007] (4) On the basis of PID control strategy based on fuzzy control theory applied to the tobacco drying machine moisture control.

[0008] 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。 [0008] in just a simple two-dimensional fuzzy controller to solve the problem of export control moisture Drying process still can not get the optimum process parameter settings, and change the inlet flow rate and inlet water for different modes, the need fuzzy control rule table adjustment, which brought inconvenience to industrial production.

发明内容 SUMMARY

[0009] 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。 [0009] The present invention solves the technical problem, for the deficiencies of the prior art, there is provided a head end section Drying process control variable optimization method, and the dry stage cut tobacco outlet head increase of moisture as quickly as possible, and rapidly reach steady state, so that the tail dry stage cut tobacco decreases slowly as the water outlet, thus effectively reducing the quantity of dry head and tail section, improve the control performance drying process; more effectively overcome the incoming water flow and changes drying influence the process of head and tail section, to avoid inconvenience to enter manual tuning process variable parameters.

[0010] 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: [0010] To solve the above technical problem, the technical solution employed in the present invention is: A head end section Drying process control variable optimization method is:

[0011] 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; [0011] 1) The operational flow Drying machine, establish Drying process middle wire inlet flow rate, inlet water, barrel temperature, air temperature, moisture exhaust damper, the timing relationship between the outlet of water, while the dry head stage without accordance Drying Process leaf wire exit moisture measurement value, the dry end of the wire non-leaf stage with the inlet water flow characteristics of the inlet of the detection value, using the cubic function cubic-RBF-ARX model as radial basis functions are established drying process with the dry tail dry stage head Cubic-RBF-ARX model stage;

[0012] 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; [0012] 2) The historical operating data Drying head tail section, structured nonlinear parameter optimization procedure were optimized Drying Dry head tail dry stage and stage Cubic-RBF-ARX model;

[0013] 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; [0013] 3) Drying process according to the optimized phase and dry head Cubic-RBF-ARX model tail dry stage, moisture exhaust damper using double S phase function describes the dry head, optimal input air temperature curve, the temperature of the tube ; described step function using a dry phase inlet flow header optimum input curve; optimal input curve exponential function tail dry stage described moisture exhaust damper, air temperature, motor cylinder barrel moderate frequency;

[0014] 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。 [0014] 4) The method 列维布格奈奎 Stewart, minimum error calculated by Cubic-RBF-ARX model allows optimization of the dry tail dry stage head outlet water phase predicted value of the set value and the outlet water , to find out the optimal parameter input stage dry head curve drying tail dry stage of the process to accommodate variations in the incoming case to reduce the quantity of dry tail dry stage.

[0015] 所述步骤1)中,烘丝机干头阶段Cubic-RBF-ARX模型为: [0015] step 1), the first stage Dryer Drying Cubic-RBF-ARX model:

Figure CN103610227BD00081

Figure CN103610227BD00091

[0019] 其中,yH(tH)表示烘丝机干头阶段Cubic-RBF-ARX模型的出口水分; [0019] wherein, yH (tH) represents the water outlet head Dryer Drying stage Cubic-RBF-ARX model;

Figure CN103610227BD00092

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-l)为入口流量和入口水分的状态变量;np H, nqH,dH和mH均表示干头阶段Cubic-RBF-ARX模型的阶次;Zf,Zf分别为干头阶段 Respectively represent moisture exhaust throttle opening degree Cubic-RBF-ARX model dry first stage, air temperature, cylinder temperature, the inlet flow rate and an inlet water; XH (tH-l) is the state the inlet flow and the inlet water variables; np H, nqH , dH and orders have indicated mH dry Cubic-RBF-ARX model head stage; Zf, Zf are first dry stage

Figure CN103610227BD00093

Cub i c-RBF-ARX模型输出项与输入项的RBF神经网络的中. RBF neural network Cub i c-RBF-ARX model output in terms of entry.

Figure CN103610227BD00094

为干头阶段Cubic-RBF-ARX模型的标量权系数;M · I |F表示矩阵的Frobenius范数;|H(tH)是干头阶段Cubic-RBF-ARX模型的建模误差,为高斯白噪声;T QH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,T 2为从有入口水分检测值到有出口水分检测值的时间,T 3为从有入口水分检测值到烘丝筒入口的时间,! Scalar weights Cubic-RBF-ARX model dry first stage; M · I | F represents Frobenius matrix norm; | H (tH) is a modeling error Cubic-RBF-ARX model dry first stage, is Gaussian white noise; T QH dryer drying of the head stage Cubic-RBF-ARX model modeling sampling time, T1 is an inlet flow rate detection value from the inlet moisture detection time value, T 2 is the detected value from the inlet water has outlet moisture detection time value, T 3 is the detected value of the moisture from the inlet tube to the inlet drying time! \为叶丝在烘丝筒烘干的时间。 \ Leaf silk yarn in the drying cylinder drying time.

[0020] 所述步骤1)中,烘丝机干尾阶段Cubic-RBF-ARX模型为: [0020] step 1), the tail Dryer Drying stage Cubic-RBF-ARX model:

[0021] [0021]

Figure CN103610227BD00101

「00241 其中,vT (tT)衷示烘玆机干尾阶段Cubic-RBF-ARX模型的出口水分; "00241 where, vT (tT) illustrates co hereby Dryer drying stage tail Cubic-RBF-ARX model water outlet;

Figure CN103610227BD00102

分别表示干尾阶段Cub i c-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_l)为热风风温和筒体电机频率的状态变量;npT,nqT,dT和m %表示干尾阶段Cubic-RBF-ARX模型的阶次; ,<;«分别为干尾阶段cub i c-RBF-ARX模型输出项与输入项的RBF神经网络的中心; Denote cylinder temperature tail dry stage Cub i c-RBF-ARX model, hot air temperature, moisture exhaust throttle opening, the inlet flow rate, inlet water and the frequency of the motor cylinder; XT (tT_l) to moderate hot air cylinder motor frequency state variables; order npT, nqT, dT and m% represents Cubic-RBF-ARX model dry tail stage; <; << were dry tail phase cub i c-RBF-ARX model output term to the input terms RBF Center for neural network;

Figure CN103610227BD00103

为干尾阶段Cubic-RBF-ARX模型的标量权系数; f(tT)是干尾阶段Cubic-RBF-ARX模型建模误差,为高斯白噪声;IV为烘丝机干尾阶段Cubic-RBF-ARX模型建模采样时间。 Scalar weights Cubic-RBF-ARX model tail dry stage; f (tT) tail dry stage Cubic-RBF-ARX modeling error is Gaussian white noise; IV to the tail dry stage cut tobacco drying machine Cubic-RBF- ARX ​​modeling sampling time.

[0025] 所述步骤2)中,烘丝机干头阶段Cubic-RBF-ARX模型优化如下: [0025] step 2), the first stage Dryer Drying Cubic-RBF-ARX model optimization as follows:

Figure CN103610227BD00111

[0027] 其中,严(严)是烘丝机干头阶段出口水分的实际值,产(严)是在实际输入作用下,由烘丝机干头阶段Cub i c-RBF-ARX模型计算出的出口水分的预测值; [0027] wherein, Yan (Yan) is the actual value of the outlet water Drying Dryer first stage, yield (Yan) under actual input action, calculated by Drying Dryer first stage Cub i c-RBF-ARX model for an the predicted value of the water outlet;

Figure CN103610227BD00112

%烘丝机干头阶段Cubic-RBF-ARX模型的线性参数; Drying machine head% dry stage linear parameter Cubic-RBF-ARX model;

Figure CN103610227BD00113

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。 A nonlinear parameter Cubic-RBF-ARX model head Dryer Drying stage; Nh Drying machine for the Cubic-RBF-ARX model modeling data length header dry stage.

[0028] 烘丝机干尾阶段Cubic-RBF-ARX模型优化如下: [0028] Dryer Drying stage tail Cubic-RBF-ARX model optimization as follows:

Figure CN103610227BD00114

[0030] 其中,/(r)是烘丝机干尾过程中出口水分的实际值;r(r〇是在实际输入作用下,由烘丝机干尾阶段Cub i C-RBF-ARX模型计算出的出口水分的预测值; [0030] wherein, / (r) is the actual value of the tail Dryer Drying process water outlet; R & lt (r〇 under actual input action, calculated by the tail Dryer Drying stage Cub i C-RBF-ARX model water outlet predicted value;

Figure CN103610227BD00115

为烘丝机干尾阶段Cubic-RBF-ARX模型的线性参数, Drying machine for the tail dry stage linear parameter Cubic-RBF-ARX model,

Figure CN103610227BD00116

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。 A nonlinear parameter Cubic-RBF-ARX model tail dry stage cut tobacco drying machine; Nt of tobacco drying machine as Cubic-RBF-ARX model modeling data length header dry stage.

[0031] 所述步骤3)中: [0031] step 3):

[0032] 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: Double S function expression [0032] Drying machine for describing the first stage of the dry moisture exhaust damper, air temperature, barrel temperature optimum curve for the input:

Figure CN103610227BD00117

[0034] 其中,ts为输入的时间,单位为s ; λ i,λ4, \5分别为双S型函数的起点、转折点及终点值;λ2, λ6分别为双S型函数的两条对称轴中心位置;λ 3, λ 7分别为双S型函数上升或下降的速度;λ 3, λ 7大于0时表示S型函数上升,λ 3, λ 7小于0时表示S型函数下降; c=l,2, 3, Usl(ts)是排潮风门的设定值;Us2(ts)是风温的设定值;U s3(ts)是筒温的设定值。 [0034] wherein, TS is the input of the time, in units of s; λ i, λ4, \ 5 are double S starting function, the turning point and the end point value; λ2, λ6 are two pairs of S-shaped function symmetry axis center position; λ 3, λ 7 are double-S-shaped function increase or decrease speed; λ 3, when λ 7 is greater than 0 represents S type function rising, λ 3, λ 7 is less than 0 represents S type function decline; c = l, 2, 3, Usl (ts) is a set value of moisture exhaust damper; Us2 (ts) is a set value of the air temperature; U s3 (ts) is a set value of the barrel temperature.

[0035] 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: Step function expression [0035] used to describe the optimal inlet flow input curve Dryer Drying head stage is:

Figure CN103610227BD00118

[0037] 其中,tT为输入的时间,单位为s ; κ κ 2, κ 3分别为阶跃函数的上升速度、上升时间与终值。 [0037] wherein, tT is the time input unit s; κ κ 2, κ 3 is a step function increase in velocity, the rise time and the final values.

[0038] 所述步骤4 )中,烘丝机干头阶段Cub i c-RBF-ARX模型计算出的出口水分预测值 [0038] step 4), the first stage Dryer Drying Cub i c-RBF-ARX model to calculate the predicted values ​​of the outlet water

Figure CN103610227BD00121

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Cub i c-RBF-ARX的输入变量 By setting the input variable optimization curve Dryer Drying substituting first stage of the process variables of each dry first stage constructed Cub i c-RBF-ARX of

Figure CN103610227BD00122

中得到。 Obtained. 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ysrt(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 By dry stage head Cubic-RBF-ARX model to calculate the predicted values ​​of the outlet water and the outlet water setpoint ysrt (ta) error eH (ta) the smallest, i.e., a method using optimization problem is solved 列维布格奈奎 Technology

Figure CN103610227BD00123

.寻找出干头阶段排潮风门、风温、筒温的输入曲线的参数λχ和入口流量输入曲线的参数κ κ2, κ3;其中,^1,2,〜,7出=1,2,3"是干头阶段持续的时间。 Finding the first stage of the dry moisture exhaust damper, Parameter λχ inlet flow input curve and the curve of the input air temperature, a barrel temperature of κ κ2, κ3; wherein ^ 1,2, ..., 7 = 2,3 "dry first phase duration.

[0039] 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: [0039] step 3), the end of the stage is used to describe dry moisture exhaust damper, the optimal expression of an exponential function of the input air temperature profile, the cylinder barrel mild motor frequency is:

Figure CN103610227BD00124

[0041] 式中Uzl(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。 [0041] wherein Uzl (tz), Uz2 (tz), Uz3 (tz), Uz4 (tz) represent the end stage of the dry moisture exhaust damper, optimal input air temperature curve, the motor cylinder barrel moderate frequency.

[0042] 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 [0042] step 4), Cubic-RBF-ARX model tail dry stage calculated predicted value of the water outlet

Figure CN103610227BD00125

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Cub i c-RBF-ARX模型的输入变量》6V)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浐P)与出口水分设定值y'srt(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 By setting the input variable optimization curve substituting tail Dryer Drying stages of process variables constructed tail dry stage Cub i c-RBF-ARX model "6V) obtained; tail dry stage by Cubic-RBF- water outlet ARX model predictive value calculated Chan P) and the water outlet of the minimum set value y'srt (tb) error eT (tb), i.e., using the method of optimization problem is solved 列维布格奈奎 Technology

Figure CN103610227BD00126

,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apg;其中,g=l,2, 3 ;M'是干尾阶段持续时间。 Find out the tail dry stage moisture exhaust damper, air temperature, motor cylinder barrel moderate frequency input parameters apg optimal curve; wherein, g = l, 2, 3; M 'is the duration of the tail dry stage.

[0043] 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。 [0043] Compared with the prior art, the present invention has beneficial effects: dry method of the present invention allows the first leaf stage wire exit moisture increased as quickly as possible, and rapidly reach a steady state, the tail dry stage cut tobacco can water was slowly lowered as the outlet, thus effectively reducing the quantity of dry head and tail section, improve the control performance drying process with great economic value; the method of the present invention is considered to quantity between the respective input variables dynamic characteristics, can be more effectively overcome the effects of changes in water flow and incoming drying head and tail section, the control section when the head and tail of the inlet flow to the inlet water applied in different patterns cut tobacco; method of the present invention is based on the model identification optimization of the optimal input setting curves, avoiding the manual tuning process variable input parameters inconvenience.

附图说明 BRIEF DESCRIPTION

[0044] 图1为烘丝机工艺过程示意图。 [0044] FIG. 1 is a schematic process Drying machine.

具体实施方式 detailed description

[0045] 烘丝机工艺过程如图1所示。 [0045] Drying process unit as shown in FIG. 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。 Before entering the cut tobacco cut tobacco drying step, the inlet flow is first detected yarn leaves the water inlet U4 and U5. 经过T3时间,叶丝到达烘丝机入口处。 After time T3, leaf tobacco drying machine wire to reach the entrance. 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U 1、风温U2、筒温U3等工艺变量参数值。 Moisture exhaust throttle opening when the cut tobacco Drying cylinder drying, the system periodically sampling cylinder U 1, air temperature U2, U3 cylinder temperature and other process variable parameters. 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。 The drying cycle duration time T4, after the cut tobacco drying Drying decanted from the barrel outlet, and the measured water content value y leaf wire exit at the outlet. 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。 A flow detection value from the inlet to the outlet moisture detection value to be experienced a longer period of time, for example, a production line Drying takes about 340s. 另外,烘丝机的输入/输出变量间也具有较大的时滞。 Further, between the input / output variables Drying machine also has a large delay.

[0046] 当检测到有入口流量时,表明烘丝过程开始运行。 [0046] When detecting the inlet flow rate, indicating Drying process runs. 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。 Drying processes have the initial operation leaves yarn inlet and the inlet water flow rate detection value, there is no outlet water detected value of cut tobacco, when the first dry Drying process stage begins. 根据烘丝过程干头阶段的特性,建立Cubic-RBF-ARX模型结构: Drying processes according to characteristics of the first stage of the dry, model structure Cubic-RBF-ARX:

Figure CN103610227BD00131

Figure CN103610227BD00141

[0050] 其中,yH(tH)表示烘丝机干头阶段CubiC-RBF-ARX模型的出口水分; [0050] wherein, yH (tH) represents the water outlet head Dryer Drying stage CubiC-RBF-ARX model;

Figure CN103610227BD00142

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-l)为入口流量和入口水分的状态变量;np H, nqH,dH和mH均表示干头阶段Cubic-RBF-ARX模型的阶次; Respectively represent moisture exhaust throttle opening degree Cubic-RBF-ARX model dry first stage, air temperature, cylinder temperature, the inlet flow rate and an inlet water; XH (tH-l) is the state the inlet flow and the inlet water variables; np H, nqH , dH have indicated mH and the order of the first stage of the dry Cubic-RBF-ARX model;

Figure CN103610227BD00143

分别为干头阶段Cub i c-RBF-ARX模型输出项与输入项的RBF神经网络的中心 Stem head center stage Cub i c-RBF-ARX model output items are the entry and RBF neural network

Figure CN103610227BD00144

Figure CN103610227BD00145

为干头阶段Cubic-RBF-ARX模型的标量权系数;M · I |F表示矩阵的Frobenius范数;|H(tH)是干头阶段Cubic-RBF-ARX模型的建模误差,为高斯白噪声;T QH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,T 2为从有入口水分检测值到有出口水分检测值的时间,T 3为从有入口水分检测值到烘丝筒入口的时间,! Scalar weights Cubic-RBF-ARX model dry first stage; M · I | F represents Frobenius matrix norm; | H (tH) is a modeling error Cubic-RBF-ARX model dry first stage, is Gaussian white noise; T QH dryer drying of the head stage Cubic-RBF-ARX model modeling sampling time, T1 is an inlet flow rate detection value from the inlet moisture detection time value, T 2 is the detected value from the inlet water has outlet moisture detection time value, T 3 is the detected value of the moisture from the inlet tube to the inlet drying time! \为叶丝在烘丝筒烘干的时间。 \ Leaf silk yarn in the drying cylinder drying time.

[0051] 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。 [0051] When the normal inlet flow becomes 0, marking the start of the dry end of the process, when the outlet water dropped to 3%, marking the end of the whole machine Drying Drying process. 干尾过程中无入口流量检测值,但有出口水分检测值。 Tail dry process without the inlet flow rate detection value, but with an outlet moisture detection value. 根据烘丝机干尾过程段的特性,建立如下的Cubic-RBF-ARX模型: The characteristics of the tail Dryer Drying process section, following the establishment of Cubic-RBF-ARX model:

[0052] [0052]

Figure CN103610227BD00151

[0055] 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Cub i c-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_l)为热风风温和筒体电机频率的状态变量;npT,nqT,dT和m %表示干尾阶段Cubic-RBF-ARX模型的阶次; zP' 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; [0055] wherein, yT (tT) represents the outlet water Cubic-RBF-ARX model Drying Dryer tail stage; denote cylinder temperature of the dry tail phase Cub i c-RBF-ARX model, hot air temperature, moisture exhaust damper opening, the inlet flow rate, inlet water and the frequency of the motor cylinder; XT (tT_l) state of mild hot air cylinder motor variable frequency; order npT, nqT, dT and m% represents Cubic-RBF-ARX model tail dry stage views; zP 'are centered on the end of a dry RBF neural network stage Cubic-RBF-ARX model output term to the input terms;

Figure CN103610227BD00152

为干尾阶段Cubic-RBF-ARX模型的标量权系数; f(tT)是干尾阶段Cubic-RBF-ARX模型建模误差,为高斯白噪声;IV为烘丝机干尾阶段Cubic-RBF-ARX模型建模采样时间。 Scalar weights Cubic-RBF-ARX model tail dry stage; f (tT) tail dry stage Cubic-RBF-ARX modeling error is Gaussian white noise; IV to the tail dry stage cut tobacco drying machine Cubic-RBF- ARX ​​modeling sampling time.

[0056] 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。 [0056] The present invention is structured nonlinear parameter optimization method (SNPOM) method to estimate the model. 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。 In order to make Cubic-RBF-ARX model can be constructed in the above described global dynamic characteristics of head and tail Drying section, we first use the model to optimize SNPOM method, step prediction error parameters the minimum case, and as long as the parameters optimize the initial predicted model parameters at the target value. 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。 Then, using 列维布格奈奎 Gault method (LMM) to optimize the optimal model parameters to predict long-term performance.

[0057] 烘丝机干头阶段Cubic-RBF-ARX模型(1)的参数优化问题如下: [0057] The first stage Dryer Drying Cubic-RBF-ARX model (1) the parameters of the optimization problem as follows:

Figure CN103610227BD00161

[0059] 其中,产(严)是烘丝机干头阶段出口水分的实际值,r (严)是在实际输入作用下,由烘丝机干头阶段Cub i c-RBF-ARX模型计算出的出口水分的预测值; [0059] wherein the yield (Yan) is the actual value of the outlet water Drying Dryer first stage, r (Yan) under actual input action, calculated from the drying wire Dryer first stage Cub i c-RBF-ARX model the predicted value of the water outlet;

Figure CN103610227BD00162

为烘丝机干头阶段Cubic-RBF-ARX模型的线性参数 Linear parameter Cubic-RBF-ARX model head Dryer Drying Phase

Figure CN103610227BD00163

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。 A nonlinear parameter Cubic-RBF-ARX model head Dryer Drying stage; Nh Drying machine for the Cubic-RBF-ARX model modeling data length header dry stage.

[0060] 烘丝机干尾阶段Cubic-RBF-ARX模型(3)的参数优化问题如下: [0060] Dryer Drying stage tail Cubic-RBF-ARX model (3) of the parameter optimization problem as follows:

Figure CN103610227BD00164

[0062] 其中,/(r)是烘丝机干尾过程中出口水分的实际值;r(r)是在实际输入作用下,由烘丝机干尾阶段Cub i c-RBF-ARX模型计算出的出口水分的预测值; [0062] wherein, / (r) is the actual value Drying machine dry end of the process the outlet water; r (r) is the actual input action, calculated by Drying Dryer End stage Cub i c-RBF-ARX model water outlet predicted value;

Figure CN103610227BD00165

为烘丝机干尾阶段Cubic-RBF-ARX模型的线性参数 Linear parameter Cubic-RBF-ARX model stage tail Dryer Drying

Figure CN103610227BD00166

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。 A nonlinear parameter Cubic-RBF-ARX model tail dry stage cut tobacco drying machine; Nt of tobacco drying machine as Cubic-RBF-ARX model modeling data length header dry stage.

[0063] 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。 [0063] Drying Dry head stage Cubic-RBF-ARX model of the process according to the estimated optimal design input curve for each process variable, to adapt to changes in the incoming case to minimize the quantity of dry dry phase of the head. 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。 The present invention uses a double S-shaped function will be described first stage of the dry moisture exhaust damper, optimal input air temperature curve, the barrel temperature, using step-function input curve to describe the optimal inlet flow.

[0064] 双S型曲线公式如下: [0064] S-shaped double curve following formula:

Figure CN103610227BD00167

[0066] 其中,ts为输入的时间,单位为s ; λ i,λ4, \5分别为双S型函数的起点、转折点及终点值;λ 2, λ 6分别为双S型函数的两条对称轴中心位置;λ 3, λ 7分别为双S型函数上升或下降的速度;λ 3, λ 7大于〇时表示S型函数上升,λ 3, λ 7小于〇时表示S型函数下降; c=l,2, 3, Usl(ts)是排潮风门的设定值;Us2(ts)是风温的设定值;U s3(ts)是筒温的设定值。 [0066] wherein, TS is the input of the time, in units of s; λ i, λ4, \ 5 are double S starting function, the turning point and the end point value; λ 2, λ 6 are two pairs of S-shaped function symmetry axis center position; λ 3, λ 7 are double-S-shaped function increase or decrease speed; λ 3, λ 7 represents S type function rising, λ 3, when λ 7 is smaller than the square represents S-shaped function decline greater than square; c = l, 2, 3, Usl (ts) is a set value of moisture exhaust damper; Us2 (ts) is a set value of the air temperature; U s3 (ts) is a set value of the barrel temperature.

[0067] 描述入口流量输入曲线的阶跃型函数公式如下: [0067] The step-described function formula inlet flow input curve as follows:

Figure CN103610227BD00171

[0069] 其中,tT为输入的时间,单位为s ; κ ρ κ 2, κ 3分别为阶跃函数的上升速度、上升时间与终值。 [0069] wherein, tT is the time input unit s; κ ρ κ 2, κ 3 is a step function increase in velocity, the rise time and the final values.

[0070] 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4 (〇,4(〇,4(〇,4(〇中,可得到干头阶段出口水分的预测值37 /?(〇: [0070] The optimized set of process variables for each curve (7-8) into constructed CubiC-RBF-ARX model (1) 4 input variables (square, 4 (square, 4 (square, 4 (square in ? a prediction value obtained dry phase outlet header 37 water / (○:

Figure CN103610227BD00172

[0072] 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AiQ=Ij,"'?)参数和入口流量最优输入曲线的Kj(j=l,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: [0072] The 列维布格奈奎 Technology (Levenberg-Marquardt Method, LMM) method, calculated by the model to the water outlet of the prediction error value and the minimum water content set value of the outlet, to find out the first stage of the dry moisture exhaust damper , air temperature, cylinder temperature optimal input curve AiQ = Ij, " '?) the input parameters and the optimal inlet flow curve Kj (j = l, 2,3) parameter set values ​​and the water outlet head on a dry phase Stem head dynamic error between the model predictions (9):

Figure CN103610227BD00173

[0074] yset (ta)是出口水分设定值。 [0074] yset (ta) is the water outlet setpoint.

[0075] 干头阶段工艺变量最优设定的优化问题如下: [0075] The first stage of the optimization problem dry optimal set of process variables as follows:

Figure CN103610227BD00174

[0077] M是干头阶段持续时间。 [0077] M is the duration of the first stage dry. 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。 Setting parameter values ​​to obtain optimal curve by solving the above optimization problem to design the optimal input curve Dryer Drying head at various stages of the process variables.

[0078] 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。 [0078] Drying process tail dry stage Cubic-RBF-ARX model estimated according to the optimal design input curve for each process variable, to adapt to changes in the incoming case to minimize the quantity of dry tail dry stage. 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: Using the exponential function tail dry stage described moisture exhaust damper, optimal input air temperature curve, the motor cylinder barrel moderate frequency, the exponential curve formula is as follows:

Figure CN103610227BD00175

[0080] 式中Uzl(tz)、Uz2(tz)、U z3(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。 [0080] wherein Uzl (tz), Uz2 (tz), U z3 (tz), Uz4 (tz) represent the end stage of the dry moisture exhaust damper, optimal input air temperature curve, the motor cylinder barrel moderate frequency. 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《ff),中,可得到干尾阶段出口水分的预测值: The optimized set of process variables for each curve (12) into constructed Cubic-RBF-ARX model (3) of the input variable "ff), the obtained dry water outlet end of stage prediction value:

Figure CN103610227BD00176

[0082] 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apg;其中,g=l,2,3。 [0082] The 列维布格奈奎 Stewart (the LMM) method, calculated by the model to the water outlet of the prediction error value and the minimum outlet water setpoint, locate a discharge end of tidal damper dry, air temperature, cylinder optimal motor cylinder moderate frequency input apg curve; wherein, g = l, 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: Water outlet tail dry stage based on an error between the setting value and the dry end of the dynamic model prediction value (13) as:

Figure CN103610227BD00177

[0084] ysrt⑴是出口水分设定值。 [0084] ysrt⑴ export water setpoint.

[0085] 干尾阶段工艺变量最优设定的优化问题如下: [0085] Optimization tail dry stage optimal set of process variables as follows:

Figure CN103610227BD00181

[0087] M是干尾阶段持续时间。 [0087] M is the duration of the tail dry stage. 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。 Setting parameter values ​​to obtain optimal curve by solving the above optimization problem to design the optimal input tail Dryer Drying curves at various stages of the process variables.

Claims (3)

  1. 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) 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; 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; 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 A head end section Drying process variables optimization control method, characterized in that the method: 1) The operational flow Drying machine, establish the middle silk Drying process flow inlet, water inlet, barrel temperature, wind temperature, moisture exhaust damper, the timing relationship of the outlet water, the first stage while dry wire exit moisture detecting no leaf tobacco drying process value based on the dry non-leaf stage tail yarn inlet and the inlet flow characteristics of the moisture detection values, as a function of the radial cubic Cubic-RBF-ARX model basis functions are established drying process stage and dry head Cubic-RBF-ARX model tail dry stage; 2) drying the historical operating data head tail section, structured nonlinear parameters optimization methods optimization drying process Stem head stage and Cubic-RBF-ARX model dry end of stage; 3) according to drying process Stem head stage and Cubic-RBF-ARX model dry end of stage optimized, double S-shaped function optimal input curve moisture exhaust damper head described dry phase, air temperature, the cartridge temperature; described step function using a dry phase inlet flow header optimum input curve; described exponential function tail dry stage moisture exhaust damper 、风温、筒温和筒体电机频率的最优输入曲线; 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; 所述步骤1)中,烘丝机干头阶段Cubic-RBF-ARX模型为: , Air temperature, optimal input curve cylindrical barrel moderate frequency motor; 4) using the method of Stewart 列维布格奈奎, by optimizing the first stage and the dry stage of the dry tail Cubic-RBF-ARX model calculated water outlet predicted value and the set value of the water outlet smallest error to find out the optimal parameter input stage dry head curve drying tail dry stage of the process to accommodate variations in the incoming case to reduce the quantity of dry tail dry stage ; step 1), the first stage dryer drying Cubic-RBF-ARX model:
    Figure CN103610227BC00021
    Figure CN103610227BC00031
    其中,yH(tH)表示烘丝机干头阶段Cubic-RBF-ARX模型的出口水分; <(产),Oii),"f(〇, "fC <(产)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(t Hl)为入口流量和入口水分的状态变量;npH,nqH,d H和m H均表示干头阶段Cubic-RBF-ARX模型的阶次;$',Zf 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;II · I If表示矩阵的Frobenius范数;ξ H(tH)是干头阶段Cubic-RBF-ARX模型的建模误差,为高斯白噪声;Tci11为烘丝机干头阶段Cubic-RBF-ARX模型建模采样时间,T 从有入口流量检测值到有入口水分检测值的时间,T2S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,T 4为叶丝在烘丝筒烘干的时间; 所述步骤1)中,烘丝机干尾阶段Cubic-RBF Wherein, yH (tH) denotes an outlet water Cubic-RBF-ARX model Drying Dryer first stage; <(yield), Oii), "f (square," fC <(yield) represent dry first stage Cubic-RBF moisture exhaust throttle opening -ARX model, air temperature, cylinder temperature, and the inlet water flow inlet; XH (t Hl) and the inlet of the inlet flow of water state variables; npH, nqH, d H m H and each represents a dry head order Cubic-RBF-ARX model stage; $ ', Zf are dry first stage RBF neural network center Cubic-RBF-ARX model output term to the input terms; dry head phase scalar weights Cubic_RBF_ARX model; II · I If represented Frobenius matrix norm; ξ H (tH) is the first phase of the modeling error dry Cubic-RBF-ARX model is Gaussian white noise; Tci11 dry first stage Cubic-RBF-ARX model drying machine building mode sampling time, T from the flow rate detecting values ​​inlet to time the inlet water detection value, T2S from the inlet moisture detection value the time with an outlet moisture measurement value, T3 for the inlet of water detected value to a drying wire tube from the inlet time, T 4 is the cut tobacco drying cylinder drying time; step 1), the tail dryer drying stage Cubic-RBF -ARX模型为: -ARX model:
    Figure CN103610227BC00032
    其中: among them:
    Figure CN103610227BC00041
    其中,yT(tT)表示烘丝机干尾阶段Cubic-RBF-ARX模型的出口水分; < (iK (〇,<(/), :),分别表示干尾阶段Cub i c-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-l)为热风风温和筒体电机频率的状态变量;npT,nqT,dT和m %表示干尾阶段Cubic-RBF-ARX模型的阶次; ζί;ν,ζί;"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; ,<^(),' 6Clg 为干尾阶段Cubic-RBF_ARX 模型的标量权系数; f(tT)是干尾阶段Cubic-RBF-ARX模型建模误差,为高斯白噪声;IV为烘丝机干尾阶段Cubic-RBF-ARX模型建模采样时间; 所述步骤2)中,烘丝机干头阶段Cubic-RBF-ARX模型优化如下: Wherein, yT (tT) represents a water outlet Cubic-RBF-ARX model tail dry stage cut tobacco drying machine; <(iK (square, <(/), :), respectively tail dry stage Cub i c-RBF-ARX model the barrel temperature, hot air temperature, moisture exhaust throttle opening, the inlet flow rate, inlet water and the frequency of the motor cylinder; XT (tT-l) is a gentle hot air cylinder motor frequency state variables; npT, nqT, dT and m order% represents Cubic-RBF-ARX model dry tail stage; ζί; ν, ζί; "respectively, the center RBF neural network dry end of stage Cubic-RBF-ARX model output term to the input terms;, <^ () , '6Clg scalar weights Cubic-RBF_ARX model dry tail stage; f (tT) is the dry end of stage Cubic-RBF-ARX modeling error is Gaussian white noise; IV is a tobacco drying machine dry end of stage Cubic-RBF -ARX modeling sampling time;) in the step 2, the first stage dryer drying Cubic-RBF-ARX model optimization as follows:
    Figure CN103610227BC00042
    其中,,(P)是烘丝机干头阶段出口水分的实际值,^V)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; 奸广Λ气二",<二4» 严=1,.·.,叫=1,,..,》产}为烘丝机干头II ,0 f!,_/ ,0 " J 阶段Cubic-RBF-ARX模型的线性参数;祀Z(,,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; 烘丝机干尾阶段Cubic-RBF-ARX模型优化如下: Wherein ,, (P) is the actual value of the water outlet head Dryer Drying stage, ^ V) is under the action of the actual input, the predicted value calculated by the first stage the dry Cubic-RBF-ARX model Drying water outlet ; rape gas two wide Λ '<= 4 »strict = 1, ·, called = 1 ,, ..,.."} is produced dryer drying head II, 0 f, _ /, 0 "J stage! linear parameter Cubic-RBF-ARX model; Si Z (,, non-linear parameter Cubic-RBF-ARX model head dryer drying stage; Nh is the head dryer drying stage Cubic-RBF-ARX model modeling data length; tail dry stage drying machine Cubic-RBF-ARX model optimization as follows:
    Figure CN103610227BC00051
    其中,,(〇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卜<|4'0,扮乂,气=1,…,n/7ry 阶段Cubic-RBF-ARX模型的线性参数,θ「ν = (Zf 'Zf卞=1,· ·,"/}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; 所述步骤3)中: 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: Wherein ,, (square is the actual value of the tail Dryer Drying process water outlet; under action of the actual input, the predicted value calculated by the tail dry stage Cubic-RBF-ARX model Drying water outlet; [Theta] Bu <| linear parameter Cubic-RBF-ARX model 4'0, play qe, gas = 1, ..., n / 7ry stage, [theta] "ν = (Zf 'Zf BIAN = 1, ·," /} of drying dryer Nonlinear parameter Cubic-RBF-ARX model tail stage, Nt is the first stage dryer drying Cubic-RBF-ARX model modeling data length;) in the step 3: drying dryer for describing a first stage moisture exhaust damper, air temperature, optimum barrel temperature profile double S input function expression is:
    Figure CN103610227BC00052
    其中,ts为输入的时间,单位为s ; λ λ4, \5分别为双S型函数的起点、转折点及终点值;λ2, \6分别为双S型函数的两条对称轴中心位置;λ 3, λ 7分别为双S型函数上升或下降的速度;λ 3, λ 7大于〇时表示S型函数上升,λ 3, λ 7小于〇时表示S型函数下降;c = 1,2, 3, Usl (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; 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: Wherein, TS is the input of the time, in units of s; λ λ4, \ 5 are double S starting function, the turning point and the end point value; λ2, \ 6 are axial center position of two pairs of symmetrical S-shaped function; [lambda] 3, λ 7 double S function respectively increase or decrease the speed; λ 3, λ 7 when S is greater than the square type function indicates increased, λ 3, λ 7 when less than the square represents S type function decline; c = 1,2, 3, Usl (ts) is a set value of moisture exhaust damper; Us2 (ts) is a set value of the air temperature; Us3 (ts) is a set value of the barrel temperature; dryer drying for describing the first stage inlet flow the optimal input function expression is a step profile:
    Figure CN103610227BC00053
    其中,tT为输入的时间,单位为S ; K K2, K3分别为阶跃函数的上升速度、上升时间与终值; 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: Wherein, tT is the time input unit is S; K K2, K3 are rising speed of a step function, the rise time and the final value; 3) in the step for describing tail dry stage moisture exhaust damper, the air temperature expression exponential function curve of the optimum input frequency of the motor cylinder barrel is moderate:
    Figure CN103610227BC00054
    P = 1,2, 3, 4 式中Uzl (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。 P = 1,2, 3, 4 wherein Uzl (tz), Uz2 (tz), Uz3 (tz), Uz4 (tz) represent the end stage of the driest moisture exhaust damper, the air temperature, the cylinder drum motor frequency moderate preferably input curve.
  2. 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Mt s)、Ut (tT)代入所述烘丝机干头阶段Cubic-RBF-ARX模型的输入变量<(Π ,》f(0,(0, 4(0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产(Ο;通过使干头阶段Cubic-RBF-ARX模型计算出的出口水分预测值浐(O与出口水分设定值y srt(ta)的误差eH(ta)最小,即采用列M 维布格奈奎尔特方法求解优化问题ininJ = ,寻找出干头阶段排潮风门、风温、筒Av. κσ 温的输入曲线的参数λχ和入口流量输入曲线的参数KK 2,K 3;其中,X = 1,2,…,7 ;g =1,2, 3 ;M是干头阶段持续的时间。 2. Drying the head end section of the process variables according to claim 1, optimal control, wherein said step 4), the head Dryer Drying optimization stage set curve Mt s), Ut (tT ) into said cut tobacco drying machine dry head stage Cubic-RBF-ARX model input variables <(Π, "f (0, (0, 4 (0, the obtained drying dryer first stage Cubic-RBF-ARX model calculated Outputs water outlet of the predicted value (o; head by a dry phase Cubic-RBF-ARX model to calculate the predicted values ​​of the outlet water Chan (O moisture set value and the minimum outlet y srt (ta) error eH (ta), i.e., a method using column M 维布格奈奎 Technology optimization problems ininJ =, find the first stage of the dry moisture exhaust damper, and an inlet flow parameters λχ input curve input air temperature curve, the cartridge Av. κσ temperature parameters KK 2 , K 3; wherein, X = 1,2, ..., 7; g = 1,2, 3; M is a first phase duration dry.
  3. 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"「(广),"[(广),"【#),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尹Y);通过使干尾阶段的Cubic-RBF-ARX模型计算出的出口水分预测值/f)与出口水分设定值y' srt(tb)的误差eT(tb)最小,即采用列维布格奈奎尔特方M 法求解优化问题min./' = (^),寻找出干尾阶段排潮风门、风温、筒温和筒体电机频率aps k=\ 最优输入曲线的参数a pg;其中,g = 1,2, 3 ;M'是干尾阶段持续时间。 The head end section Drying process variables of the optimization control method according to claim 1, wherein said step 4), the end of the optimization phase Dryer Drying curves set Uzp (tz) substituting the input variables Cubic-RBF-ARX model drying machine dry end of phase "" (wide) "[(Canton)," [#), to give Cubic-RBF-ARX model drying dryer tail phase calculated water outlet predicted value of the Y Yin); calculated by the tail dry stage Cubic-RBF-ARX model predicted value of the water outlet / f) and the outlet water setpoint y 'srt (tb) error eT (tb) minimum , i.e. using 列维布格奈奎 Technology M square method to solve the optimization problem min./ '= (^), locate a discharge end of tidal damper dry, air temperature, motor cylinder barrel moderate frequency aps k = \ optimal input curve parameters a pg; wherein, g = 1,2, 3; M 'is the duration of the tail dry stage.
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