抄纸过程智能控制策略研究
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摘要
造纸工业从古老的手工抄纸到现代造纸技术,发展至今,尽管已经取得了许多成就,满足了人们对纸品日益不断增长的需求,但随着社会的发展,人们的这种需求已经不仅仅是体现在数量上的简单满足,还体现在对纸品的质量、功能以及生产纸品的效益上。而纸机自动控制是现代造纸工业的重要组成部分,那么如何从控制技术的角度解决和提高生产满足数量、质量、功能及效益的成纸品,即是当前和今后造纸工业寻求突破的一个主要方向。
针对上述问题,论文以成纸的定量、水分、厚度、灰分等为研究对象,从控制的角度来探讨抄纸过程中如何有效地提高成纸的质量技术指标。在纸机抄造控制系统中曾尝试采用先进控制算法来解决影响成纸各控制指标波动的问题,但至今实际使用的算法大多依然是采样PI控制。因此,本课题以陕西科技大学承担的国家九五攻关项目“造纸过程优化控制系统”为工程背景,通过对纸机的机理模型进行分析与建模,从具有智能控制思想的预测控制理论及算法分析、控制策略实现及其在纸机抄纸过程质量控制系统中的应用等方面开展研究工作。提出成纸综合质量指标优化控制体系,实现质量指标的智能优化控制;提出水分干燥曲线以耗能为目标函数的优化方法,建立烘缸表面温度与袋区通风的横向水分控制;提出纸页厚度与灰分指标的控制策略,实现纸页厚度与灰分的高精度自动控制;并结合实践,建立抄纸过程CIPS(Computer Integrated Process System),实现理论研究对工程实践所具有的直接指导意义与使用效能。
首先,通过对中定量纸机从网前箱到卷取部的各部进行机理分析,寻求出抄纸对象的输入输出关系特性,在考虑抄纸过程对象的不确定性、不完全性、非线性、时滞性和强耦合特性且忽略一些次要扰动的前提下,建立近似真实过程的一般纸机抄纸过程数学模型。并根据抄纸工艺流程,分析各路参量信号的检测,制定相对应的多回路控制策略。
然后,基于抄纸过程数学模型及控制策略的基础上,针对抄纸过程的复杂多变量大时滞对象特性,提出基于预测控制思想的先进控制算法。所采用的动态矩阵控制DMC(Dynamic Matrix Control),其预测模型可以从过程对象中通过阶跃响应来获取,算法简单,计算量较小,同时具有较强的抗干扰性能。对于多变量耦合系统,DMC控制还具有特有的隐性解耦能力,不需要传统解耦控制方法。同时考虑到现场可操控性,利用PID与DMC控制策略的结合形式,形成改进PIDDMC控制算法,具有更好的动态性能。广义预测控制采用对象受控自回归积分滑动平均CARIMA模型(Controlled Auto Return Integral Moving Average Model),可在大于对象纯滞后的有限时域内进行长程预测;在目标函数中采用加权的控制增量作用,从而获取最优控制律,达到预测未来输出的目的。而基于对象受控自回归滑动平均CARMA模型(Controlled Auto Return Moving Average Model)的模型预测函数控制,通过对基函数的选取简化了优化变量维数,在求解模型输出预测时,推导了直接分离优化变量系数的递推算法,从而避免了Diophantine方程求解过程,减少了在线运算量,提高了算法的实时控制性能。
再者,由于抄纸过程对象复杂性、不确定性以及非线性等特性,在上述常规预测控制的控制思想下,借助于神经网络,利用神经网络具有较好逼近实际被控过程的能力,实现抄纸过程对象的综合智能预测控制。其采用基于NARMA模型(Nonlinear Auto Return Moving Average Model)的输入输出辨识,采用另一个神经网络进行实际输出误差预测,克服传统预测误差校正精度不高的缺点,同时通过对象模型辨识可以直接获取控制律,避免了复杂的非线性求解和运算过程。所提出的神经网络预测控制算法能够克服干扰和系统模型不确定的影响,在抄纸过程对象的控制中能够获得较好的效果。
最后,通过对上述智能控制算法的研究,实施具体的抄纸过程控制策略以及工程应用。其中,提出抄纸过程定量、水分、厚度及灰分的纵横向控制的实现方法,重点探讨了各质量指标的横向控制问题,对于横向水分控制,本文提出了烘缸表面温度横向控制方法与袋区通风控制方法,提出并建立基于DMC的横向水分多变量控制系统;对于横向厚度控制,采用具有可控中高的电磁加热调节系统;对于灰分控制,从传感器检测信号的角度探讨保证高精度灰分控制的可行性。并根据实用性要求,提出基于多控制器的抄纸过程控制系统无扰动切换技术,实现多模式控制的切换;提出解决现场信号易受干扰的滤波方法,有效保证检测信号的精确度。在结合上述各控制参量及其算法实现技术的基础上,提出基于计算机CIPS的抄纸过程DCS控制系统,选用Windows xp为软件平台,采用WinCC为开发软件环境,设计先进控制算法软件包,成功应用于一套纸机的QCS质量控制系统中,整个抄纸系统在集成计算机控制下,能够处于高产、优质、低耗的最优运行状态。
From ancient manual papermaking craft to modern automatic paper production technology, papermaking industry has obtained many achievements and meanwhile has met human’s increasing requirements of paper product. However with the development of society, people’s requirements are not only simply the quantity of paper product but also the quality and function of paper product as well as efficiency of paper production. Here the papermaking automatic control is one of the most important parts in papermaking technology, so from the angle of control how to solve and improve the quantity, quality, function and efficiency of paper product is the main direction of papermaking industry nowadays.
According to the above problems, the paper takes the most important performance indexes such as basis weight, moisture content, caliper, and ash as the plants, and from the view of control, the methods are analyzed in detail to improve the control performances in the papermaking process. Although many advanced control algorithms were applied to solve the fluctuation of the paper basis weight and moisture content and other indexes as well before, the real useful and practical method is still sampling PI control which is prevailing all along. So based on the national key project“papermaking process optimal control system”awarded to Shaanxi University of Science and Technology, the paper establishes the paper machine’s mathematic models, and recurring to intelligent predictive control theory, algorithm, strategy and practice, the paper machine basis weight, moisture content, caliper and ash control systems are well studied, then the intelligent optimal control structure of papermaking process is realized. Thereinto, moisture content drying curve with target function of funminimum energy consumption is proposed, meanwhile the cross-directional moisture content control with dryer surface temperature and bag zone air blowing method are analyzed. Caliper and ash control are also analyzed to abtain automatic tracking characteristics. At last with practices the CIPS is applied, and efforts to get direct guidance of engineering and practice efficiency are achieved.
Firstly, according to the analysis of middle basis weight paper machine sections from headbox to reel part, the input and output characteristics of papermaking plants are obtained to establish paper machine’s mathematic model. Because of paper machine plant’s uncertainty, inadequacy, nonlinearity, time-delay and strong coupling, some unnecessary disturbances are ignored and approximate mathematic model can be achieved. Hence according to the technical process and signal measurement loop, multi-loop control strategies are set correspondingly.
Secondly, according to the papermaking process mathematic models and control strategies, advanced control algorithm based on predictive control is applied to papermaking process control system so as to settle the complicated multi-variable and large time-delay plants. The dynamic matrix control algorithm gets the predictive model from plant’s step response, its algorithm is simplicity with little calculation as well a strong anti-disturbance. For multi-variable coupling system, DMC has the latent decoupling ability. Meanwhile, combined PID with DMC, novel PIDDMC algorithm is proposed to get better dynamic performance. And global predictive control applies CARIMA model to predict within finite time domain for large time-delay plant, by successively solving Diophantine equation and adding the action of control increment in target function, optimized control law is got to obtain the purpose of future output prediction. While the model predictive function control based on CARMA selects the base function to decrease the dimension of optimal variable and develops the successively algorithm to get model output prediction, by the way the solving process of Diophantine equation is ignored, online calculating work is decreased and real time performance is improved.
Thirdly, because of the characteristics of plant complication, uncertainty, nonlinearity and time-delay in papermaking process, traditional predictive control methods also have faults. So combined with the neural network which has the merits of plant’s model identification and self-tuning, the integrated neural network predictive control is proposed to settle the complicated control parameters of the papermaking process. Neural network has the ability of approaching real process, and here NARMA model with BP network is used, meanwhile another neural network is used to predict real output error. By this way the control law can be directly obtained according to the plant model identification, and complicated nonlinear solution and calculation load are avoided as well. Meanwhile by the use of neural network, new neural network predictive controller can optimize the plant’s control performance by the cost of moderate memory space, and the influence of disturbance and uncertainty can be minimized.
Finally, with the research of above intelligent control algorithms, the technical realization and excutation of paper quality control system QCS is proposed. Here the paper basis weight, moisture content, caliper and ash control methods are analyzed. The emphasis is focus on the cross-directional control besides the basis weight and moisture content. To the moisture content, dryer surface temperature cross-direction control and bag zone air blowing control methods are analyzed, hence the DMC cross-directional moisture content control system is established. To the paper thickness control, electromagnetic heating adjustor for the controlled middle-high roller is set. To the ash control, from the view of sensor signal detection, feasibility of high accuracy control is developed. Meanwhile with the practice, multi-controller control system of papermaking process is proposed and switching method with no disturbance is realized. And filter technique is applied to solve signal disturbance for good accuracy. With above analysis, papermaking process distributed control system based on computer is proposed with hardware and software project, which is called CIPS. And with Windows xp, WinCC and control algorithm package, papermaking process control system is applied to a paper machine production line and the system can achieve optimal running states with high quantity, fine quality and low loss.
针对上述问题,论文以成纸的定量、水分、厚度、灰分等为研究对象,从控制的角度来探讨抄纸过程中如何有效地提高成纸的质量技术指标。在纸机抄造控制系统中曾尝试采用先进控制算法来解决影响成纸各控制指标波动的问题,但至今实际使用的算法大多依然是采样PI控制。因此,本课题以陕西科技大学承担的国家九五攻关项目“造纸过程优化控制系统”为工程背景,通过对纸机的机理模型进行分析与建模,从具有智能控制思想的预测控制理论及算法分析、控制策略实现及其在纸机抄纸过程质量控制系统中的应用等方面开展研究工作。提出成纸综合质量指标优化控制体系,实现质量指标的智能优化控制;提出水分干燥曲线以耗能为目标函数的优化方法,建立烘缸表面温度与袋区通风的横向水分控制;提出纸页厚度与灰分指标的控制策略,实现纸页厚度与灰分的高精度自动控制;并结合实践,建立抄纸过程CIPS(Computer Integrated Process System),实现理论研究对工程实践所具有的直接指导意义与使用效能。
首先,通过对中定量纸机从网前箱到卷取部的各部进行机理分析,寻求出抄纸对象的输入输出关系特性,在考虑抄纸过程对象的不确定性、不完全性、非线性、时滞性和强耦合特性且忽略一些次要扰动的前提下,建立近似真实过程的一般纸机抄纸过程数学模型。并根据抄纸工艺流程,分析各路参量信号的检测,制定相对应的多回路控制策略。
然后,基于抄纸过程数学模型及控制策略的基础上,针对抄纸过程的复杂多变量大时滞对象特性,提出基于预测控制思想的先进控制算法。所采用的动态矩阵控制DMC(Dynamic Matrix Control),其预测模型可以从过程对象中通过阶跃响应来获取,算法简单,计算量较小,同时具有较强的抗干扰性能。对于多变量耦合系统,DMC控制还具有特有的隐性解耦能力,不需要传统解耦控制方法。同时考虑到现场可操控性,利用PID与DMC控制策略的结合形式,形成改进PIDDMC控制算法,具有更好的动态性能。广义预测控制采用对象受控自回归积分滑动平均CARIMA模型(Controlled Auto Return Integral Moving Average Model),可在大于对象纯滞后的有限时域内进行长程预测;在目标函数中采用加权的控制增量作用,从而获取最优控制律,达到预测未来输出的目的。而基于对象受控自回归滑动平均CARMA模型(Controlled Auto Return Moving Average Model)的模型预测函数控制,通过对基函数的选取简化了优化变量维数,在求解模型输出预测时,推导了直接分离优化变量系数的递推算法,从而避免了Diophantine方程求解过程,减少了在线运算量,提高了算法的实时控制性能。
再者,由于抄纸过程对象复杂性、不确定性以及非线性等特性,在上述常规预测控制的控制思想下,借助于神经网络,利用神经网络具有较好逼近实际被控过程的能力,实现抄纸过程对象的综合智能预测控制。其采用基于NARMA模型(Nonlinear Auto Return Moving Average Model)的输入输出辨识,采用另一个神经网络进行实际输出误差预测,克服传统预测误差校正精度不高的缺点,同时通过对象模型辨识可以直接获取控制律,避免了复杂的非线性求解和运算过程。所提出的神经网络预测控制算法能够克服干扰和系统模型不确定的影响,在抄纸过程对象的控制中能够获得较好的效果。
最后,通过对上述智能控制算法的研究,实施具体的抄纸过程控制策略以及工程应用。其中,提出抄纸过程定量、水分、厚度及灰分的纵横向控制的实现方法,重点探讨了各质量指标的横向控制问题,对于横向水分控制,本文提出了烘缸表面温度横向控制方法与袋区通风控制方法,提出并建立基于DMC的横向水分多变量控制系统;对于横向厚度控制,采用具有可控中高的电磁加热调节系统;对于灰分控制,从传感器检测信号的角度探讨保证高精度灰分控制的可行性。并根据实用性要求,提出基于多控制器的抄纸过程控制系统无扰动切换技术,实现多模式控制的切换;提出解决现场信号易受干扰的滤波方法,有效保证检测信号的精确度。在结合上述各控制参量及其算法实现技术的基础上,提出基于计算机CIPS的抄纸过程DCS控制系统,选用Windows xp为软件平台,采用WinCC为开发软件环境,设计先进控制算法软件包,成功应用于一套纸机的QCS质量控制系统中,整个抄纸系统在集成计算机控制下,能够处于高产、优质、低耗的最优运行状态。
From ancient manual papermaking craft to modern automatic paper production technology, papermaking industry has obtained many achievements and meanwhile has met human’s increasing requirements of paper product. However with the development of society, people’s requirements are not only simply the quantity of paper product but also the quality and function of paper product as well as efficiency of paper production. Here the papermaking automatic control is one of the most important parts in papermaking technology, so from the angle of control how to solve and improve the quantity, quality, function and efficiency of paper product is the main direction of papermaking industry nowadays.
According to the above problems, the paper takes the most important performance indexes such as basis weight, moisture content, caliper, and ash as the plants, and from the view of control, the methods are analyzed in detail to improve the control performances in the papermaking process. Although many advanced control algorithms were applied to solve the fluctuation of the paper basis weight and moisture content and other indexes as well before, the real useful and practical method is still sampling PI control which is prevailing all along. So based on the national key project“papermaking process optimal control system”awarded to Shaanxi University of Science and Technology, the paper establishes the paper machine’s mathematic models, and recurring to intelligent predictive control theory, algorithm, strategy and practice, the paper machine basis weight, moisture content, caliper and ash control systems are well studied, then the intelligent optimal control structure of papermaking process is realized. Thereinto, moisture content drying curve with target function of funminimum energy consumption is proposed, meanwhile the cross-directional moisture content control with dryer surface temperature and bag zone air blowing method are analyzed. Caliper and ash control are also analyzed to abtain automatic tracking characteristics. At last with practices the CIPS is applied, and efforts to get direct guidance of engineering and practice efficiency are achieved.
Firstly, according to the analysis of middle basis weight paper machine sections from headbox to reel part, the input and output characteristics of papermaking plants are obtained to establish paper machine’s mathematic model. Because of paper machine plant’s uncertainty, inadequacy, nonlinearity, time-delay and strong coupling, some unnecessary disturbances are ignored and approximate mathematic model can be achieved. Hence according to the technical process and signal measurement loop, multi-loop control strategies are set correspondingly.
Secondly, according to the papermaking process mathematic models and control strategies, advanced control algorithm based on predictive control is applied to papermaking process control system so as to settle the complicated multi-variable and large time-delay plants. The dynamic matrix control algorithm gets the predictive model from plant’s step response, its algorithm is simplicity with little calculation as well a strong anti-disturbance. For multi-variable coupling system, DMC has the latent decoupling ability. Meanwhile, combined PID with DMC, novel PIDDMC algorithm is proposed to get better dynamic performance. And global predictive control applies CARIMA model to predict within finite time domain for large time-delay plant, by successively solving Diophantine equation and adding the action of control increment in target function, optimized control law is got to obtain the purpose of future output prediction. While the model predictive function control based on CARMA selects the base function to decrease the dimension of optimal variable and develops the successively algorithm to get model output prediction, by the way the solving process of Diophantine equation is ignored, online calculating work is decreased and real time performance is improved.
Thirdly, because of the characteristics of plant complication, uncertainty, nonlinearity and time-delay in papermaking process, traditional predictive control methods also have faults. So combined with the neural network which has the merits of plant’s model identification and self-tuning, the integrated neural network predictive control is proposed to settle the complicated control parameters of the papermaking process. Neural network has the ability of approaching real process, and here NARMA model with BP network is used, meanwhile another neural network is used to predict real output error. By this way the control law can be directly obtained according to the plant model identification, and complicated nonlinear solution and calculation load are avoided as well. Meanwhile by the use of neural network, new neural network predictive controller can optimize the plant’s control performance by the cost of moderate memory space, and the influence of disturbance and uncertainty can be minimized.
Finally, with the research of above intelligent control algorithms, the technical realization and excutation of paper quality control system QCS is proposed. Here the paper basis weight, moisture content, caliper and ash control methods are analyzed. The emphasis is focus on the cross-directional control besides the basis weight and moisture content. To the moisture content, dryer surface temperature cross-direction control and bag zone air blowing control methods are analyzed, hence the DMC cross-directional moisture content control system is established. To the paper thickness control, electromagnetic heating adjustor for the controlled middle-high roller is set. To the ash control, from the view of sensor signal detection, feasibility of high accuracy control is developed. Meanwhile with the practice, multi-controller control system of papermaking process is proposed and switching method with no disturbance is realized. And filter technique is applied to solve signal disturbance for good accuracy. With above analysis, papermaking process distributed control system based on computer is proposed with hardware and software project, which is called CIPS. And with Windows xp, WinCC and control algorithm package, papermaking process control system is applied to a paper machine production line and the system can achieve optimal running states with high quantity, fine quality and low loss.
引文
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