PTA装置氧化反应器自动升降负荷的先进控制

针对PTA装置氧化反应器升降负荷过程中存在的滞后大、动态响应慢、非线性严重、回路间关联性强等控制难点,在多变量预测控制器基础上设计了自动升降负荷系统,极大提高了生产操作平稳度,降低了物耗和能耗及产品质量的波动,取得良好的控制效果和显著的经济效益。     

基于闭环耦合度的大系统常规PID控制回路的投用规则

化工过程全流程为具有高维输入输出的多变量大系统,内部存在一定程度的耦合作用,因此其底层常规PID控制系统的设计、投用和整定也必然与整个系统的耦合程度有关。首先,提出了闭环相对增益和耦合度的概念,分析闭环相对增益在理想控制条件下与开环相对增益的等效性,发现闭环控制回路的反馈作用引起操纵变量和被控变量的关联,由此加剧整个系统的耦合程度,因此闭环控制回路并非投用得越多越好。然后,提出了基于闭环耦合度的化工过程大系统常规PID控制回路的投用规则,根据被控过程开环相对增益阵确定控制回路变量配对和投用顺序,依次投用各控制回路并计算控制回路的耦合度,当耦合度超过阈值时停止剩余控制回路的投用。最后,以典型多变量TE过程为例验证了该投用规则的有效性。     

加热炉支路平衡控制中流量控制回路的取舍分析

由于加热炉支路平衡控制是一个带约束的多变量强耦合的控制问题,具有非线性、大滞后及强干扰等的特点,多偏差法的多层串级控制结构会增加系统的复杂性和不确定性。针对上述问题提出了一种改进的多偏差控制方案,通过舍去流量控制回路,避免了回路过多造成的系统稳定性变差的问题,简化了控制结构,改善了控制性能。     

基于粒子群优化算法的球磨机制粉系统PID-ANN解耦控制器

球团厂钢球磨煤制粉系统是多变量强耦合、时滞、非线性以及生产工况变化大的复杂对象,其自动控制问题一直是控制界关注的热点。基于粒子群算法具有对整个参数空间进行高效并行搜索的特点以及PID神经网络的自调节和自适应特性,设计了具有PID结构的多变量自适应神经网络控制器。PID神经网络解耦控制方法被用来消除回路之间的耦合,神经网络连接权值由粒子群算法进行学习优化。仿真研究表明所建模型和所提控制方法具有较好的控制品质、良好的自适应解耦能力和自学习功能。该控制策略可在大范围内克服系统的非线性和强耦合问题,具有很高的工程实用价值。     

基于PID对角优势补偿阵的过程多变量控制系统设计

化工过程一般为多变量系统,输入输出变量之间往往存在耦合作用,常规分散PID控制系统难以保证控制质量。为了削弱多变量系统的耦合作用,基于对角优势的设计准则,通过在若干频率点加权优化,设计PID动态预补偿阵。然后利用正Nyquist阵列设计法对补偿后的系统设计控制器,基于对角优势系统的Nyquist稳定判据,通过绘制优势度曲线和Gershgorin带判断系统的优势程度,初步确定反馈矩阵的稳定参数范围,再按照单输入单输出系统设计动态补偿器,使得系统满足动态控制品质要求。最后通过示例说明,该方法设计的集中控制系统与分散控制相比,控制性能具有一定的优势,且方法简便,容易实现。     

基于PID对角优势补偿阵的过程多变量控制系统设计

化工过程一般为多变量系统,输入输出变量之间往往存在耦合作用,常规分散PID控制系统难以保证控制质量。为了削弱多变量系统的耦合作用,基于对角优势的设计准则,通过在若干频率点加权优化,设计PID动态预补偿阵。然后利用正Nyquist阵列设计法对补偿后的系统设计控制器,基于对角优势系统的Nyquist稳定判据,通过绘制优势度曲线和Gershgorin带判断系统的优势程度,初步确定反馈矩阵的稳定参数范围,再按照单输入单输出系统设计动态补偿器,使得系统满足动态控制品质要求。最后通过示例说明,该方法设计的集中控制系统与分散控制相比,控制性能具有一定的优势,且方法简便,容易实现。     

冷连轧机厚控系统的PI型广义预测控制

针对冷连轧带钢自动厚度控制系统的多变量、强耦合、非线性及纯延时等特点,提出具有简化PI结构的广义预测控制策略。将广义预测控制的目标函数改造成PI形式后,利用PI的反馈信息改善系统性能指标、抑制超调。简化后的PIGPC控制策略,计算量减少,提高了控制的实时性;同时利用预测信息提高了系统的稳定性;通过滚动优化,改善了系统的自适应抗干扰能力。仿真实验结果表明:采用简化后的PIGPC控制策略,有效地改善了系统的动态特性,抑制了超调,提高了跟踪速度和控制精度。     

基于ANFIS与多模型的一类非线性系统的自适应控制方法

针对一类不确定的离散时间零动态不稳定非线性系统,提出了一种基于自适应神经模糊推理系统(ANFIS)与多模型的非线性自适应控制方法。该方法由线性鲁棒自适应控制器,基于ANFIS的非线性自适应控制器以及切换机制组成。线性控制器用来保证闭环系统输入输出信号有界,非线性控制器用来改善系统性能。切换机制通过对上述两种控制器的切换,保证闭环系统输入输出有界的同时,改善系统性能。在采用ANFIS作为系统未建模动态补偿器时,首先用一个连续、单调、可逆的一一映射把可能无界的未建模动态的定义域转化成一个有界闭集,保证了ANFIS的万能逼近特性成立的前提条件。而且,ANFIS能减小BP神经网络收敛速度慢和容易陷入局部极小的问题,改善了控制效果。建立了保证系统稳定性的引理,并给出了闭环系统的稳定性和收敛性分析。通过仿真比较,说明了所提方法的有效性。     

计算机辅助蒸馏过程模拟及控制(Ⅰ)——多组分复杂蒸馏控制的相对增益矩阵计算研究

本文应用Bristol的相对增益概念,对具有非线性多变量和相关多回路的复杂蒸馏过程,建立起5×5调节和被调参数对的相对增益计算方法,以研究它在设计中的控制策略问题.该方法改进了2×2相对增益子集分析方法的不足,达到了比通常应用的方法更准确和全面地反映出控制回路间存在的相关作用的目的,从而找到在全局意义下的控制策略.     

强制循环蒸发系统线性自抗扰解耦控制的鲁棒设计

针对强制循环蒸发系统液位与出料密度两个回路的非线性耦合问题,提出了一种基于粒子群算法的线性定常自抗扰解耦控制设计。首先通过引入虚拟控制量,将对象解耦配置为两个单输入单输出子系统,并对每个回路设计降维线性扩张状态观测器。随后,对观测器动态线性化得到的近似积分器环节进行比例控制。最后,在可能的大工况内通过粒子群算法优化控制增益耦合矩阵和比例增益。该算法使用观测器估计并补偿动态耦合部分,降低了控制器对数学模型的依赖程度;使用粒子群算法优化定常控制增益矩阵,避免了实时测量出料温度,降低了对传感测量的要求,提高了可靠性并降低了实施难度。数学仿真结果表明该算法能有效地消除液位回路和出料密度回路的耦合作用,在大工况内具有很强的鲁棒性。     

Intelligent decoupling PID control for the forced-circulation evaporation system

The control objective of the forced-circulation evaporation process of alumina production is not only to avoid large fluctuations of the level, but also to ensure the product density to track its setpoint quickly. Due to the existence of strong coupling between the level loop and the product density loop, and high nonlinearities in the process, the conventional control strategy cannot achieve satisfactory control performance, and thus the production demand cannot bemet. In this paper, an intelligent decoupling PID controller including conventional PID controllers, a decoupling compensator and a neural feedforward compensator is proposed. The parameters of such controller are determined by generalized predictive control law. Real-time experiment results show that the proposed method can decouple the loops effectively and thus improve the evaporation efficiency.     

Model predictive control for multivariable unstable processes with constraints on manipulated variables

The original MPC(Model Predictive Control) algorithm cannot be applied to open loop unstable systems, because the step responses of the open loop unstable system never reach steadystates. So when we apply MPC to the open loop unstable systems, first we have to stabilize them by state feedback or output feedback. Then the stabilized systems can be controlled by MPC. But problems such as valve saturation may occur because the manipulated input is the summation of the state feedback output and the MPC output. Therefore, we propose Quadratic Dynamic Matrix Control(QDMC) combined with state feedback as a new method to handle the constraints on manipulated variables for multivariable unstable processes. We applied this control method to a single-input-single-output unstable nonlinear system and a multi-input-multi-output unstable system. The results show that this method is robust and can handle the input constraints explicitly and also its control performance is better than that of others such as well tuned PI control. Linear Quadratic Regulator (LQR) with integral action.     

Model predictive control for multivariable unstable processes with constraints on manipulated variables

The original MPC(Model Predictive Control) algorithm cannot be applied to open loop unstable systems, because the step responses of the open loop unstable system never reach steady states. So when we apply MPC to the open loop unstable systems, first we have to stabilize them by state feedback or output feedback. Then the stabilized systems can be controlled by MPC. But problems such as valve saturation may occur because the manipulated input is the summation of the state feedback output and the MPC output. Therefore, we propose Quadratic Dynamic Matrix Control(QDMC) combined with state feedback as a new method to handle the constraints on manipulated variables for multivariable unstable processes. We applied this control method to a single-input-single-output unstable nonlinear system and a multi-input-multi-output unstable system. The results show that this method is robust and can handle the input constraints explicitly and also its control performance is better than that of others such as well tuned PI control. Linear Quadratic Regulator (LQR) with integral action.     

高效再生催化裂化装置多稳态分析：反应温度开/闭环控制条件对热反馈机制的影响

针对催化裂化反应-再生系统在提升管反应温度开环和闭环控制条件下的输出与输入多稳态问题,分析了烧焦罐式高效再生催化裂化反应-再生系统在两种条件下随着CO助燃剂添加量变化时的多稳态分布。在反应温度开环条件下,因再生温度与反应温度的耦合程度较低,使系统移热曲线呈单调递增,导致了系统出现3个稳态操作点。在反应温度闭环控制条件下,提升管反应器和再生器间热反馈机制发生改变,由于再生剂循环量可以作为额外的自由度对再生温度和反应温度之差进行补偿,再生器和提升管反应器的耦合程度增强,使得系统只会在助燃剂添加量极低时才会出现多个稳态点,而在基准操作条件下只有一个稳态点,规避了系统在提升管反应温度开环时的多个稳态点的问题。     

Attention LSTM network identification method based on factory data

The control system of chemical enterprises is becoming more and more complex, and identifying the controlled object model is the primary task of automatic control and optimization design. In view of the problem that most chemical process identification experiments need to apply test signals to the process, which may lead to production interruption or safety accidents, a long short-term memory(LSTM) nonlinear dynamic model identification algorithm combined with attention mechanism is proposed to adapt to plant time series data with characteristics of high dimension, strong coupling and nonlinearity. Based on LSTM model, the algorithm considers the importance of the input variables to the target variables, pays more attention to the key features that affect the output results in the input sequence, and improves the generalization ability of the LSTM model. The LSTM network model based on the daily operation data of the plant can be used as the digital virtual device of the identified object, and the local linear model can be identified offline on the virtual device by using the designed test data. The identification experiments on Tennessee-Eastman (TE) process verify the effectiveness of this method.     

基于工厂数据的注意力LSTM网络辨识方法

化工企业控制系统日益复杂,辨识被控对象模型是自动控制和优化设计的首要任务。针对化工过程多数辨识实验需要对过程施加测试信号,可能导致生产中断或引发安全事故的问题,利用长短时记忆（long short-term memory,LSTM）网络对含高维度、强耦合、非线性等特点的工厂时序数据具有的强适应性的特点,提出一种结合注意力机制思想的LSTM非线性动态模型辨识算法。该算法在LSTM模型基础上考虑输入变量对目标变量的重要性,为输入序列中影响输出结果的关键特征分配更多注意力,提高了LSTM模型的泛化能力。基于工厂日常运行数据构建LSTM网络模型可作为被辨识对象的数字化虚拟装置,利用人工测试信号在虚拟装置上离线辨识局部线性模型。在Tennessee-Eastman（TE）过程上的辨识实验验证了本文方法的有效性。     

FEEDBACK LINEARIZATION DESIGNS FOR UNCERTAIN NONLINEAR TIME-DELAY PROCESSES: A COMPARATIVE STUDY

This work concerns the phenomena in which the feedback linearization control is applied to uncertain nonlinear time-delay processes. Under the I/O linearization algorithm, both nonlinear controllers are used to stabilize the closed-loop system with transformed delay inputs. When the effect of input perturbations can converge to zero or asymptotically vanish, these nonlinear feedback designs with only an adjustable parameter can directly improve the tracking performance. The simple linearizing controller can directly regulate the system output at unstable operating point. Combined with deadtime compensation the nonlinear predictive controller with the aid of appropriate state prediction is valid for the real process in the presence of large time delay. Finally, via computer simulation and test of control ability of both feedback control designs the useful comparative results are presented.     

An Efficient Measure for Quantification of Nonlinearity in Chemical Engineering Processes Based on I/O Steady-State Loci

Nonlinearity is virtually ubiquitous in chemical engineering plants, and assessing the degree of nonlinearity involved in a process is of special interest for process control purposes. In this paper, we introduce a simple nonlinearity measure to quantify the extent of nonlinearity in a dynamic system based on its normalized steady-state input/output loci. Our nonlinearity measure obviates the limitations of previous metrics in terms of computational effort and correct identification of highly nonlinear relationships. The measure is satisfactorily applicable to various I/O relationships—from truly linear to sinusoidal, for instance. In order to illustrate the efficiency of the proposed measure, four numerical examples concerning a double-effect evaporator, a jacketed continuously stirred tank reactor (CSTR) with an irreversible reaction, a CSTR involving van de Vusse reactions, and the Henson–Seborg–Pottmann CSTR are presented.     

草甘膦生产中母液浓缩系统的工艺改造

本文介绍了草甘膦母液浓缩系统的工艺改造。用自然循环蒸发器取代原设计的强制循环蒸发器；用自排不凝性气体大气冷凝器取代真空泵以维持系统的真空度。经过改造，全系统具有流程简单合理、蒸发速率快，操作简便及节能显著等优点。