基于PBM的L-谷氨酸粒度分布控制优化

针对β型L-谷氨酸冷却结晶过程,为获取期望粒度分布,采用特征曲线法（MOCH）来建立关于粒度相关生长率的种群平衡方程（PBE）,然后通过对种群平衡模型（PBM）参数辨识后确定最优过饱和度及控温曲线。由于辨识模型参数的目标函数具有非线性和非凸型性,因而采用少量经济性的批量冷却结晶实验,结合图像分析晶种和产品粒度分布得到的统计数据,拟合模型参数。根据实际要求的结晶过程时间,为达到目标粒度分布,通过优化结晶过程的过饱和度获得最优调温曲线,实现基于恒定过饱和度的晶体生长过程优化控制。实验结果表明通过优化的控温曲线,实现了基于最优过饱和度控制的期望目标粒度分布。     

基于PBM的L-谷氨酸粒度分布控制优化

针对β型L-谷氨酸冷却结晶过程,为获取期望粒度分布,采用特征曲线法(MOCH)来建立关于粒度相关生长率的种群平衡方程(PBE),然后通过对种群平衡模型(PBM)参数辨识后确定最优过饱和度及控温曲线。由于辨识模型参数的目标函数具有非线性和非凸型性,因而采用少量经济性的批量冷却结晶实验,结合图像分析晶种和产品粒度分布得到的统计数据,拟合模型参数。根据实际要求的结晶过程时间,为达到目标粒度分布,通过优化结晶过程的过饱和度获得最优调温曲线,实现基于恒定过饱和度的晶体生长过程优化控制。实验结果表明通过优化的控温曲线,实现了基于最优过饱和度控制的期望目标粒度分布。     

基于分布函数矩的聚合物分子量分布预测控制

聚合物分子量分布（molecular weight distribution,MWD）是聚合产物重要的质量指标,由于无法在线测量,使得直接质量控制至今难以实现。在利用Legendre正交多项式组合神经网络建立聚合反应分子量分布灰箱模型的基础上,把MWD这个三维空间控制问题解构为以其矩向量为特征的二维时间域的控制问题,提出了通过控制分布的矩值实现分子量分布的预测控制方法。目标函数以矩值误差平方和为基础,考虑控制变量的约束条件,同时引入可测低阶矩的修正项,使得分子量分布的部分闭环反馈控制得以实现。该方法以实验室规模的苯乙烯聚合过程为对象进行了仿真建模与控制研究,获得良好的控制效果,证明了方法的有效性。     

基于PLC的浇模型冰激凌控制系统

以西门子S7200 CPU224为核心,浇模型冰激凌机为控制对象,通过采用SCR顺序控制图的设计方法编写程序,利用触摸屏和计算机,以达到实时监控冰激凌生产为目标,实现其生产全过程控制。重点阐述了系统控制原理,提出了通过控制过热度的策略对制冷剂流量进行PID智能控制方法,给出了PLC控制变频器和制冷机组的实现方法、主要数据点表、生产的实时监控图和历史趋势曲线,达到理想的控制效果。     

不对称系统输入反馈预测控制及可控性分析

研究了生产过程中一类特殊的非线性系统,不对称系统的预测控制方法。在正反方向的控制作用下,这类系统表现出不对称动态特性。此类系统的理论研究特别是控制方法研究十分有限。针对基于正反方向上的线性模型设计的正反模型预测控制方法与传统预测控制方法在结构上的差异进行了分析,说明了由于这种结构上的差异可能导致的模型失配及控制效果不佳,然后采用输入反馈方法对不对称系统正反模型预测控制方法进行了修正,将实际施加给对象的控制作用通过反馈形式纳入到下一步正反方向控制律的求解中。并且在无约束的条件下分析了正反模型预测控制方法的可控性。最后通过pH值控制的仿真实验验证了此两种不对称系统正反模型预测控制方法的控制效果。     

污水处理过程的多目标多模型预测控制方法研究

针对污水处理过程节能降耗问题以及污水处理过程的高度非线性、强耦合、不确定性等特点.以基于活性污泥2号模型ASM2的A2/O污水处理过程为研究对象,提出了污水处理过程的多目标多模型预测控制方法.该方法首先采用聚类-PLS方法建立污水过程的多模型预测模型,然后构建了包含出水水质区间控制和经济性能指标的多目标优化结构的预测控制策略.仿真结果表明,与设定值预测控制方法相比,多目标优化预测控制策略在保证出水水质的前提下,能有效地节约能耗费用.     

基于控制规则调整的自适应模糊控制器

工业对象大多是具有非线性、大时滞、高阶次的复杂对象 ,常规的控制方法往往难以适应这些对象的变化。文中提出了一种自适应模糊控制器 ,它能在控制过程中不断调整和修改控制规则 ,以适应对象和环境的变化。对控制方法与常规PID控制、Smith预估控制、基本模糊控制进行了仿真比较。仿真曲线表明 ,自适应模糊控制器的控制性能明显好于其它 3种控制方法     

苯酐生产中固定床反应器模型的研究

固定床气相反应器是苯酐生产中的主要设备,通过对比苯酐生产中不同固定床气相反应器操作法的优缺点,选择了低空气比率法作为固定床气相反应器研究对象。引入苯酐固定床气相反应器的反应动力学模型、转化率及温度分布模型、固定床反应器传热系数模型,并通过计算机对数学模型进行模拟计算得到数据,绘图得到相关曲线。通过对曲线进行分析,得出了与实际数据相符合的结论,该模型的建立不但可以优化固定床气相反应器的设计、生产,而且对苯酐生产中固定床气相反应器控制起到了指导作用。     

基于小波神经网络的通用模型控制

针对通用模型控制要求被控对象有显式解的局限性,应用小波神经网络来建立非线性被控对象的逆模型.再结合通用模型控制算法,将非线性过程模型直接嵌入到控制器中,来实现对被控对象的逆控制.其参考轨迹是一条典型的二阶曲线,控制器参数具有明显的物理意义,且易于整定.仿真结果验证了该控制策略的有效性.     

基于模糊模型参考学习控制的焦炉温度控制

焦炉是一个大惯性、纯滞后、非线性、时变和参数分布的系统,很难对其建立精确的数学模型.现有的线性系统控制方法对焦炉的温度控制存在精度不高、不能适应工况变化等问题.而模糊控制又因规则和隶属度函数的粗糙性导致系统在稳态时出现振颤,模型参考自适应控制需要对被控对象进行阶次辨识,而且算法复杂.针对上述问题,提出一种新的控制策略,使用模糊模型参考学习控制算法来对焦炉的温度进行控制,该方法结合模糊控制和自适应控制的优点,通过在线学习算法适应工况变化,提高了控制精度.对焦炉简化模型进行的仿真实验结果表明,系统具有良好的动态和稳态性能,有效减少了炉温波动,具有一定的推广应用价值.     

EFFECTIVE IMPLEMENTATION OF CONSERVATIVE MODEL BASED CONTROL ALGORITHM

Recently a digital control algorithm, known as Conservative Model Based Controller (CMBC) with superior performance compared to many other currently popular controllers was discussed in the literature. It provides offset-free performance; however a large number of terms will have to be included in the algorithm to achieve this. This paper describes two modifications to ensure zero offset with limited number of terms in the algorithm. These modifications are evaluated through simulation by considering a few typical first order with dead time (FODT) processes. Both modifications provide offset-free performance, and either could be employed depending on the nature of the process response data on hand.     

Two-layer model predictive control for chemical process model with integrating controlled variables

This paper proposes an overall solution to the two-layer model predictive control (MPC) for the integrating controlled variables in the process model. The scheme includes three modules, that is, the open-loop prediction module, the steady-state target calculation (SSTC) module, and the dynamic control module. Based on the real-time output measurements and past inputs, the open-loop prediction module predicts the future outputs in the presence of disturbances. The economic optimization of SSTC is comprised of the feasibility stage and the economics stage, considering constraints of multi-priority ranks. The dynamic control module receives the steady-state targets from SSTC and calculates the control signals. The optimization problems of SSTC and dynamic control operate with the same frequency. This overall method guarantees the consistency of three modules with respect to the model, the constraints, and the targets. The simulation example illustrates that steady-state targets are adjusted dynamically after the occurrence of disturbances, and offset-free control is achieved.     

Application of offset-free Koopman-based model predictive control to a batch pulp digester

This work presents the application of a Koopman operator approach to a batch pulp digester. To manufacture paper products with desired properties, it is essential to consider both macroscopic and microscopic attributes of pulp. However, the complexity of multiscale dynamics of pulping processes hinders proper control system design. Therefore, we utilize extended dynamic mode decomposition (EDMD), which is based on Koopman operator theory, to derive a global linear representation of a pulp digester. Then, we design an offset-free Koopman-based model predictive control (KMPC) system to regulate the Kappa number and cell wall thickness (CWT) of fibers at a batch pulp digester while compensating for the influence of plant-model mismatch and disturbance during operation. The numerical experiments demonstrate that the linear state-space model, obtained via EDMD, properly predicts the behavior of a batch pulp digester, and the designed offset-free KMPC system successfully drives the Kappa number and CWT to set-point values.     

A multiple model,state feedback strategy for robust control of non-linear processes

The major limitation of reported multiple model approaches is that robustness against process/controller disturbances cannot be addressed for processes consisting of hybrid stable/unstable regimes, or with chaotic dynamics. In this paper, a significantly modified multiple model approach is developed to achieve robust control with global stability. The new advances include: (1) stabilization of open-loop unstable plants using a state feedback strategy, (2) incorporation of an adjustable pre-filter to achieve offset-free control, (3) implementation of a Kalman filter for state estimation, and (4) connection of the multiple model approach with non-linear model predictive control to achieve a precise control objective. The improved controller design method is successfully applied to two non-linear processes with different chaotic behaviour. Compared with conventional methods without model modifications, the new approach has achieved significant improvement in control performance and robustness with a dramatically reduced number of local models.     

Conditions for offset elimination in state space receding horizon controllers: A tutorial analysis

An offset-free control is one that drives the controlled outputs to their desired targets at steady state. In the linear model predictive control (MPC) framework, the elimination of steady-state offset may seem a little obscure, since the closed-loop optimization tends to hide the integral action. Theoretically, implementing a well-posed optimization problem and having unbiased steady-state predictions are sufficient conditions to eliminate the output offset. However, these basic conditions are not always achieved in practical applications, especially when state-space models are used to perform the output predictions. This paper presents a detailed practical analysis of the existing strategies to eliminate offset when using linear state-space models with moderated uncertainties. The effectiveness of these strategies is demonstrated by simulating three different control problems: a linear SISO system where the effect of using the estimation of the control variable is highlighted, a continuous stirred tank reactor (CSTR) with non-linear dynamics and the consequent model uncertainty and, a 2 × 2 system representing a distillation column that verifies the consistency of previous results and extends the conclusions to higher dimension systems.     

Nonlinear internal model control strategy for neural network models

A nonlinear internal model control (NIMC) strategy based on neural network models is proposed for SISO processes. The neural network model is identified from input—output data using a three-layer feedforward network trained with a conjugate gradient algorithm. The NIMC controller consists of a model inverse controller and a robustness filter with a single tuning parameter. The proposed strategy includes time delay compensation in the form of a Smith predictor and ensures offset-free performance. Extensions for measured disturbances are also presented. The NIMC approach is currently restricted to processes with stable inverses. Two alternative implementations of the control law are discussed and simulations results for a continuous stirred tank reactor and pH neutralization process are presented. The results for these two highly-nonlinear processes demonstrate the ability of the new strategy to outperform conventional PID control.     

An optimal filter based MPC for systems with arbitrary disturbances

In this study, a linear model predictive control (MPC) approach with optimal filters is proposed for handling unmeasured disturbances with arbitrary statistics. Two types of optimal filters are introduced into the framework of MPC to relax the assumption of integrated white noise model in existing approaches. The introduced filters are globally optimal for linear systems with unmeasured disturbances that have unknown statistics. This enables the proposed MPC to better handle disturbances without access to disturbance statistics. As a result, the effort required for disturbance modeling can be alleviated. The proposed MPC can achieve offset-free control in the presence of asymptotically constant unmeasured disturbances. Simulation results demonstrate that the proposed approach can provide an improved disturbance õrejection performance over conventional approaches when applied to the control of systems with unmeasured disturbances that have arbitrary statistics.     

Model based robust control approach for batch crystallization product design

The paper presents a novel control approach for crystallization processes, which can be used for designing the shape of the crystal size distribution to robustly achieve desired product properties. The approach is based on a robust optimal control scheme, which takes parametric uncertainties into account to provide decreased batch-to-batch variability of the shape of the crystal size distribution. Both open-loop and closed-loop robust control schemes are evaluated. The open-loop approach is based on a robust end-point nonlinear model predictive control (NMPC) scheme which is implemented in a hierarchical structure. On the lower level a supersaturation control approach is used that drives the system in the phase diagram according to a concentration versus temperature trajectory. On the higher level a robust model-based optimization algorithm adapts the setpoint of the supersaturation controller to counteract the effects of changing operating conditions. The process is modelled using the population balance equation (PBE), which is solved using a novel efficient approach that combines the quadrature method of moment (QMOM) and method of characteristics (MOC). The proposed robust model based control approach is corroborated for the case of various desired shapes of the target distribution.     

控制图在复极电解槽控制中的应用

根据离子膜制碱工艺的特点，与控制图的应用理论相结合，寻找控制对象并选择控制图的类型。通过选择合适的工艺参数，使用控制图的数据处理技术对实际数据进行处理，绘制出控制图，利用参数波动符合正态分布的特点对控制图的波动情况进行分析，用来诊断电解槽运行的受控情况。     

燃煤锅炉煤中汞的迁移和分配规律研究

建立了固体、液体中汞的直接测定方法。采用直接测汞法,样品无需任何消解,无论固体样品还是液体样品均可用汞原子蒸气在253.65 nm处的特征吸收进行测定,方法灵敏度高、简便、快速、准确。通过对某燃煤锅炉装置所有排放污染物中汞的分析,确定了燃煤锅炉煤中汞的迁移和分配规律,为汞污染治理提供了技术支持。