瓶用聚酯装置酯化段软测量模型及参数估计

为开发瓶用聚酯装置酯化段先进控制软件 ,从工程角度出发分析研究了对苯二甲酸与乙二醇直接酯化过程 ,建立了简化有效的酯化过程主、副反应动力学模型。结合无约束非线性最优化方法 ,利用现场分析数据对模型进行了参数估计和校验 ,仿真结果表明所建模型是合理和有效的     

On Bayesian analysis of nonlinear continuous‐time autoregression models

Abstract. This article introduces a method for performing fully Bayesian inference for nonlinear conditional autoregressive continuous‐time models, based on a finite skeleton of observations. Our approach uses Markov chain Monte Carlo and involves imputing data from times at which observations are not made. It uses a reparameterization technique for the missing data, and because of the non‐Markovian nature of the models, it is necessary to adopt an overlapping blocks scheme for sequentially updating segments of missing data. We illustrate the methodology using both simulated data and a data set from the S & P 500 index.     

基于状态校正的积分对象预测控制算法及其应用

针对具有大滞后、强动态干扰的积分特性对象,提出了基于一阶状态校正的预测控制算法。算法考虑预测状态与其对应的实际测量值偏差的积分作用,在一阶维度上施加状态校正,从而大幅提高控制器的预测准确性与应对模型失配的能力。通过仿真对比验证了状态校正算法的有效性。本算法已实际应用于某炼厂闪蒸罐的液位控制,取得了较好的控制效果。     

Achieving state estimation equivalence for misassigned disturbances in offset‐free model predictive control

Integrated white noise disturbance models are included in advanced control strategies, such as Model Predictive Control, to remove offset when there are unmodeled disturbances or plant/model mismatch. These integrating disturbances are usually modeled to enter either through the plant inputs or the plant outputs or partially through both. There is currently a lack of consensus in the literature on the best choice for the structure of this disturbance model to obtain good feedback control. We show that the choice of the disturbance model does not affect the closed‐ loop performance if appropriate covariances are used in specifying the state estimator. We also present a data based autocovariance technique to estimate the appropriate covariances regardless of the plant's true unknown disturbance source. The covariances estimated using the autocovariance technique and the resulting estimator gain are shown to compensate for an incorrect choice of the source of the disturbance in the disturbance model. © 2009 American Institute of Chemical Engineers AIChE J, 2009     

Nonlinear stochastic modeling to improve state estimation in process monitoring and control

State estimation from plant measurements plays an important role in advanced monitoring and control technologies, especially for chemical processes with nonlinear dynamics and significant levels of process and sensor noise. Several types of state estimators have been shown to provide high‐quality estimates that are robust to significant process disturbances and model errors. These estimators require a dynamic model of the process, including the statistics of the stochastic disturbances affecting the states and measurements. The goal of this article is to introduce a design method for nonlinear state estimation including the following steps: (i) nonlinear process model selection, (ii) stochastic disturbance model selection, (iii) covariance identification from operating data, and (iv) estimator selection and implementation. Results on the implementation of this design method in nonlinear examples (CSTR and large dimensional polymerization process) show that the linear time‐varying autocovariance least‐squares technique accurately estimates the noise covariances for the examples analyzed, providing a good set of such covariances for the state estimators implemented. On the estimation implementation, a case study of a chemical reactor demonstrates the better capabilities of MHE when compared with the extended Kalman filter. © 2010 American Institute of Chemical Engineers AIChE J, 2011     

非线性系统的一种智能PID控制算法及应用

讨论了一种基于专家知识的智能 Ｐ Ｉ Ｄ控制算法。根据专家知识与现场经验,实时修正 Ｐ Ｉ Ｄ 参数,并根据系统响应的在线识别进行知识调整。两个具有明显非线性时变特性对象的仿真结果表明,该算法具有良好的控制特性与鲁棒性,可望被改进为一种实时在线的计算机控制策略而加以实施。     

On the tuning of predictive controllers: Impact of disturbances,constraints, and feedback structure

The impact of problem formulation modifications on predictive controller tuning is investigated. First, the proposed tuning method is shown to adapt to disturbance characteristic changes and thus, takes full economic advantage of the scenario. The second topic concerns point‐wise‐in‐time constraints and the impact of constraint infeasibility. Specifically, we shift the tuning question from selection of nonintuitive weighting matrix parameters to that of a few key parameters and results in a rather intuitive trade‐off between expected profit and expected constraint violations. Finally, we show that simple modifications will allow for the consideration of various feedback structures, including computational delay and partial state information. The overall conclusions of the work are that the results of the automated algorithm will help build an intuitive understating of the dynamics of the process and ultimately result in a higher level trade‐off between profit and constraint observance. © 2014 American Institute of Chemical Engineers AIChE J, 60: 3473–3489, 2014     

A superharmonic prior for the autoregressive process of the second‐order

Abstract. The Bayesian estimation of the spectral density of the AR(2) process is considered. We propose a superharmonic prior on the model as a non‐informative prior rather than the Jeffreys prior. Theoretically, the Bayesian spectral density estimator based on it dominates asymptotically the one based on the Jeffreys prior under the Kullback–Leibler divergence. In the present article, an explicit form of a superharmonic prior for the AR(2) process is presented and compared with the Jeffreys prior in computer simulation.     

Model‐based measurement of particle size distributions in layering granulation processes

In this work, we present a method for the online estimation of particle size distributions in layering granulation processes using a methodology known as model‐based measurement or state estimation. After presenting the necessary model equations for two practically relevant processes it is investigated which quantities of the processes have to be measured to estimate the size distribution. For the different processes square‐root unscented Kalman filters (SR‐UKF) are designed and tested in simulations to demonstrate the feasibility of this approach. © 2010 American Institute of Chemical Engineers AIChE J, 2011     

On‐line optimization of free radical bulk polymerization reactors in the presence of equipment failure

An on‐line optimizing control scheme has been developed for bulk polymerization of free radical systems. The effects of random errors, as well as one kind of a major disturbance (heating system failure), have been studied. A model‐based, inferential state estimation scheme was incorporated to estimate, on‐line, the parameters of the model (and thereby, the monomer conversion and molecular weight of the polymer) using experimental data on temperature and viscosity. A sequential quadratic programming technique was used for this purpose. A major disturbance, such as heating system failure, leads to a deteriorated final product unless an on‐line optimal temperature trajectory (history) is recomputed and implemented on the reactor. Genetic algorithm was used for this purpose. It has been found that, if the “sensing” of the major temperature deviation from the optimal value and rectification of the heating system is achieved well in advance of the onset of the Trommsdroff effect, use of a reoptimized temperature history is sufficient to produce the desired product without significantly altering reaction time. However, if such a disturbance occurs late, a single‐shot intermediate addition of an optimal amount of initiator needs to be used in addition to changing the temperature history to produce polymers having the desired properties in the minimum reaction time. Other types of failures can similarly be handled using the methodology developed. © 1999 John Wiley & Sons, Inc. J Appl Polym Sci 71: 2101–2120, 1999