一种基于状态估计的间歇过程模型实时更新预测控制策略简

Nonlinear model predictive control （NMPC） is an appealing control technique for improving the per- formance of batch processes, but its implementation in industry is not always possible due to its heavy on-line computation. To facilitate the implementation of NMPC in batch processes, we propose a real-time updated model predictive control method based on state estimation. The method includes two strategies： a multiple model building strategy and a real-time model updated strategy. The multiple model building strategy is to produce a series of sim- plified models to reduce the on-line computational complexity of NMPC. The real-time model updated strategy is to update the simplified models to keep the accuracy of the models describing dynamic process behavior. The method is validated with a typical batch reactor. Simulation studies show that the new method is efficient and robust with respect to model mismatch and changes in process parameters.     

Kinetic models analysis

This paper introduces a freeware program for models analysis, which implements a robust method for parameter estimation of nonlinear models, able to detect outliers. A new class of criteria is proposed in order to achieve an optimal experimental design. Finally a strategy for model discrimination is presented. Since this is a critical point, the correct meaning of statistical tests and regions of confidence is proposed. At last, it is explained why it is better to avoid any approach that makes use of probability to discriminate among models.     

基于适应策略差分进化算法的化工反应动力学参数估值

为了更准确地通过煤烟气成分推测汞元素氧化程度,建立精确的汞氧化过程动力学模型至关重要。目前已有的汞氧化过程动力学模型中存在一些难以确定的未知参数,为了确定模型中这些参数的最优值,本文提出一种改进的适应策略差分进化算法（ASDE）。该算法引入变异策略、缩放因子（F）和交叉参数（CR）候选集合,同时为集合中每个候选参数赋予一定的选择概率。在进化搜索过程中,以历史成功搜索信息为基础,实时更新每个候选集合中各参数对应的选择概率,并根据选择概率自适应为下一时刻进化群体中每个个体分配变异策略和对应控制参数。将改进算法用于汞氧化过程动力学参数估值问题,实验结果显示,相对其他6种算法,改进算法ASDE求解得到模型更加接近实际,是一种求解化工反应动力学参数估值问题的有效方法。     

Soft Sensor design for a Topping process in the case of small datasets

In this paper, a new strategy to cope with the identification of nonlinear models of industrial processes, when a limited number of experimental data is available, is proposed. The approach is intended to improve the generalization capabilities of the model and it is based on the integration of bootstrap resampling, noise injection and neural model stacking. A number of algorithms to stack the first level neural models are also compared. The method proposed has been applied to develop a Soft Sensor for the estimation of the Freezing Point of Kerosene in an atmospheric distillation unit (Topping) working in a refinery in Sicily, Italy. The improvements obtained thanks to the strategy proposed, with respect to a classical neural model, are shown in the paper.     

An adaptive strategy for solving kinetic model concomitant estimation — Reduction problems

In order to simultaneously estimate the parameters and to reduce a complex kinetic model, an adaptive strategy which combines effective adaptive random search (ARS) and statistical ridge analysis steps is developed. As demonstrated, this strategy can save computational time because the estimation is not repeated with each reduced model. The use of ARS is preferred for highly nonlinear models and cases having multiple parameter constraints, guaranteeing reliability for interactively obtaining the global reduced model parameter solution.     

Offset‐Free Inferential Feedback Control of Distillation Compositions Based on PCR and PLS Models

An offset‐free inferential feedback control strategy for distillation composition control using principal component regression (PCR) and partial least squares (PLS) models is presented in this paper. PCR and PLS model based software sensors are developed from process operational data so that the top and bottom product compositions can be estimated from multiple tray temperature measurements. The PCR and PLS software sensors are then used in the feedback control of the top and bottom product compositions. With this strategy the problem of substantial time delay in composition analyzer based control and of substantial bias in single tray temperature control can be overcome. A practically very important issue in software sensor based feedback control is that static control offsets often exist due to a static estimation bias, especially when the process operating condition changes. A technique for eliminating the static estimation bias and the resulting static control offsets through mean updating of process measurements is proposed in this paper. Applications to a simulated methanol‐water separation column demonstrate the effectiveness of this control strategy.     

Kinetic parameter estimation from TGA: Optimal design of TGA experiments

This work presents a general methodology to determine kinetic models of solid thermal decomposition with thermogravimetric analysis (TGA) instruments. The goal is to determine a simple and robust kinetic model for a given solid with the minimum of TGA experiments. From this last point of view, this work can be seen as an attempt to find the optimal design of TGA experiments for kinetic modelling. Two computation tools were developed. The first is a nonlinear parameter estimation procedure for identifying parameters in nonlinear dynamical models. The second tool computes the thermogravimetric experiment (here, the programmed temperature profile applied to the thermobalance) required in order to identify the best kinetic parameters, i.e. parameters with a higher statistical reliability. The combination of the two tools can be integrated in an iterative approach generally called sequential strategy. The application concerns the thermal degradation of cardboard in a Setaram TGA instrument and the results that are presented demonstrate the improvements in the kinetic parameter estimation process.     

Precise parameter estimation for chemical batch reactions in heterogeneous medium

A sequential experimental strategy for precise parameter estimation has been used in the case of liquid-liquid dispersions in batch-stirred tank reactors where slow chemical reactions take place. The mathematical model for a batch reaction in a stirred tank reactor is formulated as a system of non-linear differential equations standing for the mass balance of each component. Physical kinetic parameters and chemical kinetic parameters which arise from this model are estimated simultaneously. The estimation problem is posed as a weighted least squares problem and solved by using a standard Levenberg-Marquardt algorithm. In this work, we intend to show how it is possible to develop efficient experimental design strategies that lead to an accurate estimation of the parameters involved in phenomenological models and most particularly in kinetic models. Three design criteria for designing the experiments have been employed in order to increase the precision on the parameter estimates of the model. A standard non-linear sequential quadratic programming method ensures the determination of the operating conditions which define the experimental design. The well-known alkaline hydrolysis of esters in aqueous phase has been treated as a numerical application example.     

Sequential design including initial experimental runs for a kinetic study of propylene oxidation

A sequential design strategy for selecting experimental runs to obtain model discrimination and precise parameter estimation is tested via a simulation study of propylene oxidation kinetics. The strategy is used to design all runs including the preliminary ones which were arbitrarily chosen by earlier researchers. To design initial runs, crude initial parameter guesses may be used in the rival models until least squares estimates can be calculated. Even under conditions of very bad initial guesses and high error variances, this procedure selects whichever model is the correct one and estimates with precision its parameters, in fewer runs than previously reported.     

自适应混合粒子群约束优化算法及其在软测量模型参数估计中的应用

对于软测量模型参数估计问题, 针对传统梯度法求解非线性最小二乘模型时依赖初值、需要追加趋势分析进行验证和无法直接求解复杂问题的缺陷, 提出将参数估计化为约束优化问题, 使用混合优化算法求解的新思路。为此提出一种自适应混合粒子群约束优化算法(AHPSO-C)。在AHPSO-C算法中, 为平衡全局搜索(混沌粒子群)和局部搜索(内点法), 引入自适应内点法最大函数评价次数更新策略。对12个经典测试函数的仿真结果表明, AHPSO-C是求解约束优化问题的一种有效算法。将算法用于淤浆法高密度聚乙烯(HDPE)串级反应过程中熔融指数软测量模型参数估计, 验证了方法的可行性与优越性。     

In situ particle size estimation for crystallization processes by multivariate image analysis

Crystal size estimation from in situ images has received attention recently as a means to estimate product properties in real-time. In this paper, an automated image analysis strategy that combines classical image analysis techniques with multivariate statistics has been developed for online analysis of in situ images from crystallization process. The strategy introduces a novel image segmentation step based on information extracted from multivariate statistical models. Experimental results for batch cooling crystallization of monosodium glutamate show that the strategy effectively extracts crystal size and shape information from in situ images. The robustness and efficiency of the method has been established by comparing its performance with those obtained by manual analysis of the images. The method yields reasonably good estimates of particle length and is also fast enough to provide online measurements for the purpose of online optimization and control of a typical crystallization process.     

Calibration of adhesion models based on the extended Kalman filtering

ABSTRACT

This paper presents a strategy to be used for the calibration of adhesion models using a modified extended Kalman filter algorithm. A constitutive model coupling unilateral contact, adhesion and damage is considered for the analysis. The effect of the uncertainties in the constitutive adhesion parameters onto the adhesion reaction force is performed considering four uncertain scenarios. Furthermore, the effect of the speed in the loading-unloading of the system is also taken into account for these analyses. The parameter estimation strategy is assessed considering noise-corrupted synthetic data, two loading profiles and two possible measurement scenarios. The results indicate the key-role played by the sensitivity analysis when estimating the constitutive adhesion parameters and highlights the low sensitivity of system responses with respect to the viscous parameter when compared to the elastic parameter and also to the parameter associated with the energy of decohesion.     

Measurement of eddy diffusivity in bubble column and validation based on the intermittency models

This paper discusses methods for the estimation of eddy diffusivity using the instantaneous velocity-time data measured in a bubble column reactor. In the first method, the analysis uses the eddy isolation methodology with a correction for the bubble-beam path interruption in the data. The correction is estimated from the observation of the time-varying data acquisition rate in the time series. The method is oblivious to the type of anemometer used for the data acquisition and is useful for all kinds of multiphase measurements. For validation of the results, we have proposed a strategy based on the synergistic combination of energy spectrum and the intermittency models for revealing different stages in the turbulent cascades. The method uses the actual scales in the cascade for the estimation of eddy diffusivity and hence such a combination has resulted in a robust validation tool. The comparison of the estimations based on the standard k-? model and integral length scales is also discussed.     

Bootstrap-based bandwidth choice for log-periodogram regression

Abstract.  The choice of the bandwidth in the local log-periodogram regression is of crucial importance for estimation of the memory parameter of a long memory time series. Different choices may give rise to completely different estimates, which may lead to contradictory conclusions, for example about the stationarity of the series. We propose here a data-driven bandwidth selection strategy that is based on minimizing a bootstrap approximation of the mean-squared error (MSE). Its behaviour is compared with other existing techniques for optimal bandwidth selection in a MSE sense, revealing its better performance in a wider class of models. The empirical applicability of the proposed strategy is shown with two examples: the widely analysed in a long memory context Nile river annual minimum levels and the input gas rate series of Box and Jenkins.     

A Stochastic Approximation Algorithm for the Adaptive Control of Time Series Following Generalized Linear Models

A recursive estimation method for time series models following generalized linear models is developed in two ways. The estimation procedure, suitably modified, gives rise to a stochastic approximation scheme. We use the modified estima-tion procedure to illustrate a connection between control theory and generalized linear models by employing a logistic regression model.     

Multiscale modelling strategy for structured catalytic reactors

Structured reactors, and especially arranged reactors, are multiscale systems, in which the design on the small scale essentially determines the macroscopic reactor behaviour. In the present contribution, we propose a modelling strategy for the design and optimisation of an arranged flat-bed reforming reactor. It consists of three model levels, each describing the structure of the reactor on a different scale and detail level. The elements of this hierarchy are consistent and compatible, which means that results obtained with one model can be transferred to the other models. The possible applications of this model hierarchy range from estimation of pressure drop coefficients up to the integration of the structured reactor into a process plant.     

Optimum reparameterization of power function models

Power function models are frequently used to describe rates of adsorption (as in the common Freundlich model) and chemical reaction (for estimation of reaction orders). When power function models are used to fit available experimental data, correlations among obtained parameter estimates are normally very high, which may cause significant numerical problems during the estimation of the model parameters and lead to misinterpretation of the statistical significance of final results. In this work, a reparameterization technique is presented to allow for reduction of parameter correlations in power function models. Afterwards, the two-step parameter estimation procedure [Schwaab, M., Pinto, J.C., 2007. Optimum reference temperature for reparameterization of the Arrhenius equation. Part 1: problems involving one kinetic constant. Chemical Engineering Science 62, 2750-2764; Schwaab, M., Lemos, L.P., Pinto, J.C., 2008b. Optimum reference temperature for reparameterization of the Arrhenius equation. Part 2: problems involving multiple reparameterizations. Chemical Engineering Science 63, 2895-2906.] is used for optimum reparameterization and estimation of uncorrelated model parameters in power function models.     

Iterative model-based experimental design for spherical agglomeration processes

Spherical agglomeration (SA) is a process intensification strategy, which can reduce the number of unit operations in pharmaceutical manufacturing. SA merges drug substance crystallization with drug product wet granulation, reducing capital, and operating costs. However, SA is a highly nonlinear process, thus for its efficient operation model-based design and control strategies are beneficial. These require the development of a high-fidelity process model with appropriately estimated parameters. There are two major problems associated with the development of a high-fidelity process models—(i) selection of the appropriate model corresponding to the underlying process mechanisms, and (ii) accurate estimation of the parameters. This work focuses on the identification of the best fitting model that correlates with experimental observations using cross-validation experiments. Further, an iterative model-based experimental design strategy is developed, which uses D-optimal experimental design criterion to minimize the number of experiments necessary to obtain accurate parameter estimates.     

基于O3/UV法在线COD检测的气体溶解量估计方法

针对臭氧协同紫外方法(O₃/UV)检测化学需氧量(COD)时存在溶解性气体影响测量精度的问题，提出了一种COD检测过程中气体溶解量的估计方法，用于对COD检测模型的补偿。采集不同浓度的COD标准水样在消解过程中的测量数据和实验分析数据，基于PLS-LSSVMs建立溶解氧量和溶解二氧化碳量的估计模型，将模型的输出作为COD检测模型的补偿项。实验结果表明，基于PLS-LSSVMs建立的模型比PLS或者LSSVMs单独建立的模型估计精度高。采用溶解气体量估计模型进行补偿后的O₃/UV法检测COD与国标法测量结果相对误差均小于5%。对提高O₃/UV法检测COD精度具有重要意义。     

Cascade closed-loop optimization and control of batch reactors

In this paper, a cascade closed-loop optimization and control strategy for batch reactors is proposed. Based on the reduction of a physical conservation model a cascade system is developed, which can effectively combine optimization and control to achieve good on-line optimization and tracking performance under the common condition where incomplete knowledge of the reaction system exists. A two-tier estimation scheme using a nonlinear observer for heat production rate and reaction rates is also developed. In the reaction rate estimation, calorimetric information is used. The on-line closed-loop optimization strategy uses a descending horizon dynamic optimization algorithm based on nonlinear programming and an additive unknown disturbance for feedback. A simple adaptive nonlinear tracking system is designed based on the generic model control concept. The efficiency of this strategy is demonstrated through simulations on a batch reactor under various operation conditions, such as noisy measurements, varying initial states and model mismatch.