A STUDY OF EXPERIMENTAL DESIGN OPTIMALITY FOR WIENER SYSTEMS

In the context of nonlinear dynamic system identification for Hammerstein systems, Rollins et al. (2003a) studied the information efficiency of the following two competing experimental design approaches: statistical design of experiments (SDOE) and pseudo-random sequences design (PRSD). The focus of this study is the Wiener system and evaluates SDOE against PRS under D-optimal efficiency. Three cases are evaluated and the results strongly support SDOE as the better approach.     

System identification of the human thermoregulatory system using continuous-time block-oriented predictive modeling

This article presents preliminary results of a new research program for identifying predictive models for human thermoregulatory (HT) response using only an individual's attributes and their physical property data to build the model. This program is being developed in phases and this article presents results of the first phase. This initial phase demonstrates that the proposed semi-empirical (i.e., gray-box), continuous-time, block-oriented modeling (BOM) approach [Rollins, et al. 2003. A continuous time nonlinear dynamic predictive modeling method for Hammerstein processes. Industrial and Engineering Chemistry Research 42, 861-872; Bhandari and Rollins, 2003. A continuous-time MIMO Wiener modeling method. Industrial and Engineering Chemistry Research 42, 5583-5595.] is capable of accurately predicting HT response. This ability is demonstrated using real data from literature [Hardy and Stolwijck, 1966. Partitional calorimetric studies of man during exposures to thermal transients. Journal of Applied Physiology 21(6), 1799-1806.] and computer generated data from a HT semi-theoretical model with qualitatively accurate physiological behavior [Wissler, 1963. An analysis of factors affecting temperature levels in the nude human. Temperature—its Measurement and Control in Science and Industry 3(3), 603-612; Wissler, 1964. Mathematical model of the human thermal system. Bulletin of Mathematical Biophysics 26, 147-166.]. A critical strength of the proposed gray-box BOM approach is the use of physically interpretable structures and model coefficients. This article discusses how this strength can be exploited to identify a predictive HT response model for an individual without using environmental chamber data of the individual.     

A coupling model for EGSB bioreactors: Hydrodynamics and anaerobic digestion processes

The aim of this paper is to present a coupling model for calculating both the hydrodynamic and anaerobic digestion processes in expanded granular sludge bed (EGSB) bioreactors for treating wastewaters. The bioreactor is modeled as a dynamic (gas-solid-liquid) three-phase system. An existing set of experimental data of three case studies based on the start-up and operational performance of EGSB reactors is used to adjust and validate the model. A novel parameter, the specific rate of granule rupture, is defined for calculating the biomass transport phenomena. Values around 1 × 10⁻²⁰ dm d² g⁻¹ are calculated for this parameter. Bioreactor performances were analyzed through the main variable profiles such as pH, COD, VFA and VSS concentration. A good agreement was obtained among experimental and predicted values. It seems to indicate that the proposed EGSB model is able to reproduce the main biological and hydrodynamic successes in the bioreactor.     

Improved kernel PCA-based monitoring approach for nonlinear processes

Conventional kernel principal component analysis (KPCA) may not function well for nonlinear processes, since the Gaussian assumption of the method may be violated through nonlinear and kernel transformation of the original process data. To overcome this deficiency, a statistical local approach is incorporated into KPCA. Through this method, a new score variable which was called improved residual in the statistical local approach is constructed. The new variable approximately follows Gaussian distribution, in spite of which distribution the original data follows. Two new statistics are constructed for process monitoring, with their corresponding confidence limits determined by a χ² distribution. Besides of the improvement made on KPCA, the new joint local approach-KPCA method also shows superiority on detection sensitivity, especially for small faults slow changes of the process. The new method is exemplified using a numerical study and also tested in the complicated Tennessee Eastman (TE) benchmark process.     

Dynamic composition estimation for a ternary batch distillation

A novel Kalman estimator has been proposed to provide the estimates of dynamic composition in a ternary batch distillation process operated in an optimal-reflux policy. The estimator is formulated based on a sequence of reduced-order process models representing a whole batch behavior. Therefore, the full-order models are first developed around different pseudo-steady-state operating conditions along batch optimal profiles. Then they reduce their orders to achieve all state observability and controllability by a balanced truncation method. In the estimator scheme, the reduced models as well as relevant covariance matrices of process noise are pre-scheduled and switched according to any desired periods. Four important issues have been studied including selection of a sensor frequency, effects of an integrating step size, a state initialization and a measurement noise. The performances of the reduced estimator have been investigated and compared with those of a conventional nonlinear estimator. Simulation results have demonstrated that the performances of the novel linear estimator are reasonably good and almost identical to the nonlinear estimator in all cases, though the linear estimator performs rather sensitively to the effect of high measurement noise. Nevertheless, it has been found to be applicable to implement in real plants with much lower computation effort, easier state initialization and unrequired a priori knowledge of thermodynamics.     

Integrated FCC riser—regenerator dynamics studied in a fluid catalytic cracking pilot plant

In this paper a dynamic simulator of the fluid catalytic cracking (FCC) pilot plant, operating in the Chemical Process Engineering Research Institute (CPERI, Thessaloniki, Greece), is presented. The operation of the pilot plant permits the execution of case studies for monitoring of the dynamic responses of the unit, by imposing substantial step changes in a number of the manipulated variables. The comparison between the dynamic behavior of the unit and that predicted by the simulator arise useful conclusions on both the similarities of the pilot plant to commercial units, along with the ability of the simulator to depict the main dynamic characteristics of the integrated system. The simulator predicts the feed conversion, coke yield and heat of catalytic reactions in the FCC riser on the basis of semi-empirical models developed in CPERI and simulates the regenerator according to the two-phase theory of fluidization, with a dilute phase model taking account of postcombustion reactions. The riser and regenerator temperature, the stripper and regenerator pressure drop and the composition of the regenerator flue gas are measured on line and are used for verification of the ability of the simulator to predict the dynamic transients between steady states in both open- and closed-loop unit operation. All the available process variables such as the reaction conversion, the coke yield, the carbon on regenerated catalyst and the catalyst circulation rate are used for the validation of the steady-state performance of the simulator. The comparison between the dynamic responses of the model and those of the pilot plant to step changes in the feed rate and preheat temperature reveals the ability of the simulator to accurately depict the complex pilot process dynamics in both open- and closed-loop operation. The dynamic simulator can serve as the basis for the development of a model-based control structure for the pilot plant, alongside its use as a tool for off-line process optimization studies.     

基于JIT-MOSVR的软测量方法及应用

针对传统多模型软测量方法在面对复杂、多变工况时缺少在线更新机制、更新时输出精度降低等问题,提出了一种基于即时学习算法（JIT）的多模型在线软测量方法（MOSVR）。离线阶段首先采用模糊C均值聚类（FCM）对训练数据进行聚类,接着采用SVR建立初始模型集。在线部分以多模型输出作为主要输出,当出现新工况时,通过在线模型更新策略（OSMU）将输出模式切换为JIT,同时多模型集进行在线更新。该方法不仅拥有多模型输出的快速性、精确性,而且在模型更新时通过JIT模式还能保证输出的连续性、稳定性、精确性。最后将该软测量方法进行了数值仿真并运用到乙烷浓度软测量中,验证了该方法的有效性。     

分析化学实验教学改革的探索

以培养创新型、应用性人才为目标,对分析化学实验教学方式进行了改革,在实验教学中实行了累积计分制,增设了开拓学生思维能力的设计型实验和适用于社会需求的开放型实验,激发了学生的创新学习的动力,培养了学生的实践能力,形成了自主、创新、综合提高的教学特色。     

An aggregation model reduction method for one‐dimensional distributed systems

A method for deriving reduced dynamic models of one‐dimensional distributed systems is presented. It inherits the concepts of the aggregated modeling method of Lévine and Rouchon originally derived for simple staged distillation models and can be applied to both spatially discrete and continuous systems. The method is based on partitioning the system into intervals of steady‐state systems, which are connected by dynamic aggregation elements. By presolving and substituting the steady‐state systems, a discrete low‐order dynamic model is obtained. A characteristic property of the aggregation method is that the original and the reduced model assume identical steady states. For spatially continuous systems, the method is an alternative to discretization methods like finite‐difference and finite‐element methods. Implementation details of the method are discussed, and the principle is illustrated on three example systems, namely a distillation column, a heat exchanger, and a fixed‐bed reactor. © 2011 American Institute of Chemical Engineers AIChE J, 2012     

一种非线性系统在线辨识的选择性递推方法

针对非线性系统的在线辨识, 提出了一种选择性递推岭参数极限学习机方法。首先, 推导了岭参数极限学习机模型节点增加的递推算法, 以有效地更新在线模型。其次, 结合训练模型的相对误差, 提出模型节点递推增加的选择性策略, 以限制模型的复杂度, 获得更简单的递推辨识模型。通过一个典型非线性化工过程的在线辨识, 从多方面比较验证了所提出方法的简单有效, 更适合非线性过程的在线辨识。     

即时局部建模在填料塔液泛气速预测的应用

填料塔在工业生产中应用广泛,准确预测填料塔的液泛气速具有重要的应用价值。实际的填料类型多种多样,获取的填料数据也存在差异,单一全局模型的预测效果受到一定的限制。首先给出了岭参数极限学习机模型及其节点增加的递推算法,以有效更新在线模型。结合即时学习方式,提出了局部递推岭参数极限学习机在线建模方法,用于填料塔液泛气速的预测。实验结果表明所提出方法能更充分挖掘数据间的相关信息,预测效果优于相应的全局模型。     

交替充汽方式下双蒸汽蓄热器系统动态特性仿真

为了研究双蒸汽蓄热器系统的动态特性,采用集总参数法建立相应的数学模型,进行直接交替和间接交替两种充汽方式的动态仿真。仿真结果表明：直接交替充汽方式充汽时间减少2.6 s,压力波动更小;相同充汽方式下,汽包压力波动 < 过热蒸汽压力波动 < 供汽母管压力波动;直接交替充汽方式燃油量节省率为7.45%。通过数据对比分析可得：直接交替充汽方式具有充汽速度快、压力波动小、燃油经济性好的优点。     

Integrated design and control under uncertainty: Embedded control optimization for plantwide processes

High performance processes should operate close to design boundaries and specification limits, while still guaranteeing robust performance without design constraint violations. Since design chemical process is operating close to tighter boundaries safely; much attention has been devoted to integrating design and control, in which the design decisions, dynamics, and control performance are considered simultaneously in some optimal fashion. However, rigorous methods for solving design and control simultaneously lead to challenging mathematical formulations which easily become computationally intractable. In an earlier paper of our group, a new mathematical methodology to reduce the combinatorial complexity of integrating design and control was introduced (Malcolm et al., 2007). We showed that substantial problem size reduction can be achieved by embedding control for specific process designs. In this paper, we extend the embedded control methodologies to plantwide flowsheet. The case study for the reactor-column flowsheet will demonstrate the current capabilities of the methodology for integrating design and control under uncertainty.     

Optimal experiment design for nonlinear dynamic (bio)chemical systems using sequential semidefinite programming

Optimal experiment design (OED) for parameter estimation in nonlinear dynamic (bio)chemical processes is studied in this work. To reduce the uncertainty in an experiment, a suitable measure of the Fisher information matrix or variance–covariance matrix has to be optimized. In this work, novel optimization algorithms based on sequential semidefinite programming (SDP) are proposed. The sequential SDP approach has specific advantages over sequential quadratic programming in the context of OED. First of all, it guarantees on a matrix level a decrease of the uncertainty in the parameter estimation procedure by introducing a linear matrix inequality. Second, it allows an easy formulation of E‐optimal designs in a direct optimal control optimization scheme. Finally, a third advantage of SDP is that problems involving the inverse of a matrix can be easily reformulated. The proposed techniques are illustrated in the design of experiments for a fed‐batch bioreactor and a microbial kinetics case study. © 2014 American Institute of Chemical Engineers AIChE J, 60: 1728–1739, 2014     

Nonlinear dynamic process monitoring based on dynamic kernel PCA

Nonlinear dynamic process monitoring based on dynamic kernel principal component analysis (DKPCA) is proposed. The kernel functions used in kernel PCA (KPCA) are profitable for capturing nonlinear property of processes and the time-lagged data extension is suitable for describing dynamic characteristic of processes. DKPCA enables us to monitor an arbitrary process with severe nonlinearity and (or) dynamics. In this respect, it is a generalized concept of multivariate statistical monitoring approaches. A unified monitoring index combined T² with SPE is also suggested. The proposed monitoring method based on DKPCA is applied to a simulated nonlinear process and a wastewater treatment process. A comparison study of PCA, dynamic PCA, KPCA, and DKPCA is investigated in terms of type I error rate, type II error rate, and detection delay. The monitoring results confirm that the proposed methodology results in the best monitoring performance, i.e., low missing alarms and small detection delay, for all the faults.