Multivariate time delay analysis based local KPCA fault prognosis approach for nonlinear processes☆

Currently, some fault prognosis technology occasionally has relatively unsatisfied performance especially for in-cipient faults in nonlinear processes duo to their large time delay and complex internal connection. To overcome this deficiency, multivariate time delay analysis is incorporated into the high sensitive local kernel principal com-ponent analysis. In this approach, mutual information estimation and Bayesian information criterion (BIC) are separately used to acquire the correlation degree and time delay of the process variables. Moreover, in order to achieve prediction, time series prediction by back propagation (BP) network is applied whose input is multivar-iate correlated time series other than the original time series. Then the multivariate time delayed series and future values obtained by time series prediction are combined to construct the input of local kernel principal component analysis (LKPCA) model for incipient fault prognosis. The new method has been exemplified in a sim-ple nonlinear process and the complicated Tennessee Eastman (TE) benchmark process. The results indicate that the new method has superiority in the fault prognosis sensitivity over other traditional fault prognosis methods. ? 2016 The Chemical Industry and Engineering Society of China, and Chemical Industry Press. Al rights reserved.     

Inventory pinch gasoline blend scheduling algorithm combining discrete- and continuous-time models

This work introduces multi-period inventory pinch-based algorithm to solve continuous-time scheduling models (MPIP-C algorithm), a three level method which combines discrete-time approximate scheduling with continuous-time detailed scheduling and with inventory pinch-based optimization of operating states. When applied to gasoline blending, the top level computes optimal recipes for aggregated blends over periods initially delineated by inventory pinch points. Discrete-time middle level uses fixed blend recipes to compute an approximate schedule, i.e. what, when, and how much to produce; it also allocates swing storage and associated product shipments with specific storage. Continuous-time model at the third level computes when exactly to start/stop an operation (blend, tank transfer, shipment). MPIP-C algorithm solves linear or nonlinear problems 2–3 orders of magnitude faster than full-space models.     

Improved continuous-time model for gasoline blend scheduling

This work introduces a reduced-size continuous-time model for scheduling of gasoline blends. Previously published model has been modified by (i) introducing new model features (penalty for deliveries in order to reduce sending material from different product tanks to the same order, product and blender-dependent minimum setup times, maximum delivery rate from component tanks, threshold volume for each blend), (ii) by reducing the number of integer variables, and (iii) by adding lower bounds on the blend and switching costs, which significantly improve convergence. Nonlinearities are introduced by ethyl RT-70 equations for octane blending. Medium-size linear problems (two blenders, more than 20 orders, 5 products) are solved to optimality within one or two minutes. Previously unsolved large scale blending problems (more than 35 orders, 5 product, 2 or 3 blenders) have also been solved to less than 0.5% optimality gap.     

Half order plus time delay (HOPTD) models to tune PI controllers

Methods based on the first‐order plus time delay (FOPTD) model are very popular for tuning proportional‐integral (PI) controllers. The FOPTD model‐based methods are simple and their utility has been proved with many successful applications to a wide range of processes in practice. However, even for some overdamped processes where the FOPTD model seems to be applied successfully, these empirical FOPTD model‐based methods can fail to provide stable tuning results. To remove these drawbacks, a PI controller tuning method based on half‐order plus time delay (HOPTD) model is proposed. Because FOPTD model‐based methods can be applied to higher order processes, the proposed HOPTD model‐based method can be applied to higher order processes as well. It does not require any additional process information compared to the FOPTD model‐based method and hence can be used for overdamped processes in practice, complementing the traditional FOPTD model‐based methods. © 2016 American Institute of Chemical Engineers AIChE J, 63: 601–609, 2017     

Iterative identification of output error model for industrial processes with time delay subject to colored noise

To dealwith colored noise and unexpected load disturbance in identification of industrial processes with time delay, a bias-eliminated iterative least-squares (ILS) identification method is proposed in this paper to estimate the output error model parameters and time delay simultaneously. An extended observation vector is constructed to establish an ILS identification algorithm. Moreover, a variable forgetting factor is introduced to enhance the convergence rate of parameter estimation. For consistent estimation, an instrumental variable method is given to deal with the colored noise. The convergence and upper bound error of parameter estimation are analyzed. Two illustrative examples are used to show the effectiveness and merits of the proposed method.     

Experimental Validation of the Heat and Mass Transfer Model for Convective Drying

The aim of this article is to present a self-consistent mathematical model describing the heat and mass transfer phenomena during the convective drying both in the constant and in the falling drying rate periods. This general model is developed on the basis of the theory of mixtures and the thermodynamics of irreversible processes. The boundary conditions are formulated and the numerical algorithm enabling calculation of the temperature and the drying curves in the two mentioned periods of drying is constructed. In this paper much effort is devoted to the experimental validation of the model. The convective drying of a cylindrical sample made of kaolin was examined both experimentally and numerically for comparison and the distribution of temperature and the drying curves were determined. A very good agreement of the experimental and theoretical results is stated.     

Unbiased identification of continuous-time parametric models using a time-weighted integral transform

A time-weighted integral transform is presented to identify a continuous SISO or MIMO parametric model based on a single dynamic test under open-loop or closed-loop operation. Moving-horizon algorithms are proposed to obtain unbiased estimates of the model parameters. The off-line algorithm in a least-squares form and the on-line algorithm in a recursive form are provided. An effective technique based on pattern recognition is also developed to determine the system order and time delay from observed data in a simple manner. Furthermore, the proposed method can be easily applied as a model reduction technique that results in an ideal model with delay for any specified order.     

Estimation of time-varying time delay and parameters of a class of jump Markov nonlinear stochastic systems

It is a challenging problem to estimate time-varying time delay and parameters, especially for systems subject to disturbances with unknown statistics in measurements. The desirable filter should be sensitive to unmodeled dynamics caused by random changes in time delay and parameters, and also be robust to disturbances. Recently, we proposed a finite-horizon robust Kalman filter (RKF) through designing and simultaneously minimizing the upper bounds of unknown covariances of prediction errors, filtering residuals and estimation errors. Unfortunately, unmodeled dynamics and disturbances must be hypothesized to be zero-mean white noises in the RKF. To cope with more general unmodeled dynamics and/or disturbances, a class of jump Markov stochastic systems (JMSS) subject to unmodeled dynamics and disturbances is considered in this article so that a priori system information, such as the value range of unknown and/or randomly changing parameters, can be introduced. Through combining the RKF with the interacting multiple model (IMM) estimation technique, a RKF/IMM algorithm is proposed for such JMSS. Then it is applied to estimate time-varying time delay and parameters of a continuous stirred tank reactor (CSTR) with sensors subject to Gaussian disturbances with unknown means and/or covariances. The RKF/IMM algorithm is compared with the extended Kalman filter (EKF), the strong tracking filter (STF) and the RKF through computer simulations. The results show that, in the case that measurement disturbances are zero-mean noise with unknown covariances, the RKF/IMM and RKF achieve almost the same accurate estimates, which are superior to those of the STF and EKF; in the case that such disturbances have unknown covariances and time-varying means, only the RKF/IMM remains the ability to estimate time-varying time delay and parameters. Furthermore the RKF/IMM has unique ability to identify the disturbance mean no matter whether it is constant or time-varying. Moreover, the RKF/IMM algorithm is shown having strong robustness against the a priori filter parameters, this may be attractive in practical applications.     

Residual Autocorrelation Distribution in the Validation Data Set

Testing model performance on a data set other than the data set used for estimation is common practice in econometrics, technological stochastic modelling and environmetrics. In this paper, using an ARMAX model, the asymptotic distribution of the residual autocorrelations in the validation data set is given and a χ² test for overall residual incorrelation is considered.     

一种可变步长时延估计方法

推导基于参数模型的自适应时延估计算法,分析提高收敛速度的可变步长方法,针对实际应用,给出一种利用功率因子估值估计实际环境中噪声与信号功率的方法。实验结果表明,基于可变步长的自适应时延估计算法具有更快的收敛速度和更小的稳态均方估计误差。     

Kaye温度验证系统在原料药生产设备验证中的应用

主要描述了Kaye温度验证系统的组成、功能及其在原料药生产设备验证的应用。     

Validation of a Mathematical Model by Studying the Effects of Recirculation of Drying Gases

Drying is a costly and energy-consuming process. Consequently, it is of great practical importance to improve its energy efficiency. Our objective, therefore, is to develop and validate a theoretical drying model by studying the recirculation of drying gases. The results show that the amount of recovered energy over the condenser and the dryer efficiency increase with increased recirculation within our test range, implying an energy-efficient operation. The validation shows that the model correlates well with industrial data collected from a full-scale dryer (diameter 3 m, length 9 m, capacity 5 MW) at a Swedish wood fuel pellet plant. Accordingly, it should be possible to use the model industrially to predict capacity changes and recovered energy when changes in drying gas recirculation are made.     

Dynamic flexibility analysis of chemical reaction systems with time delay: Using a modified finite element collocation method

Chemical reaction systems are often complex dynamic time-delay systems that have to operate successfully in the presence of uncertainties. Under these circumstances, flexibility analysis comes to be much important to the design and operation of time-delay chemical reaction systems. In this work, a modified finite element collocation method was proposed to carry out flexibility analysis of chemical reaction systems with time delay. The proposed method is combined with the linear quadratic regulator (LQR) and Lagrange polynomial for the optimal solution of control variables and state variables respectively. The method is investigated by two typical chemical reaction systems with time delay. All the results demonstrate that the proposed modified finite element collocation method may provide a powerful tool for studying the dynamic flexibility of chemical reaction systems with time delay.     

湿热灭菌柜的性能验证

湿热灭菌柜是一种常见的消毒灭菌装置。该设备性能验证中,要选用合适的灭杀方法、选取适宜的灭菌阶段冷点,并保证空气过滤器的完整性和在线灭菌。     

SDG-based Model Validation in Chemical Process Simulation

Signed direct graph (SDG) theory provides algorithms and methods that can be applied directly to chemical process modeling and analysis to validate simulation models, and is a basis for the development...     

Systematic Strategies in High-Performance Liquid Chromatography Method Development and Validation

This paper concerns the systematic strategies in high-performance liquid chromatography method development and validation. Currently, there is no single source and/or a lack of detailed recommendations on method validation that helps analytical scientists to perform validation in a systematic manner in pharmaceutical industry. Therefore industry depends on the scientist's knowledge and experience to develop simple and efficient methods of analysis. This is why much effort in this paper is focused on the development of validated methods in the pharmaceutical industry. Therefore, guidelines were given for analytical development and validation in this field; the methyl p-hydroxybenzoate and propyl p-hydroxybenzoate was used as model. In addition, a rapid method using HPLC coupled with photdiode array detection was developed and validated for the simultaneous determination of methyl p-hydroxybenzoate and propyl p-hydroxybenzoate in a liquid pharmaceutical sample.     

ON q-CONDITIONED PARTIAL CORRELATIONS

Abstract. In attempting to develop a procedure for fitting linear multiple autoregressive-moving average models to observed data, perhaps the most difficult problem is to achieve a reasonable initial model selection. A recent paper by Jenkins and Alavi suggests, as one possibility, the examination of so-called q -conditioned partial correlations. We show that the sampling properties of these statistics are such as to render them of dubious value for this purpose.     

探讨拉拔对延期体延期时间精度的影响

延期体在延期雷管中起着延时作用,延期时间精度则是体现延期体性能的重要指标之一。通过对延期体燃烧过程的理论分析,寻找影响延期体延期时间精度的因素。并从理论和实验数据出发,探讨拉拔对延期体延期时间精度的影响。     

Nonlinear ARMA models with functional MA coefficients

Abstract. In the present article, we propose and study a new class of nonlinear autoregressive moving‐average (ARMA) models, in which each moving‐average (MA) coefficient is enlarged to an arbitrary univariate function. We first provide a sufficient condition for the existence of the stationary solution and further discuss the moment structure. We investigate the estimation method to the proposed models. The global estimates of parameters and local linear estimates of functional coefficients are obtained by using a back‐fitting algorithm. For testing whether the functional coefficients are some specified parametric forms, a bootstrap test approach is provided. The proposed models are illustrated by both simulated and real data examples.     

工业过程时滞对象辨识方法的改进研究及应用

实施基于IMC的先进控制技术,关键在于对象辨识的准确性.模型参数和状态方程初值同时辨识的方法的提出,提高了模型辨识的准确性,为解决该方法曲线拟合虽好而参数偏差过大的问题,提出了过程时滞对象求解的改进方法.改进方法提高了对象辨识的准确性,仿真实验和实际应用证明了这种方法的有效性.该方法的提出为提高模型辨识的准确性提供了一种新方案.