基于功率谱比对的液氯输送管道泄漏检测方法

针对液氯输送管道泄漏检测难以获取泄漏信号样本用于特征提取和诊断建模的难题,提出了一种基于功率谱比对的无泄漏信号样本管道泄漏检测方法。鉴于相同类型传感器应用于不同介质时表现出相似的输出信号特性,通过分析、比较现有的气体管道泄漏信号和正常信号的频域特征,提出了基于双移动窗和功率谱比对的频域特征提取方法。以液氮模拟输送管道正常信号为目标类数据,在无泄漏信号样本条件下建立了基于SVDD的面向液氯输送管道泄漏检测的诊断模型,实现泄漏的可靠检测。现场模拟检验结果表明：该方法能够有效实现液氯输送管道泄漏检测。     

Periodic solutions in a porous catalyst pellet—homoclinic orbits

The analysis performed as well as extensive numerical simulations have revealed the possibility of the generation of homoclinic orbits as a result of homoclinic bifurcation in the model which describes transport phenomena and chemical reaction in a porous catalyst pellet. A method has been proposed for the development of a special type of diagrams—the so-called bifurcation diagrams. These diagrams comprise the locus of homoclinic orbits together with the lines of limit points bounding the region of multiple steady states as well as the locus of the points of Hopf bifurcation. Thus, they define a set of parameters for which homoclinic bifurcation can take place. They also make it possible to determine conditions under which homoclinic orbits are generated.Two kinds of homoclinic orbits have been observed, namely semistable and unstable orbits. It is found that the character of the homoclinic orbit depends on the stability features of the limit cycle which is linked with the saddle point.Very interesting dynamic phenomena are associated with the two kinds of homoclinic orbits; these phenomena have been illustrated in the solution diagrams and phase diagrams.     

工业PTA溶剂脱水过程动态模拟与分析

Dynamic model for dehydration process of industrial purified terephthalic acid solvent is investigated to understand and characterize the process.A temperature differential expression is presented,which ensures the equation to convergence and short computation time.The model is used to study the dynamic behavior of an azeotropic distillation column separating acetic acid and water using n-butyl acetate as the entrainer.Responses of the column to feed flow and aqueous reflux flow are simulated.The movement of temperature front is also simulated.The comparison between simulation and industrial values shows that the model and algorithm are effective.On the basis of simulation and analysis,control strategy,online optimization and so on can be implemented effectively in dehydration process of purified terephthalic acid solvent.     

Description of flow regime transitions in bubble columns via laser Doppler anemometry signals processing

This work aims at studying the non-linear dynamics and the flow regime transitions in bubble column reactors. For this purpose, various signal processing techniques e.g. frequency analysis, fractal analysis and deterministic chaos analysis have been applied to laser Doppler velocimetry signals. The system considered is a two-dimensional reactor allowing LDV measurements at higher void fractions than in three-dimensional systems. Each signal processing technique presents a specific capacity to describe a regime transition or a feature of the flow structure. Use of these various techniques have highlighted the occurrence of two states in the transition regime and yielded detailed information on the physical mechanisms responsible for these transitions.     

动态转录调控微生物代谢途径研究进展

传统的微生物代谢工程主要是通过过表达或敲除关键基因来实现产物产量最大化,但会造成代谢流失衡,生产效能降低。而对微生物代谢途径进行动态调控可维持细胞生长,平衡代谢流,提高生产效率。本文根据信号分子的来源不同,将微生物在转录水平的动态调控分为两种：一种是在光、温度、化学诱导剂的外源信号刺激下,利用响应该信号的启动子等元件调控下游代谢途径的人工诱导动态调控;另一种为在胞内代谢物水平或细胞密度改变的内源信号感应下,利用启动子、转录因子、核糖核酸开关调节关键基因的细胞自主诱导动态调控。本文同时介绍了转录水平动态调控策略在微生物代谢工程中的应用实例,以期对代谢途径的多个基因实现连续动态表达以及适配表达,有效提高目标产物的产量。     

Context-based recognition of process states using neural networks

Patterns whose interpretation varies across contexts are common in many engineering domains. The resulting one-to-many mapping between patterns and their classes cannot be adequately handled by traditional pattern recognition approaches. This important class of pattern recognition problems although common has not received any attention in chemical engineering domain. In this paper, we show that identification of the state of chemical or biological processes is context dependent. Two types of features are important for context-based pattern recognition—primary features, which determine the class of a pattern, and contextual features, which cannot themselves predict the class, but can improve the effectiveness of the primary features. Process measurements can be used as primary features for identifying the current process state, and the previous process state provides the context in which the primary features have to be interpreted. We also propose a dynamic neural network architecture for context-based operating state identification. Three variations of the architecture, each using a different approach to identify change of context, are described. These are illustrated using two case studies for operating state identification—the startup of a simulated fluidized catalytic cracking unit and operation of a lab-scale fermentation process. A comparison with traditional neural networks reveals that the performance of the proposed context-based pattern recognition architecture is superior in all cases.     

An online method for detection and reduction of chattering alarms due to oscillation

Chattering alarms, which repeatedly and rapidly make transitions between alarm and normal states in a short time period, are the most common form of nuisance alarms that severely degrade the performance of alarm systems for industrial plants. One reason for chattering alarms is the presence of oscillation in process signals. The paper proposes an online method to promptly detect the chattering alarms due to oscillation and to effectively reduce the number of chattering alarms. In particular, a revised chattering index is proposed to quantify the level of chattering alarms; the discrete cosine transform-based method is used to detect the presence of oscillation; two mechanisms by adjusting the alarm trippoint and using a delay timer are exploited to reduce the number of chattering alarms. An industrial case study is provided to illustrate the effectiveness of the proposed method.     

Surrogate-based optimization of a periodic rescheduling algorithm

Periodic rescheduling is an iterative method for real-time decision-making on industrial process operations. The design of such methods involves high-level when-to-schedule and how-to-schedule decisions, the optimal choices of which depend on the operating environment. The evaluation of the choices typically requires computationally costly simulation of the process, which—if not sufficiently efficient—may result in a failure to deploy the system in practice. We propose the continuous control parameter choices, such as the re-optimization frequency and horizon length, to be determined using surrogate-based optimization. We demonstrate the method on real-time rebalancing of a bike sharing system. Our results on three test cases indicate that the method is useful in reducing the computational cost of optimizing an online algorithm in comparison to the full factorial sampling.     

Learning dynamic fault models based on a fuzzy set covering method

The synthesis of a real-time diagnostic expert system to monitor plant performance and identify faults in the event of process failure as well as signal potential failures is described. The crucial features which have been included are system reliability, use of dynamic trends in data and parameters to identify problem as well as the ability to handle complex knowledge. The emphasis here is on how a dynamic simulator is used to incorporate a learning algorithm based on a fuzzy set covering method for formulating a knowledge model of the operational characteristics, which enables debugging and testing of the system to be carried out continuously. The procedure is illustrated by reference to the operation of refinery crude oil distillation columns.     

基于子波分析的过程数据多分辨率分析处理

针对化工过程数据的非平稳性 ,提出了一种基于子波分析的过程数据多分辨率分析处理算法 ,分别对阶跃、尖峰冲激等时变特征进行检测 ,并对白噪声、异常突变等进行有效处理 .以原油分馏过程实际生产数据进行验证 ,结果表明了该方法对过程数据处理的必要性和有效性 .     

Dynamic bayesian approach to gross error detection and compensation with application toward an oil sands process

This paper is concerned with developing an online algorithm for detecting and estimating systematic errors (gross errors) in mass and energy balances from measurement data. This method has its application in diagnosing problems in an oil sands process. Conventional techniques for detecting gross errors presently exist for offline application. The proposed online method entitled Dynamic Bayesian Gross Error Detection (DBGED) is a dynamic Bayesian analogue of traditional gross error detection, and can be considered as a type of Switching Kalman Filter. As such, related topics such as Kalman Filtering, observability and Dynamic Bayesian Inference are discussed. In addition to detecting gross errors, the DBGED also estimates detected gross error magnitudes in real time (as an augmented state variable) so that future measurements can be corrected. When the estimate converges to yield satisfactory prediction errors, gross error estimation is stopped and instruments are corrected with a constant gross error correction term. DBGED performance is demonstrated through a simulation example and an example of an industrial application.     

Dynamische Prozeßsimulation in der industriellen Praxis

Dynamic process simulation in industrial practice. Dynamic process simulation is gaining in importance in process development and plant design. In addition to simulation tools for steady state calculations and optimization, recent years have seen increased development of software tools suitable for dynamic simulation. In particular, dynamic process models have been developed for various unit operations at universities and in industry. Application of the models includes synthesis and analysis of the control structure, start-up and shut down, safety assessments, optimization, and training. In addition, dynamic flowsheet programs analogous to the steady state programs were developed. Wider use of these program systems requires increased flexibility, solving procedures, user interfaces, model library, interfaces to external programs, etc. The present article is intended not only to describe the state of the art and describe some typical applications, but in particular to examine the demands placed on dynamic process simulation and the simulators and to delineate the current limitations of dynamic process simulation from an industrial viewpoint.     

Rigorous design and control of thermally integrated pressure-swing reactive distillation process for isobutyl acetate production considering the effect of column pressures

By studying the effect of pressure on relative volatility, azeotrope composition, and net reaction rate, a rigorous pressure-swing thermally integrated reactive distillation (PST-RD) process for isobutyl acetate synthesis is proposed. The steady-state and dynamic characteristics of the PST-RD process are evaluated based on Aspen and Aspen Dynamic. Steady-state optimization results show that the energy consumption and the total annual cost (TAC) reduced by 42.32% and 34.20% compared with the conventional two-column process, respectively. Subsequently, two control structures of the PST-RD process under a rigorous model are developed and evaluated by large disturbances in throughput and feed composition. Dynamic simulation results show that peak dynamic transients can be effectively reduced by adding a Q/F feedforward control structure. Furthermore, the effective heat integration of high-pressure (HP) and low-pressure (LP) columns can be realized by implementing composition/pressure cascade control when the capacity of the LP column increases.     

A general framework and optimization models for the scheduling of continuous chemical processes

We present a framework for the formulation and solution of continuous process scheduling problems. We focus on modeling transient operations such as startups, shutdowns, and transitions between steady states. First, we show how the concept of processing tasks can be generalized to represent continuous processes, including their transient operations. Second, we discuss how to systematically calculate the parameters describing material consumption/production and utility consumption during transient operations. Finally, we present new mixed-integer linear programming formulations for the scheduling of continuous chemical production.     

动态过程优化算法的计算量比较

A comparison of arithmetic operations of two dynamic process optimization approaches called quasi-sequential approach and reduced Sequential Quadratic Programming (rSQP) simultaneous approach with respect to equality constrained optimization problems is presented. Through the detail comparison of arithmetic operations, it is concluded that the average iteration number within differential algebraic equations (DAEs) integration of quasi-sequential approach could be regarded as a criterion. One formula is given to calculate the threshold value of average iteration number. If the average iteration number is less than the threshold value, quasi-sequential approach takes advantage of rSQP simultaneous approach which is more suitable contrarily. Two optimal control problems are given to demonstrate the usage of threshold value. For optimal control problems whose objective is to stay near desired operating point, the iteration number is usually small. Therefore, quasi-sequential approach seems more suitable for such problems.     

基于吸引子比较法的卧式搅拌床反应器声发射故障诊断

在卧式搅拌床反应器丙烯聚合工艺中,结块是严重影响装置长期稳定运行和产品质量的隐患之一。本文以时域分析的角度,提出了一种基于吸引子比较法的声发射结块检测技术。该方法通过比较当前状态与正常状态的声发射信号,并借助统计量S值对两个时间序列的分布进行检验,从而判断两者是否由同一机理产生。当S<3时,则表明当前状态为正常;当S>3时,则表明反应器内有95%的可能性发生了结块。冷模实验结果表明,在合理选择传感器位置的前提下,通过对原始声发射信号进行消噪、归一化等预处理后,吸引子比较法能够灵敏地检测出卧式搅拌床反应器内粒径分布的细微变化,继而预警结块的发生。     

Identification and prediction of air core diameter in a hydrocyclone by a novel online sensor based on digital signal processing technique

A hydrocyclone is a particle separation device widely used in chemical and allied process industries in which a particle-fluid mixture is injected tangentially creating a strong swirling, recirculation flow. The particle separation efficiency increases by suppressing the air core, so online prediction of air core formation has significant importance in the industrial operations. Performance of hydrocyclone is greatly influenced by shape and size of air core. A novel type of senor technique has been developed to identify and predict the air core diameter from online live data using data acquisition card. The true signal amplitudes change as a function of the time was used with noise interruption for random changes in amplitude. Noises are eliminated by using moving average technique. The slope of the curve is continuously tracked to determine sudden or abrupt change and indicates the formation of air core. It has been observed that a strong air core of diameter 0.95 cm to 1.2 cm was formed during experimentation and matched with predicted values over an entire flow regime. The experimental and finally an alarm is designed which gives alerts once air core is formed and calculates air core diameter. For calculating air core diameter a polynomial equation is fitted between pressure difference and the pressure transmitter reading. A simple moving average with a smooth width of 10 was used for prediction of air core. Experimental results indicate that the digital signal sensor techniques identify the air core and measure air core diameter very accurately and can be used in many mining and mineral based chemical and allied process industries.     

Fault detection during process transitions: a model-based approach

Startup, shutdown and other transitions are integral to batch and continuous process operations. Operators usually execute transitions in manual mode. Processes are therefore prone to operator errors in addition to process faults during transitions. If undetected, such abnormalities can lead to process downtime and in the worst case, accidents. Although essential, fault detection during transitions has received little attention in literature. This paper presents a novel multiple filters and observers based fault detection scheme using (i) a nonlinear process model, and (ii) knowledge of the standard operating procedure for executing the transition. Extended Kalman filters, Kalman filters, and open-loop observers are used to estimate process states during the transition and generate residuals. These residuals indicate deviations from normal operation due to process faults and operator errors. The model-based scheme has been implemented in Matlab/Simulink and found to successfully detect faults during the startup of an experimental pH neutralization CSTR.     

Optimization of plastisol processes by dynamic mechanical analysis

Dynamic mechanical analysis (DMA) can be a particularly useful tool for studying PVC plastisol manufacturing processes. DMA temperature sweeps are uniquely able to characterize plastisol gelation and fusion behavior under low shear stress conditions that are similar to conditions found in many commercial plastisol processing operations. Dynamic mechanical analysis is also well‐suited for studying plastisol melt viscosities at low shear rates such as might be encountered in a flexible PVC foaming process or rotomolding process. Likewise, DMA rate sweeps or strain sweeps can give insights into self‐association and flow properties in a plastisol which ordinary viscometers cannot provide. J. VINYL ADDIT. TECHNOL., 13:151–154, 2007. © 2007 Society of Plastics Engineers     

Identification of flow regime transitions in an annulus sparged internal loop airlift reactor based on higher order statistics and Winger trispectrum

Higher order statistics and Wigner higher order moment spectra were used to extract useful flow regime characteristics from wall pressure fluctuation signals in an annulus sparged internal loop airlift reactor. It is found that the pressure fluctuation in the airlift reactor is a typical nonlinear and non-stationary process, which exhibits different frequency characteristics depending on flow regimes. Analysis methods based on bispectrum and Wigner trispectrum are powerful tools to reveal frequency characteristics of pressure signals. To identify flow regime transitions in the reactor, two new characteristic quantities, namely average bispectrum and generalized average frequency, are defined from bispectrum and Wigner trispectrum of the pressure signal, respectively. Two flow regime transition points corresponding to three flow regimes in the reactor are successfully detected by using these two characteristic quantities.