旋转导热污泥干燥粘滞区特性实验研究

采用旋转导热干燥机对污泥进行干燥实验研究,得到了污泥干燥特性。干燥速率随着轴转速的提高而增大,根据干燥速率,将旋转导热污泥干燥分为三个区域:糊状区、粘滞区和块状区,粘滞区泥的含水率约为55%。随着干燥的进行,粘滞区泥表面分别出现裂纹、龟裂、收缩现象,处于粘滞区的污泥干燥速率较低。结果表明:旋转导热干燥机,适当调整圆盘间距、叶轮上刮板的疏密程度以及刮刀的布置有利于"粘滞区"的突破,用于污泥干燥完全可行。     

水平分支管路气力输送阻力特性的预测

在水平T型分支管道中,用压缩空气作为输送气体,对平均粒径为0.25 mm和0.5 mm的砂石进行气力输送试验。通过试验和GRNN神经网络对输送表观气速和两分支管路流量控制阀开度发生变化时,各分支管路中的阻力特性进行了研究。结果表明:随着表观气速的逐渐减小和两分支管路流量控制阀开度差值的增加,各分支管路中的阻力特性相应地发生了变化。通过试验值和GRNN网络模拟值的对比,发现试验值和模拟值间相互吻合得较好,说明采用GRNN网络来模拟两分支管路中各自的阻力特性适应性较好。     

浅议联碱生产中自动调节阀的选型

通过对控制阀选型过程中用到的各种特性参数及其确定方法的介绍,分析了在联碱设计与生产中应如何正确、经济、合理地选用控制阀,以达到工艺要求.     

基于数据驱动方法的控制阀故障诊断

针对控制阀门的故障类型，提出一种基于数据驱动的控制阀故障诊断方法。利用实际测量值和辨识模型预估值的残差来提取阀门的内/外漏特征，基于马氏距离对提取的分类特征进行故障分类。并利用阀门输入和输出数据的差值做自相关性分析来诊断阀门的粘滞故障。基于所提方法开发了相关应用软件，所开发软件在某大型煤气化装置的应用结果证实了所提方法的可行性和有效性，为解决控制阀门的故障诊断提供了一条新的有效途径。     

间歇式化学反应器分程控制系统的实现

针对间歇式化学反应器对生产提出的特殊要求,采用分程控制方案,用冷水阀和蒸汽阀2个控制阀分别调节冷水和蒸汽,通过阀门定位器改变控制阀的工作信号段,有效解决了反应开始前需加热、反应后需放热的问题,合理设置分程控制系统,可满足反应器的温度控制要求。     

同心套管包围的多跨换热器传热管在静水中阻尼特性的实验研究

通过求解同心套管包围的带N-1个同心支承板传热管的运动方程,导出计算挤压膜阻尼和粘滞阻尼的公式.为了验证理论分析结果,对同心套管包围的四跨传热管在静水中的挤压膜阻尼和粘滞阻尼进行了实验研究,得出了粘滞阻尼与同心套管内径以及管子与同心套管偏心率的关系;挤压膜阻尼与支承板厚度、传热管与支承板管孔间隙、与支承板管孔偏心率之间的关系.在分析实验结果的基础上,得出了传热管总阻尼的公式,并为换热器设计提供了传热管管壁间距离、支承板厚度以及支承极管孔与管子间隙的参考尺寸.     

液压控制阀的水击振动耦合模型研究

以某型舵机用三位三通换向阀为例,对液压控制阀的自激振动以及启闭产生水击诱发非线性振动现象,利用阀芯弹性力学理论和阀腔内油液运动的N—S方程建立了描述液压控制阀非线性流-固耦合振动的4-方程微分运动方程组。有利于从动态特性方面获得控制阀结构参数的影响规律,为阀的设计和改进提供理论依据。     

玄武岩熔体粘滞活化能的计算

依据玄武岩熔体粘度的实测数据计算了玄武岩熔体的粘滞活化能,讨论了粘滞活化能与熔体结构的关系。结合硅酸盐熔体粘度的Shaw公式,给出了1种玄武岩熔体粘滞活化能的理论计算方法,对玄武岩熔体粘滞活化能和熔体结构的研究有一定的借鉴和指导意义。     

MOKVELD轴流式控制阀在天津输油处SCADA系统上的应用

为了适应原油管道各种水力工况的变化,天津输油处采用了由HMI上位机、Quantum PLC、MOKVELD轴流式控制阀和智能定位器组成的管道SCADA系统,通过标准4～20mA信号+HART通信信号实现对MOKVELD轴流式控制阀的控制及阀位反馈的采集,主要介绍MOKVELD轴流式控制阀在SCADA系统中的两种调节方式和FISHER-DVC6010智能定位器在输油管道中优越的应用特性.     

上海源冠自控设备有限公司的专利产品大口径滑板阀顺利交货

<正>上海源冠自控设备有限公司研发设计的全不锈钢材质DN300mm～600mm大口径滑板阀顺利出厂,主要用于大口径主管线蒸汽、工艺气体及工艺废水等介质的压力和流量精确控制!滑板式控制阀是上海源冠自控设备有限公司研发设计的专利产品,是一种高性能、高科技、仪表化、节能环保的控制阀,着重解决传统控制阀体积庞大、反应滞后、笨重及能耗大等问题,同时满足了现代流程工业对控制阀产品要求精、巧、轻、节能及交货快捷的发展趋势。目前滑板式控制阀在外形尺寸、重量和能耗上已比     

GRAPHICAL TECHNIQUE FOR MODELING INTEGRATING (Non–SELF-REGULATING) PROCESSES WITHOUT STEADY-STATE PROCESS DATA

Model-fitting techniques for controller tuning that require the process to be initially at steady state cannot generally be used with integrating (non–self-regulating) processes. To address this issue, a graphical model-fitting technique is detailed and demonstrated for determination of first order plus dead time integrating model parameters from integrating process response plots. The resulting model parameters can be used directly in a range of tuning correlations designed specifically for integrating processes. The advantage of this technique is that it requires only two periods of constant manipulated and disturbance variables sustained just long enough for the process variable to respond and establish a clear slope. This is an important benefit because integrating processes generally cannot be maintained at an initial steady state as required when using techniques published for self-regulating processes. The result is an industry-friendly method. The method is demonstrated for level control in a pumped tank, a classical challenge in industrial practice. Both a simulation and a bench-scale experimental system are used in the demonstration studies.     

GRAPHICAL TECHNIQUE FOR MODELING INTEGRATING (Non-SELF-REGULATING) PROCESSES WITHOUT STEADY-STATE PROCESS DATA

Model-fitting techniques for controller tuning that require the process to be initially at steady state cannot generally be used with integrating (non-self-regulating) processes. To address this issue, a graphical model-fitting technique is detailed and demonstrated for determination of first order plus dead time integrating model parameters from integrating process response plots. The resulting model parameters can be used directly in a range of tuning correlations designed specifically for integrating processes. The advantage of this technique is that it requires only two periods of constant manipulated and disturbance variables sustained just long enough for the process variable to respond and establish a clear slope. This is an important benefit because integrating processes generally cannot be maintained at an initial steady state as required when using techniques published for self-regulating processes. The result is an industry-friendly method. The method is demonstrated for level control in a pumped tank, a classical challenge in industrial practice. Both a simulation and a bench-scale experimental system are used in the demonstration studies.     

CLOSED-LOOP IDENTIFICATION FOR PARAMETRIC MODELS USING FREQUENCY RESPONSE TECHNIQUES

A parametric identification technique via closed-loop testing using frequency response techniques is presented in this study. Under an operation of proportional control, a single dynamic test (such as an arbitrary pulse or step change) in the set point was introduced to the system. The closed-loop transients could numerically be translated into frequency response data by Fourier integral transforms, and the parameters of a given process model were then be obtained by a least-squares fit in the frequency domain based on Parseval's theorem. This identification technique could apply not only for self-regulating processes but also for integrating or open-loop unstable processes. Furthermore, the proposed technique was also extended to the controller settings for the feedforward-feedback control system. Simulation results demonstrated that the proposed technique could yield good model parameters and can be applied in many different types of processes.     

A theoretical investigation of enhancement of mass transfer from a packed bed using acoustic oscillations

This paper describes a theoretical investigation of the possibility of improving the performance of packed bed dryers using acoustic oscillations. It was motivated by the increasing interest in the use of acoustic oscillations to improve the performance of energy‐intensive, industrial processes. Sound propagation in a packed bed was modelled as propagation through a porous medium. The effect of acoustic oscillations on the drying rate was then investigated by numerically integrating the heat and mass transfer equations. The model used quasi‐steady correlations for heat and mass transfer. The results show that threshold values for void fractions and sound pressure levels exist, above which significant increase in drying rate can be obtained. The increase in mass transfer decreases with effective particle diameter.     

Detection and Diagnosis of Plant‐Wide Oscillations

This paper presents some emerging techniques for detection and root‐cause diagnosis of plant‐wide oscillations, and demonstrates their efficacy through a successful industrial case study. The recently proposed autocorrelation function based method (Thornhill et al., J. Proc. Control 13, 91–100, 2003a) is used for detection of oscillations in the process measurements. Signals having common oscillations are analyzed for the presence of valve stiction using higher order statistical methods (Choudhury et al., Automatica 40, 1719–1728, 2004b) . A method employing changes in controller gain is proposed for distinguishing an internally generated oscillation from an external oscillatory disturbance. This method of changing controller gain is used to confirm the presence of control valve stiction. The proposed techniques have been used successfully to identify the root cause of plant‐wide oscillations in an industrial case study using routine operating data.     

A tuning of the nonlinear PI controller and its experimental application

Many industrial chemical process control systems consist of conventional PID and nonlinear controllers, even though many advanced control strategies have been proposed. In addition, nonlinear control methods are widely used even for linear processes to achieve better control performance compared with linear PID controllers. However, there are few tuning methods for these nonlinear controllers. In this work, we suggest new controller tuning methods for the error square type of nonlinear PI controller. These control methods can be applied to a large number of linear and nonlinear processes without changing control structures. We also propose new tuning rules for integrating processes. In addition, we suggest application guidelines for performing the proposed tuning rules at the pilot scale multistage level control system. Finally, in this work we confirmed good control performances of the proposed tuning methods through both simulation studies and experimental studies.     

Robust model predictive control of an industrial partial combustion fluidized-bed catalytic cracking converter

It is presented here in the study of the application of a robust model predictive control to an industrial partial combustion fluidized-bed catalytic cracking (FCC) converter. This particular type of FCC converter shows an interesting dynamics in which most of the system outputs are integrating with respect to the manipulated inputs. Time delays are also present and the model parameters can change depending on the operating point. Then, the system model should be represented by a set of possible plants, which can stand for different operating conditions of this process system. Moreover, one needs to include a comprehensive model formulation in order to accommodate time-delays for both stable and integrating outputs. The proposed control strategy was tested through simulation for the disturbances commonly found in the FCC converter unit, taking into consideration the plant/model mismatch. Results obtained from the simulated scenarios point out a fine prospective method. The robust controller shows a good potential to be implemented in the real process.     

Control valve stiction detection using Markov transition field and deep convolutional neural network

Control valve stiction is an industrial problem that often causes oscillations in process control loops. Oscillating control loops are not capable of maintaining key process variables near or at their desired values, thus yielding low-quality products, inducing economic loss, and increasing environmental impacts. Therefore, it is of vital importance to detect stiction in industrial control valves. In this regard, the present work proposes a new method based on the Markov transition field and convolutional neural network (CNN) to identify sticky control valves in industrial control loops. The Markov transition field is employed to convert process variable (PV) and controller output (OP) into two-dimensional images, which are then utilized by CNN to learn to distinguish stiction induced oscillations from oscillations brought out by a non-stiction condition. A transfer learning strategy is adopted to improve the stiction detection capability of the proposed method. Its performance is evaluated via its application to benchmark control loops taken from the chemical, paper, mining, and metal industries. Results demonstrate that the proposed method obtains the correct verdict for the majority of the control loops studied.     

1,1-二氯乙烷萃取糠醛的模拟设计研究

文章采用Aspen Plus软件对萃取分离糠醛过程进行了模拟研究。结合Aspen Split软件,分别绘制了以氯仿、1,1-二氯乙烷、1,2-二氯乙烷为萃取剂的三元相图和剩余曲线图,并进行综合比较,最终选择1,1-二氯乙烷作为模拟萃取剂。根据相图和剩余曲线图,提出了两种可能的分离方案,并用Aspen Plus软件进行模拟分析。结果表明,两种分离方案均能得到合格的糠醛产品,但先萃取后精馏工艺(方案一)比先蒸馏后脱水工艺(方案二)节能95%。     

Multi-objective optimization for the periodic operation of the naphtha pyrolysis process using a new parallel hybrid algorithm combining NSGA-II with SQP

Nowadays naphtha pyrolysis is the most important process for ethylene production, which can bring along another important monomer, namely propylene. The demand of both the ethylene and propylene has recently increased dramatically and naphtha pyrolysis is indispensable to satisfy the demand of both crucial products simultaneously, resulting in a typical multi-objective optimization problem. The nondominated sorting genetic algorithm (NSGA-II), which has been successfully applied to many multi-objective optimization problems, cannot efficiently generate the Pareto set which spreads as widely as the true Pareto front in a limited time, meanwhile, its convergence process is rather slow and could not meet the speed requirement when used for the complicated industrial problem mentioned above. To efficiently solve the multi-objective optimization problem of the industrial complicated chemical processes, this paper first proposed a new parallel hybrid multi-objective optimization algorithm combing NSGA-II with SQP (Successive Quadratic Programming) used to improve the efficiency of the NSGA-II and the quality of the Pareto-optimal set. Then the multi-objective operation optimization model of naphtha pyrolysis was established, and at last the application of the proposed algorithm to improve the performance of an industrial naphtha pyrolysis process was presented and analyzed.