Research on Technological Process Control Model of Reverse Logistics in Manufacturing System

This paper has found out some important input factors of reverse logistics in manufacturing system throuth analysis and summary,and established four kinds of technological process control models of reverse logistics in manufacturing system according to different processing methods. These models embed each other that form a cubic control system of reverse logistics.

Hybrid Neural Network Model for RH Vacuum Refining Process Control

A hybrid neural network model,in which RH process(theoretical)model is combined organically with neural network(NN)and case-base reasoning(CBR),was established.The CBR method was used to select the operation mode and the RH operational guide parameters for different steel grades according to the initial conditions of molten steel,and a three-layer BP neural network was adopted to deal with nonlinear factors for improving and compensating the limitations of technological model for RH process control and end-point prediction.It was verified that the hybrid neural network is effective for improving the precision and calculation efficiency of the model.

Modeling of Microstructure Evolution and Mechanical Properties of Steel Plates Produced by Thermo-Mechanical Control Process and Its On-line Application

An integrated metallurgical model was developed to predict microstructure evolution and mechanical properties of low-carbon steel plates produced by TMCP. The metallurgical phenomena occurring during TMCP and mechanical properties were predicted for different process parameters. In the later passes full recrystallization becomes difficult to occur and higher residual strain remains in austenite after rolling. For the reasonable temperature and cooling schedule, yield strength of 30 mm plain carbon steel plate can reach 310 MPa. The first on-line application of prediction and control of microstructure and properties (PCMP) in the medium plate production was achieved. The predictions of the system are in good agreement with measurements.

Batch Process Modelling and Optimal Control Based on Neural Network Model

This paper presents several neural network based modelling, reliable optimal control, and iterative learning control methods for batch processes. In order to overcome the lack of robustness of a single neural network, bootstrap aggregated neural networks are used to build reliable data based empirical models. Apart from improving the model generalisation capability, a bootstrap aggregated neural network can also provide model prediction confidence bounds. A reliable optimal control method by incorporating model prediction confidence bounds into the optimisation objective function is presented. A neural network based iterative learning control strategy is presented to overcome the problem due to unknown disturbances and model-plant mismatches. The proposed methods are demonstrated on a simulated batch polymerisation process.

High-Order Volterra Model Predictive Control and Its Application to a Nonlinear Polymerisation Process

Model Predictive Control (MPC) has recently found wide acceptance in the process industry, but existing design and implementation methods are restricted to linear process models. A chemical process, however, involves severe nonlinearity which cannot be ignored in practice. This paper aims to solve this nonlinear control problem by extending MPC to accommodate nonlinear models. It develops an analytical framework for nonlinear model predictive control (NMPC). It also offers a third-order Volterra series based nonparametric nonlinear modelling technique for NMPC design, which relieves practising engineers from the need for deriving a physical-principles based model first. An on-line realisation technique for implementing NMPC is then developed and applied to a Mitsubishi Chemicals polymerisation reaction process. Results show that this nonlinear MPC technique is feasible and very effective. It considerably outperforms linear and low-order Volterra model based methods. The advantages of the developed approach lie not only in control performance superior to existing NMPC methods, but also in eliminating the need for converting an analytical model and then convert it to a Volterra model obtainable only up to the second order. Keywords Model predictive control - Volterra series - process control - nonlinear control Yun Li is a senior lecturer at University of Glasgow, UK, where has taught and researched in evolutionary computation and control engineering since 1991. He worked in the UK National Engineering Laboratory and Industrial Systems and Control Ltd, Glasgow in 1989 and 1990. In 1998, he established the IEEE CACSD Evolutionary Computation Working Group and the European Network of Excellence in Evolutionary Computing (EvoNet) Workgroup on Systems, Control, and Drives. In summer 2002, he served as a visiting professor to Kumamoto University, Japan. He is also a visiting professor at University of Electronic Science and Technology of China. His research interests are in parallel processing, design automation and discovery of engineering systems using evolutionary learning and intelligent search techniques. Applications include control, system modelling and prediction, circuit design, microwave engineering, and operations management. He has advised 12 Ph.D.s in evolutionary computation and has 140 publications.Hiroshi Kashiwagi received B.E, M.E. and Ph.D. degrees in measurement and control engineering from the University of Tokyo, Japan, in 1962, 1964 and 1967 respectively. In 1967 he became an Associate Professor and in 1976 a Professor at Kumamoto University. From 1973 to 1974, he served as a visiting Associate Professor at Purdue University, Indiana, USA. From 1990 to 1994, he was the Director at Computer Center of Kumamoto University. He has also served as a member of Board of Trustees of Society of Instrument and Control Engineers (SICE), Japan, Chairman of Kyushu Branch of SICE and General Chair of many international conferences held in Japan, Korea, Chin and India. In 1994, he was awarded SICE Fellow for his contributions to the field of measurement and control engineering through his various academic activities. He also received the Gold Medal Prize at ICAUTO’95 held in India. In 1997, he received the “Best Book Award” from SICE for his new book entitled “M-sequence and its application” written in Japanese and published in 1996 by Shoukoudou Publishing Co. in Japan. In 1999, he received the “Best Paper Award” from SICE for his paper “M-transform and its application to system identification”. His research interests include signal processing and applications, especially pseudorandom sequence and its applications to measurement and control engineering.

State Space Model Predictive Control of an Aerothermic Process with Actuators Constraints

This paper investigates State Space Model Predictive Control (SSMPC) of an aerothermic process. It is a pilot scale heating and ventilation system equipped with a heater grid and a centrifugal blower, fully connected through a data acquisition system for real time control. The interaction between the process variables is shown to be challenging for single variable controllers, therefore multi-variable control is worth considering. A multi-variable state space model is obtained from on-line experimental data. The controller design is translated into a Quadratic Programming (QP) problem, in which a cost function subject to actuators linear inequality constraints is minimized. The outcome of the experimental results is that the main control objectives, such as set-point tracking and perturbations rejection under actuators constraints, are well achieved for both controlled variables simultaneously.

基于Statistical Process Control风险等级判定及神经网络模型构建珠海市传染病指数

目的建立珠海市传染病指数预报模型,为传染病风险预测预报提供思路。方法利用统计过程控制(SPC)的控制下限、中线和控制上限划分全市2014—2017年以周次为时间计量单位的流感样病例比例、手足口病及其他感染性腹泻发病率的风险等级(布雷图指数采用5、10、20判定)。运用长短时记忆神经网络模型(LSTM)和自回归移动平均模型(ARIMA)对2018年15~19周数据进行预测。计算传染病指数并将预测值与实际值对比进而评估预测一致性。结果珠海市手足口病发病率LSTM模型中,测试集MSE为9.0441,RMSE为3.0073,训练集MSE为1.1812,RMSE为1.0868。其余模型在训练集和测试集均表现良好,没有出现过拟合现象。风险指数等级预测与实际值对比,预测一致率为96.0%。结论利用SPC划分风险等级,运用LSTM等构建传染病指数预测模型可行。

Output feedback robust model predictive control with unmeasurable model parameters and bounded disturbance

The output feedback model predictive control(MPC),for a linear parameter varying(LPV) process system including unmeasurable model parameters and disturbance(all lying in known polytopes),is considered.Some previously developed tools,including the norm-bounding technique for relaxing the disturbance-related constraint handling,the dynamic output feedback law,the notion of quadratic boundedness for specifying the closed-loop stability,and the ellipsoidal state estimation error bound for guaranteeing the recursive feasibility,are merged in the control design.Some previous approaches are shown to be the special cases.An example of continuous stirred tank reactor(CSTR) is given to show the effectiveness of the proposed approaches.

DESIGN OF AN EXPERT CONTROL SYSTEM FOR LEACHING PROCESS

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Automatic control strategy for step feed anoxic/aerobic biological nitrogen removal process

Control of sludge age and mixed liquid suspended solids concentration in the activated sludge process is critical for ensuring effective wastewater treatment. A nonlinear dynamic model for a step-feed activated sludge process was developed in this study. The system is based on the control of the sludge age and mixed liquor suspended solids in the aerator of last stage by adjusting the sludge recycle and wastage flow rates respectively. The simulation results showed that the sludge age remained nearly constant at a value of 16 d in the variation of the influent characteristics. The mixed liquor suspended solids in the aerator of last stage were also maintained to a desired value of 2500 g/m3 by adjusting wastage flow rates.

Integration Between Enterprise Process Monitoring and Controlling System and Enterprise Application

The relationships and the features of integration between Enterprise ProcessMonitoring and Controlling System (EPMCS) and Enterprise Process Related Applications (EPRA) wereanalyzed. An integration architecture centered on EPMCS was presented, in which there were fourlayers to connect from EPMCS to EPRA: EPMCS, application integration layer, transport layer andEPRA, and there were four layers used to etstablish integration: presentation layer, function layer,data layer and system layer. The frameworks to connect EPMCS and EPRA were designed, thatEnterprise-Independent Model (EIM), Enterprise-Specific Model (ESM) and meta-model to describe thesetwo models were defined. The method to integrate data based on XML was designed to exchange datafrom EPMCS to EPRA according to the mapping between EIM and ESM. The approches are suitable forintegrating EPMCS and systems in Product Data Management (PDM), project management and enterprisebusiness management.

Dynamics Models of Crank in Motor Housing Process

With regards to the assembly line of cost control of Dechang(HK)company,the motor housing’s cost control of process will be necessarily respected.Because the supply quantity is big in a machine the price of motor housing is small,so that the cost control of automatic production line is significant with modeling.It is found that the control of equipment includes in shaft and crank linkage for benefit which also needs to be controlled in detail.For the sake of benefits can we fundamentally resolve the main problem of high cost process.

Mathematical Model of Dynamic Operation and Optimum Control of Billet Reheating Furnace

This paper provides a mathematical model for the billet reheating process in furnace.A new optimum method is brought up that the objective function is the integral value of enthalpy increasing process of a billet.Different delays are simulated and calculated,some proper delay strategies are ob- tained.The on-line computer control model is de- veloped.The real production conditions simulated, the temperature deviation of drop out billet from the target temperature is kept within±15℃.

Mathematical model of metallurgical thermal processes and application

There are lots of physical changes and chemical reactions in the processes of iron and steel making, these processes are quite complex in the aspect of heat transfer.The processes of iron and steel making can be approximately divided into three kinds.The first kinds are the processes of fusion metallurgy which involve enormous chemical reactions,such as blast furnace,converter,electric furnace and coke oven.The second kinds are the processes of heating and cooling which are mainly the physical changes,such as walking-beam reheating furnace,annular heating furnace and car-type furnace.The third kinds are the processes of heat treatment which mainly adjust metallurgical structure of metal,such as roller hearth heat treatment furnace, strip continuous heat treatment vertical/horizontal furnace and HPH bell-type annealing furnace.Every process can only be finished in particular thermal equipment.And all the physical and chemical processes mentioned above must obey first principles of engineering thermodynamics,heat & mass transfer,hydromechanics, combustion,metallurgy physical chemistry etc,and which can be summarized as principle of heat transfer,mass transfer,momentum transfer and chemistry reaction.In this paper,based on first principle of heat and mass transfer in iron and steel making processes,a series of mathematical models of thermal equipments and processes are presented.Such as the model of hot-blast stoves,coke oven,CDQ-boiler system,sintering, reheating furnace,soaking furnace,annular heating furnace,roller hearth heat treatment furnace,strip continuous heat treatment vertical/horizontal furnace,HPH bell-type annealing furnace,control cooling of medium plate,burner,heat exchanger and regenerative burner etc.The on-line application of the model is based on experimental certification of the mathematical model.And finally the computer optimization system of metallurgical thermal process is obtained.

Numerical Controlled Rolling Process of Thick Metal Plate

Thick metal plate rolling process has become more and more important in building a flat roof of drilling on the bottom at sea. This is because not only the product quality requirement higher and higher but also the marketing competition. To improve the process of thick metal plate rolling and to increase productivity a numerical controlled rolling process is developed, which include the process planning, the mathematical model establishment and the numerical control system development. The process is for the 17 000 kN×3 000 mm movable up roller bending machine. According to the machine configuration non-symmetry rolling process is planed. This makes it possible to integrate all the steps of plate shaping up such as end side bending, several times of semi-shape bending and the last shape finishing. Since the process will perform under the numerical controlled condition whole steps of the process are considered can be worked in the automatic cycle. The mathematic model consists of two sections, the theory model and the experience parameters model. Which takes the original plate parameters such as geometry and mechanics, characters of the machine such as the movement limits, tonnage and so on as input and calculates all the parameters needed in process performing. Meanwhile, the mathematic Model is totally adapted to the control system. The numerical control system development scheme is based on all the works above. Here, a system plan is provided. The functional modules and hardware selection, in detail, are introduced. The system software on top level and the controlling software for controller are developed. And some unit techniques in the system such as timer setting, communication between system and controller, video integration, and the ability of resisting impact force are introduced. The process has been used successfully in production for more than two years. Practice approves that the process is robust.

Expert control strategy using neural networks for electrolytic zinc process

The most important parameters which control the electrolytic process are the concentrations of zinc and sulfuric acid in the electrolyte. An expert control strategy for determining and tracking the optimal concentrations was proposed, which uses neural networks, rule models and a single loop control scheme. First, the process was described and the strategy that features an expert controller and three single loop controllers was explained. Next, neural networks and rule models were constructed based on statistical data and empirical knowledge on the process. Then, the expert controller for determining the optimal concentrations was designed through a combination of the neural networks and rule models. The three single loop controllers used the PI algorithm to track the optimal concentrations. Finally, the implementation of the proposed strategy were presented. The run results show that the strategy provides not only high purity metallic zinc, but also significant economic benefits.

A MECHANICAL MODEL FOR THE DRY BASIS WEIGHT PLANT OF PAPER MAKING PROCESS

Of the two methods of getting the mathematical model of a given system,modeling and i-dentification,the first is of the advantage of“knowing it in the detailed inside”.In this paper,after the common technological process of paper making is approached,a simplified physicalimitation and the mathematical model are presented.The resulted model by means of modelingis of the same form with that through,identification by K.J.Astrom in 1970.The course ofmodel deriving is described in detail,from which one can see clearly how the minor factors ofthe dynamics are omitted and what may be included in the unmodeled dynamics.At the sametime,the limit to its usage is also given.

Formal Reduction of Interfaces to Large-scale Process Control Systems

A formal methodology is proposed to reduce the amount of information displayed to remote human operators at interfaces to large-scale process control plants of a certain type. The reduction proceeds in two stages. In the first stage, minimal reduced subsets of components, which give full information about the state of the whole system, are generated by determining functional dependencies between components. This is achieved by using a temporal logic proof obligation to check whether the state of all components can be inferred from the state of components in a subset in specified situations that the human operator needs to detect, with respect to a finite state machine model of the system and other human operator behavior. Generation of reduced subsets is automated with the help of a temporal logic model checker. The second stage determines the interconnections between components to be displayed in the reduced system so that the natural overall graphical structure of the system is maintained. A formal definition of an aesthetic for the required subgraph of a graph representation of the full system, containing the reduced subset of components, is given for this purpose. The methodology is demonstrated by a case study.

基于二阶价值梯度模型强化学习的工业过程控制方法

为了实现对高延时、非线性和强耦合的复杂工业过程稳定准确的连续控制,提出了一种基于二阶价值梯度模型强化学习的控制方法。首先,该方法在模型训练过程中加入了状态价值函数的二阶梯度信息,具备更精确的函数逼近能力和更高的鲁棒性,学习迭代效率更高;其次,通过采用新的状态采样策略,可以更高效地利用模型进行策略学习。最后,通过在OpenAI的Gym公共实验环境和两个工业场景的仿真环境的实验表明:基于二阶价值梯度模型对比传统的基于最大似然估计模型,环境模型预测误差显著降低;基于二阶价值梯度模型的强化学习方法学习效率优于现有的基于模型的策略优化方法,具备更好的控制性能,并减小了控制过程中的振荡现象。可见该方法能有效地提升训练效率,同时提高工业过程控制的稳定性和准确性。

基于状态观测器的增量式预测控制

通过状态观测器的状态估计代替内部模型的状态反馈,推导了基于状态观测器的增量式模型预测控制算法,通过增广状态估计实现实测的输出反馈对内部模型进行修正;其次,当模型预测控制内部模型与实际被控对象存在模型失配时,得到存在模型失配的闭环系统的状态方程,研究了闭环系统的鲁棒稳定性;最后,给出一个线性对象和一个非线性对象的仿真示例,并对线性示例进行模型失配下的稳定性分析,仿真结果表明该控制算法具有较好的控制效果,并具有一定的鲁棒稳定性。