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.

NUMERICAL SIMULATION OF THE THERMO-MECHANICAL PROCESS FOR BEAM BLANK CONTINUOUS CASTING

The aim of this study was to simulate the solidification process of beam blank continuous casting, and then find the reasons for the typical defects of the beam blank. A two-dimensional transient coupled finite element model has been developed to compute the temperature and stress profile in beam blank continuous casting. The enthalpy method was used in the heat conduction equation. The thermo-mechanical property in the mushy zone was taken into consideration in this calculation. It is shown that at the mold exit the thickness of the shell had its maximum value at the flange tip and its minimum value at the fillet. The temperature had a great fluctuation on the surface of the beam blank in the secondary cooling zone. At the unbending point, the surface temperature of the web was in the brittleness temperature range under the present condition. To ensure the quality, it is necessary to weaken the intensity of secondary cooling. At the mold exit the equivalent stress and strain have higher values at the flange tip and at the web. From the spray 1 to the unbending point, the maximum values of stress and strain gradually moved to the internal section of the flange tip and the web. However, whenever, there were bigger stress and strain values near the flange tip and the web than in the other parts, it must be very easy to generate cracks at those positions. Now, online verification of this simulation has been developed, which has proved to be very useful and efficient to instruct the practical production of beam blank continuous casting.

IMPROVING THE MECHANICAL PROPERTIES OF COPPER ALLOYS BY THERMO-MECHANICAL PROCESSING

Systematic physical simulation of thermo-mechanical processing routes has been ap-plied on a Gleeble 1500 simulator to four copper alloys (mass %) Cu-0.57Co-0.32Si,Cu-0.55Cr-0.065P, Cu-0.22Zr-0.035Si and Cu-1.01Ni-0.43Si aimed at clarifying theinfluences of processing conditions on their final properties, strength and electricalconductivity. Flow curves were determined over wide temperature and strain rateranges. Hardness was used as a measure of the strength level achieved. High hard-ness was obtained as using equal amounts (strains 0.5) of cold deformation beforeand after the precipitation annealing stage. The maximum values achieved for theCu-Co-Si, Cu-Cr-P, Cu-Zr-Si and Cu-Ni-Si alloys were 190, 165, 178 and 193 HV5,respectively. A thermo-mechanical schedule involving the hot deformation-ageing-colddeformation stages showed even better results for the Cu-Zr-Si alloy. Consequently,the processing routes were designed based on simulation test results and wires of 5 and2mm in diameters have been successfully processed in the industrial scale.

Effect of thermo-mechanical processing on microstructure and electrochemical behavior of Ti-Nb-Zr-V new metastable β titanium biomedical alloy

The influence of thermo-mechanical processing (TMP) on the microstructure and the electrochemical behavior of new metastableβ alloy Ti?20.6Nb?13.6Zr?0.5V (TNZV) was investigated. The TMP included hot working in belowβ transus, solution heat treatments at the same temperature and different cooling rates in addition to aging. Depending upon the TMP conditions, a wide range of microstructures with varying spatial distributions and morphologies of equiaxed/elongatedα andβ phases were attained, allowing for a wide range of electrochemical properties to be achieved. The corrosion behavior of the studied alloy was evaluated in a Ringer’s solution at 37 °C via open circuit potential?time and potentiodynamic polarization measurements.

EVOLUTION OF MICROSTRUCTURES AND HARDNESS DURING CONTINUOUS THERMO-MECHANICAL PROCESSING OF 6201 ALUMINUM ALLOY

Continuous thermo-mechanical processing (CTMP) of 6201 aluminum alloy was simulated on Gleeble-1500. The deformed specimens were analyzed by the observation of TEM and the measurement of hardness. It was shown that rapid solid solution and aging treatment can be effectively combined in one procedure by the strain induced during CTMP. The deformation temperature is ranging from 540* C to 300* C, the hardness increases directly before the 6th pass followed by a slight drop, the amount of precipitates increases with the holding time after deformation. Uniformly distributed and stabilized Mg2Si precipitates, as well as dislocation substructure can be observed on deformed specimens which have been subsequently held at 300℃ for 60 seconds.

Optimization study of station track utilization in high-speed railroad based on constraints of control in random origin and process

Purpose-The purpose of this paper is to eliminate the fluctuations in train arrival and departure times caused by skewed distributions in interval operation times.These fluctuations arise from random origin and process factors during interval operations and can accumulate over multiple intervals.The aim is to enhance the robustness of high-speed rail station arrival and departure track utilization schemes.Design/methodologylapproach-To achieve this objective,the paper simulates actual train operations,incorporating the fluctuations in interval operation times into the utilization of arrival and departure tracks at the station.The Monte Carlo simulation method is adopted to solve this problem.This approach transforms a nonlinear model,which includes constraints from probability distribution functions and is difficult to solve directly,into a linear programming model that is easier to handle.The method then linearly weights two objectives to optimize the solution.Findings-Through the application of Monte Carlo simulation,the study successfully converts the complex nonlinear model with probability distribution function constraints into a manageable linear programming model.By continuously adjusting the weighting coefficients of the linear objectives,the method is able to optimize the Pareto solution.Notably,this approach does not require extensive scene data to obtain a satisfactory Pareto solution set.Originality/value-The paper contributes to the field by introducing a novel method for optimizing high-speed rail station arrival and departure track utilization in the presence of fluctuations in interval operation times.The use of Monte Carlo simulation to transform the problem into a tractable linear programming model represents a significant advancement.Furthermore,the method's ability to produce satisfactory Pareto solutions without relying on extensive data sets adds to its practical value and applicability in real-world scenarios.

Evolution of Microstructure in a Cu-Cr in situ Composite Produced by Thermo-Mechanical Processing

This paper studied the microstructure evolution of a deformation-processed Cu-7Cr in situ composite prepared by thermo-mechanical processing. The longitudinal and transverse sectional microstructures were analyzed using an optical microscope and a scanning electronic microscope. In the longitudinal section, the initially randomly distributed Cr dendrites in the as-cast Cu-7Cr alloy were transformed into the fibres aligned parallel to the drawing axis;the Cr dendrites experienced breaking, flattening and rotating, lapping and merging, and homogenizing and refinement during thermo-mechanical processing. In the transverse section, the initially randomly distributed Cr dendrites in the as-cast Cu-7Cr alloy were changed into the curvy ribbon like fibres;the Cr dendrites underwent breaking, flattening and rotating, folding and twisting, and irregularizing and refinement during thermo-mechanical processing.

Study on the Interactivity of Controlled and Automatic Information Processing in L2 Reading

L2 reading is not only an important channel for people to obtain information and knowledge,but also the main way for people to learn a foreign language.Reading information processing can be divided into controlled processing and automatic processing.Controlled information processing is a conscious and resource-intensive processing model,while automatic information processing is an unconscious and automatic processing model.This study investigates the characteristics and interactivity of controlled and automatic information processing in L2 reading,and explores the roles of controlled and automatic information processing strategies in improving L2 reading ability.The findings are as follows:(a)controlled and automatic information processing is interactive in L2 reading;and(b)the uses of controlled and automatic information processing strategies are beneficial to the improvement of the reading ability of L2 learners.This study has important theoretical and practical value in improving the efficiency of L2 reading teaching and learning.

Formation Mechanism in Alloy Steel Rolling Process Using Thermo-mechanical Coupling Method

Based on the theory of elastic-plastic finite element method, the high-speed hot continuous rolling process of a billet is simulated and analyzed in vertical and horizontal passes. The billet is dragged into the passes by contact friction force between the billet and rollers. The rollers and billet are represented by respectively rigid and deformable bodies, and three-dimensional models are developed for the billet and rollers. The distribution of deformation field, effective strain, rolling force and temperature field are accurately calculated for the whole rolling process （including unstable and stable stages）. In addition, the rolling pressure on the width symmetry center is compared with that in the in-situ experimental measurements. It is revealed that various heat exchange phenomena among the billet, rollers and surroundings can result in unbalanced temperature distribution on the cross section. Rolling force and strain can change significantly when the billet is moved towards or away from the roller gap, and keep almost invariable in the stable stage. It is expected that the simulation results would be useful for practical manufacture and provide the theoretical foundation for improvement of process planning and optimization of process parameters.

Application of single neuron adaptive PID controller during the process of timber drying

The paper presents a method of using single neuron adaptive PID control for adjusting system or servo system to implement timber drying process control, which combines the thought of parameter adaptive PID control and the character of neural network on exactly describing nonlinear and uncertainty dynamic process organically. The method implements functions of adaptive and self-learning by adjusting weighting parameters. Adaptive neural network can make some output trail given hoping value to decouple in static state. The simulation result indicates the validity, veracity and robustness of the method used in the timber drying process

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。

Whole-Process Pollution Control for Cost-Effective and Cleaner Chemical Production A Case Study of the Tungsten Industry in China

In this research,a methodology named whole-process pollution control(WPPC)is demonstrated that improves the effectiveness of process optimization.This methodology considers waste/emission treatment as a step of the whole production process with respect to the minimization of cost and environmental impact for the whole process.The following procedures are introduced in a WPPC process optimization:①a material and energy flow investigation and optimization based on a systematic understanding of the distribution and physiochemical properties of potential pollutants;②a process optimization to increase the utilization efficiency of different elements and minimize pollutant emissions;and③an evaluation to reveal the effectiveness of the optimization strategies.The production of ammonium paratungstate was chosen for the case study.Two factors of the different optimization schemes-namely the cost-effectiveness factor and the environmental impact indicator-were evaluated and compared.This research demonstrates that by considering the nature of potential pollutants,technological innovations,economic viability,environmental impacts,and regulation requirements,WPPC can efficiently optimize a metal production process.

Multivariable Decoupling Predictive Control with Input Constraints and Its Application on Chemical Process

A constrained decoupling (generalized predictive control) GPC algorithm is proposed for MIMO (malti-input multi-output) system. This algorithm takes account of all constraints of inputs and their increments. By solving matrix equations, the multi-step predictive decoupling controllers are realized. This algorithm need not solve Diophantine functions, and weakens the cross-coupling of the variables. At last the simulation results demon- strate the effectiveness of this proposed strategy.

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.

Nonlinear Model Algorithmic Control of a pH Neutralization Process

Control of pH neutralization processes is challenging in the chemical process industry because of their inherent strong nonlinearity. In this paper, the model algorithmic control (MAC) strategy is extended to nonlinear processes using Hammerstein model that consists of a static nonlinear polynomial function followed in series by a linear impulse response dynamic element. A new nonlinear Hammerstein MAC algorithm (named NLH-MAC) is presented in detail. The simulation control results of a pH neutralization process show that NLH-MAC gives better control performance than linear MAC and the commonly used industrial nonlinear propotional plus integral plus derivative (PID) controller. Further simulation experiment demonstrates that NLH-MAC not only gives good control response, but also possesses good stability and robustness even with large modeling errors.

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.

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.

Intercross real-time control strategy in alternating activated sludge process for short-cut biological nitrogen removal treating domestic wastewater

To develop technically feasible and economically favorable dynamic process control(DPC)strategies for an alternating activated sludge(AAS)system,a bench-scale continuous-flow alternating aerobic and anoxic reactor,performing short-cut nitrogen removal from real domestic wastewater was operated under different control strategies for more than five months.A fixed-time control(FTC) study showed that bending-points on pH and oxidation-reduction potential(ORP)profiles accurately coincided with the major biologic...

An LMI Method to Robust Iterative Learning Fault-tolerant Guaranteed Cost Control for Batch Processes

Based on an equivalent two-dimensional Fornasini-Marchsini model for a batch process in industry, a closed-loop robust iterative learning fault-tolerant guaranteed cost control scheme is proposed for batch processes with actuator failures. This paper introduces relevant concepts of the fault-tolerant guaranteed cost control and formulates the robust iterative learning reliable guaranteed cost controller (ILRGCC). A significant advantage is that the proposed ILRGCC design method can be used for on-line optimization against batch-to-batch process uncertainties to realize robust tracking of set-point trajectory in time and batch-to-batch sequences. For the convenience of implementation, only measured output errors of current and previous cycles are used to design a synthetic controller for iterative learning control, consisting of dynamic output feedback plus feed-forward control. The proposed controller can not only guarantee the closed-loop convergency along time and cycle sequences but also satisfy the H∞performance level and a cost function with upper bounds for all admissible uncertainties and any actuator failures. Sufficient conditions for the controller solution are derived in terms of linear matrix inequalities (LMIs), and design procedures, which formulate a convex optimization problem with LMI constraints, are presented. An example of injection molding is given to illustrate the effectiveness and advantages of the ILRGCC design approach.

EXPERT OPTIMIZED CONTROL TECHNIQUE FOR ELECTROLYTIC DEPOSITION PROCESS OF HYDROMETALLURGY OF ZINC

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