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.

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.

Instrument Automation Control System Faults and Maintenance

This article explores the topic of fault diagnosis and maintenance strategies for instrument automation control systems,analyzing them through specific cases.The aim of this research is to improve the stability and reliability of the system by conducting a thorough investigation of faults and maintenance in instrument automation control systems.By doing so,this research hopes to provide a strong guarantee for the smooth progress of industrial production.

Magnetically controlled growing instrumentation for early onset scoliosis: Caution needed when interpreting the literature

BACKGROUND Magnetically controlled growing rods(MCGR) are a novel treatment option for early onset scoliosis(EOS). Although the complication profile with MCGR use has been reviewed, these reviews do not take into account important implants modifications, termed iterations, that were made due to early on postoperative complications is not well reported or understood.AIM To assess the effect of MCGR implant iterations on post-operative complications in EOS.METHODS A systematic review was performed to identify studies investigating MCGR specifically for the treatment of EOS, refined to those reporting the implant iteration, specifically the incorporation of the keeper plate to the implant design.Articles with mixed implant iteration usage were excluded. Complications following surgery were recorded as well as potential risk factors and compared between implant cohorts.RESULTS Although 20 articles were identified for inclusion, 5 included mixed implant iteration leaving a total of 271 patients identified through 15 clinical studies thatmet inclusion criteria. The average follow-up was 25.4-mo. Pre-keeper plate implants were utilized in 3 studies with a total of 49 patients. Overall, 115(42.4%)post-operative complications were identified, with 87% defined as major. The addition of the keeper plate significantly decreased the rate of post-operative complications per study(35.7% vs 80.6%, P = 0.036), and the rate of distraction failure(8.1% vs 40.8%, P = 0.02). Unplanned reoperation occurred in 69(26.7%)patients but was not different between implant iteration cohorts(25.5% without keeper plate vs 27.1% with keeper plate, P = 0.92).CONCLUSION MCGR for EOS has a cumulative complication rate of 42.4% but this is significantly reduced to 35.7% when reviewing only keeper-plate enabled implants. However, 25% of published articles included mixed implant iterations.Future studies should discern between implants iterations when reporting on the usage of MCGR for EOS.

Ageing management strategies for instrumentation and control equipment in NPPs

This paper aims to explain main ageing stressors and effects on instrumentation and control (I&C) components followed by ageing management strategies through developed design such as using freewheeling diode to eliminate relays contact ageing due to inductive loads, or through traditional practices and new online monitoring where I&C components can be checked and assessed remotely while the plant is operating. It also includes some recommendations for obsolescence management for special I&C components like microcontrollers and programmable logic controllers (PLCs).

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

Modeling analysis and optimization design of the thermostatical control system of laser instrument of the semiconductor

Influence produced by the heat effect at work of the laser instrument crystal of the semiconductor, the text designs a kind of temperature control system to the crystal of the laser instrument, using the thought and method of the classical control theory to analyze this temperature control system, and establishes mathematics model. According to mathematics model the text demonstrated the system at S field and time- area, and proposed optimizing basis to the total mark of proportion and differential parameter to con- troller PID, thus proposed a kind of temperature control scheme. And the thermostatically system is simulated by MATLAB.

Threshold decision for instrument control system and its application in nuclear power plant

Threshold decision is an important function of nuclear instrument control system based on physical parameters threshold decision. Because the conventional decision methods lack correlation with time and conditions, by analyzing the existing methods, some optimized methods are adopted. Considering safety, those methods are improved in data processing algorithms, floating threshold with multiple values, association with specific working condition, etc. These measures im- prove the nuclear instrument control system in fault tolerance and fault diagnosis, especially, the shutdown number of nucle- ar power plant decreases.

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。

INTELLIGENT VIRTUAL CONTROL:MEASURING INSTRUMENT FROM WHOLE TO PART

A new concept called intelligent virtual control (IVC), which can be drivenby measuring functions, is put forward. This small 'intelligent measurement instrument unit (IMIU)',carrying with functions of instrument, consists of different types of intelligent virtualinstrument (IVI) through individual components together as building blocks and can be displayeddirectly on the computer screen. This is a new concept of measuring instrument, and also animportant breakthrough after virtual instrument (VI). Virtual control makes instrument resourcesobtain further exploitation. It brings about a fundamental change to the design and manufacturingmode. The instrument therefore, can not only be produced directly inside a PC, but the product isinvolved in the 'green product' system. So far, all the present digital instruments will grow to bereplaced by intelligent control with green characteristics.

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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Global satisfactory control for nonlinear integrator processes with long delay

Integrator processes with long delay are difficult to control. Nonlinear characteristics of actuators make the control problem more challenging. A technique is proposed in this paper for global satisfactory control （GSC） of such processes with relay-type nonlinearity. An oscillatory control signal is injected into the nonlinear process; the amplitude and frequency of the oscillatory signal are designed to linearise the nonlinear process in the sense of harmonic analysis; and a state feedback controller is configured to implement GSC over the linearised process. An illustrative example is given to demonstrate the effectiveness of