气象观测场在汽车试验场中的应用研究

汽车试验场作为汽车开展道路测试的重要场所,用于验证汽车产品的品质以及可靠性。除了场地道路外,气象条件作为汽车道路测试的重要一环,在《GB/T12534-1990汽车道路试验方法通则》中也有明确要求,如:试验时应是无雨无雾天气,相对湿度小于95%,气温0-40℃,风速不大于3m/s。同时气象条件也作为试验场道路管控的重要依据,实时风速、雨量、能见度等信息为场地管理者发布限速、限行、封场等通知提供必要参考依据,直接影响道路测试安全管控的及时性。因此,文章从气象观测场的建设、气象服务、异常天气道路管控等方面开展气象观测场在汽车试验场中的应用研究。

Semantic model and optimization of creative processes at mathematical knowledge formation

The aim of this work is mathematical education through the knowledge system and mathematical modeling. A net model of formation of mathematical knowledge as a deductive theory is suggested here. Within this model the formation of deductive theory is represented as the development of a certain informational space, the elements of which are structured in the form of the orientated semantic net. This net is properly metrized and characterized by a certain system of coverings. It allows injecting net optimization parameters, regulating qualitative aspects of knowledge system under consideration. To regulate the creative processes of the formation and realization of mathematical know- edge, stochastic model of formation deductive theory is suggested here in the form of branching Markovian process, which is realized in the corresponding informational space as a semantic net. According to this stochastic model we can get correct foundation of criterion of optimization creative processes that leads to “great main points” strategy (GMP-strategy) in the process of realization of the effective control in the research work in the sphere of mathematics and its applications.

Detection of Oscillations in Process Control Loops From Visual Image Space Using Deep Convolutional Networks

Oscillation detection has been a hot research topic in industries due to the high incidence of oscillation loops and their negative impact on plant profitability.Although numerous automatic detection techniques have been proposed,most of them can only address part of the practical difficulties.An oscillation is heuristically defined as a visually apparent periodic variation.However,manual visual inspection is labor-intensive and prone to missed detection.Convolutional neural networks(CNNs),inspired by animal visual systems,have been raised with powerful feature extraction capabilities.In this work,an exploration of the typical CNN models for visual oscillation detection is performed.Specifically,we tested MobileNet-V1,ShuffleNet-V2,Efficient Net-B0,and GhostNet models,and found that such a visual framework is well-suited for oscillation detection.The feasibility and validity of this framework are verified utilizing extensive numerical and industrial cases.Compared with state-of-theart oscillation detectors,the suggested framework is more straightforward and more robust to noise and mean-nonstationarity.In addition,this framework generalizes well and is capable of handling features that are not present in the training data,such as multiple oscillations and outliers.

Advancement in Wide Area Monitoring Protection and Control Using PMU’s Model in MATLAB/SIMULINK

A big step forward to improve power system monitoring and performance, continued load growth without a corresponding increase in transmission resources has resulted in reduced operational margins for many power systems worldwide and has led to operation of power systems closer to their stability limits and to power exchange in new patterns. These issues, as well as the on-going worldwide trend towards deregulation of the entire industry on the one hand and the increased need for accurate and better network monitoring on the other hand, force power utilities exposed to this pressure to demand new solutions for wide area monitoring, protection and control. Wide-area monitoring, protection, and control require communicating the specific-node information to a remote station but all information should be time synchronized so that to neutralize the time difference between information. It gives a complete simultaneous snap shot of the power system. The conventional system is not able to satisfy the time-synchronized requirement of power system. Phasor Measurement Unit (PMU) is enabler of time-synchronized measurement, it communicate the synchronized local information to remote station.

Structures and Properties of Iron Matrix Composites with Tungsten Carbide Particle by EPC-V Process

In this paper the Expendable Pattern Casting with dry sand Vacuum(EPC-V) process is used to manufacture iron matrix composites with tungsten carbide particle.Microstructures of the composites layers were analyzed.The abrasive wear resistance of the composites layers were tested and compared with that of high chromium cast iron.The results show that the iron matrix composites with tungsten carbide particle have high hardness.The abrasive wear resistance of composites with tungsten carbide particle is higher than that of high chromium cast iron.The properties of the matrix materials have been improved remarkably.

Real-time model correction using Kalman filter for Raman-controlled cell culture processes

Raman spectroscopy has found extensive use in monitoring and controlling cell culture processes.In this context,the prediction accuracy of Raman-based models is of paramount importance.However,models established with data from manually fed-batch cultures often exhibit poor performance in Raman-controlled cultures.Thus,there is a need for effective methods to rectify these models.The objective of this paper is to investigate the efficacy of Kalman filter(KF)algorithm in correcting Raman-based models during cell culture.Initially,partial least squares(PLS)models for different components were constructed using data from manually fed-batch cultures,and the predictive performance of these models was compared.Subsequently,various correction methods including the PLS-KF-KF method proposed in this study were employed to refine the PLS models.Finally,a case study involving the auto-control of glucose concentration demonstrated the application of optimal model correction method.The results indicated that the original PLS models exhibited differential performance between manually fed-batch cultures and Raman-controlled cultures.For glucose,the root mean square error of prediction(RMSEP)of manually fed-batch culture and Raman-controlled culture was 0.23 and 0.40 g·L-1.With the implementation of model correction methods,there was a significant improvement in model performance within Raman-controlled cultures.The RMSEP for glucose from updating-PLS,KF-PLS,and PLS-KF-KF was 0.38,0.36 and 0.17 g·L-1,respectively.Notably,the proposed PLS-KF-KF model correction method was found to be more effective and stable,playing a vital role in the automated nutrient feeding of cell cultures.

Reducing Greenhouse Gas Emission from the Neodymium Oxide Electrolysis. Part II: Basics of a Process Control Avoiding PFC Emission

The neodymium electrolysis produces unnecessary high emission of CF4 and C2F6. These perfluorocarbons (PFCs) are potent greenhouse gases and are not filtered or destroyed in the off-gas. A process control in analogy to the aluminum electrolysis can reduce the PFC emission to a great extend and keep the process in a green process window. Therefore, a theoretical analysis is done of the cell voltage of the industrial neodymium electrolysis in dependence on the neodymium oxide concentration in the electrolyte. The analysis shows the different contributions to the cell voltage focusing on the impact of the anodic overvoltage on the cell voltage, by which the electrolysis process can be controlled. The model of the cell voltage is evaluated by laboratory neodymium electrolysis with a similar setup as the industrial cell. The relation of the oxide concentration, the anodic current density and the cell voltage with the cell resistance are measured. The continuous off-gas measurements show the gas concentration and PFC emissions. The effect of Nd2O3 feeding on the galvanostatic electrolysis is analyzed as well. Based on the results a process control strategy is proposed similar to the aluminum electrolysis strategy. The green process window is in a narrow oxide concentration range, making a continuous and precise oxide feeding essential.

Identification and Control of Typical Industrial Process

Many industrial processes such as heating furnaces have over damping dynamic characteristics. Based on an innovative impulse response model, a method of identification and control for the over damping plant is introduced in the paper. The number of parameters of the model is much less than conventional impulse response model. The model based on tuning procedure of numerical optimum PID controller parameters is presented. For an actual instance, a large scale airflow circulatory resistance furnace control system with cascades of time delays is developed. In the system, the optimum PID control is used in the inner loop. A nonlinear PI compensation control is applied in the outer loop. The coordinating control among each output is realized by a fuzzy control strategy. A process surveillance organization monitors running situation of system and tunes controller parameters.

Process Control and Optimization for Heterologous Protein Production by Methylotrophic Pichia pastoris

The methylotrophic yeast Pichia pastoris is a highly successful system for production of a variety of heterologous proteins due to its unique features/abilities for effective protein expression, and tremendous efforts have been made to increase heterologous protein productivity by P. pastoris in recent years. When new engineered yeast strains are constructed and are ready to use for industrial protein production, process control and optimization techniques should be applied to improve the fermentation performance in the following aspects: (1) increase recombinant cell concentrations in fermentor to high density during growth phase; (2) effectively induce heterologous proteins by enhancing/stabilizing titers or concentrations of the proteins during induction phase; (3) decrease operation costs by relieving the working loads of heat-exchange and oxygen supply. This article reviews and discusses the key and commonly used techniques in heterologous protein production by P. pastoris, with the focus on optimizations of fermentation media and basic operation conditions, development of optimal glycerol feeding strategies for achieving high density cultivation of P. pastoris and effective heterologous protein induction methods by regulating specific growth rate, methanol concentration, temperatures, mixture ratio of multi-carbon substrates, etc. Metabolic analysis for recombinant protein production by P. pastoris is also introduced to interpret the mechanism of sub-optimal heterologous protein production and to explore further optimal expression methods.

ROBUST SMITH PREDICTIVE CONTROLLER FOR PROCESS WITH INVERSE RESPONSE

Smith predictor known as the time delay compensator was extended to control the process with inverse response.Modern robust control theory was employed to design the robust controller,which has only one parameter to be determined with compromise among the rise time,undershoot,robustness and capability to reject disturbance of the closed loop system.The former two specifications can be assessed quantitatively and the latter two qualitatively.Examples show that the proposed method has significant improvements and wide applicable ranges for inverse response process.

Integrated Product and Process Control for Sustainable Semiconductor Manufacturing

半导体制造是有几百复杂单位操作和它的一个生产序列被策略开发总是为保证没有缺点的产品的收益质问。在这份报纸，通过集成产品和过程控制的先进控制策略被建立。建议多尺度的计划为协调设备控制，进程控制和产品质量控制包含三层。在上面的层，联机控制表演评价被使用减少优秀变化并且最大化全面产品表演(OPP ) 。它用作监督控制在中间的层更新进程控制器的配方。过程控制器作为指数地加权的移动一般水准(EWMA ) 被设计拒绝骚乱的 run-to-run 控制器那例如用坏的过程移动，飘移和工具，被施加到操作。在这个过程的设备个别地被控制维持它的最佳的运作的地位并且最大化全面设备有效性(OEE ) ，基于过程控制器给的集合点。建议综合控制计划的功效通过案例研究被表明，在的地方两 OPP (为产品) 并且 OEE (为设备) 被提高。

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.

A novel Q-based online model updating strategy and its application in statistical process control for rubber mixing

To overcome the large time-delay in measuring the hardness of mixed rubber, rheological parameters were used to predict the hardness. A novel Q-based model updating strategy was proposed as a universal platform to track time-varying properties. Using a few selected support samples to update the model, the strategy could dramatically save the storage cost and overcome the adverse influence of low signal-to-noise ratio samples. Moreover, it could be applied to any statistical process monitoring system without drastic changes to them, which is practical for industrial practices. As examples, the Q-based strategy was integrated with three popular algorithms(partial least squares(PLS), recursive PLS(RPLS), and kernel PLS(KPLS)) to form novel regression ones, QPLS, QRPLS and QKPLS, respectively. The applications for predicting mixed rubber hardness on a large-scale tire plant in east China prove the theoretical considerations.

Integrated Statistical and Engineering Process Control Based on Smooth Transition Autoregressive Model

Traditional studies on integrated statistical process control and engineering process control (SPC-EPC) are based on linear autoregressive integrated moving average (ARIMA) time series models to describe the dynamic noise of the system.However,linear models sometimes are unable to model complex nonlinear autocorrelation.To solve this problem,this paper presents an integrated SPC-EPC method based on smooth transition autoregressive (STAR) time series model,and builds a minimum mean squared error (MMSE) controller as well as an integrated SPC-EPC control system.The performance of this method for checking the trend and sustained shift is analyzed.The simulation results indicate that this integrated SPC-EPC control method based on STAR model is effective in controlling complex nonlinear systems.

Optimization of chemistry and process parameters for control of intermetallic formation in Mg sludges

Intermetallic formation in sludge during magnesium(Mg)melting,holding and high pressure die casting practices is a very important issue.But,very often it is overlooked by academia,original equipment manufacturers(OEM),metal ingot producers and even die casters.The aim of this study was to minimize the intermetallic formation in Mg sludge via the optimization of the chemistry and process parameters.The Al8Mn5 intermetallic particles were identified by the microstructure analysis based on the Al and Mn ratio.The design of experiment(DOE)technique,Taguchi method,was employed to minimize the intermetallic formation in the sludge of Mg alloys with various chemical compositions of Al,Mn,Fe,and different process parameters,holding temperature and holding time.The sludge yield(SY)and intermetallic size(IS)was selected as two responses.The optimum combination of the levels in terms of minimizing the intermetallic formation were 9 wt.%Al,0.15 wt.%Mn,0.001 wt.%(10 ppm)Fe,690℃ for the holding temperature and holding at 30 mins for the holding time,respectively.The best combination for smallest intermetallic size were 9 wt.%Al,0.15 wt.%Mn,0.001 wt.%(10 ppm)Fe,630℃ for the holding temperature and holding at 60 mins for the holding time,respectively.Three groups of sludge factors,Chemical Sludge(CSF),Physical Sludge(PSF)and Comprehensive Sludge Factors(and CPSF)were established for prediction of sludge yields and intermetallic sizes in Al-containing Mg alloys.The CPSF with five independent variables including both chemical elements and process parameters gave high accuracy in prediction,as the prediction of the PSF with only the two processing parameters of the melt holding temperature and time showed a relatively large deviation from the experimental data.The Chemical Sludge Factor was primarily designed for small ingot producers and die casters with a limited melting and holding capacity,of which process parameters could be fixed easily.The Physical Sludge Factor could be used for mass production with a single type of Mg alloy,in which the chemistry fluctuation might be negligible.In large Mg casting suppliers with multiple melting and holding furnaces and a number of Mg alloys in production,the Comprehensive Sludge Factor should be implemented to diminish the sludge formation.

Adaptive Fuzzy Sliding Mode Controller for Grid Interface Ocean Wave Energy Conversion

This paper presents a closed-loop vector control structure based on adaptive Fuzzy Logic Sliding Mode Controller (FL-SMC) for a grid-connected Wave Energy Conversion System (WECS) driven Self-Excited Induction Generator (SEIG). The aim of the developed control method is to automatically tune and optimize the scaling factors and the membership functions of the Fuzzy Logic Controllers (FLC) using Multi-Objective Genetic Algorithms (MOGA) and Multi-Objective Particle Swarm Optimization (MOPSO). Two Pulse Width Modulated voltage source PWM converters with a carrier-based Sinusoidal PWM modulation for both Generator- and Grid-side converters have been connected back to back between the generator terminals and utility grid via common DC link. The indirect vector control scheme is implemented to maintain balance between generated power and power supplied to the grid and maintain the terminal voltage of the generator and the DC bus voltage constant for variable rotor speed and load. Simulation study has been carried out using the MATLAB/Simulink environment to verify the robustness of the power electronics converters and the effectiveness of proposed control method under steady state and transient conditions and also machine parameters mismatches. The proposed control scheme has improved the voltage regulation and the transient performance of the wave energy scheme over a wide range of operating conditions.

Research on Production Process Control Method Combined Stochastic Process Algebra and Stochastic Petri Nets

A hierarchical closed-loop production control scheme integrating scheduling,control and performance evaluation is discussed.Firstly,the production process is divided into two main hierarchies:the lower level is the physical operation level and the upper one is the management level.Secondly,the schedule template for the management level and the activity template for the physical operation level are constructed separately,the tasks in the schedule have the ability to make partial decisions,and the per- formance parameters are introduced into activity template.Thirdly,the two levels use different model representations:stochastic process algebra for the management level whose output is the control commands and stochastic Petri net for the physical operation lev- el which is the execution of the control commands.Then,the integration of the two levels is the control commands mapping into the lower physical operations and the responses feeding back to the upper decision-making that are defined by some transition functions. Under the proposed scheme,the production process control of a flexible assembly is exemplified.It is concluded that the process con- trol model has partial ability to make decision on-line for uncertain and dynamic environments and facilitates reasoning about the be- haviors of the process control,and performance evaluation can be done online for real-time scheduling to ensure the global optimiza- tion.

A Pole Assignment Design Method of Process Control System for Tubular Heat Exchanger

This Paper has first studied the simplified model of tubular heat exchanger which is widely used in the industry and other field.On the basis of reference 2,a new pole assignment design method of pro-cess control system with derivative control action is found.For the above system,the method and the for-mation which calculate the feedback matrix K and gain matrix L is given,and the simulation of the system is made.