Component Content Soft-sensor Based on Neural Networks in Rare-earth Countercurrent Extraction Process

Throught fusion of the mechanism modeling and the neural networks modeling,a compo- nent content soft-sensor,which is composed of the equilibrium calculation model for multi-component rare earth extraction and the error compensation model of fuzzy system,is proposed to solve the prob- lem that the component content in countercurrent rare-earth extraction process is hardly measured on-line.An industry experiment in the extraction Y process by HAB using this hybrid soft-sensor proves its effectiveness.

Physical Safety and Cyber Security Analysis of Multi-Agent Systems:A Survey of Recent Advances

Multi-agent systems(MASs)are typically composed of multiple smart entities with independent sensing,communication,computing,and decision-making capabilities.Nowadays,MASs have a wide range of applications in smart grids,smart manufacturing,sensor networks,and intelligent transportation systems.Control of the MASs are often coordinated through information interaction among agents,which is one of the most important factors affecting coordination and cooperation performance.However,unexpected physical faults and cyber attacks on a single agent may spread to other agents via information interaction very quickly,and thus could lead to severe degradation of the whole system performance and even destruction of MASs.This paper is concerned with the safety/security analysis and synthesis of MASs arising from physical faults and cyber attacks,and our goal is to present a comprehensive survey on recent results on fault estimation,detection,diagnosis and fault-tolerant control of MASs,and cyber attack detection and secure control of MASs subject to two typical cyber attacks.Finally,the paper concludes with some potential future research topics on the security issues of MASs.

Neural Networks Based Component Content Soft-Sensor in Countercurrent Rare-Earth Extraction

The equilibrium model for multicomponent rare earth extraction is developed using neural networks, which combined with the material balance model could give online prediction of component content in countercurrent rare earth (extraction) production. Simulation experiments with industrial operation data prove the effectiveness of the hybrid soft-(sensor).

Neural-network-based two-loop control of robotic manipulators including actuator dynamics in task space

A neural-network-based motion controller in task space is presented in this paper. The proposed controller is addressed as a two-loop cascade control scheme. The outer loop is given by kinematic control in the task space. It provides a joint velocity reference signal to the inner one. The inner loop implements a velocity servo loop at the robot joint level. A radial basis function network （RBFN） is integrated with proportional-integral （PI） control to construct a velocity tracking control scheme for the inner loop. Finally, a prototype technology based control system is designed for a robotic manipulator. The proposed control scheme is applied to the robotic manipulator. Experimental results confirm the validity of the proposed control scheme by comparing it with other control strategies.

Situation and Developing Trend of Rare-Earth Countercurrent Extraction Processes Control

On the basis of the description of the rare-earth countercurrent extraction process, the on-line detecting method and equipments of rare-earth elements and the application in the process of the rare-earth countercurrent extraction are summarized. The procedure simulation of the computer, the automation control method and its current application are also mentioned in the process of rare-earth countercurrent extraction. The method of soft sensor is proposed. Optimal control method based on object-oriented rare-earth countercurrent extraction process and integrated automation system composed of process management system and process control system are presented, which are the developing direction of the automation of rare-earth countercurrent extraction process.

Integrated Automation System for Rare Earth Countercurrent Extraction Process

Lower automation level in industrial rare-earth extraction processes results in high production cost, inconsistent product quality and great consumption of resources in China. An integrated automation system for extraction process of rare earth is proposed to realize optimal product indices, such as product purity,recycle rate and output. The optimal control strategy for output component, structure and function of the two-gradcd integrated automation system composed of the process management grade and the process control grade were discussed. This system is successfully applied to a HAB yttrium extraction production process and was found to provide optimal control, optimal operation, optimal management and remarkable benefits.

Hybrid intelligent control of combustion process for ore-roasting furnace

Because of its synthetic and complex characteristics, the combustion process of the shaft ore-roasting furnace is very difficult to control stably. A hybrid intelligent control approach is developed which consists of two systems： one is a cascade fuzzy control system with a temperature soft-sensor, and the other is a ratio control system for air flow with a compensation model for heating gas flow and air-fuel ratio. This approach combined intelligent control, soft-sensing and fault diagnosis with conventional control. It can adjust both the heating gas flow and the air-fuel ratio in real time. By this way, the difficulty of online measurement of the furnace temperature is solved, the fault ratios during combustion process is decreased, the steady control of the furnace temperature is achieved, and the gas consumption is reduced. The successful application in shaft furnaces of a mineral processing plant in China indicates its effectiveness.

AN IDENTIFICATION ALGORITHM OF FUZZY MODELS

An identification approach of dynamic system is put forward in this paperwhich can provide the fuzzy models with fairly high accuracy.This method consists ofpremise structure identification,premise parameters identification,consequent structureand parameters identification.It has been applied to some industrial processes modeling.The simulation study shows its effectiveness.

Modeling and Hybrid Optimization of Batching Planning System for Steelmaking-continuous Casting Process

This paper investigates the batching problem for steelmaking and continuous casting production in an iron and steel enterprise. The tasks of this problem are to decide how to select slabs and determine their width, how to group the selected slabs into charges and then group the charges into tundishes, how to determine the sequence of charges in each tundish, and how to group tundishes into casts and determine the sequence of tundishes in each cast. The effective decision on the batching problem can help balance the requirements of the sequential process after steelmaking and continuous casting,reduce production cost, and improve slab quality. We first give the mathematical description of the original problem. Based on the analysis of width, we present a decomposition strategy to divide the model into three sub-models, i.e., charge design model,tundish design model and cast design model, while adding relevant objectives and constraints. According to the characteristics of each sub-model, we present hybrid optimization algorithms separately. Computational experiments show the strategy, models and algorithms can generate satisfactory solutions.

Nonlinear Decoupling PID Control Using Neural Networks and Multiple Models

For a class of complex industrial processes with strong nonlinearity, serious coupling and uncertainty, a nonlinear decoupling proportional-integral-differential （PID） controller is proposed, which consists of a traditional PID controller, a decoupling compensator and a feedforward compensator for the unmodeled dynamics. The parameters of such controller is selected based on the generalized minimum variance control law. The unmodeled dynamics is estimated and compensated by neural networks, a switching mechanism is introduced to improve tracking performance, then a nonlinear decoupling PID control algorithm is proposed. All signals in such switching system are globally bounded and the tracking error is convergent. Simulations show effectiveness of the algorithm.

Multivariable Adaptive Decoupling Controller Using Hierarchical Multiple Models

To solve the problem such as too many models, long computing time and so on, a hierarchical multiple models direct adaptive decoupling controller is designed. It consists of multiple levels. In the upper level, the best model is chosen according to the switching index. Then multiple fixed models are constructed on line to cover the region which the above chosen fixed model lies in.In the last level, one free-running and one re-initialized adaptive model are added to guarantee the stability and improve the transient response. By selection of the weighting polynomial matrix, it not only eliminates the steady output error and places the poles of the closed loop system arbitrarily, but also decouples the system dynamically. At last, for this multiple models switching system, global convergence is obtained under common assumptions. Compared with the conventional multiple models adaptive controller, it reduces the number of the fixed models greatly. If the same number of the fixed models is used, the system transient response and decoupling result are improved. The simulation example illustrates the power of the derived controller.

Multiple-model-and-neural-network-based nonlinear multivariable adaptive control

A multivariable adaptive controller feasible for implementation on distributed computer systems （DCS） is presented for a class of uncertain nonlinear multivariable discrete time systems. The adaptive controller is composed of a linear adaptive controller, a neural network nonlinear adaptive controller and a switching mechanism. The linear controller can provide boundedness of the input and output signals, and the nonlinear controller can improve the performance of the system. The purpose of using the switching mechanism is to obtain the improved system performance and stability simultaneously. Theory analysis and simulation results are presented to show the effectiveness of the proposed method.

Model Software Package Development for Laminar Cooling Process in Hot Mill

The use of computer simulation techniques in the control system design of the laminar cooling process in a hot mill can significantly reduce the development cost while ensuring maximum production safety. The case-based modelling strategy was proposed and implemented in a software package. The functional structure of the software package was discussed in detail. The interfaces and functions of the software package were introduced. The results of industrial experiments can be directly visualized in the software package, allowing accurate and instant comparison of the model predictions with real measurements. The software package provides the simulation platform for research into advanced modelling and control strategies of the laminar cooling process in a hot rolling mill.

Intelligent optimal control system for ball mill grinding process

Operation aim of ball mill grinding process is to control grinding particle size and circulation load to ball mill into their objective limits respectively, while guaranteeing producing safely and stably. The grinding process is essentially a multi-input multi-output system （MIMO） with large inertia, strong coupling and uncertainty characteristics. Furthermore, being unable to monitor the particle size online in most of concentrator plants, it is difficult to realize the optimal control by adopting traditional control methods based on mathematical models. In this paper, an intelligent optimal control method with two-layer hierarchical construction is presented. Based on fuzzy and rule-based reasoning （RBR） algorithms, the intelligent optimal setting layer generates the loops setpoints of the basic control layer, and the latter can track their setpoints with decentralized PID algorithms. With the distributed control system （DCS） platform, the proposed control method has been built and implemented in a concentration plant in Gansu province, China. The industrial application indicates the validation and effectiveness of the proposed method.