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Ladle Furnace Liquid Steel Temperature Prediction Model Based on Optimally Pruned Bagging

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Journal of Iron and Steel Research International 2012年第12期19卷 21-28页

作者： LU Wu MAO Zhi-zhong YUAN Ping College of Information Science and Engineering Northeastern University Shenyang 110004 Liaoning China state key laboratory of synthetical automation for process Industries University Shenyang 110004 Liaoning China

For accurately forecasting the liquid steel temperature in ladle furnace （LF）, a novel temperature predic tion model based on optimally pruned Bagging combined with modified extreme learning machine （ELM） is pro posed. By analyzing the mechanism of LF thermal system, a thermal model with partial linear structure is obtained. Subsequently, modified ELM, named as partial linear extreme learning machine （PLELM）, is developed to estimate the unknown coefficients and undefined function of the thermal model. Finally, a pruning Bagging method is pro- posed to establish the aggregated prediction model for the sake of overcoming the limitation of individual predictor and further improving the prediction performance. In the pruning procedure, AdaBoost is adopted to modify the ag- gregation order of the original Bagging ensembles, and a novel early stopping rule is designed to terminate the aggre- gation earlier. As a result, an optimal pruned Bagging ensemble is achieved, which is able to retain Bagging＇s ro- bustness against highly influential points, reduce the storage needs as well as speed up the computing time. The pro- posed prediction model is examined by practical data, and comparisons with other methods demonstrate that the new ensemble predictor can improve prediction accuracy, and is usually consisted compactly.

关键词： Bagging extreme learning machine LF liquid steel temperature prediction model AdaBoost

Two-Stage Mathematical Programming Approach for Steelmaking process Scheduling Under Variable Electricity Price

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Journal of Iron and Steel Research International 2013年第7期20卷 1-8页

作者： TAN Yuan-yuan HUANG Ying-lei LIU Shi-xin College of Information Science and Engineering Northeastern University state key laboratory of synthetical automation for process Industries Enterprise Solution Business Group Shanghai Baosight Software Limited Company

The steelmaking process scheduling problem by considering variable electricity price （SMSPVEP） was in- vestigated. A decomposition approach was proposed for the SMSPVEP. At the first stage, mathematical program-ming was utilized to minimize the maximum completion time for each cast without considering variable electricity price. At the second stage, based on obtained relative schedules of all casts, a mathematical model was formulated with an objective of minimizing the energy cost for all casts scheduling problem. The two-stage models were tested on randomly generated instances based on the practical process in a Chinese steelmaking plant. Computational results demonstrate the effectiveness of the proposed approach.

关键词： steelmaking process scheduling variable electricity price mathematical programming

Attention-Aware Heterogeneous Graph Neural Network

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Big Data Mining and Analytics 2021年第4期4卷 233-241页

作者： Jintao Zhang Quan Xu College of Sciences Northeastern UniversityShenyang 110004China state key laboratory of synthetical automation for process Industries Northeastern UniversityShenyang 110819China

As a powerful tool for elucidating the embedding representation of graph-structured data,Graph Neural Networks(GNNs),which are a series of powerful tools built on homogeneous networks,have been widely used in various data mining *** is a huge challenge to apply a GNN to an embedding Heterogeneous Information Network(HIN).The main reason for this challenge is that HINs contain many different types of nodes and different types of relationships between *** contains rich semantic and structural information,which requires a specially designed graph neural ***,the existing HIN-based graph neural network models rarely consider the interactive information hidden between the meta-paths of HIN in the poor embedding of nodes in the *** this paper,we propose an Attention-aware Heterogeneous graph Neural Network(AHNN)model to effectively extract useful information from HIN and use it to learn the embedding representation of ***,we first use node-level attention to aggregate and update the embedding representation of nodes,and then concatenate the embedding representation of the nodes on different ***,the semantic-level neural network is proposed to extract the feature interaction relationships on different meta-paths and learn the final embedding of *** results on three widely used datasets showed that the AHNN model could significantly outperform the state-of-the-art models.

关键词： Graph Neural Network(GNN) Heterogeneous Information Network(HIN) embedding

Prediction method for energy con sumption per ton of fused magnesium furnaces using data driven and mechanism model

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Control Theory and Technology 2019年第1期17卷 24-36页

作者： Dan GUO Zhiwei WU Tianyou CHAI Jie YANG Jinliang DING state key laboratory of synthetical automation for process Industries Northeastern University Shenyang Liaoning 110819 China National Engineering Research Center of Metallurgical automation (Shenyang) Shenyang Liaoning 7 70879 China

The electric energy consumed in every ton of acceptable product, namely energy consumption per ton (ECT), is an important overall index for the production process of a fused magnesium furnace. The furnace is the equipment for producing the fused magnesia. The ECT value depends on the current in the smelting process. The optimal operation for a fused magnesium furnace is supposed to have the ECT as low as possible, where the key is to predict ECT accurately. By introducing an unknown high-order non linear term, this paper builds a dynamic ECT model for differe nt production batches based on the static ECT model for one batch. The average current is taken as the input of the dynamic ECT model, which is composed of the unknown high-order nonlinear term and a nonlinear model with unknown parameters. The order of the nonlinear term is determined by the distance correlatio n and the nonlinear term is estimated by the stochastic con figuration n etwork, while the parameters of the non linear model is ide ntified by the least square method. The estimation of the nonli near term alter nates with the parameter identification. This paper proposes a prediction method for ECT, which is composed of the order identification of the non linear term, the alternating identification of the model and the ECT prediction model. The simulation experiments are conducted by the on-site data, and the results verify the effectiveness of the proposed prediction method.

关键词： Fused magnesia energy consumption per ton alternating identification stochastic configuration network distance correlation

Adaptive Neural Network Control of Thermoacoustic Instability in Rijke Tube: A Fully Actuated System Approach

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Journal of Systems Science & Complexity 2022年第2期35卷 586-603页

作者： ZHAO Yuzhuo MA Dan MA Hongwei state key laboratory of synthetical automation for process Industries and College of Information Science and Engineering Northeastern UniversityShenyang 110819China College of Information Science and Engineering Northeastern UniversityShenyang 110819China

Thermoacoustic instability phenomena often encounter in gas turbine combustors,especially for the premixed combustor design,with many possible detrimental *** a classical experiment,the Rijke tube is the simplest and the most effective illustration to study the thermoacoustic *** paper investigates the active control approach of the thermoacoustic instability in a horizontal Rijke ***’s more,the radial basis function(RBF)neural network is adopted to estimate the complex unknown continuous nonlinear heat release rate in the Rijke ***,based on the proposed second-order fully actuated system model,the authors present an adaptive neural network controller to guarantee the flow velocity fluctuation and pressure fluctuation to converge to a small region of the ***,simulation results demonstrate the feasibility of the design method.

关键词： Adaptive neural network control fully actuated system nonlinear system Rijke tube thermoacoustics instability

Hyperbolic Hybrid Switching Sliding Surface Design For Discrete Delay System With Uncertainty

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Hyperbolic Hybrid Switching Sliding Surface Design For Discr...

A junction-by-junction feedback-based strategy with convergence analysis for dynamic traffic assignment

第36届中国控制会议

作者： Qingling Zhang Huazhou Hou Institute of System Science Northeastern University state key laboratory of synthetical automation for process Industries Northeastern University

ISBN: (纸本)9781538629185

This paper investigates the sliding mode control for discrete time system with input delay and *** different hyperbolic functions are introduced during sliding surface design procedure to design a novel sliding surface,named as hyperbolic hybrid switching sliding *** of the introduction of two hyperbolic functions,we artificially achieve the switching of sliding *** switching can achieve quick response as well as low overshoot of closed loop system with easy implemented *** derived theorems verify the reaching condition of the sliding surface,and the condition to ensure the stability of closed loop ***,simulation example is presented to illustrate the effectiveness of proposed method.

关键词： Sliding mode control Switching Uncertainty Input delay Discrete time

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Science China(Information Sciences) 2016年第1期59卷 30-46页

作者： Tengfei LIU Xuesong LU Zhong-Ping JIANG state key laboratory of synthetical automation for process Industries Northeastern University Department of Electrical and Computer Engineering University of Connecticut Department of Electrical and Computer Engineering Polytechnic School of Engineering New York University

By considering the traffic assignment problem as a control problem, this paper develops a new realtime route guidance strategy for accurate convergence of the traffic flows to user equilibrium(UE) or system optimum(SO), in the presence of drivers’ response uncertainties. With the new guidance strategy, the drivers make routing decisions based on the route guidance information from junction to junction. Specifically, instead of total travel cost of every route from origin to destination, the travel cost of every alternative link plus the average cost to destination from the next junction corresponding to the alternative link is sent to the drivers at each specific junction. The drivers’ response to the route guidance information is directly modeled by the splitting rates at the junctions, which are simply negatively correlated with the comparison of related cost information and are able to take into account the drivers’ response uncertainties. With the proposed route guidance strategy, in the case of fixed travel demands, the accurate convergence of the traffic flows to a UE is guaranteed in the presence of drivers’ response uncertainties by using La Salle’s invariance principle. When marginal travel cost information, instead of travel cost information, is sent to the drivers, a system optimum can be achieved under a mild condition on the marginal cost function.

关键词： dynamic traffic assignment user equilibrium(UE) system optimum(SO) convergence La Salle’s invariance principle

Improved Disturbance Observer (DOB) Based Advanced Feedback Control for Optimal Operation of a Mineral Grinding process

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Chinese Journal of Chemical Engineering 2012年第6期20卷 1206-1212页

作者：周平向波柴天佑 state key laboratory of synthetical automation for process Industries Northeastern University automation Department Tangshan College

Advanced feedback control for optimal operation of mineral grinding process is usually based on the model predictive control (MPC) dynamic optimization. Since the MPC does not handle disturbances directly by controller design, it cannot achieve satisfactory effects in controlling complex grinding processes in the presence of strong disturbances and large uncertainties. In this paper, an improved disturbance observer (DOB) based MPC advanced feedback control is proposed to control the multivariable grinding operation. The improved DOB is based on the optimal achievable H 2 performance and can deal with disturbance observation for the nonminimum-phase delay systems. In this DOB-MPC advanced feedback control, the higher-level optimizer computes the optimal operation points by maximize the profit function and passes them to the MPC level. The MPC acts as a presetting controller and is employed to generate proper pre-setpoint for the lower-level basic feedback control system. The DOB acts as a compensator and improves the operation performance by dynamically compensating the setpoints for the basic control system according to the observed various disturbances and plant uncertainties. Several simulations are performed to demonstrate the proposed control method for grinding process operation.

关键词： disturbance observer model predictive control advanced feedback control grinding process steady-state optimization disturbance rejection

Distributed Control of a Class of Second-Order Nonlinear Multi-Agent Systems with Switching Topologies

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Distributed Control of a Class of Second-Order Nonlinear Mul...