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Modeling and optimization of feedstock allocation problem based on industrial cracking furnace system using an asynchronous updating differential evolution algorithm

Modeling and Optimization of feedstock allocation problem ba...

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第26届中国过程控制会议（CPCC2015）

作者： Lin Yue Feng Du WenLi Li JinLong Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology

This paper introduces a practical optimization model of multiple cracking furnaces system with consideration of feedstock allocation. The goal of our work is to model this problem and to find the best allocation combination of feedstock to the cracking furnaces to maximize the profit of high-added-value production. Using artificial neural network(ANN) technique, the model can be formulated as a binary-integer nonlinear programming model. However, owing to the existence of integer variables and equality constraints, it's hard to be solved by traditional evolutionary ***, an asynchronous updating differential algorithm(AUDE) is proposed in this paper to better handle discrete variables. Finally, to test the effectiveness and robustness of the proposed algorithm, simulation results on benchmark problems and practical application case shows that the proposed algorithm is robust and capable of getting satisfactory optimization results when compared with the state-of-the art evolution algorithm.

关键词： Differential evolution feedstock allocation ethylene cracking furnace modeling asynchronous updating

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A Study on SVM Based on the Weighted Elitist Teaching-Learning-Based optimization and Application in the Fault Diagnosis of chemical Process

A Study on SVM Based on the Weighted Elitist Teaching-Learni...

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The 2015 2nd International Conference on Engineering Technology and Application

作者： Junxiang Cao Jianxu Luo Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of EducationEast China University of Science and Technology

Teaching-Learning-Based optimization(TLBO) is a new swarm intelligence optimization algorithm that simulates the class learning *** to such problems of the traditional TLBO as low optimizing efficiency and poor stability,this paper proposes an improved TLBO algorithm mainly by introducing the elite thought in TLBO and adopting different inertia weight decreasing strategies for elite and ordinary individuals of the teacher stage and the student *** this paper,the validity of the improved TLBO is verified by the optimizations of several typical test functions and the SVM optimized by the weighted elitist TLBO is used in the diagnosis and classification of common failure data of tie TE chemical *** with the SVM combining other traditional optimizing methods,the SVM optimized by the weighted elitist TLBO has a certain improvement in the accuracy of fault diagnosis and classification.

关键词： TLBO algorithm support vector machine fault diagnosis TE chemical process

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Robust optimization for a Multi-Product Integrated Problem of Planning and Scheduling under Products Uncertainty

Robust Optimization for a Multi-Product Integrated Problem o...

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2015 Workshop 1

作者： Mengwen Chen Cuiwen Cao Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology

This paper presents robust optimization models for a multi-product integrated problem of planning and scheduling(based on the work of Terrazas-Moreno & Grossmann(2011) [1]) under products prices uncertainty. With the objective of maximizing the total profit in planning time horizon, the planning section determines the amount of each product, each product distributed to each market, and the inventory level in each manufacturing site during each scheduling time period;the scheduling section determines the products sequence, start and end time of each product running in each production site during each scheduling time period. The uncertainty sets used in robust optimization model are box set, ellipsoidal set, polyhedral set, combined box and ellipsoidal set, combined box and polyhedral set, combined box, ellipsoidal and polyhedral *** genetic algorithm is utilized to solve the robust optimization models. Case studies show that the solutions obtained from robust optimization models are better than the solutions obtained from the original integrated planning and scheduling when the prices are changed.

关键词： Uncertainty Robust optimization Integrated Problem of Planning and Scheduling GA

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Sensor Scheduling for Distributed Filtering under Limited Communication Resources

Sensor Scheduling for Distributed Filtering under Limited Co...

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IEEE Annual Conference on Decision and control

作者： Wen Yang Chao Yang Hongbo Shi Key Laboratory of Advanced Control and Optimization for Chemical Processes(East China University of Science and Technology) Ministry of Education

ISBN: (纸本)9781479978878

In this paper we consider the problem of sensor scheduling for distributed estimation with power constraint. To satisfy the requirement of energy consumption, we assume that each sensor sends data to its adjacent sensors with a given probability, where the state of the system is estimated by means of distributed estimators. First, we design optimal estimator gain to minimize the estimation error covariance for each sensor, and then we find an upper bound of the expected state error covariance, provide a sufficient condition to guarantee the stability of the proposed estimator. To find the optimal communication probability, we further formulate the sensor scheduling problem as an optimization problem, which is relaxed to a convex optimization in the form of LMIs.

关键词： Sensors communication resources Distribute Convex function Covariance Filtration energy consumption

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Improving the Performance of Online Classifier by Removing the Error Samples from Offline Training Data

Improving the Performance of Online Classifier by Removing t...

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2015 IEEE International Conference on Computer and Communications(ICCC 2015)

作者： Hanhan Zhang Jing Jin Sijie Zhou Yu Zhang Xingyu Wang Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology

Brain-computer interface(BCI) plays an important role in helping the people with severe motor disability. In event-related potential(ERP) based BCIs, subjects were asked to count the target stimulus in the offline experiment, the recorded electroencephalogram(EEG) data was used to train the classification mode. However, subjects may make mistakes in counting the target stimulus or be affected by the non-target stimulus. The target trials may not contain expected ERPs and the non-target trials may contain unexpected ERPs, which was called error samples. This paper intends to survey whether the classification accuracy could be improved after removing these error samples from offline training data. The result showed that the online performance of BCI system could be improved after selecting the offline samples for training the classification mode.

关键词： Brain-computer interface(BCI) P300 feature selection algorithm

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SSVEP Recognition using Multivariate Linear Regression for Brain Computer Interface

SSVEP Recognition using Multivariate Linear Regression for B...

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2015 IEEE International Conference on Computer and Communications(ICCC 2015)

作者： Haiqiang Wang Yu Zhang Jing Jin Xingyu Wang The Key Laboratory for Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology

Until now, the canonical correlation analysis(CCA)-based method has been most widely applied to steady-state visual evoked potential(SSVEP). Artificial sine-cosine signals are used as the original references in the CCA method, which could hardly reflect the real SSVEP features buried in electroencephalogram(EEG). In this study, we use principal component analysis(PCA) to extract EEG features multivariate linear regression(MLR) is implemented on EEG and the specific sample labels. Experimental results show that the proposed MLR method outperformed other two competing methods for SSVEP recognition, especially in short time window.

关键词： Brain-Computer Interface(BCI) Canonical Correlation Analysis(CCA) Electroencephalogram(EEG) Steady-State Visual Evoked Potential(SSVEP) principal component analysis(PCA) Multivariate Linear Regression(MLR)

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Partial stabilization for Boolean network with state feedback control 34

Partial stabilization for Boolean network with state feedbac...

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第三十四届中国控制会议

作者： LI Fangfei ZHAO Shouwei LI Chunxiang YU Zhaoxu Department of Mathematics East China University of Science and Technology School of Fundamental Studies Shanghai University of Engineering Science Department of Mathematics and Physics Second Military Medical University Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology

ISBN: (纸本)9781467374439

In the literatures, to design state feedback controller to partially stabilize Boolean control network is rarely *** by this, this paper studies partial stabilization for Boolean network with state feedback control. A sufficient condition for the existence of a state feedback controller to achieve the partial stabilization is established first. Then, the feedback control law is proposed. Finally, examples are presented to show the proposed design procedure.

关键词： Boolean control network,partial stabilization,feedback control design,semi-tensor product

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Distributed formation control for a multi-agent system with dynamic and static obstacle avoidances

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Chinese Physics B 2014年第7期23卷 337-342页

作者：曹建福凌志浩袁宜峰高冲 Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology

Formation control and obstacle avoidance for multi-agent systems have attracted more and more attention. In this paper, the problems of formation control and obstacle avoidance are investigated by means of a consensus algorithm. A novel distributed control model is proposed for the multi-agent system to form the anticipated formation as well as achieve obstacle avoidance. Based on the consensus algorithm, a distributed control function consisting of three terms （formation control term, velocity matching term, and obstacle avoidance term） is presented. By establishing a novel formation control matrix, a formation control term is constructed such that the agents can converge to consensus and reach the anticipated formation. A new obstacle avoidance function is developed by using the modified potential field approach to make sure that obstacle avoidance can be achieved whether the obstacle is in a dynamic state or a stationary state. A velocity matching term is also put forward to guarantee that the velocities of all agents converge to the same value. Furthermore, stability of the control model is proven. Simulation results are provided to demonstrate the effectiveness of the proposed control.

关键词： multi-agent system formation control obstacle avoidance consensus theory

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A fast MPC algorithm for reducing computation burden of MIMO

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Chinese Journal of chemical Engineering 2015年第12期23卷 2087-2091页

作者：祁荣宾梅华陈超钱锋 Key Laboratory of Advanced Control and Optimization for Chemical Process Ministry of EducationEast China University of Science and Technology

The computation burden in the model-based predictive control algorithm is heavy when solving QR optimization with a limited sampling step, especially for a complicated system with large dimension. A fast algorithm is proposed in this paper to solve this problem, in which real-time values are modulated to bit streams to simplify the multiplication. In addition, manipulated variables in the prediction horizon are deduced to the current control horizon approximately by a recursive relation to decrease the dimension of QR optimization. The simulation results demonstrate the feasibility of this fast algorithm for MIMO systems.

关键词： Fast MPC algorithm Computation burden One-bit operation Dimension reduction

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Full Form Dynamic Linearization controller Based Data-driven Model Free Adaptive control

Full Form Dynamic Linearization Controller Based Data-driven...

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第27届中国控制与决策会议

作者： Yuanming Zhu Zhongsheng Hou Shangtai Jin Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of EducationEast China University of Science and Technology Advanced Control System Laboratory Beijing Jiaotong University

ISBN: (纸本)9781479970186

This paper presents a data-driven model free adaptive control method based on full form dynamic linearization *** this framework,the controller structure is designed by the full form dynamic linearization technique on the ideal controller,and its parameters are online optimized using input and output data of the plant through the simple projection algorithm,where the accurate plant model is not *** effectiveness of the proposed method is verified by numerical simulations.

关键词： Dynamic Linearization,Data-driven,Model Free Adaptive control,Nonlinear System

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