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A novel hybrid estimation of distribution algorithm for solving hybrid flowshop scheduling problem with unrelated parallel machine

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Journal of Central South University 2017年第8期24卷 1779-1788页

作者：孙泽文顾幸生 Key Laboratory of Advanced Control and Optimization for Chemical Process Ministry of Education(East China University of Science and Technology)Shanghai 200237China

The hybrid flow shop scheduling problem with unrelated parallel machine is a typical NP-hard combinatorial optimization problem, and it exists widely in chemical, manufacturing and pharmaceutical industry. In this work, a novel mathematic model for the hybrid flow shop scheduling problem with unrelated parallel machine(HFSPUPM) was proposed. Additionally, an effective hybrid estimation of distribution algorithm was proposed to solve the HFSPUPM, taking advantage of the features in the mathematic model. In the optimization algorithm, a new individual representation method was adopted. The(EDA) structure was used for global search while the teaching learning based optimization(TLBO) strategy was used for local search. Based on the structure of the HFSPUPM, this work presents a series of discrete operations. Simulation results show the effectiveness of the proposed hybrid algorithm compared with other algorithms.

关键词： hybrid estimation of distribution algorithm teaching learning based optimization strategy hybrid flow shop unrelated parallel machine scheduling

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Wind field reconstruction for the dispersion modeling of accidental chemical spills on complex geometry

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Chinese Journal of chemical Engineering 2019年第11期27卷 2712-2724页

作者： Bing Wang Feng Qian Weimin Zhong Key Laboratory of Advanced Control and Optimization for Chemical Process Ministry of EducationEast China University of Science and TechnologyShanghai 200237China

chemical spills on complex geometry are difficult to model due to the uneven concentration distribution caused by air flow over ground obstacles. Computational fluid dynamics(CFD) is one of the powerful tools to estimate the building-resolving wind flow as well as pollutant dispersion. However, it takes too much time and requires enormous computational power in emergency situations. As a time demanding task, the estimation of the chemical spill consequence for emergency response requires abundant wind field information. In this paper, a comprehensive wind field reconstruction framework is proposed, providing the ability of parameter tuning for best reconstruction accuracy. The core of the framework is a data regression model built on principal component analysis(PCA) and extreme learning machine(ELM). To improve the accuracy, the wind field estimation from the regression model is further revised from local wind observations. The optimal placement of anemometers is provided based on the maximum projection on minimum eigenspace(MPME) algorithm. The fire dynamic simulator(FDS) generates high-resolution data of wind flow over complex geometries for the framework to be implemented. The reconstructed wind field is evaluated against simulation data and an overall reconstruction error of 9% is achieved. When used in real case,the error increases to around 12% since no convergence check is available. With parameter tuning abilities,the proposed framework provides an efficient way of reconstructing the wind flow in congested areas.

关键词： Wind field reconstruction CFD PCA Extreme learning machine Sensor placement

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Monocular depth estimation based on deep learning: An overview

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Science China(Technological Sciences) 2020年第9期63卷 1612-1627页

作者： ZHAO ChaoQiang SUN QiYu ZHANG ChongZhen TANG Yang QIAN Feng Key Laboratory of Advanced Control and Optimization for Chemical Process Ministry of EducationEast China University of Science and TechnologyShanghai 200237China

Depth information is important for autonomous systems to perceive environments and estimate their own state. Traditional depth estimation methods, like structure from motion and stereo vision matching, are built on feature correspondences of multiple viewpoints. Meanwhile, the predicted depth maps are sparse. Inferring depth information from a single image(monocular depth estimation) is an ill-posed problem. With the rapid development of deep neural networks, monocular depth estimation based on deep learning has been widely studied recently and achieved promising performance in accuracy. Meanwhile, dense depth maps are estimated from single images by deep neural networks in an end-to-end manner. In order to improve the accuracy of depth estimation, different kinds of network frameworks, loss functions and training strategies are proposed subsequently. Therefore, we survey the current monocular depth estimation methods based on deep learning in this review. Initially, we conclude several widely used datasets and evaluation indicators in deep learning-based depth estimation. Furthermore, we review some representative existing methods according to different training manners: supervised, unsupervised and semi-supervised. Finally, we discuss the challenges and provide some ideas for future researches in monocular depth estimation.

关键词： autonomous systems monocular depth estimation deep learning unsupervised learning

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Multi-objective optimization of p-xylene oxidation process using an improved self-adaptive differential evolution algorithm

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Chinese Journal of chemical Engineering 2017年第8期25卷 983-991页

作者： Lili Tao Bin Xu Zhihua Hu Weimin Zhong College of Engineering Shanghai Polytechnic University School of Mechanical Engineering Shanghai University of Engineering Science Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology

The rise in the use of global polyester fiber contributed to strong demand of the Terephthalic acid （TPA）. The liquid-phase catalytic oxidation of p-xylene （PX） to TPA is regarded as a critical and efficient chemical process in industry [ 1 ]. PX oxidation reaction involves many complex side reactions, among which acetic acid combustion and PX combustion are the most important. As the target product of this oxidation process, the quality and yield of TPA are of great concern. However, the improvement of the qualified product yield can bring about the high energy consumption, which means that the economic objectives of this process cannot be achieved simulta- neously because the two objectives are in conflict with each other. In this paper, an improved self-adaptive multi-objective differential evolution algorithm was proposed to handle the multi-objective optimization prob- lems. The immune concept is introduced to the self-adaptive multi-objective differential evolution algorithm （SADE） to strengthen the local search ability and optimization accuracy. The proposed algorithm is successfully tested on several benchmark test problems, and the performance measures such as convergence and divergence metrics are calculated. Subsequently, the multi-objective optimization of an industrial PX oxidation process is carried out using the proposed immune self-adaptive multi-objective differential evolution algorithm （ISADE）. optimization results indicate that application oflSADE can greatly improve the yield of TPA with low combustion loss without degenerating TA quality.

关键词： p-Xylene oxidation Operation condition optimization Multi-objective optimization Self-adaptive differential evolution

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Neural network-based source tracking of chemical leaks with obstacles

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Chinese Journal of chemical Engineering 2021年第5期34卷 211-220页

作者： Qiaoyi Xu Wenli Du Jinjin Xu Jikai Dong Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of EducationEast China University of Science and TechnologyShanghai 200237China Shanghai Institute of Intelligent Science and Technology Tongji UniversityShanghai 200092China

The leakage of hazardous gases poses a significant threat to public security and causes environmental *** effective and accurate source term estimation(STE)is necessary when a leakage accident ***,most research generally assumes that no obstacles exist near the leak source,which is inappropriate in practical *** solve this problem,we propose two different frameworks to emphasize STE with obstacles based on artificial neural network(ANN)and convolutional neural network(CNN).Firstly,we build a CFD model to simulate the gas diffusion in obstacle scenarios and construct a benchmark ***,we define the structure of ANN by searching,then predict the concentration distribution of gas using the searched model,and optimize source term parameters by particle swarm optimization(PSO)with well-performed cost ***,we propose a one-step STE method based on CNN,which establishes a link between the concentration distribution and the location of ***,we propose a novel data processing method to process sensor data,which maps the concentration information into feature *** comprehensive experiments illustrate the performance and efficiency of the proposed methods.

关键词： Obstacle optimization Neural networks Feature extraction Source term estimation Computational fluid dynamics (CFD)

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Multi-objective modeling and optimization for scheduling of cracking furnace systems

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Chinese Journal of chemical Engineering 2017年第8期25卷 992-999页

作者： Peng Jiang Wenli Du Key Laboratory of Advanced Control and Optimization for Chemical Process Ministry of Education School of Information Science and Engineering East China University of Science and Technology

Cracking furnace is the core device for ethylene production. In practice, multiple ethylene furnaces are usually run in parallel. The scheduling of the entire cracking furnace system has great significance when multiple feeds are simultaneously processed in multiple cracking furnaces with the changing of operating cost and yield of product. In this paper, given the requirements of both profit and energy saving in actual production process, a multi-objective optimization model contains two objectives, maximizing the average benefits and minimizing the average coking amount was proposed. The model can be abstracted as a multi-objective mixed integer non- linear programming problem. Considering the mixed integer decision variables of this multi-objective problem, an improved hybrid encoding non-dominated sorting genetic algorithm with mixed discrete variables （MDNSGA-II） is used to solve the Pareto optimal front of this model, the algorithm adopted crossover and muta- tion strategy with multi-operators, which overcomes the deficiency that normal genetic algorithm cannot handle the optimization problem with mixed variables. Finally, using an ethylene plant with multiple cracking furnaces as an example to illustrate the effectiveness of the scheduling results by comparing the optimization results of multi-objective and single objective model.

关键词： Cracking furnace systems Feed scheduling Multi-objective mixed integer nonlinear optimization Genetic algorithm

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Reaction mechanism of methyl nitrite dissociation during co catalytic coupling to dimethyl oxalate:A density functional theory study

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Chinese Journal of chemical Engineering 2016年第1期24卷 132-139页

作者： Chen Fan Man Luo Wende Xiao Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology (ECUST) School of Chemistry and Chemical Engineering Shanghai Jiao Tong University (SJTU)

Dissociation of methyl nitrite is the first step during CO catalytic coupling to dimethyl oxalate followed by hydrogenation to ethyl glycol in a typical coal to liquid process. In this work, the first-principle calculations based on density functional theory were performed to explore the reaction mechanism for the non-catalytic dissociation of methyl nitrite in the gas phase and the catalytic dissociation of methyl nitrite on Pd(111) surface since palladium supported on alpha-alumina is the most effective catalyst for the coupling. For the non-catalytic case, the calculated results show that the CH_3O–NO bond will break with a bond energy of 1.91 eV, and the produced CH_3O radicals easily decompose to formaldehyde, while the further dissociation of formaldehyde in the gas phase is difficult due to the strong C–H bond. On the other hand, the catalytic dissociation of methyl nitrite on Pd(111) to the adsorbed CH_3O and NO takes place with a small energy barrier of 0.03 eV. The calculated activation energies along the proposed reaction pathways indicate that(i) at low coverage, a successive dehydrogenation of the adsorbed CH_3O to CO and H is favored while(ii) at high coverage, hydrogenation of CH_3O to methanol and carbonylation of CH_3O to methyl formate are more preferred. On the basis of the proposed reaction mechanism,two meaningful ways are proposed to suppress the dissociation of methyl nitrate during the CO catalytic coupling to dimethyl oxalate.

关键词： Methyl nitrite Catalytic Non-catalytic Dissociation Density functional theory

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Combined Timetabling Procedure and Complete Local Search for No-Wait Job Shop Scheduling with Total Tardiness

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Journal of Donghua University(English Edition) 2014年第2期31卷 83-91页

作者：杨玉珍顾幸生 Key Laboratory of Advanced Control and Optimization for Chemical Process Ministry of EducationEast China University of Science and Technology

The strong non-deterministic polynomial-hard( NP-hard)character of job shop scheduling problem( JSSP) has been acknowledged widely and it becomes stronger when attaches the nowait constraint,which widely exists in many production processes,such as chemistry process, metallurgical process. However,compared with the massive research on traditional job shop problem,little attention has been paid on the no-wait ***,in this paper, we have dealt with this problem by decomposing it into two sub-problems, the timetabling and sequencing problems,in traditional frame work. A new efficient combined non-order timetabling method,coordinated with objective of total tardiness,is proposed for the timetabling problems. As for the sequencing one,we have presented a modified complete local search with memory combined by crossover operator and distance counting. The entire algorithm was tested on well-known benchmark problems and compared with several existing *** experiments showed that our proposed algorithm performed both effectively and efficiently.

关键词： job shop scheduling no-wait timetabling tardiness complete local search with memory

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Hybrid Discrete Harmony Search Algorithm for Flow Shop Scheduling with Limited Buffers

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Journal of Donghua University(English Edition) 2014年第2期31卷 171-178页

作者：崔喆顾幸生 Key Laboratory of Advanced Control and Optimization for Chemical Process Ministry of EducationEast China University of Scienceand Technology

The flow shop scheduling problem with limited buffers( LBFSP) widely exists in manufacturing systems. A hybrid discrete harmony search algorithm is proposed for the problem to minimize total flow time. The algorithm presents a novel discrete improvisation and a differential evolution scheme with the jobpermutation-based representation. Moreover,the discrete harmony search is hybridized with the problem-dependent local search based on insert neighborhood to balance the global exploration and local exploitation. In addition, an orthogonal experiment design is employed to provide a receipt for turning the adjustable parameters of the algorithm. Comparisons based on the Taillard benchmarks indicate the superiority of the proposed algorithm in terms of effectiveness and efficiency.

关键词： multiproduct processes scheduling problem limited buffers total flow time harmony search

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Active training sample selection and updating strategy for near-infrared model with an industrial application

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Chinese Journal of chemical Engineering 2019年第11期27卷 2749-2758页

作者： Kaixun He Kai Wang Yayun Yan College of Electrical Engineering and Automation Shandong University of Science and TechnologyQingdao 266590China Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of EducationEast China University of Science and TechnologyShanghai 200237China

Training sample selection is widely accepted as an important step in developing a near-infrared(NIR) spectroscopic model. For industrial applications, the initial training dataset is usually selected empirically. This process is time-consuming, and updating the structure of the modeling dataset online is difficult. Considering the static structure of the modeling dataset, the performance of the established NIR model could be degraded in the online process. To cope with this issue, an active training sample selection and updating strategy is proposed in this work. The advantage of the proposed approach is that it can select suitable modeling samples automatically according to the process information. Moreover, it can adjust model coefficients in a timely manner and avoid arbitrary updating effectively. The effectiveness of the proposed method is validated by applying the method to an industrial gasoline blending process.

关键词： Near-infrared spectroscopy chemical processes Process systems Soft sensor Gasoline blending

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