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Modeling of ethylene cracking furnace yields based on transfer learning

Huagong Xuebao/CIESC Journal 2014年第12期65卷 4921-4928页

作者： Zhou, Shuheng Du, Wenli Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai200237 China

Ethylene cracker generally utilizes petroleum products as raw materials, and turn the long-chain hydrocarbon molecules into a variety of short chain hydrocarbons like gaseous hydrocarbons and a small amount of liquid hydrocarbons at high temperature, thereby obtaining ethylene, propylene and other products. Establishing the yield model for main products is of great significance to advanced control, operation optimization of the pyrolysis furnace in theory and practice. Due to the semi-continuous and periodic characteristics of cracking furnace, establishing a yield model for each condition is time-consuming. Considering the temperature, residence time and pressure have common effect on product yields, so this article uses these similarities exist in the new and old processes, supplemented with transfer learning to accomplish quick modeling for different conditions. Compared to previous studies, this method can be able to fully exploit the information contained in the historical data from a small amount of new data. Meanwhile, building a model of ethylene in the process of cracking furnace, case studies demonstrate the efficacy of the developed methodology. ©, 2014, chemical Industry Press. All right reserved.

关键词： Ethylene

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Investigating impacts of cost functions to atmospheric dispersion modeling and source term estimation in turbulent condition

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Huagong Xuebao/CIESC Journal 2020年第3期71卷 1163-1173页

作者： Dong, Jikai Du, Wenli Wang, Bing Xu, Qiaoyi Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai200237 China

Hazardous gas release will threaten social security and cause damage to property and environment. Establish an effective emergency response system with monitoring devices can help reduce the potential risk and provide evaluation for release accident emergencies. The gas concentration is detected by the gas concentration sensor arranged in the risk area, and the leakage assessment is carried out according to the meteorological conditions and the forward diffusion model. Because the data of leakage scenarios is not easy to obtain, the large eddy simulation method based on Fires Dynamics Simulator (FDS) is used in this paper to generate date through simulating release scenarios with turbulent condition. On this basis, the impacts of the cost function to the modeling process and source term estimation are studied. The results of source term estimation shows the function of the square sum of concentration deviation is better than other functions. However, the function cannot avoid the condition that the estimation index of some scenes is far from the average value because of turbulent flow. In addition, a combination of the estimation results of different cost functions can improve the situation to a certain extent. © All Right Reserved.

关键词： Cost functions

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Feed property identification of ethylene cracking based on improved fuzzy C-mean clustering algorithm

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Huagong Xuebao/CIESC Journal 2013年第12期64卷 4366-4372页

作者： Li, Jiawen Du, Wenli Li, Jinlong Qian, Feng Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai 200237 China

In ethylene cracking process, the changes of feed have many kinds, and due to its expensive feed analyzer, little industrial site equips with it, so online recognition of oil property is important to achieve cracking online optimization. As the traditional fuzzy C-means algorithm is based on the membership of the strike Euclidean distance, the algorithm contains only the mean center, bringing the unity of clustering results. To take full advantage of effective information of cracking feed, this paper proposes a fuzzy membership set method based on hybrid probabilistic model, namely through the establishment of Gaussian mixture model to achieve describing the probability distribution of clustering sample's affiliation, and use EM algorithm to estimate the model parameter's pole maximum likelihood. The algorithm can not only consider mean center of the sample, but also effectively use sample covariance and the weight coefficient information for mode discrimination. Finally, the simulation is based on classic IRIS data clustering and ethylene cracking feedstock identification, verifying the method described in this paper in the index of dunn and Xiebieni is better than fuzzy C-means clustering algorithm, showing that the method is effective. © All Rights Reserved.

关键词： Clustering algorithms

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Chaotic particle swarm optimization algorithm with harmony search for industrial applications

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Qinghua Daxue Xuebao/Journal of Tsinghua University 2012年第3期52卷 325-330页

作者： Du, Wenli Zhang, Hailong Qian, Feng Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology Shanghai 200237 China

Particle swarm optimization methods for optimization problems tend to search premature solutions. This paper presents an improved particle swarm optimization algorithm merging chaotic and harmony searches. The chaos particle swarm optimization method is stable, robust and adaptable. The harmony search algorithm is a meta heuristic algorithm for simulating band tuning to obtain an optimal harmonized process with a global search. Results for four standard test functions show that this chaos particle swarm optimization algorithm with a harmony search (CPSO-HS) can jump out of local optimums with fast convergence and good stability. This algorithm has been successfully applied to parameter estimates for a heavy oil thermal cracking model.

关键词： Particle swarm optimization (PSO)

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A novel hybrid particle swarm optimization algorithm merging crossover mutation and chaos

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Huagong Xuebao/CIESC Journal 2010年第11期61卷 2861-2867页

作者： Liu, Zhao Qi, Rongbin Qian, Feng Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai 200237 China

To solve the premature convergence problem of particle swarm optimization(PSO) in dealing with complex high dimensional function optimization, a novel hybrid particle swarm optimization algorithm merging crossover mutation and chaos(CMCPSO) was proposed. The main approaches included using chaos strategy to initiate positions and velocities of all particles in the design space, introducing crossover operation in each iteration to increase the diversity of particles, breaching the restrictions of local optimization points with a new chaotic disturbance mechanism and mutation operation during the later computation period. Four standard test functions were selected to have a simulation study on the proposed algorithm. The results showed that CMCPSO had a fast convergence rate and effective global optimization ability. © All Rights Reserved.

关键词： Merging

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Automatic prediction of sleep level during day time short nap

Automatic prediction of sleep level during day time short na...

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IEEE/Institute of Complex Medical Engineering International Conference on Complex Medical Engineering

作者： Wang, Bei Fu, Xiuyan Wang, Xingyu Nakamura, Masatoshi Key Laboratory of Advanced Control and Optimization for Chemical Processes School of Information Science and Technology East China University of Science and Technology Shanghai China Research Institute of Systems Control Institute for Advanced Research and Education Saga University Saga Japan

ISBN: (纸本)9781424493241

The day time nap sleep has the significant prophylactic function to our health and working efficiency. In this study, the prediction of sleep level for day time short nap was investigated. The ultimate purpose was to develop the automatic analysis technique for nap sleep interpretation. Firstly, the conditional probabilities of sleep stage awake, stage 1 and stage 2 were calculated for the consecutive data segments based on a set of parameters. Secondly, the sleep level of next data segment was predicted according to the obtained conditional probabilities. The prediction of sleep level reflected the future sleep tendency and was helpful for automatic nap sleep interpretation. © 2011 IEEE.

关键词： Forecasting

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Support vector regression based on multi-scale wavelet kernel for propylene concentration estimation and application

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Huagong Xuebao/CIESC Journal 2010年第6期61卷 1486-1494页

作者： Yu, Yanfang Du, Wenli Qian, Feng Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai 200237 China

Support vector regression based on multi-scale wavelet kernel has strong robustness and good generalization ability, but it is critical for it to choose appropriate model parameters. Obviously, the multi-scale kernel method has more difficulty in model selection than the single kernel approach. This paper proposed an approach about how to develop support vector regression based on multi-scale wavelet kernel. It applied quantum clustering to data partition in order to determine the scale parameter of the multi-scale kernel, resorted to the support vector data description algorithm to calculate the kernel width of the corresponding data points, and then used cultural algorithms to optimize the kernel width and the remaining parameters. The results showed that the multi-scale kernel method outperformed the single wavelet approach and the Gaussian method. The experiments about three regression data sets-Boston housing, Bodyfat and Santa demonstrated that in contrast with the Gaussian approach, the present multi-scale wavelet support vector regression made the mean squared error of test sets decrease by 6.8%, 62.0% and 91.3%, respectively. Meanwhile, the proposed approach exhibited good generalization ability for propylene concentration estimation in the bottom byproduct of propylene fractionation tower. It not only enabled the model output of training set for propylene concentration to show little difference with the actual output, but also made the relative error of test set down to 0.211. Compared with other methods, it had the minimal prediction error. © All Rights Reserved.

关键词： Errors

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DE based economic control chart design and application for a typical petrochemical process

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Frontiers of Engineering Management 2017年第3期4卷 348-356页

作者： Zhi LI Feng QIAN Wenli DU Weimin ZHONG Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology

Petrochemical industry plays an important role in the development of the national economy. Purified terephthalic acid(PTA) is one of the most important intermediate raw materials in the petrochemical and chemical fiber industries. PTA production has two parts:p-xylene(PX) oxidation process and crude terephthalic acid(CTA) hydropurification process. The CTA hydropurification process is used to reduce impurities, such as 4-carboxybenzaldehyde, which is produced by a side reaction in the PX oxidation process and is harmful to the polyester industry. From the safety and economic viewpoints, monitoring this process is necessary. Four main faults of this process are analyzed in this study. The common process monitoring methods always use T^2 and SPE statistic as control limits. However, the traditional methods do not fully consider the economic viewpoint. In this study, a new economic control chart design method based on the differential evolution(DE) algorithm is developed. The DE algorithm transforms the economic control chart design problem to an optimization problem and is an excellent solution to such problem. Case studies of the main faults of the hydropurification process indicate that the proposed method can achieve minimum profit *** method is useful in economic control chart design and can provide guidance for the petrochemical industry.

关键词： petrochemical PTA economic control chart design process monitoring DE algorithm

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Influence of flue gas radiative properties, burner geometry and shadow effects in steam cracking furnace simulations

Influence of flue gas radiative properties, burner geometry ...

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Computing and Systems Technology Division 2014 - Core Programming Area at the 2014 AIChE Annual Meeting

作者： Zhang, Yu Schietekat, Carl M. Van Geem, Kevin M. Qian, Feng Marin, Guy B. Laboratory for Chemical Technology Department of Chemical Engineering Ghent University Technologiepark 918 Zwijnaarde Belgium Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology 130 Meilong Road Shanghai China

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Opportunities and Challenges of Artificial Intelligence for Green Manufacturing in the Process Industry

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Engineering 2019年第6期5卷 995-1002页

作者： Shuai Mao Bing Wang Yang Tang Feng Qian Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of EducationEast China University of Science and TechnologyShanghai 200237China

Smart manufacturing is critical in improving the quality of the process industry. In smart manufacturing, there is a trend to incorporate different kinds of new-generation information technologies into process- safety analysis. At present, green manufacturing is facing major obstacles related to safety management, due to the usage of large amounts of hazardous chemicals, resulting in spatial inhomogeneity of chemical industrial processes and increasingly stringent safety and environmental regulations. Emerging informa- tion technologies such as arti cial intelligence (AI) are quite promising as a means of overcoming these dif culties. Based on state-of-the-art AI methods and the complex safety relations in the process industry, we identify and discuss several technical challenges associated with process safety: ① knowledge acquisition with scarce labels for process safety;② knowledge-based reasoning for process safety;③ accurate fusion of heterogeneous data from various sources;and ④ effective learning for dynamic risk assessment and aided decision-making. Current and future works are also discussed in this context.

关键词： Process industry Smart manufacturing Green manufacturing Artificial intelligence

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