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Chinese Journal of Chemical Engineering 2018年第8期26卷 1605-1612页

作者： xiaoyong gao dexian huang yongheng jiang Tao Chen Institute for Ocean Engineering China University of Petroleum department of automation Tsinghua University department of Chemical and process Engineering University of Surrey

refinery scheduling attracts increasing concerns in both academic and industrial communities in recent ***, due to the complexity of refinery processes, little has been reported for success use in real world refineries. In academic studies, refinery scheduling is usually treated as an integrated, large-scale optimization problem,though such complex optimization problems are extremely difficult to solve. In this paper, we proposed a way to exploit the prior knowledge existing in refineries, and developed a decision making system to guide the scheduling process. For a real world fuel oil oriented refinery, ten adjusting process scales are predetermined. A C4.5 decision tree works based on the finished oil demand plan to classify the corresponding category(i.e. adjusting scale). Then,a specific sub-scheduling problem with respect to the determined adjusting scale is solved. The proposed strategy is demonstrated with a scheduling case originated from a real world refinery.

关键词： refinery scheduling Decision tree C4.5 Decomposition method

scEpiTools: a database to comprehensively interrogate analytic tools for single-cell epigenomic data

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Journal of Genetics and Genomics 2024年第4期51卷 462-465页

作者： Zijing gao Xiaoyang Chen Zhen Li Xuejian Cui Qun jiang Keyi Li Shengquan Chen rui jiang Ministry of Education Key laboratory of Bioinformatics Bioinformatics Division at the Beijing National Research Center for Information Science and TechnologyCenter for Synthetic and Systems BiologyDepartment of AutomationTsinghua UniversityBeijing 100084China School of Mathematical sciences and LpMC Nankai UniversityTianjin 300071China

recent advances in single-cell sequencing technologies provide significant implications for understanding cellular heterogeneity,developmental biology,and disease *** fully exploit the potential of these data,numerous tools have been proposed for upstream and downstream *** the the single-cell rNA sequencing(scrNA-seq)community,scrNA-tools(Zappia et al.,2018)was proposed to help researchers navigate the plethora of tools by *** its inception,scrNA-tools has been widely used and its updated version further reveals trends in the field with over 1000 collected tools(Zappia and Theis,2021),providing a valuable guidance in selecting tools for analyses.

关键词： analytic selecting Tools

基于python的大规模复杂优化问题求解平台设计与应用

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计算机与应用化学 2018年第1期35卷 1-6页

作者：韩彪王潮高小永江永亨黄德先清华大学自动化系北京100084 中国石油大学(北京)海洋工程研究院北京102249

优化问题广泛存在于工业生产过程的各种应用层面,且往往描述为大规模复杂优化问题,该类问题的有效求解对于提高经济效益意义重大。现有的商业性通用建模优化软件普遍存在价格昂贵、专用性差、应用门槛高及难以维护和再开发等局限,因此... 详细信息

优化问题广泛存在于工业生产过程的各种应用层面,且往往描述为大规模复杂优化问题,该类问题的有效求解对于提高经济效益意义重大。现有的商业性通用建模优化软件普遍存在价格昂贵、专用性差、应用门槛高及难以维护和再开发等局限,因此研究一种低门槛、便捷高效的应用开发模式及开发一款理想的优化求解平台十分必要。鉴于此,本文提出基于python的大规模复杂优化问题求解平台的设计思路,并给出了一般化的设计流程。该平台在通用性、可扩展性和可移植性等方面存在优势。通过与一个基于C#编写的炼厂全厂调度优化软件连接使用,验证了本平台求解优化问题的有效性。

关键词：优化问题 python 求解平台

A Novel Scheduling Strategy for Crude Oil Blending

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Chinese Journal of Chemical Engineering 2010年第5期18卷 777-786页

作者：摆亮江永亨黄德先刘先广 Institute of process Control Engineering Department of Automation Tsinghua University Beijing 100084 China TECHFLY Company Beijing 100083 China

For those refineries which have to deal with different types of crude oil, blending is an attractive solution to obtain a quality feedstock. In this paper, a novel scheduling strategy is proposed for a practical crude oil blending process. The objective is to keep the property of feedstock, mainly described by the true boiling point (TBp) data, consistent and suitable. Firstly, the mathematical model is established. Then, a heuristically initialized hybrid iterative (HIHI) algorithm based on a two-level optimization structure, in which tabu search (TS) and differential evolution (DE) are used for upper-level and lower-level optimization, respectively, is proposed to get the model solution. Finally, the effectiveness and efficiency of the scheduling strategy is validated via real data from a certain refinery.

关键词： crude oil blending scheduling multilevel iterative algorithm intelligent optimization constrained optimization

A New Strategy of Integrated Control and On-line Optimization on High-purity Distillation process

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Chinese Journal of Chemical Engineering 2010年第1期18卷 66-79页

作者：吕文祥朱鹰黄德先江永亨金以慧 department of automation Tsinghua University Beijing 100084 China tsinghua national laboratory for information science and Techology Beijing 100084 China

For high-purity distillation processes,it is difficult to achieve a good direct product quality control using traditional proportional-integral-differential(pID)control or multivariable predictive control technique due to some difficulties,such as long response time,many un-measurable disturbances,and the reliability and precision issues of product quality *** this paper,based on the first principle analysis and dynamic simulation of a distillation process,a new predictive control scheme is proposed by using the split ratio of distillate flow rate to that of bottoms as an essential controlled ***,a new strategy with integrated control and on-line optimization is developed,which consists of model predictive control of the split ratio,surrogate model based on radial basis function neural network for optimization,and modified differential evolution optimization algorithm. With the strategy,the process achieves its steady state quickly,so more profit can be *** proposed strategy has been successfully applied to a gas separation plant for more than three years,which shows that the strategy is feasible and effective.

关键词： distillation process control split ratio surrogate model optimization modified differential evolution

化工过程深度神经网络软测量的结构与参数自动调整方法

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化工学报 2018年第3期69卷 900-906页

作者：王康成尚超柯文思江永亨黄德先清华大学自动化系北京100084 清华信息科学与技术国家实验室(筹) 北京100084

深度学习在流程工业的软测量领域已经得到了应用。然而,深度神经网络(DNN)的结构和参数需要人工调整,这需要扎实的机器学习知识基础和丰富的参数调整经验,烦琐的调整过程限制了深度学习在化工领域的推广应用。在大量实验的基础上,对DNN... 详细信息

深度学习在流程工业的软测量领域已经得到了应用。然而,深度神经网络(DNN)的结构和参数需要人工调整,这需要扎实的机器学习知识基础和丰富的参数调整经验,烦琐的调整过程限制了深度学习在化工领域的推广应用。在大量实验的基础上,对DNN的每个关键参数的选取过程进行了系统化的分析,提出了几乎无须人工干预的基于DNN软测量的结构和参数自动调整方法,极大地简化了参数调整过程,能够给工程技术人员学习及应用深度学习提供参考。对原油蒸馏装置及煤气化装置的案例分析验证了所提出方法的有效性和通用性。

关键词：深度学习预测参数调整算法神经网络

Matched Filter Based Spectrum Sensing When primary User Has Multiple power Levels

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china Communications 2015年第2期12卷 21-31页

作者： ZHANG Xinzhi gao Feifei CHAI rong jiang Tao Key Lab of Mobile Communication technology Chongqing University of Posts and Telecommunications department of automation Tsinghua UniversityState Key Lab of Intelligent Technologies and SystemsTsinghua National Laboratory for Information Science and Technology(TNList) Beijing Wuhan national laboratory for Optoelectronics Department of Electronic and InformationHuazhong University of Science and Technology

In this paper,we investigate the matched filter based spectrum sensing in a more reasonable cognitive radio(Cr) scenario when the primary user(pU) has more than one transmit power levels,as regulated in most standards,i.e.,IEEE 802.11 Series,GSM,LTE,LTE-A,*** new multiple primary transmit power(MpTp) scenario is specialized by two different targets:detecting the presence of pU and identifying the power *** to the traditional binary sensing where only the presence of pU is checked,SU may attain more information about the primary network(making Cr more "intelligent") and design the subsequent optimization *** key technology is the multiple hypothesis testing as opposed to the traditional binary hypothesis *** discuss two situations under whether the channel phase is known or not,and we derive the closed form solutions for decision regions and several performance metrics,from which some interesting phenomenons are observed and the related discussions are *** examples are provided to corroborate the proposed studies.

关键词： spectrum sensing power recognition matched filter multiple primary transmit power multiple hypothesis testing power mask

Multiobjective adaptive car-following control of an intelligent vehicle based on receding horizon optimization

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science china(information sciences) 2023年第4期66卷 301-302页

作者： Hongbo gao Juping ZHU Fei ZHANG ruidong YAN Junjie ZHOU Lei jiang Jianqiang WANG Keqiang LI department of automation University of Science and Technology of China Institute of Advanced technology University of Science and Technology of China department of Computational Mathematics School of Mathematical Sciences Anhui University School of Traffic and Transportation Beijing Jiaotong University Autonomous Driving Business Unit Chery Automobile School of College of Computer science and technology Zhejiang University china North Vehicle research Institute School of Automotive Engineering Tsinghua University

Dear editor, Vehicle control is one of the key steps of intelligent driving [1]. Algorithms based on receding horizon optimization(rHO) can predict future trajectories and handle multiobjective constraint conditions; therefore, rHO-based methods have attracted considerable attention in the field of vehicle control. Although existing methods use rHO to design controllers, they do not simultaneously meet the multiobjective optimal control performance requirements on tracking, fuel economy, and ride comfort.

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