1.
Yuanming Zhu
East China University of Science and Technology
(朱远明,华东理工大学)
Yuanming ZHU
received his B.S. and Ph. D. degree in BJTU and work as a vice professor in
ECUST. The candidate of "Sailing Plan Project" in Shanghai. He has
undertaken more than 10 national scientific research projects. His research
interest includes data-driven control, robust control and intelligent control
for industry process. Recent years, his research direction is aimed at the
national major strategic demands, committed to the research of key theories and
technologies for cement green manufacturing. He has published more than 20
academic papers, including in TNNLS, TII, IJRNC, IECR, etc.; applied for more
than 10 national invention patents and obtained 6 software copyrights in the
field of optimization and control for cement manufacturing.
Title: Data-driven control technology in cement industry
The
dry process for manufacturing of cement consists of
three stages: raw material preparation, clinker calcination, and cement
grinding, which involves the mixing of gas and solid phases and
high-temperature chemical reactions. However, the sampling delay for
composition analysis, the lack of online measurement for critical process
variables (such as fineness of product, the decomposition rate of raw meal, and
the concentration of the free calcium oxide in clinker), and the variety of
fuel sources and quality, have posed great challenges to the quality control
and energy and emission reduction. This talk will discuss some key data-driven
technologies of intelligent control for cement industry. 1. An embedding
learning algorithm based soft-sensor; 2. Parameterized controller based
data-driven control; 3. A deep reinforcement learning approach. Meanwhile, some
application scenarios will be included.
2.
Minglei Yang
East China University of Science and Technology
(杨明磊,华东理工大学)
Yang
Minglei was born in 1985. He received his B.S. and Ph. D. degree in Chemical
Engineering from East China University of Science and Technology. He is now the
vice director of automation research institute. For the past 10 years, he has
been devoted to the steady / dynamic state of complex petrochemical processes,
multi-scale mechanism modeling, process optimization and decision making. As
the project leader, he undertook 2 National Natural Science Foundation Projects
and 1 basic scientific research project of Central University project. As the
technical director, he undertook over 10 projects from industry and MIIT, such
as the development of large-scale petrochemical process mechanism modeling,
plan optimization, profit maximization for reforming process, etc. The
developed technics have been successfully applied in Sinopec Jiujiang Branch,
Zhenhai Branch, Shanghai Branch and Yangzi Branch. He has published over 30
papers in Chemical Engineering journals, and authorized over 20 Chinese patents.
In 2019, he won the First prize of Shanghai Science and Technology Progress
Award and Technological Invention.
Title: Value chain evaluation and optimization for petrochemical
process
Abstract:
Smart manufacturing is very hot and popular in petrochemical industry in China
due to the great improvements in economic profit and operation efficiency. This
report is to introduce three important technologies applied in Smart Factory:
(1) rapid characterization of refinery oil. Using near infrared spectroscopy
technology, online evaluation of crude oil, distillate oil, product oil and
other oil products can provide accurate and fast information for business
optimization; (2) digital twin model of the whole plant. The plant-wide model
is built by combing the mechanism and operation data, so as to improve the
accuracy in predicting the yield and properties of products; (3) plant-wide
optimization. By integrating the mechanism model in planning, the crude oil
purchase, resource distribution, and unit operation mode are optimized to
maximize the production value of the whole plant. Finally, several application
cases in smart manufacturing for petrochemical industry are shared.
3.
Hao Chen
Chinese Academy of Sciences
(陈豪,中国科学院海西研究院)
Hao Chen, a researcher of Haixi
Institute, Chinese Academy of Sciences & PI. He received his Bachelor degree
in the major of automation from National University of Defense Technology in
2006, Master degree in the major of control system from The University of
Sheffield, UK in 2009, Doctor’s degree in the major of Cybernetic from
University of Reading, UK in 2013. From January 2014 to July 2015, he engaged
in industrial data analysis and soft-sensor research in the University of
Alberta, Canada / Syncrude oil company. Now he is the director of Fujian
Provincial key laboratory for Intelligent Identification and Control of Complex
Dynamic System, the committee member of data driven control, learning and
optimization Committee of Chinese Association of Automation, the vice director
of Fujian Provincial Industrial Internet Intelligent Sensing and Decision
Engineering Research Center. He presided over nearly 30 national / Chinese
Academy of Sciences / local scientific research projects, applied for /
authorized 36 national invention patents, and published nearly 50 academic
papers. He has won the Youth Promotion Association of Chinese Academy of
Sciences, Fujian Natural Science Foundation for Distinguished Young Scholars
and Fujian May 4th Youth medal. He
committed to the research road of combining theory with application. The
production optimization series technology and industrial software that
developed by the team have been successfully applied in nearly ten leading
enterprises in the industry, such as Sunner Group, Seven Group, Shuhua Sports,
etc.
Title: Exploration on the Application of Industrial Big Data in Manufacturing
Industry
Abstract:
With the deepening of the national integration policy between informatization
and industrialization, China has gradually transformed from a big manufacturing
country to a big data manufacturing country. This report explores how the
massive industrial big data can enable industrial development through the
actual case of the intelligent control and operation and maintenance of sewage
treatment system in the farming industry of Sunner Group. At present, the
industrial sewage treatment equipment basically adopts simple open loop
control, and even many technological operations still have a trial-and-error
adjustment by the manual mode, which also makes the treatment equipment in
harsh environment require a lot of manual operation and maintenance. Aiming at
the decentralized chicken farming model of Sunner Group, a mobile and
distributed industrial sewage treatment scheme is adopted, which greatly
improves the treatment efficiency and reduces the cost. At present, most of the
water quality automatic detection device is based on the reagent detection
method of the national standard method, which has long measurement cycle and
high cost, and cannot realize real-time water quality detection. The big
data-driven soft sensor designed in the case can obtain low-cost online water
quality measurement data, thus realizing closed-loop sewage treatment and
real-time predictive operation and maintenance.