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CAST10  August 2017

CAST10 August 2017

Subject:

ADCHEM 2018 invited session "Process monitoring in the era of Big Data"

From:

Jin Wang <[log in to unmask]>

Reply-To:

Jin Wang <[log in to unmask]>

Date:

Thu, 31 Aug 2017 14:29:14 -0400

Content-Type:

text/plain

Parts/Attachments:

Parts/Attachments

text/plain (50 lines)

Dear Colleagues,

We are organizing an invited session "Process monitoring in the era of Big 
Data" at the 10th IFAC Symposium on Advanced Control of Chemical 
Processes, which will be held in Shenyang, China, July 25 - 27, 2018. The 
conference website is http://adchem2018.org/.

With ever-accelerating advancement of information, communication, sensing 
and characterization technologies, such as industrial Internet of Things 
(IoT) and high-throughput instruments, the amount of data collected and 
stored by chemical and process industry has grown exponentially. Driven by 
the availability of these data, it is expected that a new generation of 
networked, information based technologies, data analytics, and predictive 
modeling will provide unprecedented embedded computing capabilities as 
well as access to previously unimagined potential uses of data and 
information. 

How to create manufacturing intelligence from real-time data to support 
accurate and timely decision-making is expected to contribute 
significantly to the advancement of process operation. In the past 25 
years, multivariate statistical process data analytic tools, such as 
principal component analysis, partial least squares, have been effectively 
used in process operation and control. However, some existing challenges 
such as strong nonlinear dynamics and time varying characteristics, and 
the new challenges associated with Big Data (i.e., volume, variety, 
velocity and veracity), are driving for the development of new approaches 
to process monitoring. 

The aim of this invited session on “Process monitoring in the era of Big 
Data” is to call for contributions that apply and develop big data 
analytics and modeling for the purpose of process operation, monitoring 
and control. Contributions are especially sought on how data mining, 
multivariate statistical and machine learning methods might be developed 
and applied to data-enhanced operations, that can effectively address the 
existing and new challenges, including but not limited to process 
nonlinearities, multi-modal distribution, time-varying characteristics, 
and 4Vs of big data.

We would like to invite you submitting a paper to this session. If you are 
interested in doing so, please send a tentative title before September 
20th by email to [log in to unmask]

Best regards,
Jin Wang & Jinsong Zhao

_________________________
Session organizers:
Jin Wang, Auburn University, USA ([log in to unmask])
Jinsong Zhao, Tsinghua University, P.R. China 
([log in to unmask])

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