Tridiagonal Solutions > Events > Data Science & The Chemical Process Industries
  • Data Science & The Chemical Process Industries

  • Save to Calendar
  • 27/09/2018 - 11:30 am - 12:30 pm CDT/CST
  • Facebook
    Google+
    https://tridiagonal.com/events/data-science-and-the-chemical-process-industries">
    Twitter
    LinkedIn
  • Data science is revolutionizing today’s world in multiple ways – phrases such as “big data”, “predictive analytics”, “digitalization” and “digital transformation” seem to have become part of everyone’s vocabulary. At first glance, it might appear that these ideas and the technology associated with them are completely new and untested. This has resulted in a slower-than-desirable rate of acceptance of data science by the larger process engineering community.

    In this webinar, we will endeavor to dispel some of the myths that surround data science and its practice. Data science is a combination of various tools, algorithms, and statistical/machine learning principles used to discover hidden patterns in data (from various levels of the enterprise). Such patterns could then be used to make informed decisions on business-critical issues. Data-driven decisions can ultimately lead to increased profitability and improved operational efficiency, business performance and workflows.

    From a current practice perspective, data science has been most impactful in the following three major areas: a) system identification for model-based control, b) fault detection/isolation (and in general performance assessment) of assets and c) reduced order modeling of complex systems. We will describe each of these applications in some detail and give examples. We will also review opportunities for improvement – both technical and practical – that will enable a wider adoption of data science techniques.  

    Who should attend this webinar:

    • –Process Engineering Heads / Managers
    • –Manufacturing Managers
    • –Senior Process Engineers
    • –Product Development Managers

<<Sep 2018>>
MTWTFSS
27 28 29 30 31 1 2
3 4 5 6 7 8 9
10 11 12 13 14 15 16
17 18 19 20 21 22 23
24 25 26 27 28 29 30