The Advanced Analytics Techniques – What. Why. How
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- 09/06/2022 - 10:00 am - 11:00 am CDT
(Soft Sensors. Connected Analytics. Digital Twin)
Learn How to Build intelligence for Operation through Soft Sensors, Create Connected Analytics for System-level KPI’s, Practical Workflow & implementation of Digital Twin
- Soft-sensors are the key metrics that dictate the efficiency and performance of any process or the asset. The evaluation of these parameters become a necessity when monitoring the real-time performance of the system and enabling the future predictions for failure preventions and performance assessment. It also considers the physics-inspired data modeling approach, wherein the constraints are built-in a model using synthetic data. How to create and implement such Soft Sensors for assets / processes will be showcased in this Webinar.
- Most of the data analytics applications is centered around monitoring current state of the system or predicting the future state / behavior. This is more ‘Asset-centric’ view / analysis of the data. However, the processes /assets are connected and so are the dependencies (for e.g., output of one system could be an input to other). We are introducing a new (advanced) analytics technique – ‘Connected Analytics’ to analyze the network of process to identify the precursor failures/abnormalities with respect to the subjected process.
- The success of Digital Twin implementation depends on the right representation of – a. Process Knowledge & understanding, b. Defining right Operations KPIs. It needs a robust underlying and overlying technology to make Digital Twin a success. This webinar will showcase the practical approach, methods and solution to implement Digital Twin.
The session will showcase prevalent prerequisites and use cases for these advanced analytics techniques.
Meet our experts and get insights into Approach, Methods, Solutions, and how we enable it through our ‘Pilot Guided Analytics’ framework.
Who should attend:
- Process R&D Heads
- Production / Operations Head
- Maintenance Heads
- Plant heads
- Technology leaders
- Data Science leaders
- Data Analysts