Aug 2023
7 Mon
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11 Fri 09:00 AM – 06:00 PM IST
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Aug 2023
7 Mon
8 Tue
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11 Fri 09:00 AM – 06:00 PM IST
12 Sat
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Narayanan Subramaniam
Link to presentation: https://docs.google.com/presentation/d/18IgJG7hvPgcQZOVoE9cQpYlp0B6fLrwd/edit?usp=sharing&ouid=116463250234490514040&rtpof=true&sd=true
Industry (Minerals & Mining, Steel, Cement, Oil & Gas to name a few) comprise nearly 50% of total Green-House-Gas (GHG) Emissions and in the context of the Climate Crisis it is imperative to De-Carbonize existing processes in these industries while new processes with reduced carbon footprint are being introduced subject to availability of technology and suitable financing.
While there has been progress made on sensorization and digitization of process lines in the above mentioned industries, it is imperative to take a step back to assess the essential design considerations around sensor redundancy, calibration, data silos, scalable and highly available data pipelines, as well as the balance of Edge versus SaaS while coming up with an effective IoT-based IT/OT system design that captures real-time process and emissions (CO2, CH4, NOX, SOX, VOC) data from terrestrial and arial (satellite, drone) based systems.
In addition, all AI/ML based models need to have a proper foundation and insights of the real-time processes being modelled so that there is an explainable and rational basis that validates the reliability of AI/ML based models with deep domain expertise and tribal knowledge maintained by highly experienced process line operators. Design around process time lags, process and equipment degradation, limitations of forecasting and what-if analysis, real-world input raw material and environmental variations are few of the major challenges in applying AI/ML in these complex domains where MLOps maturity becomes all the more relevant.
This talk will help to shed light on the real-world design considerations based on the observed challenges and domain insights for a specific set of use industrial cases, which in a generic sense could be applied across the board.
For more context refer to: https://open.spotify.com/episode/4uCkDOqpwwKfnuHsoH2eRg , https://www.youtube.com/watch?v=22-kX5AGhE0&t=4 and https://www.youtube.com/watch?v=XyLljj2rI-Y&t=448s
I am currently the Founder & CTO of https://vanashri.com, helping companies in leveraging technology for a more sustainable planet, with past Sustainability experience in the Electric Vehicles space, and in Industrial Decarbonization in the Steel and Mining sectors.
Aug 2023
7 Mon
8 Tue
9 Wed
10 Thu
11 Fri 09:00 AM – 06:00 PM IST
12 Sat
13 Sun
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Narayanan Subramaniam
@narayanan_subramaniam Submitter
Thank you Nischal, Zainab and Shilpa. As mentioned to Zianab over LinkedIn, I plan to cover one or more specific example(s) (with some generality to ensure no IP is violated), as well as real-world design considerations based on the observed challenges and domain insights for a specific set of use industrial cases, which in a generic sense could be applied across the board.
Shilpa Jain
@shilpajain1510 Editor
Hello Narayan,
The topic of your talk on Industrial AI is indeed great and highly relevant. As Zainab rightly mentioned, it would be beneficial to include specific details about the talk, such as an in-depth solution you have implemented to solve a particular problem in this field.
Additionally, it would be helpful to provide key takeaways for the audience.
These could include general principles, best practices, or lessons learned from your experience in the field of Industrial AI. By highlighting the main learnings, the audience can gain actionable insights that they can apply to their own work or projects.
Zainab Bawa
@zainabbawa Editor & Promoter
Hello Narayan, thanks for the submission. As the next steps, shortlisted talks have to prepare a draft outline and present it to the editors between 15 and 20 June. The editorial team will set up a call with the editors and you to go over the outline and to get feedback. Let us know if this timeline works for you.
One of the feedback for the talk, when preparing the outline is to get into specifics and make this talk a takeaway story. For example, you can get into the details of an initial pilot that you deployed and share the insights from the deployment. Or, you can share details of a problem and how you tried to solve it using AI, elaborating on the challenges your team and you faced.
Look forward to hearing back.
Nischal HP
@nischalhp Editor
Hello Narayanan,
Thank you for the submission. The outline for the talk and the topic looks great. You will hear from us soon with the next steps.