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AI Reinforcement Learning in Industrial OT Environments - Ep. 038
Live from ICC 2025 in Sacramento, we sit down with Bhavnesh Patel, Go-to-Market lead at RL Core, to unpack how reinforcement learning (RL) is moving from labs to real-world OT/SCADA. We cover RL Core’s agent-based approach (observe → recommend → limited control → scale), where it fits alongside Ignition 8.3, OPC UA, and control systems, and why continuous processes (water/wastewater, solar + storage, oil sands upstream) see fast time-to-value—chemical/energy reductions, smarter setpoints, and adaptable control.
What you’ll learn
- RL vs. traditional MPC and why “data → action” beats “model → action” for many sites
- How RL Core deploys safely in stages and builds operator trust
- Where RL is working today: water/wastewater, H₂S scrubbing, solar + batteries, oil sands
- Practical paths for integrators and operators to pilot and scale
Guest: Bhavnesh Patel — RL Core (OT software startup applying RL to industrial processes)
RL Core: https://rlcore.ai/
Opsite Energy (presenting sponsor): https://opsiteenergy.com/
Chapters
00:00 Intro
00:10 Welcome & ICC 2025 vibe
01:06 Who is Bhavnesh & RL Core’s focus
02:36 ICC “Level Up,” Ignition 8.3, “Prove It”
03:08 What RL Core does (reinforcement learning for OT)
04:04 RL vs basic PID vs MPC; adaptability without re-modeling
05:07 Agent learns by doing; data → action
06:00 Where it sits: SCADA/Ignition + OPC UA (read/write)
07:00 Safe rollout: observe → tiny control → recommendations → expand
08:56 Example outcomes (chemical/energy reductions; continuous improvement)
10:22 Use cases in compression fleets & oil & gas
11:58 Early-stage focus, ideal partners, role of SIs
13:36 “Intrinsically safe” mindset and guardrails
16:00 Continuous vs batch; why ROI is stronger in continuous
16:54 Industries seeing impact (water/wastewater, solar + storage, oil sands)
18:40 Solar + battery dispatch with policy/financial constraints
19:42 Readiness checklist & engagement model
20:59 Closing + next steps
Uygar Duzgun / “Fast Life” / courtesy of www.epidemicsound.com
View all episodes
By Opsite EnergyLive from ICC 2025 in Sacramento, we sit down with Bhavnesh Patel, Go-to-Market lead at RL Core, to unpack how reinforcement learning (RL) is moving from labs to real-world OT/SCADA. We cover RL Core’s agent-based approach (observe → recommend → limited control → scale), where it fits alongside Ignition 8.3, OPC UA, and control systems, and why continuous processes (water/wastewater, solar + storage, oil sands upstream) see fast time-to-value—chemical/energy reductions, smarter setpoints, and adaptable control.
What you’ll learn
- RL vs. traditional MPC and why “data → action” beats “model → action” for many sites
- How RL Core deploys safely in stages and builds operator trust
- Where RL is working today: water/wastewater, H₂S scrubbing, solar + batteries, oil sands
- Practical paths for integrators and operators to pilot and scale
Guest: Bhavnesh Patel — RL Core (OT software startup applying RL to industrial processes)
RL Core: https://rlcore.ai/
Opsite Energy (presenting sponsor): https://opsiteenergy.com/
Chapters
00:00 Intro
00:10 Welcome & ICC 2025 vibe
01:06 Who is Bhavnesh & RL Core’s focus
02:36 ICC “Level Up,” Ignition 8.3, “Prove It”
03:08 What RL Core does (reinforcement learning for OT)
04:04 RL vs basic PID vs MPC; adaptability without re-modeling
05:07 Agent learns by doing; data → action
06:00 Where it sits: SCADA/Ignition + OPC UA (read/write)
07:00 Safe rollout: observe → tiny control → recommendations → expand
08:56 Example outcomes (chemical/energy reductions; continuous improvement)
10:22 Use cases in compression fleets & oil & gas
11:58 Early-stage focus, ideal partners, role of SIs
13:36 “Intrinsically safe” mindset and guardrails
16:00 Continuous vs batch; why ROI is stronger in continuous
16:54 Industries seeing impact (water/wastewater, solar + storage, oil sands)
18:40 Solar + battery dispatch with policy/financial constraints
19:42 Readiness checklist & engagement model
20:59 Closing + next steps
Uygar Duzgun / “Fast Life” / courtesy of www.epidemicsound.com