Industry40.tv

Reinforcement Learning Agents for Industrial Plant Optimization: Kyrill Schmid - Lead AI Engineer at MaibornWolff GmbH

Most industrial processes still run on the same foundation:

- Hard-coded logic in PLCs that follows predefined rules.
- The intuition of process and plant engineers, built from years of experience.

This combination has powered industry for decades, but it has limits.

When the challenge involves many interacting variables, unknown relationships, and non-linear effects, traditional control starts to strain.

Why?

Because fixed rules can’t adapt fast enough to changing conditions, and even the best human intuition can only process so much complexity at once.

Instead of relying on fixed instructions, RL agents learn directly from real-time feedback.

They can:
✅ Adapt continuously to new conditions.
✅ Handle high-dimensional problems with countless variables.
✅ Uncover novel, more efficient strategies that humans might overlook.

The result?

An optimization layer that works alongside your existing control system, making it smarter, more adaptive, and capable of delivering gains where complexity used to be a roadblock

In the latest episode of the AI in Manufacturing podcast, I sat down with Dr. Kyrill Schmid, the Lead AI Engineer at MaibornWolff GmbH, to discuss the application of reinforcement learning agents for optimizing industrial plants.

Autonomous AI Agents for Industrial Process Optimization: Bryan DeBois - Director of Industrial AI at RoviSys

Can AI agents really make decisions in high-stakes industrial environments?

Generative AI agents, on their own, do not have a robust understanding of cause-and-effect for real-world decision-making.

However, when combined with Deep Reinforcement Learning, AI agents gain the ability to reason, learn from interaction, and make decisions that solve operational problems in complex, real-world environments, like the plant floor.

Case in point.

Bryan DeBois and his team at RoviSys developed an Autonomous AI agent to manage a notoriously difficult glass bottle production process, where small disruptions like temperature fluctuations can quickly push the process out of specification.

Here’s how they approached it:

✅ 𝐒𝐭𝐞𝐩 1 - 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐓𝐞𝐚𝐜𝐡𝐢𝐧𝐠
They captured the knowledge and decision-making strategies of expert human operators and used this to train the AI agent, essentially teaching it how to respond to different operating conditions.

✅ 𝐒𝐭𝐞𝐩 2 - 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧 𝐒𝐮𝐩𝐩𝐨𝐫𝐭 𝐌𝐨𝐝𝐞
Initially, the agent didn’t control the process directly. It simply made recommendations.

Operators reviewed the suggestions and gave feedback using a simple green/red button system. This built trust and allowed the team to validate the AI’s decisions without risk.

✅ 𝐒𝐭𝐞𝐩 3 - 𝐂𝐥𝐨𝐬𝐞𝐝 𝐋𝐨𝐨𝐩 𝐂𝐨𝐧𝐭𝐫𝐨𝐥
Only after months of successful operation in support mode did they enable full automation.

Even then, strict safety measures were in place:
⇨ Limited control authority
⇨ Clearly defined operating boundaries
⇨ Automatic handover to human operators if conditions exceeded the agent’s training

The Results:
⇨ Human operators typically needed 7–20 minutes to bring the process back into spec
⇨ The AI agent consistently did it in under 5 minutes
⇨ And it maintained safety by operating strictly within validated limits

In the latest episode of the AI in Manufacturing podcast, I sat down with Bryan, Director of Industrial AI at RoviSys, to dive deeper into how manufacturers can leverage AI and autonomous agents to optimize manufacturing operations and improve efficiency

The Seven Core Capabilities of an Industrial Data Platform: David Ariens - Founder, The IT/OT Insider.

The industrial data stack was never built for enterprise-wide intelligence. It was built in silos, optimized for local decisions.

As a result, it is not designed to support unified, contextualized, and scalable data management across an organization.

And that’s why Industrial Data Platforms are essential for scaling digital transformation. 

To help organizations understand what makes such a platform effective, David Ariens and The IT/OT Insider team created the Industrial Data Platform Capability Map, outlining the seven key capabilities every platform should have:

1. 𝐂𝐨𝐧𝐧𝐞𝐜𝐭𝐢𝐯𝐢𝐭𝐲 – a secure and scalable connectivity layer to integrate different data sources into the Industrial Data Platform.

2. 𝐂𝐨𝐧𝐭𝐞𝐱𝐭𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 & 𝐃𝐚𝐭𝐚 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 – delivering data enriched with the right context, so users don’t have to gather and piece together information from multiple sources manually.

3. 𝐃𝐚𝐭𝐚 𝐐𝐮𝐚𝐥𝐢𝐭𝐲 – Detecting and fixing data issues in your pipeline, from sensor to final report.

4. 𝐃𝐚𝐭𝐚 𝐁𝐫𝐨𝐤𝐞𝐫 𝐚𝐧𝐝 𝐒𝐭𝐨𝐫𝐞 – The ability to ingest, store, and manage contextualized data at scale, enabling efficient data subscription and large-scale querying. 

5. 𝐄𝐝𝐠𝐞 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 – The capability to perform analytics and machine learning within the data platform, or at the edge, close to where the data is generated.

6. 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 – The capability to deliver high-quality, contextualized data to users through intuitive and accessible interfaces for fast, informed decision-making.

7. 𝐃𝐚𝐭𝐚 𝐒𝐡𝐚𝐫𝐢𝐧𝐠 – The capability to openly expose platform data to external users and applications through standard interfaces and integrations.

 

In the latest episode of the AI in Manufacturing podcast, I sat down with David, Founder of IT/OT Insider, to dive deeper into these capabilities and how organizations can implement them.

We also discussed the IT/OT Academy, an online training program designed to help IT and OT professionals build a shared vocabulary, framework, and collaboration strategy to move digital initiatives beyond pilot projects and into full-scale plant deployment.

Edge AI in the Digitalization of Industrial Process: Rainer Maidel - Business Development Manger, BE.Services

Instead of sending data to the cloud for processing, Edge AI analyzes data right where it’s generated, on the machine, in the plant, in real time.

It’s the difference between reacting later and responding now.

What Happens When You Keep Intelligence at the Source?

𝐑𝐞𝐚𝐥-𝐭𝐢𝐦𝐞 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧𝐬
A conveyor motor vibrates abnormally.
Edge AI detects the anomaly instantly and slows the line before damage occurs.

𝐒𝐦𝐚𝐫𝐭𝐞𝐫 𝐌𝐚𝐢𝐧𝐭𝐞𝐧𝐚𝐧𝐜𝐞
Time-series models forecast when a press will wear out, so teams fix it during scheduled downtime, not after it fails.

𝐐𝐮𝐚𝐥𝐢𝐭𝐲 𝐂𝐨𝐧𝐭𝐫𝐨𝐥 𝐚𝐭 𝐭𝐡𝐞 𝐄𝐝𝐠𝐞
Cameras inspect every product.
Edge AI flags visual defects without ever uploading a frame to the cloud.


In the latest episode of the AI in Manufacturing podcast, I sat down with Rainer Maidel, Business Development Manager at BE.services GmbH, the creators of Coligo Edge AIoT Software. We had an in-depth discussion about the application of Edge AI in the digitalization of industrial processes.

Superintelligence for Oil, Gas and Petrochemicals: Callum Adamson - Co-Founder and CEO, Orbital

The first foundation model purpose-built for refining and petrochemicals?

Here's the thing.

The oil, gas, and petrochemical industry is under pressure like never before.

⇨ Demand is set to double in 15 years
⇨ Facilities are shutting down
⇨ Energy transition is colliding with operational cost realities

At the same time, companies are being told AI will solve it all.

But here’s the truth.

Most AI was built for the internet, not industrial plants.
❌ It can’t explain its decisions
❌ It hallucinates
❌ It’s fragile with messy, real-world data
❌ It struggles with incomplete time series and unstructured reports

Now apply that to a refinery running 24/7, filled with volatile compounds and extreme conditions.

And you start to see the problem.

AI that can’t be trusted is worse than no AI at all.

That’s why Callum Adamson and his team built Orbital. The first foundation model designed specifically for refining and petrochemicals.

Instead of trying to stretch general-purpose AI into high-consequence environments, Orbital is:
✅ Purpose-built
✅ Physics-aware
✅ Production-grade

But more importantly, it takes a Tri-Modal Architecture that combines the following into federated intelligence:
1. 𝐓𝐢𝐦𝐞 𝐒𝐞𝐫𝐢𝐞𝐬 𝐌𝐨𝐝𝐞𝐥 – For signals and sensor data
2. 𝐏𝐡𝐲𝐬𝐢𝐜𝐬-𝐁𝐚𝐬𝐞𝐝 𝐌𝐨𝐝𝐞𝐥 – For real-world grounding
3. 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐌𝐨𝐝𝐞𝐥 – For intuitive interaction and explanation

In the latest episode of the AI in Manufacturing podcast, I sat down with Callum, who is the Co-Founder and CEO of Applied Computing, to discuss the application of Superintelligenece in Oil, Gas, and Petrochemicals.

CDOT AI Code - A New Language for Parts: Serra Tuzcuoglu CEO and Co Founder, Cosmodot - CDOT AI Code

Part traceability in manufacturing has long relied on traditional barcodes that fail where it matters most: under heat, blasting, and coating, e.t.c.

 

As a result, manufacturers normally place barcodes after key part transformations.

 

That means, for 70%+ of the production process, you're flying blind. You're guessing which parts went through which treatments.

 

And when something fails? You're looking at massive recalls, supplier penalties, and lost time.

 

What if we could code physical parts in a way that never fades?

 

That’s exactly what Serra Tuzcuoglu and her Co-Founder invented. CDOT AI Code, a frequency-based, AI-readable identifier that can survive the harshest industrial conditions.

 

Unlike traditional visual patterns, it uses signal recognition instead of contrast, enabling it to be read even when surfaces are scratched, painted, or distorted.

 

A global appliance manufacturer gave them a challenge: “If your code can survive enamelling and high heat, we’ll use it.”

 

It did. That was the beginning. CDOT AI Code Code is now deployed globally, from Renault crankshafts to Ford EV battery trays, military tanks to printing cylinders.

 

In the latest episode of the AI in Manufacturing podcast, I sat down with Serra Tuzcuoglu, the CEO and Co-Founder of COSMODOT, to discuss:

 

✅ How CDOT AI Code works

✅ Solution Architecture & Integration into the factory network

✅ Readiness for AI-based quality analytics and creation of digital twins.

✅ Real-world examples and Case Studies

Building and Scaling AI-Driven Transformation in Manufacturing: Jonathan Alexander - Global Manufacturing AI and Advanced Analytics Manager, Albemarle Corporation

Learn how Jonathan and his team at Albemarle Corp went from pilots to $150M in annual improvements through a business-first, scalable AI strategy.

In the latest episode of the AI in Manufacturing podcast, I spoke with Jonathan Alexander, Global Manufacturing AI and Advanced Analytics Manager at Albemarle Corporation, about building, scaling and sustaining AI-driven Transformation in Manufacturing.

Here’s the outline of our conversation:
⇨ Key Data Challenges in Implementing AI at Scale
⇨ Data Contextualization for Analytics and Decision Making
⇨ Data Architecture & Interoperability
⇨ Standardization & Scaling of AI Applications
⇨ Driving Sustained Action from AI Insights
⇨ Sustaining AI Adoption & Value Creation on the Plant-Floor
⇨ Change Management & Culture

AI Agents for Industrial Sales and Application Engineers: Fay Goldstein - Co-Founder and CEO, Folio

Industrial teams still rely on fragmented and manual processes to match complex product specs with use-case-specific needs.

Take this example:

You're selling a vision sensor to a factory. To get it right, you need to know:

⇨ What’s the size and speed of the conveyor line?
⇨ Is the plant located in Munich or Arizona?
⇨ Will this sensor withstand that temperature range?
⇨ What PLC is the customer using — Siemens or Rockwell?
⇨ Will the sensor integrate without conflict?
⇨ Are there newer models in the portfolio that fit better?
⇨ Can it be installed without disrupting production?

Now imagine trying to answer all of that...
⇨ Using PDFs.
⇨Email chains.
⇨ Gut instinct.
⇨ And hoping Bob from Engineering isn’t on vacation.

With an AI Agents trained on your connected industrial knowledge:
✅ All technical documentation, manuals, spec sheets, CAD drawings, becomes queryable
✅ Reps and engineers can ask natural-language questions and get verified answers
✅ Compliance, compatibility, and environmental fit can be checked in seconds
✅ Human experts stay in the loop, but no longer stuck in the weeds

I recently sat down with Fay Goldstein Co-Founder and CEO of Folio to discuss the application of AI Agents for Industrial Sales and Application Engineers.

ABOUT FOLIO:

Folio’s AI platform empowers industrial sales and application engineers by turning technical specs, configuration data, and application info into instant answers, recommendations, and agentic workflows, speeding work, cutting errors, and boosting revenue for industrial manufacturers and distributors. Learn more at www.folio.build

ABOUT FAY:

Fay Goldstein is the Co-Founder and CEO of Folio, an AI-powered platform that transforms how manufacturers and distributors sell and support complex and technical industrial product portfolios. Before founding Folio, she spent her summers managing direct and online sales at local automotive AC condenser and compressor shop, led strategic GTM and communications at an automotive telematics data company, and worked at an early-stage venture capital firm, where she supported dozens of early-stage startups on their initial GTM and communication strategies. Fay graduated magna cum laude from Florida International University and holds an MBA from Reichman University.

 

CONNECT WITH FAY
🌐 Website: https://www.folio.build/
💼 LinkedIn: https://www.linkedin.com/in/faygoldstein/

Building and Scaling Closed-Loop AI for Manufacturing Operations: Dr. Nikita Golovko - Software Architect for Industrial AI, Siemens

In theory, AI should learn, adapt, and improve continuously. But in reality, most deployments are static and disconnected from the evolving complexity of shop floor operations.

Most businesses lack tools to close the loop between:

⇨ Data collection
⇨ AI training
⇨ Deployment
⇨ Continuous retraining
⇨ Business impact validation

And they struggle to connect domain experts with data scientists.

To learn more about building and scaling closed-loop AI for industrial operations I recently sat down with Dr. Nikita Golovko who is a Software Architect for Industrial AI at Siemens.

Edge AI Architecture For Integrating Industrial AI Into Control Systems: Ander Garcia Gangoiti - Director of Data Intelligence, Vicomtech

Many small and mid-sized manufacturers want to explore AI to improve efficiency, reduce waste, or make their processes smarter.

 

However, this process requires OT and IT knowledge not present in many

industrial companies, mainly SMEs.

 

Ander Garcia Gangoiti and his team built a micro-service edge architecture based on MQTT, TimescaleDB, Node-Red and Grafana stack to ease the integration of soft AI models into industrial system.

 

The architecture has been successfully validated controlling the vacuum

generation process of an industrial machine.

 

Soft AI models applied to real-time data of the machine analyze the vacuum value to decide when the most suitable time is:

⇨ to start the second pump of the machine,

⇨ to finish the process, and

⇨ to stop the process due to the detection of humidity.

 

Ander is the Director of Data Intelligence for Industry at Vicomtech and I recently sat down with him on the AI in Manufacturing podcast.