Reinforcement Learning for Process Optimisation
Stop just predicting what will happen. Let us build an agent that actively makes your processes better, learning and adapting in real-time.
Is This Your Challenge?
Your business runs on complex, dynamic systems where small inefficiencies lead to major costs. You have sensor data and maybe even digital twins, but your current models can only forecast problems—they can't solve them.
Common Challenges:
- Material or Energy Waste: Production lines with inconsistent quality, oscillations, or excessive resource consumption
- Multi-variable Optimization: Systems with hundreds of interacting variables that need simultaneous optimization
- Traditional Controllers Fail: PID controllers lead to oscillations and can't model complex physics
- Reactive Not Proactive: Current systems only react after problems occur, rather than preventing them
Our Solution: RL Controllers & Digital Twins
INM Consulting designs and deploys Reinforcement Learning-based controllers that learn the optimal strategy for your unique environment, actively controlling processes to maximize efficiency and minimize waste.
Digital Twin Simulation
We build a high-fidelity digital twin of your process by parsing and modeling real-time sensor data. This simulation environment allows us to train and test controllers safely before deployment.
RL Agent Training
Using this simulation, we design and train a novel RL agent to take autonomous actions (adjust temperatures, pressures, schedules) that minimize your loss function. The agent learns optimal strategies through millions of simulated scenarios.
Deployment & Continuous Learning
We deploy the trained controller as a robust API that integrates with your existing production systems. The agent continues to learn and adapt from real-world performance, constantly improving its strategy.
Why RL Beats Traditional Control Systems
Traditional PID Controllers
- ❌ React only after errors build up
- ❌ Control one variable at a time
- ❌ Can't model complex physics
- ❌ Lead to oscillations
- ❌ Fixed parameters
Reinforcement Learning
- ✅ Predict and prevent problems
- ✅ Control multiple variables simultaneously
- ✅ Learn complex relationships
- ✅ Minimize oscillations
- ✅ Continuously adapt and improve
Real-World Applications
🏭 Production Line Control
Optimize manufacturing processes with hundreds of variables, reducing waste and improving quality consistency.
⚡ Energy Management
Minimize energy consumption while maintaining performance targets across complex industrial facilities.
🚚 Supply Chain Optimization
Optimize inventory levels, routing, and scheduling decisions in dynamic environments with changing constraints.
🔬 Chemical Process Control
Control reaction conditions in real-time to maximize yield and minimize byproducts in chemical manufacturing.
Featured Project: Manufacturing
Industry: Manufacturing
Challenge: Production line experiencing losses that needed optimization.
Solution: We developed a digital twin of the production line by parsing real-time measurements (temperatures, pressures, setpoints), trained an RL controller to minimize the loss function, and deployed the controller on the production line.
Results: Successfully deployed RL controller with real-time control capabilities, replacing traditional control methods.
Read Full Case Study →Ready to Optimize Your Processes?
Let's discuss how Reinforcement Learning can transform your operations from reactive to proactive.