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      Deep Reinforcement Learning
      • 19 Sep 2024
      • 3 Minutes to read
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      Contents

      Deep Reinforcement Learning

      • Updated on 19 Sep 2024
      • 3 Minutes to read
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      Article summary

      What is a Deep Reinforcement Learning (DRL) Model and when should I use one?

      Deep Reinforcement Learning (DRL) combines reinforcement learning (RL) with deep learning techniques to create models that can make complex decisions and learn optimal actions through trial and error. In DRL, an agent learns to make decisions by interacting with an environment, receiving feedback in the form of rewards or penalties, and using this feedback to improve its performance over time while it is training. Below are a few scenarios where a DRL model can be beneficial when integrated with a plant's control system.

      Optimizing Production Processes

      Purpose: To enhance the efficiency and quality of production or manufacturing operations.

      How It Helps:

      • Model Application: A DRL model learns to adjust equipment settings, production speed, and other parameters to maximize throughput and minimize defects.

      • Integration: The model interacts with the production environment, receiving feedback on performance metrics such as production rate and defect levels. It refines its actions based on this feedback to continuously improve the process.

      Example: In a plastic molding facility, a DRL model optimizes the speed and pressure settings of the molding machines to increase production efficiency while reducing the rate of defective products.

      Energy Management in Buildings

      Purpose: To reduce energy consumption while maintaining optimal indoor conditions.

      How It Helps:

      • Model Application: A DRL model controls HVAC systems, lighting, and other energy-consuming devices based on real-time data and forecasts.

      • Integration: The model receives feedback on energy usage and indoor climate conditions, adjusting system settings to balance energy savings with occupant comfort.

      Example: In an office building, a DRL model manages HVAC operations by predicting occupancy patterns and external weather conditions to minimize energy consumption and maintain a comfortable indoor environment.

      Maintenance Prevention

      Purpose: To control a piece of equipment in a way that prolongs its life.

      How It Helps:

      • Model Application: A DRL model is trained to control within a list of constraints while still maintaining performance. The reward function should prioritize keeping the equipment safe and allow the process to take the backseat if it means damaging equipment.

      • Integration: The model is given control of the equipment and is able to drive the output in a way that maximizes its life span.

      Example: In a gas plant, a level control valve is constantly hunting for a setpoint in a volatile system. This is causing wear on the valve seat, which is becoming a headache for the maintenance department. A DRL model is put in control that is incentivized to move the output as little as possible when the plant is in a state that will allow it (Such as a surge drum having sufficient room for an upset), while also allowing the valve control to become aggressive when the system is trending towards an upset. This reduces unnecessary valve cycling and prolongs time between maintenance outages.

      DRL vs. PID: The Reward Function

      In a Deep Reinforcement Learning (DRL) model, the reward function is a crucial component that defines the goal the model is trying to achieve. While a PID controller typically focuses on minimizing error between the setpoint and the process variable, a DRL model can handle multiple objectives simultaneously. For example, a custom reward function can account for efficiency, energy consumption, safety, and product quality, allowing the model to balance competing goals in ways that PID controllers cannot. Also, PID loops can struggle with non-linear, time-varying systems, requiring constant tuning updates to adapt to changes in the process. In contrast, a DRL model can learn from the environment and adjust its actions in real-time based on a custom reward function that reflects the plant’s current needs, making it more adaptive and capable of handling complex interactions.

      Conclusion

      In summary, Deep Reinforcement Learning (DRL) offers a promising alternative to traditional control methods in industrial plants, particularly in managing complex, non-linear processes. By training on countless process conditions and adapting to those changes, DRL models can optimize control strategies more effectively than linear controllers like PIDs. This adaptability allows DRL to handle the intricate dynamics of processes such as chemical reactions, ultimately improving efficiency, reducing the need for manual intervention, and enhancing overall process control. As industrial systems grow more complex, DRL has the potential to play a pivotal role in driving smarter, autonomous operations.