The global shift to sustainability, coupled with fluctuations in raw material prices and strengthening market competition, has changed the landscape of optimising industrial processes.
The order is clear. Increases efficiency, minimizes maintenance overhead and reduces environmental impact.
Navigating this narrow operating corridor requires a sophisticated combination of real-time analytical insights and intelligent adaptive control.
This article explores the strategic deployment of online process analyzers and presenting a powerful, scalable and secure approach to real-time process optimization, as well as how deep reinforcement learning (DRL) is being deployed.
It outlines core technical considerations, addresses inherent challenges, and positions DRL as a compelling alternative to legacy process control methods in complex, nonlinear industrial environments.
Modern optimization is essential
While traditional process control systems are reliable within design parameters, they struggle to deal with the complexity and variations of today's industrial environment, particularly those that can handle renewable ingredients or operate under a variety of load conditions.
Modern process systems need to meet broader raw material variability, stricter environmental compliance, and tighter margins.
This requires features that exceed the performance of predictive modeling, continuous learning, and real-time control – Classic Rule-based or Static Model Prediction Controllers.
Machine learning (ML) technologies, particularly DRL, have emerged as frontier solutions for this challenge.
When embedded in a digital twin, a data-driven replica of a physical process, ML allows optimization algorithms to simulate, learn and evolve optimal control strategies without putting real assets at risk.
However, the effectiveness of this approach depends on careful system design, robust data infrastructure, and ongoing model improvements.
Machine learning promises and pitfalls in process control
The essence of ML in an industrial context is to identify complex dependencies between process parameters based on historical data. However, this strength is also in Achilles heels.
The ML model is essentially restricted to the statistical domain of trained data.
The predictive reliability of the digital twin is reduced when process optimization algorithms evaluate or maneuver the system to a state that has not been previously visited, particularly in a state that deviates greatly from the historical pattern.
This limitation presents two important technical challenges.
- Extrapolation beyond the trained domain: The more the model works from a historically visited state, the higher the risk of prediction errors.
- Navigation of nonlinear response surfaces: Real-world industrial processes are often very nonlinear, with multiple local minimums in optimization situations.
This makes global optimization computationally expensive and time-limited, especially in real-time applications.
DRL provides a robust and practical way to overcome both challenges when properly trained.
Deep Reinforcement Learning: Optimal Learning Control Policy
Inspired by the biological learning process, Renforce Learning (RL) involves training agents who interact with the environment by performing actions, receiving feedback (rewards), and implementing decision policies to maximize cumulative rewards.
DRL takes this further by using deep neural networks to approximate complex policy functions, allowing control over high-dimensional nonlinear systems.
In process control terminology, “agent” corresponds to a control algorithm, “action”, to an instrumental variable (MV), and “state” encapsulates the current configuration of process parameters.
“Reward” is usually a function that measures performance (e.g. energy efficiency, emissions, costs, etc.).
The goal of the DRL agent is to map policies that map all related processes to the optimal set of control actions.
Importantly, this learning occurs within a simulation environment using digital twins, avoiding the risk of actual trial and error.
Safe learning using digital twins and online analyzers
Direct training of DRL agents in the live process is generally unrealistic due to the risk of unintended consequences.
Instead, the Digital Twin provides a secure, fidelity simulation platform for agents to learn the optimal policy. However, as explained, the digital twins themselves are as good as the data they saw.
To address this, closed-loop strategies have been adopted, including DRL controls, online analyzers, and continuous model updates.
- Deploying control policy: Trained DRL controllers are deployed to guide the actual process.
- Status expansion via online analyzer: When the controller explores new process states, the online analyzers and outlets installed in the main processes capture accurate data about system behavior in these new regions.
- Model refinement: New data is used to retrain or update digital twins, improving prediction accuracy for newly visited operating domains.
This loop allows for safe and gradual expansion of the controller's operational envelope without exposing the actual system to unverified conditions.
Additionally, using the constrained reward feature – penalizing excursions into the low confidence area of the digital twin ensures that DRL agents learn responsibly.
Temporal dynamics and state vector design
In traditional control systems, historical data is used to estimate trends and to infer system inertia. The DRL system also needs to consider time dependencies.
This means that the state vector (data input to the DRL controller) must include not only the current sensor measurements, but also the history of past control actions and fault associations.
For example, in processes with delayed response characteristics (such as thermal systems), it is important to include the time frame of the previous MV of the state vector.
This design requirement should be reflected in the digital twins that need to accept and process these time data inputs during training.
DRL vs. traditional model predictive control (MPC)
Nonlinear Model Predictive Control (NMPC) is a powerful optimization approach, but its real-time applicability is limited by high computational demand and the risk of being trapped in local minimals. in contrast:
- DRL is pre-trained and avoids expensive online optimization calculations.
- DRL investigates the full process landscape during simulation and learns robust policies with less trends in local optimizers.
- DRL is scalable and is suitable for optimizing complex multi-unit systems or dynamic supply chains.
Forward Pass: Scalable and Sustainable Control System
The strategic integration of DRL with online process analyzers and digital twin infrastructure provides an attractive roadmap for the processing industry. It is possible:
- Real-time, adaptive control of nonlinear multivariate systems
- Gradual learning that enables safe investigation of new behavioral domains
- Sustainable performance through reduced energy use, emissions and raw waste
Furthermore, this architecture is highly scalable. As data volumes increase and AI models improve, DRL-based control systems become increasingly autonomous and can adapt not only to operational changes, but also to changing market demand and regulatory frameworks.
Conclusion
Deep reinforcement learning, together with online process analyzers and the ever-updated digital twin, represents a paradigm shift in industrial automation.
This allows for highly adaptive, safe and scalable process optimization. This can meet sustainability and profitability requirements on comparable measures.
This approach is not theoretical. It has already been deployed in sectors ranging from chemical production to energy systems and advanced manufacturing.
With its robust design and responsible deployment, DRL can become the backbone of next-generation industrial control architectures, empowering smart factories of the future.
Modcon Systems Ltd. recognized this transformation and played an active role in accelerating recruitment.
By integrating cutting-edge inline process analyzers, including DRL-based optimizer, with AI-driven control strategies, ModCon offers sophisticated solutions for real-time decision-making in complex industrial environments.
The analyzer provides accurate, continuous data streams essential to maintaining the latest digital twins, and the process control platform is built to accommodate scalable ML models.
Through its commitment to field measurement, predictive analytics, and control innovation, ModCon helps industries bridge the gap between theoretical AI models and real-world operational excellence.
