The global shift towards sustainability, coupled with fluctuating raw material prices and intensified market competition, has transformed the landscape of industrial process optimization.
The imperative is clear: increase efficiency, minimize maintenance overhead, and reduce environmental impact – all while maintaining profitability.
To navigate this narrow operational corridor, modern industrial systems require a sophisticated combination of real-time analytical insight and intelligent, adaptive control.
This article explores how Deep Reinforcement Learning (DRL), combined with the strategic deployment of on-line process analyzers, presents a powerful, scalable, and safe approach to real-time process optimization.
It outlines the core technical considerations, addresses inherent challenges, and positions DRL as a compelling alternative to legacy process control methods in complex, non-linear industrial environments.
The Modern Optimization Imperative
Traditional process control systems, while reliable within their design parameters, often struggle to cope with the complexity and variability of today’s industrial environments – particularly those processing renewable feedstocks or operating under variable load conditions.
Modern process systems must accommodate broader feedstock variability, stricter environmental compliance, and tighter margins.
This requires predictive modelling, continuous learning, and real-time control – capabilities that exceed the performance of classic rule-based or even static model-predictive controllers.
Machine Learning (ML) technologies, and specifically DRL, have emerged as a frontier solution to this challenge.
When embedded within a digital twin – a data-driven replica of the physical process – ML enables optimization algorithms to simulate, learn, and evolve optimal control strategies without risking the real asset.
The effectiveness of this approach, however, hinges on careful system design, robust data infrastructure, and continuous model refinement.
The Promise and Pitfalls of Machine Learning in Process Control
The essence of ML in industrial contexts lies in identifying complex dependencies between process parameters based on historical data. However, this strength is also its Achilles heel.
ML models are inherently limited to the statistical domain of the data on which they were trained.
When a process optimization algorithm evaluates or steers the system into previously unvisited states – especially those that deviate significantly from historical patterns – the predictive reliability of the digital twin diminishes.
Two key technical challenges arise from this limitation:
- Extrapolation beyond trained domains: The further the model operates from historically-visited states, the higher the risk of prediction error.
- Navigating non-linear response surfaces: Real-world industrial processes are often highly non-linear, with multiple local minima in the optimization landscape.
This makes finding global optima computationally expensive and time-constrained – particularly in real-time applications.
DRL, when trained appropriately, offers a robust and practical way to overcome both challenges.
Deep Reinforcement Learning: Learning Optimal Control Policies
Inspired by biological learning processes, Reinforcement Learning (RL) involves training an agent to interact with an environment by performing actions, receiving feedback (rewards), and refining its decision policy to maximize cumulative reward.
DRL takes this further by using deep neural networks to approximate complex policy functions, enabling control in high-dimensional, non-linear systems.
In process control terms, the “agent” corresponds to the control algorithm, “actions” correspond to manipulated variables (MVs), and “states” encapsulate the current configuration of process parameters.
The “reward” is typically a function that measures performance (for example, yield, energy efficiency, emissions, cost).
The goal of the DRL agent is to learn a policy that maps every relevant process state to the optimal set of control actions.
Importantly, this learning can – and should – occur within a simulation environment using a digital twin, avoiding the risks of real-world trial and error.
Safe Learning with Digital Twins and Online Analyzers
Direct training of DRL agents on live processes is generally impractical due to the risk of unintended consequences.
Instead, a digital twin provides a safe, high-fidelity simulation platform for the agent to learn optimal policies. However, as discussed, digital twins themselves are only as good as the data they’ve seen.
To address this, a closed-loop strategy involving DRL control, online analyzers, and continuous model updating is employed:
- Control Policy Deployment: The trained DRL controller is deployed to guide the real process.
- State Expansion via Online Analyzers: As the controller explores new process states, online analyzers installed at key process inlets and outlets capture precise data about system behavior in these new regions.
- Model Refinement: The new data is used to retrain or update the digital twin, improving prediction accuracy in newly visited operating domains.
This loop allows for safe, staged expansion of the controller’s operational envelope without exposing the real system to unvalidated conditions.
Furthermore, the use of constrained reward functions – penalizing excursions into low-confidence regions of the digital twin – ensures that the DRL agent learns responsibly.
Temporal Dynamics and State Vector Design
In traditional control systems, historical data is often used to estimate trends or infer system inertia. DRL systems must also account for temporal dependencies.
This means that the state vector – the data input to the DRL controller – should not only include current sensor readings but also a relevant history of past control actions and disturbances.
For instance, in processes with delayed response characteristics (such as thermal systems), including a time window of previous MVs in the state vector is crucial.
This design requirement must be mirrored in the digital twin, which should accept and process these temporal data inputs during training.
DRL vs. Traditional Model Predictive Control (MPC)
Non-linear Model Predictive Control (NMPC) is a powerful optimization approach, but its real-time applicability is limited by high computational demands and the risk of getting trapped in local minima. In contrast:
- DRL is pre-trained, avoiding expensive online optimization calculations.
- DRL explores the full process landscape during simulation, learning robust policies less prone to local optima.
- DRL is scalable, making it well-suited for optimizing complex multi-unit systems or dynamic supply chains.
The Path Forward: Scalable, Sustainable Control Systems
The strategic integration of DRL with on-line process analyzers and digital twin infrastructure offers a compelling roadmap for the processing industries. It enables:
- Real-time, adaptive control of non-linear, multivariate systems
- Incremental learning, allowing safe exploration of new operating domains
- Sustainable performance, through reduced energy use, emissions, and raw material waste
Moreover, this architecture is highly extensible. As data volumes grow and AI models improve, DRL-based control systems will become increasingly autonomous, capable of adapting not only to operational changes but also to shifts in market demand and regulatory frameworks.
Conclusion
Deep Reinforcement Learning, in conjunction with on-line process analyzers and continuously updated digital twins, represents a paradigm shift in industrial automation.
It enables highly adaptive, safe, and scalable process optimization – meeting the demands of sustainability and profitability in equal measure.
This approach is not theoretical. It is already being deployed in sectors ranging from chemical production to energy systems and advanced manufacturing.
With robust design and responsible deployment, DRL has the potential to become the backbone of next-generation industrial control architectures – empowering the smart factories of the future.
Modcon Systems Ltd. has recognized this transformation and taken a proactive role in accelerating its adoption.
By integrating state-of-the-art in-line process analyzers with AI-driven control strategies, including DRL-based optimizers, Modcon provides advanced solutions for real-time decision-making in complex industrial environments.
Their analyzers deliver precise, continuous data streams essential for maintaining up-to-date digital twins, while their process control platforms are built to accommodate scalable ML models.
Through its commitment to in-situ measurement, predictive analytics, and control innovation, Modcon is helping industries bridge the gap between theoretical AI models and real-world operational excellence – delivering measurable value in efficiency, safety, and sustainability.
