Unlock the true potential of your plant with AI-enabled Real-Time Process Optimization
As the fertilizer industry embraces digital transformation, AI-driven Real-Time Optimization (RTO) is emerging as a key enabler of value generation from existing assets. At Baker Hughes, our approach combines deep domain expertise with AI/ML-driven insights to help producers unlock hidden performance, improve energy efficiency, and boost margins.
In this blog series, we’ll explore how Baker Hughes is deploying AI-enabled process optimization in industrial settings—and how our Cordant™ Process Optimization solution is helping energy and chemical producers thrive in a tightening market.
The complexity of industrial processes and the dynamic challenges they face
The fertilizer industry continues to face dynamic challenges. Volatile markets, aging infrastructure, and increasing sustainability demands are just the beginning. Operationally, the industry is grappling with:
- Supply chain disruptions
- Skilled labor shortages
- Volatile feedstock and product prices
- Dynamic operating conditions
- Pressure to reduce emissions
Digital adaptation has been underway for decades, with major strides made in the 1990s and 2000s thanks to increased computing power. Today, tools like Advanced Process Control (APC), and Model Predictive Control (MPC) are widely used—but they often fall short of delivering plant-wide optimization.
Smarter tools, not bigger budgets
Historically, process optimization relied on physics-based models—effective but rigid and resource-intensive. APC helped stabilize operations, but it wasn’t designed to optimize across the entire plant. RTO offered promise but required long implementation timelines and high maintenance.
Now, AI-driven optimization is changing the game.
By continuously analyzing live data and adjusting key process levers—like reformer outlet temperature, steam-to-carbon ratio, and compressor suction pressure—plants can close the gap between current performance and true potential. The result? Increased throughput, reduced energy consumption, and improved operating margins.
AI-solution with a real-world impact
A major ammonia producer in the Middle East implemented our Cordant™ AI-powered optimization to address production losses caused by dynamic operating conditions. The outcome was a 1.2–1.8% increase in daily production, achieved over and above what APC systems had already delivered.
That kind of improvement—especially in high-cost or resource-intensive environments—translates into significant value.
What sets Cordant™ Process Optimization apart
Some might say, “Process Optimization is Process Optimization.” But when you look beyond the numbers, what sets AI-driven optimization apart is its data-driven approach, uniquely combined with actual physical models.
“Calling it a new era for fertilizer production might be a stretch, but the challenges facing the fertilizer industry aren’t going away. But with the right tools, producers can turn complexity into opportunity. By embracing AI-powered process optimization, they can unlock hidden value in their existing assets—without the need for costly upgrades or overhauls. This isn’t just about automation. It’s about building a smarter, more resilient, and more sustainable future for fertilizer production.”
- Jesper Naimi Funch Poulsen, Senior Sales Manager, Cordant Solutions – Process Optimization
At the heart of this transformation is of course the AI-powered process modeling. Unlike traditional linear models, which often oversimplify complex plant behavior, these systems use custom non-linear models built on pre-designed templates tailored for industrial processes. This means they can capture the true dynamics of a plant—how variables interact, how conditions shift, and how performance evolves over time.
And development, model training and deployment is fast. While traditional model development, based on thermodynamics and physics, might take a year or more, our AI enabled solution can be up and running in just 3–4 months.
The solution is supported by our engineering insights and expertise, with domain experts playing a critical role in selecting the right inputs, outputs, and intermediate variables to ensure the models reflect real-world causal relationships. The result? A model that’s not only accurate but also adaptable—designed to evolve with the plant as conditions change.
These models are deployed in the cloud, enabling real-time data processing and end-to-end tracking. Operators get reliable insights, fast—without waiting for manual updates or recalibrations. And because the system continuously monitors performance, it can flag deviations and recommend corrective actions before issues escalate.
What’s more, these AI-driven solutions work independently of existing APC systems, meaning they can unlock additional efficiency gains without disrupting current operations. This was the case for the ammonia producer who deployed our AI-driven process optimization and achieved results above and beyond what APC alone could offer. So, what does this mean for operators?
It means they’re no longer just reacting to or chasing setpoints. They’re making informed decisions, guided by predictive insights and real-time simulations. They can run “what-if” scenarios, visualize performance gaps, and steer the plant toward optimal conditions—all through an intuitive interface designed for real-world use.
In short, AI-driven RTO doesn’t just improve numbers—it elevates the role of the operator, turning them into strategic decision-makers at the heart of a smarter, more resilient plant.