TL;DR

• Modular LNG projects prioritize speed and capital efficiency.
• Automation must scale to avoid becoming a bottleneck.
• Industrial AI accelerates configuration and engineering workflows.
• Simulation tools enable faster, more accurate project design.
• AI advisors improve uptime and operator decision-making.

Why this matters now

LNG projects have changed dramatically in recent years. The traditional mega-train plants producing 5+ million tons per annum (mtpa) have given way to more modular projects, deploying a series of smaller, 1.5 mtpa trains that can be completed and start production much faster.

The benefit of this more modular strategy is the ability to free up revenue much more quickly, which can in turn be used to construct more trains, creating a system of continuous capital recycling.

However, as Emerson’s Nathan Pettus explored in a recent article in LNG Industry, this new strategy comes with its own challenges:

“Automation strategy will always be a bottleneck, unless it scales and accelerates in parallel. Teams not only need ways to deploy equipment quickly, but also to ensure it delivers the highest operational efficiency so they can drive fast return on investment—the core goal behind this new strategy.”

This highlights the critical role automation plays in enabling modular LNG strategies to succeed.

Takeaway: Automation must scale alongside modular LNG projects to maintain speed and return on investment.

Yet, in parallel with this shift, the industry is facing a workforce shortage, leaving projects without the deep expertise that historically enabled successful execution. As expert personnel become harder to find, maintaining operational excellence and efficient timelines becomes increasingly challenging. Fortunately, a new solution is emerging:

“Just as LNG projects of all sizes begin to gain increased momentum, an optimal technology for project improvement and driving increased operational excellence has emerged in the marketplace: artificial intelligence (AI).”

This signals a transition toward AI-enabled execution models that reduce dependence on scarce expertise.

Takeaway: Industrial AI is becoming essential to sustaining performance in a constrained labor environment.

Learning from the past

One of the key advantages Emerson has gained from participating in thousands of LNG projects over many decades is the creation of proven automation design frameworks. These repeated implementations have generated vast amounts of data on what works—and what doesn’t.

Industrial AI tools, such as those embedded within DeltaV™ Revamp, leverage both historical project data and domain expertise to evaluate and optimize control system configurations.

“Armed with this data, the tools can quickly identify best practices in control configuration. The software then automatically generates configuration templates for new control solutions, dramatically reducing the manual workflows that delay results and lead to errors in configuration.”

This approach enables engineering teams to move faster while reducing risk.

Takeaway: AI-driven configuration tools accelerate engineering workflows and reduce errors in control system design.

The result is a significant reduction in engineering burden, enabling lean teams to deliver fully integrated digital workflows with greater speed and consistency.

Simulating success

Another area where AI is delivering value is in the creation of simulation models for tools such as Aspen HYSYS® and DeltaV Mimic.

Because AI can process large volumes of data far faster than human teams, it allows engineers to generate and refine models more efficiently using both first-principles knowledge and historical project data.

“These hybrid models, built on decades of engineering expertise, collect and analyse an organisation’s data. They then combine it with the insights and guidance from machine learning to simulate more plant conditions and automatically generate a wider range of solutions, making it easier for engineers of any experience level to generate right-the-first time results.”

This enables the creation of more robust digital twins, allowing teams to validate design decisions, test workflows, and conduct safety and energy analyses long before equipment is deployed.

Takeaway: AI-powered simulation improves design accuracy and reduces risk through better, earlier validation.

Making every operator the best operator

Beyond engineering and design, AI is also transforming plant operations through embedded advisor tools within the control system.

These AI advisors continuously monitor plant conditions and provide early warnings of potential issues—often before alarms are triggered.

“This allows the software to alert teams before alarms trigger, which empowers plant personnel to schedule maintenance when it is convenient, rather than when it is critical. The resulting avoidance of unplanned downtime is often the key enabler of a team’s ability to drive the extra production necessary to fill one additional ship.”

This capability shifts operations from reactive to proactive, improving uptime and overall production performance.

Takeaway: AI advisors enable proactive maintenance, reducing downtime and improving production reliability.

In addition, these systems provide decision support by guiding users toward relevant data and recommended actions, helping less experienced personnel ramp up faster while enabling experienced staff to solve problems more efficiently.

Plan for AI from the beginning

The value of industrial AI spans the full lifecycle of an LNG project—from initial design through execution and into ongoing operations.

By planning for AI adoption early in the project lifecycle, teams can establish a strong foundation for long-term value creation, ensuring systems are designed to fully leverage AI-driven capabilities.

The most effective approach is to implement fit-for-purpose industrial AI tools that are already integrated into the automation platforms operators use every day.

Takeaway: Early integration of industrial AI enables continuous value across the entire LNG project lifecycle.