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For decades, process simulation and optimization (PSO) software has been built on a foundation of first-principles engineering—rigorous physics, thermodynamics, and mass and energy balances. These models have enabled engineers to design plants, size equipment, analyze safety scenarios, and improve operating performance with a high degree of confidence. However, as industrial systems have grown more complex and the pace of change has accelerated, traditional simulation approaches alone are no longer sufficient.
Today, the PSO market is undergoing a fundamental transformation driven by artificial intelligence (AI) and, more importantly, hybrid modeling—the integration of AI techniques with physics-based models. This shift is redefining how engineers design, operate, and optimize industrial assets across their entire lifecycle.
From First Principles to Hybrid Intelligence
Traditional process simulation relies on first-principles models derived from well-understood physical and chemical laws. These models are highly accurate and trusted, but they often require deep domain expertise, extensive configuration effort, and significant time to calibrate—particularly when applied to complex real-world operations.
AI changes this equation by introducing data-driven learning into the modeling process. Machine learning algorithms can analyze large volumes of plant, pilot, or simulation data to identify relationships that are difficult or impractical to express explicitly in equations. When combined with first-principles models, the result is hybrid modeling—a new class of models that are both physically consistent and empirically accurate.
Hybrid models preserve the engineering “guardrails” provided by physics while leveraging AI to accelerate model creation, calibration, and ongoing adaptation. Rather than replacing engineering expertise, AI augments it, allowing models to better reflect how plants actually operate under varying conditions.
Where Hybrid Modeling Fits in the New Industrial AI Landscape
As industrial organizations navigate the hype surrounding Artificial Intelligence, it is critical to distinguish between general-purpose chatbots and deterministic, industrial-grade intelligence. As outlined by Colin Masson in his recent research, Decoding Industrial AI: A New Voyage of Discovery, we must systematically deconstruct AI based on operational realities rather than treating it as a monolithic software feature.
Under the new ARC 3-Axis Industrial AI Models Taxonomy, the AI and hybrid modeling solutions transforming process simulation map precisely as "Process Optimizers". They are defined by three distinct dimensions:
Axis 1 (The "What"): They target Operations & Process Control.
Axis 2 (The "How"): They utilize Physics-Informed & Hybrid Models, moving beyond standard machine learning to embed first-principles and physical laws directly into the neural network's loss function.
Axis 3 (The "Context"): Crucially, they operate at Level 3: Domain-Specific. Unlike Level 2 "Industry-Aware" copilots that rely on text and manuals, Level 3 models are tightly bounded by the immutable laws of physical reality.
By embedding the actual physical, chemical, and thermodynamic equations into the AI's mathematical architecture, these hybrid models are physically constrained. They effectively prevent the AI from "physically hallucinating" impossible scenarios, providing the rigorous domain foundation needed to safely optimize and simulate complex industrial processes.
Lowering the Barrier to High-Fidelity Modeling
One of the most significant impacts of AI-enabled hybrid modeling is its ability to lower the expertise barrier traditionally associated with simulation software. Historically, building and maintaining high-fidelity models required specialized simulation experts. This has become increasingly problematic as many industrial organizations face a shrinking pool of experienced engineers and the retirement of institutional knowledge.
Hybrid modeling addresses this challenge by automating many of the most time-consuming and expertise-intensive tasks, such as parameter estimation and model calibration. AI embedded in PSO tools can assist users in setting up models, tuning them to match actual plant behavior, and even identifying inconsistencies or errors. This enables a broader group of users—process engineers, operations staff, and planners—to benefit from simulation without needing to become simulation specialists.
In effect, hybrid modeling helps democratize access to advanced simulation capabilities while ensuring that models remain safe, reliable, and grounded in engineering reality.
Enabling Lifecycle Model Reuse
Another defining advantage of hybrid modeling is its ability to support model reuse across the asset lifecycle. Traditionally, different models were created for different purposes—conceptual design, FEED, safety analysis, planning, operations, and training—often by different teams using different tools. This led to duplication of effort, inconsistencies, and models that quickly became outdated.
Hybrid modeling supports a “single source of truth” approach, where a core high-fidelity model can be adapted for multiple use cases. Reduced-order and surrogate models derived using AI can be deployed for fast what-if analysis, planning optimization, or real-time decision support, all while remaining synchronized with the underlying first-principles model.
This model-alliance concept enables organizations to maintain consistency across CapEx and OpEx activities, reduce risk during project execution, and improve collaboration between engineering, planning, and operations teams.
Supporting Sustainability and Energy Transition Goals
Sustainability has emerged as a major driver of PSO adoption, and hybrid modeling plays a critical role in meeting these objectives. Many decarbonization and energy-transition projects—such as hydrogen production, carbon capture, bio-refining, and electrification—introduce new operating regimes and uncertainties that are difficult to model using first principles alone.
Hybrid models can incorporate historical and experimental data to improve accuracy in these emerging applications while still respecting physical constraints. This enables engineers to evaluate trade-offs between efficiency, cost, and environmental impact more quickly and with greater confidence.
As regulatory pressure, investor scrutiny, and consumer expectations continue to rise, the ability to simulate sustainability outcomes alongside economics is becoming a strategic necessity.
AI as an Enabler, not a Replacement
Despite the growing role of AI, human domain expertise remains essential. AI models require engineering context to operate safely and effectively. Constraints such as mass balance, thermodynamic feasibility, and equipment limits provide the structure that ensures AI-driven insights are realistic and actionable.
Rather than replacing engineers, hybrid modeling embeds expert knowledge into the modeling process, preserving best practices and making them accessible to a wider audience. This partnership between human expertise and artificial intelligence is what allows PSO solutions to scale across organizations and deliver sustained value.
Looking Ahead
AI and hybrid modeling are no longer experimental concepts in process simulation—they are rapidly becoming the standard. As industrial assets grow more complex and organizations face increasing pressure to improve performance, safety, and sustainability, hybrid models provide a pragmatic path forward.
By combining the trust and rigor of first-principles engineering with the speed and adaptability of AI, hybrid modeling enables faster decisions, better outcomes, and more resilient operations. For the PSO market, this represents not just an incremental improvement but a fundamental shift in how value is created across the industrial lifecycle.
ARC is kicking off new research on the Process Simulation and Optimization Software market. Please contact Emilio Posa ([email protected]) and Peter Reynolds ([email protected]) for more information. Planned publication of this research is Q3 of 2026.