TL;DR Summary Box
Industrial process simulation software is evolving from isolated engineering tools into integrated digital platforms that support optimisation, operational decision-making and sustainability initiatives across the full lifecycle of industrial processes.
Modern simulation environments now enable interoperability between thermodynamic packages, unit operations, optimisation tools and external engineering software through standards such as CAPE-OPEN. This allows engineers to reuse validated calculation models across design, revamping, operational support and process optimisation applications.
Advanced thermodynamic modelling remains central to simulation accuracy, particularly for complex systems involving electrolytes and emerging industrial processes. Open architectures also allow integration with scripting languages such as Python, enabling automated optimisation studies, API-driven workflows and hybrid simulation approaches combining physical models with artificial intelligence and machine learning.
Process simulation software is increasingly used to improve energy efficiency, reduce water consumption and support sustainability targets through tools such as pinch analysis, exergy analysis and lifecycle assessment integration. The next generation of industrial simulation platforms will focus on openness, interoperability and AI-assisted engineering optimisation.
From standalone process simulators to integrated engineering platforms
Process simulation was born in the 1970s. Originally, process simulation software was standalone software, closed black boxes that users had to use as they were.
While this approach has proven effective for many years, it increasingly shows its limitations when simulation has to interact with a broader digital ecosystem.
One of the major evolutions, which started in the middle of the 1990s, is the opening of software: towards external tools on the one hand and being able to interoperate between them on the other hand, thanks to integrated communication standards.
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Modern process simulation software is becoming a critical tool for chemical engineering, industrial optimisation and digital transformation initiatives. As industrial facilities seek greater efficiency, sustainability and operational flexibility, simulation platforms are increasingly required to integrate with broader engineering, automation and data analysis environments.
Expanding the role of process simulation in industrial operations
Today, simulation models are no longer used only at the design stage. They are reused for revamping studies, operational support, optimisation, data reconciliation, and sometimes real-time decision support.
In this context, the ability to extract, reuse and orchestrate calculation components independently of a specific graphical environment becomes essential. A component-based approach allows simulation to be integrated as a building block within larger workflows, rather than being confined to a single tool.
Why interoperability matters in modern process simulation
This evolution does not aim at replacing existing simulators, but at extending their usefulness and lifespan by making them interoperable with other engineering and data-driven environments. In process industries, CAPE-OPEN is a commonly used standard today, as much for thermodynamics as for unit operations.
As an example, the most efficient thermodynamic calculation engine currently available, such as Simulis Thermodynamics, can use thermodynamic “packages” from other suppliers or, conversely, use the calculation methods of these software packages thanks to these standards.
Thus, the same thermodynamic package can be used throughout the life of a process: from its configuration based on available experimental data, to the control or optimisation of a process, through its design or revamping phases.
“A component-based approach allows simulation to be integrated as a building block within larger workflows, rather than being confined to a single tool.”
Thanks to a software component approach, the calculation codes, but also the communication interfaces (to define the calculations but also to interpret the results) are identical and ensure total coherence in the calculations carried out, regardless of the environment used (Microsoft Excel®, Matlab® or any process simulation software).
The same is true for unit operations, which makes it possible to use specific modules from any process simulation software (for example XIST from HTRI for tube and shell heat exchangers, or ProSec from Fives ProSim for brazed plate and fin heat exchangers, to mention only some modules related to heat exchangers).
This way of working allows the user to continue using their favourite software, for thermodynamics or unit operations (even in-house software), without any programming effort. The process simulation software must also allow the user to develop their own models, with the tools (Matlab®, Microsoft Excel®…) or the programming languages of their choice (C++, C#, Python…), as much for the thermodynamics as for the unit operations or the solvers or optimisers used.
For example, with our steady state simulation and optimisation software ProSimPlus, it is possible to delegate the management of the calculation sequence to an external program written in the Python language. This functionality has been designed especially for the use of external optimisers but it can also be used in the context of a machine learning system, a sensitivity study or other uses.
Thermodynamic modelling as a long-term engineering asset
In many industrial projects, the choice of a thermodynamic model is often seen as a preliminary step, sometimes revisited only when simulation results become inconsistent with plant data. Experience shows that this approach is risky.
The robustness of a simulation model over time depends largely on the consistency and traceability of its thermodynamic assumptions. Treating thermodynamics as a shared and reusable asset, rather than as an internal configuration of a given simulator, makes it possible to maintain coherence across studies, teams and applications, especially for complex systems such as electrolytes or emerging processes.
Advanced thermodynamic calculations in process simulation
Thermodynamic modelling is essential for process simulation, the quality of the results of a simulation being directly linked to the good modelling of the phase equilibria and the various physico-chemical properties of the systems to be treated.
Numerous developments are currently being carried out in research laboratories, in particular models based on statistical mechanics (SAFT based models) or numerical chemistry (COSMO based models), and the process simulation software must allow access to these new models.
The native integration of these models in the process simulation software is of course a way to give access to these models more powerful than the existing traditional models, but the process simulation software must also offer the possibility to users to use any model, thus allowing the engineers in the industry to have quick access to the results of the university research.
On the other hand, more and more systems encountered in industry contain electrolytic species. This field of thermodynamics is particularly complex and requires significant R&D efforts today to meet the associated challenges.
“Treating thermodynamics as a shared and reusable asset, rather than as an internal configuration of a given simulator, makes it possible to maintain coherence across studies, teams and applications.”
Using process simulation software to improve energy efficiency and sustainability
In the current context, industry must tend towards an optimal efficiency, for energy consumption, of course but also for the natural resources used, in particular water. A process simulation and optimisation software must therefore be able to provide indicators of these consumptions as well as ways of improvement.
Process simulation software has to provide for example analysis modules based on the pinch technology which allow users to determine the theoretical minimum quantity of thermal energy and/or water that should be used by the process, and thus to compare the consumption of the simulated configuration with these optima.
These optimisation capabilities are increasingly important as industrial operators seek to reduce carbon emissions, improve energy efficiency and minimise operating costs across complex production facilities.
From the information generated by these modules, the software can propose heat exchanger networks but also water networks allowing to get closer to these theoretical optima.
Exergy analysis is also useful to allow the user to quickly visualise the parts of their process that destroy exergy, i.e. those that are not efficient from an energy point of view. This information is thus particularly relevant to consider areas for improvement.
Exergy analysis helps engineers identify thermodynamic inefficiencies within industrial processes and prioritise areas where energy recovery and optimisation can deliver the greatest operational improvements.
Water optimisation and lifecycle analysis in industrial simulation
For the optimisation of water consumption, this involves recycling, which often requires purification steps. These treatment operations must also be implemented in the process simulation software, especially those using membranes.
The life cycle analysis (LCA) is also an interesting tool for the environmental optimisation of a process. It is therefore necessary to allow coupling between process simulation software and LCA tools, the former generating the information necessary for the calculations of the latter, the results of the LCA modules can be used as optimisation criteria.
Python APIs and scripting in modern process simulation
The growing use of scripting languages such as Python in process engineering is sometimes perceived as a trend driven by data science or artificial intelligence. In practice, scripting primarily responds to concrete engineering needs: automation of sensitivity analyses, coupling with optimisers, management of calculation sequences, and integration with external data sources.
Exposing simulation capabilities through APIs improves transparency, reproducibility and long-term maintainability of engineering studies.
Coupling first‑principles process simulation models with Python, through an API, combines the robustness of physical modelling with the flexibility of modern computational workflows. Physical models ensure consistency, extrapolation capability, and thermodynamic rigor, while Python enables automation, orchestration, and integration with optimisation, data analysis, and machine‑learning tools.
This approach allows engineers to reuse validated models beyond a simulator and embed simulation naturally into broader engineering and decision‑support workflows. Fives ProSim has put a lot of effort and R&D into a new process simulation solution that will push further the boundaries of process simulation and optimisation. This next evolution is coming soon and will create an interesting bridge with artificial intelligence.
“Coupling first‑principles process simulation models with Python, through an API, combines the robustness of physical modelling with the flexibility of modern computational workflows.”
How artificial intelligence complements physical process models replacing, physical models
Artificial intelligence, already widely used in many fields (image processing, finance and banking…) is not yet very mature in the simulation field. However, artificial intelligence and data-driven models offer promising perspectives for process simulation, especially to reduce computation times or model localised phenomena.
However, they do not replace first-principles models when extrapolation is required. Hybrid approaches combining rigorous simulation and data-driven models are therefore the most promising path, provided that openness and physical consistency are preserved.
FAQs
What is process simulation software used for?
Process simulation software is used to model industrial and chemical processes for design, optimisation, troubleshooting and operational improvement. It helps engineers evaluate process performance before implementation.
Why is thermodynamic modelling important in process simulation?
Thermodynamic modelling is essential because it determines the accuracy of phase equilibria, heat transfer and physico-chemical property calculations used throughout the simulation process.
What is CAPE-OPEN in process simulation?
CAPE-OPEN is an interoperability standard that allows different process simulation components and thermodynamic packages to communicate and work together across multiple software platforms.
How is Python used in process simulation?
Python is used to automate calculations, perform sensitivity analyses, integrate optimisation tools and connect simulation software with machine learning and data analysis workflows.
What is exergy analysis in industrial processes?
Exergy analysis evaluates where energy inefficiencies occur within a process by identifying where useful energy is destroyed. This helps engineers improve process efficiency and reduce energy consumption.
How does process simulation support sustainability?
Process simulation helps reduce energy use, water consumption and carbon emissions by enabling engineers to optimise industrial processes before physical implementation.
Can artificial intelligence replace physical simulation models?
Artificial intelligence can complement physical simulation models by improving speed and supporting data analysis but first-principles models remain essential for reliable engineering predictions and extrapolation.
Why is interoperability important in industrial simulation software?
Interoperability allows engineers to connect simulation tools, thermodynamic packages and optimisation platforms together which improves flexibility, consistency and long-term usability across engineering projects.
