equinor/neqsim

What is NeqSim?

NeqSim (Non-Equilibrium Simulator) is a comprehensive Java library for fluid property estimation, process simulation, and engineering design. It covers the full process engineering workflow, from thermodynamic modeling and PVT analysis through equipment sizing, pipeline flow, safety studies, and field development economics.

Developed at NTNU and maintained by Equinor, NeqSim is used for real-world oil & gas, carbon capture, hydrogen, and energy applications.

Use it from Java, Python, Jupyter notebooks, .NET, MATLAB, or let an AI agent drive it via natural language.

Key capabilities

See the full documentation, Java Wiki, or ask questions in Discussions.

Quick Start

Python - try it in 30 seconds

pip install neqsim

from neqsim import jneqsim

# Create a natural gas fluid
fluid = jneqsim.thermo.system.SystemSrkEos(273.15 + 25.0, 60.0)  # 25 C, 60 bara
fluid.addComponent("methane", 0.85)
fluid.addComponent("ethane", 0.10)
fluid.addComponent("propane", 0.05)
fluid.setMixingRule("classic")

# Run a flash calculation
ops = jneqsim.thermodynamicoperations.ThermodynamicOperations(fluid)
ops.TPflash()
fluid.initProperties()

print(f"Gas density:    {fluid.getPhase('gas').getDensity('kg/m3'):.2f} kg/m3")
print(f"Gas viscosity:  {fluid.getPhase('gas').getViscosity('kg/msec'):.6f} kg/(m*s)")
print(f"Z-factor:       {fluid.getPhase('gas').getZ():.4f}")

Java - add to your project

Maven Central (simplest - no authentication needed):

<dependency>
  <groupId>com.equinor.neqsim</groupId>
  <artifactId>neqsim</artifactId>
  <version>3.10.0</version>
</dependency>

import neqsim.thermo.system.SystemSrkEos;
import neqsim.thermodynamicoperations.ThermodynamicOperations;

SystemSrkEos fluid = new SystemSrkEos(273.15 + 25.0, 60.0);
fluid.addComponent("methane", 0.85);
fluid.addComponent("ethane", 0.10);
fluid.addComponent("propane", 0.05);
fluid.setMixingRule("classic");

ThermodynamicOperations ops = new ThermodynamicOperations(fluid);
ops.TPflash();
fluid.initProperties();

System.out.println("Density: " + fluid.getDensity("kg/m3") + " kg/m3");

AI agent - describe your problem in plain English

@solve.task hydrate formation temperature for wet gas at 100 bara

The agent scopes the task, builds a NeqSim simulation, validates results, and generates a Word + HTML report with no coding required.

What can you do with NeqSim?

from neqsim import jneqsim

fluid = jneqsim.thermo.system.SystemSrkEos(273.15 + 15.0, 100.0)
fluid.addComponent("methane", 0.90)
fluid.addComponent("CO2", 0.05)
fluid.addComponent("nitrogen", 0.05)
fluid.setMixingRule("classic")

ops = jneqsim.thermodynamicoperations.ThermodynamicOperations(fluid)
ops.TPflash()
fluid.initProperties()

print(f"Density:      {fluid.getDensity('kg/m3'):.2f} kg/m3")
print(f"Molar mass:   {fluid.getMolarMass('kg/mol'):.4f} kg/mol")
print(f"Phases:       {fluid.getNumberOfPhases()}")

from neqsim import jneqsim

fluid = jneqsim.thermo.system.SystemSrkEos(273.15 + 30.0, 80.0)
fluid.addComponent("methane", 0.80)
fluid.addComponent("ethane", 0.12)
fluid.addComponent("propane", 0.05)
fluid.addComponent("n-butane", 0.03)
fluid.setMixingRule("classic")

Stream = jneqsim.process.equipment.stream.Stream
Separator = jneqsim.process.equipment.separator.Separator
Compressor = jneqsim.process.equipment.compressor.Compressor
ProcessSystem = jneqsim.process.processmodel.ProcessSystem

feed = Stream("Feed", fluid)
feed.setFlowRate(50000.0, "kg/hr")

separator = Separator("HP Separator", feed)
compressor = Compressor("Export Compressor", separator.getGasOutStream())
compressor.setOutletPressure(150.0, "bara")

process = ProcessSystem()
process.add(feed)
process.add(separator)
process.add(compressor)
process.run()

print(f"Compressor power: {compressor.getPower('kW'):.0f} kW")
print(f"Gas out temp:     {compressor.getOutletStream().getTemperature() - 273.15:.1f} C")

from neqsim import jneqsim

fluid = jneqsim.thermo.system.SystemSrkEos(273.15 + 5.0, 80.0)
fluid.addComponent("methane", 0.90)
fluid.addComponent("ethane", 0.06)
fluid.addComponent("propane", 0.03)
fluid.addComponent("water", 0.01)
fluid.setMixingRule("classic")
fluid.setMultiPhaseCheck(True)

ops = jneqsim.thermodynamicoperations.ThermodynamicOperations(fluid)
ops.hydrateFormationTemperature()

print(f"Hydrate T: {fluid.getTemperature() - 273.15:.2f} C")

from neqsim import jneqsim

fluid = jneqsim.thermo.system.SystemSrkEos(273.15 + 40.0, 120.0)
fluid.addComponent("methane", 0.95)
fluid.addComponent("ethane", 0.05)
fluid.setMixingRule("classic")

Stream = jneqsim.process.equipment.stream.Stream
PipeBeggsAndBrills = jneqsim.process.equipment.pipeline.PipeBeggsAndBrills

feed = Stream("Inlet", fluid)
feed.setFlowRate(200000.0, "kg/hr")

pipe = PipeBeggsAndBrills("Export Pipeline", feed)
pipe.setPipeWallRoughness(5e-5)
pipe.setLength(50000.0)       # 50 km
pipe.setDiameter(0.508)        # 20 inch
pipe.setNumberOfIncrements(20)
pipe.run()

outlet = pipe.getOutletStream()
print(f"Outlet pressure: {outlet.getPressure():.1f} bara")
print(f"Outlet temp:     {outlet.getTemperature() - 273.15:.1f} C")

Explore 30+ Jupyter notebooks in examples/notebooks/:

• Phase envelope calculation
• TEG dehydration process
• Vessel depressurization / blowdown
• Heat exchanger thermal-hydraulic design
• Production bottleneck analysis
• Risk simulation and visualization
• Data reconciliation and parameter estimation
• Reservoir-to-export integrated workflows
• Multiphase transient pipe flow

Agentic Engineering & MCP Server

LLMs reason well but hallucinate physics. NeqSim is exact on thermodynamics but needs context. Together, they form a complete engineering system. The LLM reasons. NeqSim computes. Provenance proves it.

MCP Server - give any LLM access to rigorous thermodynamics

The NeqSim MCP Server lets any MCP-compatible client (VS Code Copilot, Claude Desktop, Cursor, etc.) run real calculations. Install in seconds:

# Docker (no Java needed)
docker pull ghcr.io/equinor/neqsim-mcp-server:latest

Every response includes provenance metadata (EOS model, convergence, assumptions, limitations). See the MCP Server docs and setup guide.

AI task-solving workflow

@solve.task TEG dehydration sizing for 50 MMSCFD wet gas

The agent creates a task folder, runs NeqSim simulations, validates results, and generates a Word + HTML report with no coding required. See the tutorial or workflow reference.

Use NeqSim in Java

<dependency>
  <groupId>com.equinor.neqsim</groupId>
  <artifactId>neqsim</artifactId>
  <version>3.10.0</version>
</dependency>

The Quick Start above shows the core pattern (create a fluid, run a flash, and read properties). For process simulation, add equipment to a ProcessSystem and call run(); see the Java Getting Started Guide for full examples.

1. Configure authentication in your Maven settings.xml:

<servers>
  <server>
    <id>github</id>
    <username>YOUR_GITHUB_USERNAME</username>
    <password>${env.GITHUB_TOKEN}</password>
  </server>
</servers>

2. Add to your pom.xml:

<repositories>
  <repository>
    <id>github</id>
    <url>https://maven.pkg.github.com/equinor/neqsim</url>
  </repository>
</repositories>

Learn more: Java Getting Started Guide | JavaDoc | Wiki | Colab demo

Use NeqSim in Python

pip install neqsim

NeqSim Python gives you direct access to the full Java API via the jneqsim gateway. All Java classes are available, including thermodynamics, process equipment, PVT, standards, and more.

from neqsim import jneqsim

# All Java classes accessible through jneqsim
SystemSrkEos = jneqsim.thermo.system.SystemSrkEos
ProcessSystem = jneqsim.process.processmodel.ProcessSystem
Stream = jneqsim.process.equipment.stream.Stream
# ... 200+ classes available

Explore 30+ ready-to-run Jupyter notebooks in examples/notebooks/.

Other language bindings

Develop & Contribute

Clone and build

git clone https://github.com/equinor/neqsim.git
cd neqsim
./mvnw install        # Linux/macOS
mvnw.cmd install      # Windows

Run tests

./mvnw test                                    # all tests
./mvnw test -Dtest=SeparatorTest               # single class
./mvnw test -Dtest=SeparatorTest#testTwoPhase  # single method
./mvnw checkstyle:check spotbugs:check pmd:check  # static analysis

Open in VS Code

The repository includes a ready-to-use dev container; just open the repo in VS Code with container support:

git clone https://github.com/equinor/neqsim.git
cd neqsim
code .

Architecture

graph TB
    subgraph core["NeqSim Core (Java 8+)"]
        THERMO["Thermodynamics<br/>60+ EOS models"]
        PROCESS["Process Simulation<br/>33+ equipment types"]
        PVT["PVT Simulation"]
        MECH["Mechanical Design<br/>& Standards"]
    end

    subgraph access["Access Layers"]
        PYTHON["Python / Jupyter<br/>pip install neqsim"]
        JAVA["Java / Maven<br/>Direct API"]
        MCP["MCP Server (Java 17+)<br/>LLM integration"]
        AGENTS["AI Agents<br/>VS Code Copilot"]
    end

    PYTHON --> THERMO
    PYTHON --> PROCESS
    JAVA --> THERMO
    JAVA --> PROCESS
    MCP --> THERMO
    MCP --> PROCESS
    AGENTS --> MCP
    AGENTS --> PYTHON

Which entry point should I use?

Java version matrix

Core modules

For details see docs/modules.md.

Contributing

We welcome contributions of all kinds: bug fixes, new models, examples, documentation, and notebook recipes. AI-assisted PRs are first-class contributions; see CONTRIBUTING.md.

New here? Three commands to get started:

git clone https://github.com/equinor/neqsim.git && cd neqsim
pip install -e devtools/    # one-time: registers the `neqsim` command
neqsim onboard             # interactive setup (Java, Maven, build, Python, agents)

Tip: Using a virtual environment (python -m venv .venv then activate it) avoids PATH issues on all platforms. See devtools/README.md if neqsim is not found, or use python -m neqsim_cli as a fallback.

Or skip local setup entirely: Open in GitHub Codespaces, with everything pre-installed in the browser.

Then explore and contribute:

neqsim try                 # interactive playground - experiment with NeqSim instantly
neqsim contribute          # guided wizard - picks the right path for you
neqsim doctor              # quick diagnostic if something isn't working

• CONTRIBUTING.md - Code of conduct, PR process, AI-assisted contributions
• VISION_AGENTS.md - What belongs in the agentic system (core vs. community)
• Developer setup guide - Build, test, and project structure
• Contributing structure - Where to place code, tests, and resources

Where to start

Skills are markdown files containing engineering knowledge (code patterns, design rules, troubleshooting tips) that AI agents load automatically when solving related tasks. Contributing a skill is the easiest way to make the agentic system smarter, with no Java required.

Community Skill Catalog

Browse and install community-contributed skills, or publish your own:

neqsim skill list                    # browse the catalog
neqsim skill install <name>          # install a skill
neqsim skill publish user/repo-name  # publish yours (creates a draft PR)

See the Skills Guide for the full walkthrough, community-skills.yaml for the catalog, and .github/skills/README.md for the quick contribution guide.

All tests and ./mvnw checkstyle:check must pass before a PR is merged.

Documentation & Resources

Authors

Even Solbraa (esolbraa@gmail.com), Marlene Louise Lund

NeqSim development was initiated at NTNU. A number of master and PhD students have contributed to its development, and we greatly acknowledge their contributions.

License

Apache-2.0