DWSIM Python Automation — Screening Task

This project demonstrates fully automated, headless simulation of process flowsheets in DWSIM using Python. The workflow programmatically constructs and evaluates a Plug Flow Reactor (PFR) and a rigorous distillation column, followed by parametric sweep studies.

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1. Setup

Requirements

• Python Version: 3.10 or 3.11 (recommended for pythonnet compatibility)
• DWSIM Version: 8.x (standard Windows installation)

Dependencies

Install required Python libraries:

pip install -r requirements.txt

Running the Script

python run_screening.py

• The simulation runs completely headless (no GUI interaction).
• Progress is logged to console and simulation.log.
• Results are continuously written to results.csv.

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Demo

🎥 Watch Demo Video

This video demonstrates:

• Headless execution of the automation script
• Parametric sweep in real time
• Generated outputs (CSV and plots)

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2. Workflow Overview

2.1 Flowsheet Construction

• Uses DWSIM Automation3 API via pythonnet

• No prebuilt flowsheets or GUI usage

• Programmatically creates:

  • Property package (Peng-Robinson)

  • Material and energy streams

  • Reaction set (n-pentane isomerization)

  • Unit operations:

    • Plug Flow Reactor (RCT_PFR)
    • Rigorous Distillation Column (RigorousColumn)

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2.2 Parametric Sweep

Part A & C — PFR Study

• Variables:

  • Reactor Volume: 0.5 → 10 m³
  • Temperature: 350 → 450 K

• Total cases: 25

Part B & C — Distillation Column Study

• Variables:

  • Reflux Ratio: 1.2 → 4.0
  • Number of Stages: 10 → 25

• Total cases: 20

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3. Assumptions

• Isothermal PFR: Reactor temperature is fixed; DWSIM computes heat duty required to maintain isothermal conditions.

• Property Package: Peng-Robinson (PR) — suitable for non-polar hydrocarbon systems and VLE prediction.

• Reaction Model: First-order kinetic model for n-pentane isomerization:

  • Pre-exponential factor: (1.2 \times 10^8 , s^{-1})
  • Activation energy: (65 , kJ/mol)

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Execution Snapshot

Below is a sample terminal output demonstrating fully automated, headless execution of the simulation workflow:

Highlights:

• Headless execution using DWSIM Automation API (no GUI interaction)
• Automated parametric sweep across PFR and distillation column cases
• Real-time logging of simulation progress and outputs
• Successful execution of all simulation cases without failure

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4. Outputs

4.1 results.csv

Contains all simulation cases (45 total) with:

This dataset includes:

• Input parameters
• Simulation outputs
• Success flags and error handling

Metadata

• case_type, success_flag, error_message

Input Variables

• V, T, RR, N

Key Performance Indicators (KPIs)

• conversion
• distillate_purity
• nC5_outlet_flow
• iC5_outlet_flow
• temperature_out
• heat_duty
• condenser_duty
• reboiler_duty

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4.2 Plots (plots/ directory)

Generated visualizations showing parametric trends:

Insights from plots:

• Conversion increases with temperature and reactor volume
• Distillation purity improves with reflux ratio
• Energy consumption increases with separation performance

1. pfr_conversion_vs_volume.png Effect of reactor size on conversion

2. pfr_conversion_vs_temperature.png Temperature dependence of reaction kinetics

3. col_purity_vs_reflux.png Impact of reflux ratio on separation

4. col_duty_vs_stages.png Energy requirement vs number of stages

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5. Engineering Insights

• Reaction Kinetics: Conversion increases strongly with temperature due to Arrhenius dependence.
• Reactor Design: Larger reactor volumes increase residence time, improving conversion at lower temperatures.
• Thermodynamic Limitation: Separation of n-pentane and isopentane is difficult due to close boiling points (~10°C difference).
• Column Behavior: Increasing reflux ratio improves purity but significantly increases energy consumption.
• Optimal Trade-off: High purity requires both higher reflux and more stages, leading to diminishing returns.

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6. Key Features

• Fully headless simulation (no GUI)
• Programmatic flowsheet construction
• Robust error handling and logging
• Automated parametric sweep execution
• Structured data export and visualization

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7. File Structure

project/
│── run_screening.py
│── requirements.txt
│── README.md
│── results.csv
│── simulation.log
│── simulation_report.txt
│── plots/

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8. Notes

• All simulations executed successfully (45/45 cases).
• The workflow is scalable for larger screening and optimization studies.

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9. Summary

This project demonstrates the ability to:

• Build complete process flowsheets programmatically using DWSIM Automation API
• Perform headless simulation without GUI interaction
• Implement parametric sweeps for reactor and separation units
• Extract and analyze key engineering performance metrics
• Generate structured outputs and visualizations

The work reflects a strong integration of process engineering fundamentals with Python-based automation.

Additional Note

The separation of n-pentane and isopentane is thermodynamically limited due to their close boiling points. This is reflected in the simulation results where purity plateaus despite increasing reflux ratio and stages.