PD_ISING: Process Design Optimization using Ising Models

A comprehensive repository for process design optimization using both classical and quantum computing approaches. This project contains case studies for pharmaceutical manufacturing flowsheet optimization and ionic liquid reactor-separator network optimization.

Overview

This repository provides optimization frameworks for process design problems, comparing various optimization methods in solving integer/qubo-ising problems, including branch and bound, simulated annealing,quantum computing, and entropy computing approaches.

Subprojects

1. ds-mfg/ - Flowsheet Optimization for Drug Substance Manufacturing

   • Pharmaceutical manufacturing process optimization
   • See ds-mfg/README.md for detailed documentation

2. il-rxtor-sep-opt/ - Ionic Liquid Reactor-Separator Network Optimization

   • Quantum Annealing and Simulated Annealing implementations
   • See il-rxtor-sep-opt/README.md for detailed documentation

Project Structure

pd_ising/
├── ds-mfg/                      # Drug Substance Manufacturing Flowsheet Optimization
│   ├── discrete_ip/             # Discrete IP and QUBO implementation
│   ├── simulation/              # Simulation code and results
│   ├── data/                    # Flowsheet data files
│   └── README.md                # Detailed documentation
├── il-rxtor-sep-opt/            # Ionic Liquid Reactor-Separator Network Optimization
│   ├── discrete_ip/             # Discrete IP implementation
│   ├── discrete_qubo/           # Discrete QUBO implementation
│   ├── original_mip/            # Original MIP implementation
│   ├── data/                    # Input data files
│   └── README.md                # Detailed documentation
├── images/                      # Project images and diagrams
└── README.md                    # This file

Quick Start

Prerequisites

• Python: 3.8 or higher
• Julia: 1.11.5 or higher (for some subprojects)
• D-Wave Ocean SDK: For quantum annealing (optional)
• Gurobi: For MIP/IP optimization (optional)

Installation

Each subproject has its own installation instructions. Please refer to the respective README files:

• ds-mfg: See ds-mfg/README.md for installation details
• il-rxtor-sep-opt: See il-rxtor-sep-opt/README.md for installation details

Common Dependencies

Most subprojects require:

pip install pyomo pandas numpy matplotlib 

For annealing features:

pip install dwave-ocean-sdk neal

For quadratic unconstrained binary optimization (qubo) features:

• Please refer to https://github.com/JuliaQUBO/QUBO.jl

Configuration

D-Wave Configuration

To use quantum annealing features:

export DWAVE_API_TOKEN="your-dwave-api-token"
export DWAVE_API_URL="https://cloud.dwavesys.com/sapi"

QCI Configuration

To use QCI entropy computing services:

export QCI_API_TOKEN="your-qci-api-token"

Gurobi Configuration

For MIP/IP optimization, ensure Gurobi is properly licensed and configured.

Contributing

We welcome contributions and extensions to this repository! Each subproject has specific contribution guidelines.

General Contribution Guidelines

1. Follow the existing code style and conventions
2. Add comprehensive docstrings for new functions
3. Include type hints where applicable
4. Update documentation as needed
5. Add tests for new functionality when appropriate

Documentation

• Subproject READMEs: Each subproject contains detailed documentation
• Jupyter Notebooks: Interactive examples and tutorials in subprojects
• Code Comments: Comprehensive inline documentation throughout

References

Drug Substance Manufacturing

1. Casas-Orozco, D., Laky, D. J., Wang, V., Abdi, M., Feng, X., Wood, E., Reklaitis, G. V., & Nagy, Z. K. (2023). Techno-economic analysis of dynamic, end-to-end optimal pharmaceutical campaign manufacturing using pharmapy. AIChE Journal, 69(9).

2. Laky, D. J., Casas-Orozco, D., Laird, C. D., Reklaitis, G. V., & Nagy, Z. K. (2022). Simulation-optimization framework for the digital design of pharmaceutical processes using pyomo and pharmapy. Industrial and Engineering Chemistry Research, 61, 16128–16140.

3. Barhate, Y., Laky, D. J., Casas-Orozco, D., Reklaitis, G. V., & Nagy, Z. K. (2025). Hybrid rule-based and optimization-driven framework for the synthesis of end-to-end optimal pharmaceutical processes. AIChE J., e18888. doi:10.1002/aic.18888

Ionic Liquid Reactor-Separator Optimization

Iftakher, A., & Hasan, M. M. F. (2024). Exploring Quantum Optimization for Computer-aided Molecular and Process Design. Systems and Control Transactions, 3, 292-299. https://doi.org/10.69997/sct.143809

License

This code is provided for research and educational purposes. Please refer to the original references for proper attribution.

Support

For questions and support:

• Check the documentation in subproject README files
• Review the Jupyter notebooks for examples
• Open an issue for bugs or feature requests

Acknowledgments

• Original research authors cited in References
• D-Wave Systems for quantum computing infrastructure
• QCi for entropy computing infrastructure
• Gurobi for optimization solver
• The quantum computing and optimization communities