🧪 Safety-by-Design Framework for Ethanol-Water Separation Entrainer Selection

An Active Learning Framework for Simultaneous Optimization of Thermodynamic Efficiency and Inherent Safety in Extractive Distillation Entrainers

🎯 Project Overview

This research framework implements a "Safety-by-Design" approach to entrainer selection for ethanol-water separation. Unlike traditional methods that optimize efficiency first and apply safety as a retroactive constraint, this framework treats safety and efficiency as simultaneous objectives within a Multi-Objective Bayesian Optimization (MOBO) loop.

The Problem

In industrial ethanol-water separation via extractive distillation:

• Traditional approach: Maximize efficiency first → Apply safety constraints later
• Result: Selection of hazardous solvents (e.g., benzene - a known carcinogen)
• Consequence: Expensive containment and mitigation strategies

Our Solution

A five-phase computational pipeline that:

1. Maps the chemical space to identify promising molecular "hot spots"
2. Selects candidates using three parallel AI/algorithmic engines
3. Expands the search via graph-based molecular similarity traversal
4. Optimizes simultaneously for safety and efficiency using MOBO + qEHVI
5. Validates rigorously through process simulation

🏗️ Architecture

┌─────────────────────────────────────────────────────────────────────────┐
│                    ENTRAINER SELECTION FRAMEWORK                        │
├─────────────────────────────────────────────────────────────────────────┤
│                                                                         │
│  Phase I: Domain Mapping          Phase II: Multi-Vector Selection      │
│  ┌─────────────────────┐         ┌─────────────────────────────────────┐│
│  │ Literature Survey   │         │  Engine A    Engine B   Engine C    ││
│  │ Database Scoping    │────────▶│  Graph-RAG   TRIZ      RDKit        ││
│  │ Cluster Definition  │         │  (AI)       (Heuristic)(Algorithmic)││
│  │ 100K+ → 500 clusters│         │     └────────┬─────────┘            ││
│  └─────────────────────┘         └──────────────┼──────────────────────┘│
│                                                 │                       │
│                                                 ▼                       │
│  Phase III: Deep Traversal       Phase IV: Bayesian Optimization        │
│  ┌─────────────────────┐         ┌─────────────────────────────────┐    │
│  │ Neo4j Graph DB      │         │  Gaussian Process Surrogate     │    │
│  │ Similarity Expansion│────────▶│  qEHVI Acquisition Function    │    │
│  │ 75-150 → 150-300    │         │  Pareto Frontier Identification │    │
│  └─────────────────────┘         └──────────────┬──────────────────┘    │
│                                                 │                       │
│                                                 ▼                       │
│                           Phase V: Simulation & Validation              │
│                           ┌─────────────────────────────────┐           │
│                           │  DWSIM Process Simulation       │           │
│                           │  Final Top 10 Ranking           │           │
│                           │  Pareto-Optimal Library Output  │           │
│                           └─────────────────────────────────┘           │
└─────────────────────────────────────────────────────────────────────────┘

🔬 Research Hypotheses

ID	Hypothesis	Validation Phase	Success Metric
H1	Pareto frontier exhibits convex structure with identifiable knee points	Phase IV	≥1 knee point identified
H2	qEHVI achieves equivalent hypervolume with ≤30% computational budget	Phase IV	HV_30% ≥ 0.95 × HV_100%
H3	Consensus safety scoring reduces uncertainty by ≥25%	Phase II	σ_reduction ≥ 25%

📁 Project Structure

entrainer-selection/
├── src/
│   ├── core/              # Shared infrastructure (config, logging, models)
│   ├── phase1/            # Domain Mapping & Cluster Definition
│   ├── phase2/
│   │   ├── engine_a/      # Graph-RAG with Gemini
│   │   ├── engine_b/      # TRIZ Multi-Agent System
│   │   └── engine_c/      # Cheminformatics & Diversity
│   ├── phase3/            # Graph Traversal & Expansion
│   ├── phase4/            # MOBO & Active Learning
│   └── phase5/            # DWSIM Simulation & Validation
├── config/
│   ├── infra_config.yaml  # Database, API, logging settings
│   └── science_config.yaml # SMARTS patterns, thresholds, thermodynamics
├── data/
│   ├── raw/               # API query results
│   ├── processed/         # Cleaned datasets
│   └── results/           # Output from each phase
├── notebooks/             # Jupyter notebooks for exploration
├── tests/                 # Unit and integration tests
├── docs/                  # Extended documentation
│   └── phases/            # Detailed phase documentation
├── backlog/               # Development task tracking
└── scripts/               # Utility scripts

🚀 Quick Start

Prerequisites

• Python 3.11+
• Neo4j Community Edition (for Graph-RAG)
• DWSIM (for Phase V simulation - Windows only)

Installation

# Clone the repository
git clone https://github.com/yourusername/entrainer-selection.git
cd entrainer-selection

# Create virtual environment
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install dependencies
pip install -e ".[dev]"

# Set up configuration
cp config/infra_config.example.yaml config/infra_config.yaml
# Edit config files with your API keys and database credentials

Environment Variables

# Required API Keys
export GOOGLE_API_KEY="your-gemini-api-key"
export PUBCHEM_API_KEY="optional-for-higher-rate-limits"

# Database Configuration
export NEO4J_URI="bolt://localhost:7687"
export NEO4J_USER="neo4j"
export NEO4J_PASSWORD="your-password"

📖 Documentation

Document	Description
TECH_STACK.md	Complete technology stack and dependencies
ARCHITECTURE.md	System architecture and data flow
Phase Documentation	Detailed documentation for each phase
CONTRIBUTING.md	Contribution guidelines
API Reference	Module and function documentation

🧬 Key Technologies

Category	Technologies
Core Language	Python 3.11+
Cheminformatics	RDKit, PubChemPy
Machine Learning	BoTorch, GPyTorch, PyTorch
Graph Database	Neo4j + ChromaDB
LLM Integration	Google Gemini API
Process Simulation	DWSIM (COM automation)
Thermodynamics	thermo (UNIFAC)

📊 Expected Outcomes

1. Pareto-Optimal Library: High-dimensional dataset identifying "Knee Points" - optimal safety/efficiency trade-offs
2. Quantifiable Metrics: 20% reduction in inherent risk with <8% efficiency penalty
3. Reproducible Workflow: Dockerized pipeline adaptable to other separation problems
4. Benchmark Comparison: Validated against ethylene glycol (industry standard) and benzene (historical negative control)

🤝 Contributing

We welcome contributions! Please see CONTRIBUTING.md for guidelines.

📜 License

This project is licensed under the MIT License - see LICENSE for details.

📚 References

• Altshuller, G. (1999). The Innovation Algorithm: TRIZ
• Laroche, L. et al. (1991). "Homogeneous Azeotropic Distillation" [DOI: 10.1021/ie00020a013]
• Perry's Chemical Engineers' Handbook, 9th Edition
• BoTorch Multi-Objective Optimization: botorch.org

📧 Contact

For questions or collaboration inquiries, please open an issue or contact the maintainers.

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Status: 🚧 Active Development | Current Phase: Infrastructure Setup