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Anaerobic Design Server

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Enables anaerobic digester design from feedstock characterization to dynamic simulation using a complete mADM1 model with 63 components, including AI-powered st

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Описание

Enables anaerobic digester design from feedstock characterization to dynamic simulation using a complete mADM1 model with 63 components, including AI-powered state generation and thermodynamic pH solving.

README

⚠️ DEVELOPMENT STATUS: This project is under active development and is not yet production-ready. APIs, interfaces, and functionality may change without notice. Use at your own risk for evaluation and testing purposes only. Not recommended for production deployments.

MCP (Model Context Protocol) server for anaerobic digester design using the complete mADM1 (Modified ADM1) process model with 63 components (62 state variables + H2O).

Overview

This server provides an end-to-end workflow for designing anaerobic digesters treating high-strength wastewater, from feedstock characterization through dynamic simulation. It leverages QSDsan's mADM1 model with phosphorus/sulfur/iron extensions for nutrient recovery and biogas production modeling.

Status: In Development (Active)

Key Features

Core Process Model

  • Complete mADM1 Model (63 Components): Full Modified ADM1 with P/S/Fe extensions

    • 27 core ADM1 components (sugars, amino acids, VFAs, biomass)
    • 3 EBPR components (X_PHA, X_PP, X_PAO)
    • 7 sulfur species (SO4, H2S, 4 SRB types, S0)
    • 9 iron species (Fe3+, Fe2+, 7 HFO variants)
    • 13 mineral precipitates (struvite, HAP, FeS, etc.)
    • 4 additional cations (K, Mg, Ca, Al)
    • 1 solvent (H2O as component index 62)
  • AI-Powered ADM1 State Generation: Codex MCP server converts feedstock descriptions into complete 63-component states

  • Thermodynamically Rigorous pH Solver: Production charge balance with all ionic species

  • Dynamic Simulation: QSDsan's AnaerobicCSTR reactor with 4-component biogas tracking (H2, CH4, CO2, H2S)

Enhanced Design Tools (Week 1-2 Implementation)

  • Mixing Module (utils/mixing_calculations.py): Physics-based power calculations for mechanical and pumped mixing

    • Eductor/jet pump support with fluids.jet_pump integration (prevents 5× pump oversizing)
    • Non-Newtonian rheology corrections (Metzner-Otto method)
    • Multiple impeller types (pitched blade, Rushton, marine propeller)
  • Rheology Module (utils/rheology.py): TSS-dependent viscosity for accurate mixing power

    • WEF MOP-8 validated correlations
    • Power-law parameters (Baudez et al. 2011)
    • Temperature corrections
  • Substrate-Aware Biomass Yield (utils/heuristic_sizing.py:80-267): Complete ADM1 pathway analysis

    • Accounts for multi-step metabolism (not just first-step yields)
    • Cascading yields: substrate → intermediates → acetate/H₂ → CH₄
    • Substrate-specific yields: carbs (0.116), proteins (0.106), lipids (0.073-0.078) kg TSS/kg COD
    • Based on ADM1 stoichiometry and product split fractions
    • Known Limitation: Does not account for SRT-dependent endogenous decay (under investigation)
  • Thermal Integration: Direct MCP access to heat-transfer-mcp server

    • Feedstock heating load calculations
    • Tank heat loss (insulated vessels, weather data integration)
    • Heat exchanger sizing (plate, shell-tube, coil)
  • Complete Workflow: Parameters → ADM1 generation → Validation → Sizing + Mixing + Thermal → Simulation

Quick Start

Installation

# Clone repository
cd /path/to/mcp-servers/anaerobic-design-mcp

# Install dependencies (requires Python 3.10+)
pip install -r requirements.txt

# Install QSDsan
pip install qsdsan

# Add to Claude Desktop MCP configuration
# Edit: %APPDATA%/Claude/claude_desktop_config.json (Windows)
#   or: ~/Library/Application Support/Claude/claude_desktop_config.json (macOS)

MCP Configuration:

{
  "mcpServers": {
    "anaerobic-design": {
      "command": "C:/path/to/venv/Scripts/python.exe",
      "args": [
        "-m",
        "server"
      ],
      "cwd": "C:/path/to/mcp-servers/anaerobic-design-mcp"
    }
  }
}

Basic Usage

Step 1: Reset and collect parameters

# Reset design state
mcp__anaerobic-design__reset_design()

# Collect basis of design parameters
mcp__anaerobic-design__elicit_basis_of_design(
    parameter_group="all",
    current_values={
        "Q": 1000,           # m3/d
        "Temp": 35,          # °C
        "cod_mg_l": 50000,   # mg/L
        "tss_mg_l": 35000,   # mg/L
        "vss_mg_l": 28000,   # mg/L
        "tkn_mg_l": 2500,    # mg-N/L
        "tp_mg_l": 500,      # mg-P/L
        "pH": 7,
        "alkalinity_meq_l": 50
    }
)

Step 2: Generate ADM1 state using Codex (CRITICAL - Do not skip!)

# Call Codex MCP to generate complete 63-component mADM1 state
mcp__ADM1-State-Variable-Estimator__codex(
    prompt="""
Generate complete mADM1 state variables for:

Feedstock: High-strength municipal wastewater sludge
COD: 50,000 mg/L | TSS: 35,000 mg/L | VSS: 28,000 mg/L
TKN: 2,500 mg-N/L | TP: 500 mg-P/L | pH: 7.0

Save to: ./adm1_state.json (all 63 components in kg/m³)
""",
    cwd="/path/to/anaerobic-design-mcp"
)

# Load generated state
mcp__anaerobic-design__load_adm1_state(file_path="./adm1_state.json")

Step 3: Validate and size

# Validate ADM1 state against measured parameters
python utils/validate_cli.py validate \
    --adm1-state adm1_state.json \
    --user-params '{"cod_mg_l": 50000, "tss_mg_l": 35000}' \
    --tolerance 0.15

# Run heuristic sizing
mcp__anaerobic-design__heuristic_sizing_ad(
    use_current_basis=True,
    target_srt_days=20
)

Step 4: Run QSDsan simulation

# Execute simulation (300-600 seconds to reach TRUE steady state)
python utils/simulate_cli.py --basis simulation_basis.json --adm1-state adm1_state.json --heuristic-config simulation_heuristic_config.json --hrt-variation 0.2

# Parse results (token-efficient: 7 KB vs 159 KB full file)
python utils/parse_simulation_results.py

Output: Three comprehensive tables showing:

  1. Performance metrics (COD removal, methane yield, biomass yields)
  2. Inhibition analysis (pH, ammonia, H2, H2S effects)
  3. Precipitation metrics (struvite, HAP, FeS, etc.)

Optional chemical dosing (if needed for pH control):

python utils/simulate_cli.py --basis simulation_basis.json --adm1-state adm1_state.json --heuristic-config simulation_heuristic_config.json --hrt-variation 0.2 --naoh-dose 2840 --fecl3-dose 100

Available MCP Tools

Core Workflow:

  • elicit_basis_of_design - Collect design parameters
  • load_adm1_state - Load Codex-generated ADM1 state
  • validate_adm1_state - Verify state matches targets
  • heuristic_sizing_ad - Size digester and MBR
  • simulate_ad_system_tool - Run QSDsan dynamic simulation

State Management:

  • get_design_state - Check workflow progress
  • reset_design - Start new project

Optional Analysis:

  • compute_bulk_composites - Calculate COD/TSS/VSS/TKN/TP from state
  • check_strong_ion_balance - Verify charge balance
  • analyze_stream_details - Component-level analysis
  • assess_process_health - Inhibition factors
  • evaluate_sulfur_balance - H2S pathways

Architecture

anaerobic-design-mcp/
├── server.py                        # FastMCP server (13 tools)
├── tools/                           # MCP tool implementations
│   ├── basis_of_design.py          # Parameter collection
│   ├── validation.py               # ADM1 state validation
│   ├── sizing.py                   # Heuristic sizing
│   └── simulation.py               # QSDsan integration
├── utils/
│   ├── qsdsan_madm1.py             # 63-component mADM1 model
│   ├── qsdsan_reactor_madm1.py     # Custom AnaerobicCSTR
│   ├── qsdsan_simulation_sulfur.py # Simulation engine
│   ├── inoculum_generator.py       # Enhanced inoculum
│   ├── parse_simulation_results.py # Token-efficient result parser
│   ├── validate_cli.py             # CLI validation tools
│   └── simulate_cli.py             # CLI simulation wrapper
├── core/
│   └── state.py                    # Design state management
└── docs/                           # Complete documentation
    ├── INDEX.md                    # Navigation hub
    ├── architecture/               # System design
    ├── bugs/                       # Bug tracking
    ├── development/                # Refactoring history
    └── diagnostics/                # Analysis guides

Current Capabilities

Implemented Features

  • Thermodynamic Rigor: Production charge balance solver with all 63 components
  • Biogas Tracking: 4-component biogas (H2, CH4, CO2, H2S) with Henry's law equilibrium
  • Methane Yield: 97.4% match to theoretical (validation against COD mass balance)
  • pH Accuracy: 6.5-7.5 range for typical digesters (fixed critical R units bug)
  • Unit Consistency: Aligned with QSDsan conventions (fixed 1000× gas production bug)

Critical Fixes Applied

  1. Enhanced Inoculum (6× Methanogen Boost) (2025-10-29): CRITICAL FIX for pH collapse during startup
    • Doubled methanogen boost factor from 3× → 6× in utils/inoculum_generator.py
    • Prevents VFA accumulation and pH drop from 7.0 → 4.8
    • Enables stable operation WITHOUT chemical supplementation (NaOH, Na2CO3)
    • Validated through comparative simulations: 3× boost = FAILED, 6× boost = SUCCESS
  2. pH Solver Bugs (2025-10-21): Fixed 10^29× error in Ka values, restored pH to 6.7-7.5 range
  3. Production PCM (2025-10-18): 9 fixes for complete charge balance with all ionic species
  4. 1000× Unit Error (2025-10-21): Fixed gas transfer units, restored methane production
  5. QSDsan Alignment (2025-10-22): Eliminated divergence from upstream conventions

See docs/bugs/CRITICAL_FIXES.md for details.

Known Limitations

  • Non-deterministic regression tests: Catastrophic failure case shows variable TAN (10,000-77,000 mg-N/L)
  • Reactor maintenance burden: Custom AnaerobicCSTR class requires manual sync with QSDsan updates
  • Simplified precipitation: Unity activity coefficients (Davies equation not yet implemented)

Documentation

  • CLAUDE.md - System prompt for Claude Code (workflow instructions)

Quick Links

Requirements

  • Python: 3.10+
  • QSDsan: 1.3+ (includes mADM1 process model)
  • FastMCP: 0.1.0+
  • NumPy: 1.24+
  • Pandas: 2.0+

See pyproject.toml for complete dependencies.

License

MIT License (see LICENSE file)

Support

  • Issues: Submit via GitHub Issues
  • Documentation: See docs/architecture/ directory for technical references

Citation

If you use this server in research, please cite:

Development Status

Current Version: 0.1.0-dev (In Development)

Recent Updates:

  • 2025-11-22: Pre-release cleanup (removed obsolete docs and dead code)
  • 2025-11-04: Background Job Pattern implementation (prevents STDIO blocking)
  • 2025-10-29: Enhanced inoculum (6× methanogen boost) - CRITICAL FIX for startup stability
  • 2025-10-29: Token-efficient result parser (95% reduction: 7 KB vs 159 KB)
  • 2025-10-26: Comprehensive documentation consolidation
  • 2025-10-22: QSDsan convention alignment
  • 2025-10-21: Critical pH and unit conversion fixes
  • 2025-10-18: Production PCM solver implementation
  • 2025-10-18: Full mADM1 (63 components) integration

Last Updated: 2025-11-22

from github.com/puran-water/anaerobic-design-mcp

Установка Anaerobic Design Server

У этого сервера нет опубликованного пакета — он собирается из исходников. Открой репозиторий и следуй инструкции в README.

▸ github.com/puran-water/anaerobic-design-mcp

FAQ

Anaerobic Design Server MCP бесплатный?

Да, Anaerobic Design Server MCP бесплатный — установка в пару кликов через Unyly без оплаты.

Нужен ли API-ключ для Anaerobic Design Server?

Нет, Anaerobic Design Server работает без API-ключей и переменных окружения.

Anaerobic Design Server — hosted или self-hosted?

Self-hosted: сервер запускается локально на твоей машине командой из раздела установки.

Как установить Anaerobic Design Server в Claude Desktop, Claude Code или Cursor?

Открой Anaerobic Design Server на unyly.org, выбери вкладку своего клиента (Claude Desktop, Claude Code, Cursor) и нажми Install — конфиг сгенерируется автоматически, без правки JSON.

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