Anaerobic Design MCP Server
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In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Anaerobic Design MCP ServerDesign a digester for 50,000 mg/L COD sludge at 1000 m3/d"
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Here is a step-by-step guide with screenshots.
Anaerobic Design MCP Server
⚠️ 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)
Related MCP server: Aerobic Design MCP Server
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 mixingEductor/jet pump support with
fluids.jet_pumpintegration (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 powerWEF 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 analysisAccounts 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.pyOutput: Three comprehensive tables showing:
Performance metrics (COD removal, methane yield, biomass yields)
Inhibition analysis (pH, ammonia, H2, H2S effects)
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 100Available MCP Tools
Core Workflow:
elicit_basis_of_design- Collect design parametersload_adm1_state- Load Codex-generated ADM1 statevalidate_adm1_state- Verify state matches targetsheuristic_sizing_ad- Size digester and MBRsimulate_ad_system_tool- Run QSDsan dynamic simulation
State Management:
get_design_state- Check workflow progressreset_design- Start new project
Optional Analysis:
compute_bulk_composites- Calculate COD/TSS/VSS/TKN/TP from statecheck_strong_ion_balance- Verify charge balanceanalyze_stream_details- Component-level analysisassess_process_health- Inhibition factorsevaluate_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 guidesCurrent 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
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.pyPrevents 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
pH Solver Bugs (2025-10-21): Fixed 10^29× error in Ka values, restored pH to 6.7-7.5 range
Production PCM (2025-10-18): 9 fixes for complete charge balance with all ionic species
1000× Unit Error (2025-10-21): Fixed gas transfer units, restored methane production
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:
QSDsan framework: https://github.com/QSD-Group/QSDsan
ADM1 model: Batstone et al. (2002) "The IWA Anaerobic Digestion Model No. 1 (ADM1)"
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
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