Smart Infrastructure Decision-Support Tool
Transforming static wastewater treatment designs into dynamic, carbon-optimized, and financially resilient asset management strategies.

Traditional municipal sewerage planning relies on static numbers that ignore real-world changes like weather storms, population surges, energy tariff hikes, and carbon taxes.

AIMeCHA changes that by running stochastic simulations (thousands of real-world scenarios) to help municipal engineers and utilities:

• 📉 Reduce Energy Demand by up to 24.8% using dynamic Variable Frequency Drive (VFD) controls.
• 🌱 Cut Scope 2 Carbon Emissions by up to 31.2% via solar microgrid and smart blower optimization.
• 💰 Save up to 18.5% in Lifecycle Costs by timing equipment upgrades using Real Options Analysis (ROA).

  1. STOCHASTIC LOADING               2. ELECTROMECHANICAL PHYSICS
  ┌─────────────────────────┐         ┌─────────────────────────┐
  │ • Hydraulic Growth (GBM)│  ────►  │ • Dynamic Blower Power  │
  │ • BOD Load (OU Process) │         │ • Raw Sewage Pumping    │
  │ • Wet-Weather Inflow    │         │ • Auxiliary Mixing      │
  └─────────────────────────┘         └─────────────────────────┘
               │                                   │
               ▼                                   ▼
  3. CARBON & COST TRACKING           4. REAL OPTIONS DECISION
  ┌─────────────────────────┐         ┌─────────────────────────┐
  │ • Dynamic Grid Emissions│  ────►  │ • Phased Capacity Sizing│
  │ • Solar Microgrid Offsets│        │ • High-Efficiency Upgrades│
  │ • Carbon Tax Liabilities│         │ • Optimal Timing Window │
  └─────────────────────────┘         └─────────────────────────┘

git clone https://github.com/your-username/stochastic-sewerage-roa.git
cd stochastic-sewerage-roa

pip install -r requirements.txt

streamlit run app.py

Your browser will automatically open http://localhost:8501 with the interactive UI engine!

├── app.py              # Main interactive web dashboard & computational engine
├── requirements.txt    # Required Python packages (Streamlit, Pandas, NumPy, Plotly)
└── README.md           # Visual guide and project documentation

This software implements the mathematical formulations established in:

• Hosdi, H. B. (2026a). Stochastic cost modeling and real options analysis for municipal sewerage infrastructure development.
• Hosdi, H. B. (2026b). Stochastic hydraulic design and real options sizing of municipal sewerage systems.
• Hosdi, H. B. (2026c). Stochastic Lifecycle Energy-Carbon Optimization and Real Options Sizing for Municipal Sewerage Systems.

Created by EUR ING Tc. Hazwan B. Hosdi (Planning and Engineering Department, Indah Water Konsortium / WorldQuant University).

Abstract

Municipal sewerage networks and wastewater treatment facilities represent major capital-intensive infrastructure characterized by high operational energy demands (Scope 2 emissions) and significant regulatory exposure. Traditional deterministic design approaches often evaluate capital expenditure, hydraulic capacity, and environmental compliance in silos, leaving municipal authorities vulnerable to escalating carbon pricing, energy tariff volatility, and strict net-zero mandates.

This repository contains the full manuscript, computational model, and simulation datasets for an integrated continuous-time stochastic framework that couples hydraulic loading models with lifecycle energy-to-emissions conversion and Real Options Analysis (ROA). Building upon prior investigations into municipal cost-overrun mitigation and energy-efficient hydraulic sizing (Hosdi 2026a, b), the model evaluates how stochastic boundary conditions—such as fluctuating biological oxygen demand (BOD) loads, dynamic electricity pricing, and carbon tax regimes—impact long-term asset performance.

By employing real options flexibility, the framework determines the optimal timing and scale for phased infrastructure interventions, such as Variable Frequency Drive (VFD) retrofits, diffuser aeration upgrades, and solar PV renewable energy integration. A computational case study demonstrates that simultaneously optimizing hydraulic capacity and greenhouse gas (GHG) mitigation pathways yields substantial reductions in cumulative carbon output (-42.1%) while safeguarding public budgets against unforeseen regulatory penalties (-90.1%) and lowering 50-year Whole Life Cycle Costing (WLCC) by 33.7%.

Keywords

Stochastic modeling Real options analysis Municipal infrastructure Sewerage systems Lifecycle assessment Energy efficiency Carbon accounting Infrastructure resilience Streamlit Python

Related Identifiers (Trilogy Linkage)

1. Hosdi, H. B. (2026a). Stochastic cost modeling and real options analysis for municipal sewerage infrastructure development: Mitigating unforeseen budget overruns. Journal of Infrastructure Systems (Under Review, Manuscript ISENG-3320).

2. Hosdi, H. B. (2026b). Stochastic hydraulic design and real options sizing of municipal sewerage systems: Balancing capital outlays, energy efficiency, and regulatory compliance. Journal of Water Resources Planning and Management (Under Review, Manuscript WRENG-7980).