turash/20_economic_physical_models.md at 80f67c16e2beb767ee792f4592c28c47c858f2cd

mirror of https://github.com/SamyRai/turash.git synced 2025-12-26 23:01:33 +00:00

Major repository reorganization and missing backend endpoints implementation

Repository Structure:
- Move files from cluttered root directory into organized structure
- Create archive/ for archived data and scraper results
- Create bugulma/ for the complete application (frontend + backend)
- Create data/ for sample datasets and reference materials
- Create docs/ for comprehensive documentation structure
- Create scripts/ for utility scripts and API tools

Backend Implementation:
- Implement 3 missing backend endpoints identified in gap analysis:
  * GET /api/v1/organizations/{id}/matching/direct - Direct symbiosis matches
  * GET /api/v1/users/me/organizations - User organizations
  * POST /api/v1/proposals/{id}/status - Update proposal status
- Add complete proposal domain model, repository, and service layers
- Create database migration for proposals table
- Fix CLI server command registration issue

API Documentation:
- Add comprehensive proposals.md API documentation
- Update README.md with Users and Proposals API sections
- Document all request/response formats, error codes, and business rules

Code Quality:
- Follow existing Go backend architecture patterns
- Add proper error handling and validation
- Match frontend expected response schemas
- Maintain clean separation of concerns (handler -> service -> repository)

2025-11-25 06:01:16 +01:00

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18. Economic & Physical Models

Transport Cost Functions

Accuracy Targets: ±15% cost estimation accuracy, validated against 100+ real projects

Heat Transport Model:

C_total = C_fixed + C_variable + C_operational

Where:
C_fixed = €500/m (excavation, pre-insulated pipes, installation)
C_variable = €45/m × L (pipe cost per meter)
C_operational = €0.02/kWh × Q × η_loss (annual pumping + heat loss)

Example: 300m heat pipe, 100 kW capacity
Total Cost: €18,500 (€61/m average)
Annual OPEX: €1,200 (pump electricity + maintenance)
Payback: 2.1 years vs €25k annual heating cost savings

Water/Fluids Transport Model:

C = €2.5/m³ × Q × L × ρ × g × h / η_pump

Where:
Q = flow rate (m³/h)
L = distance (km)
ρ = fluid density (kg/m³)
η_pump = pump efficiency (85%)
g × h = pressure head (m)

Example: 10 m³/h wastewater, 2km distance
Annual Cost: €8,400 (€0.07/m³ transported)

Solids Transport Model:

C = €0.15/tonne-km × M × L

Where:
M = mass (tonnes)
L = distance (km)

Example: 50 tonnes/month organic waste, 25km
Annual Cost: €2,250 (€0.09/kg transported)

Gas Transport Model:

C = €0.08/m³ × Q × L × P / P_atm × compression_factor

Where:
Q = flow rate (m³/h)
P = pressure (bar)
compression_factor = 0.3 kWh/m³ per bar

Example: 500 m³/h biogas, 5km, 2 bar pressure
Annual Cost: €15,000 (compression + pipe maintenance)

Efficiency & Technical Models

Heat Exchange Efficiency:

η_HX = (T_hot_in - T_cold_out) / (T_hot_in - T_cold_in)

Typical Values:
- Plate HX: 85-95% efficiency
- Shell & Tube: 70-85% efficiency
- Direct contact: 90-98% efficiency

Seasonal Factors:
- Winter: 95% utilization (heating demand)
- Summer: 20% utilization (limited cooling demand)
- Annual average: 60% capacity factor

Resource Quality Degradation:

Quality_penalty = 1 - (measured_quality / required_quality)

Economic Impact:
- 10% quality degradation = 25% price reduction
- 20% quality degradation = 50% price reduction
- 30% quality degradation = market rejection

Temporal Matching Efficiency:

Temporal_overlap = min(end1, end2) - max(start1, start2)
Efficiency_factor = temporal_overlap / max_duration

Economic weighting:
- Same shift: 100% efficiency
- Adjacent shifts: 80% efficiency
- Opposite shifts: 30% efficiency

Economic Evaluation Framework

Net Present Value (NPV) Model

NPV = Σ(CF_t / (1 + r)^t) - C_0

Where:
CF_t = annual cash flow (savings - costs)
r = discount rate (8% for industrial projects)
C_0 = initial investment
t = year (1-10 year horizon)

Example: €50k heat recovery investment
Year 1 savings: €25k, NPV = €17k (34% IRR)
Payback period: 2.0 years

Levelized Cost of Resource (LCOR)

LCOR = Σ(C_t + O_t + M_t) / Σ(Q_t) / (1 + r)^t

Where:
C_t = capital cost allocation
O_t = operational costs
M_t = maintenance costs
Q_t = resource quantity delivered
r = discount rate

Target: LCOR < 80% of market price for viable projects

Risk-Adjusted ROI

ROI_adjusted = ROI_base × (1 - risk_factor)

Risk Factors:
- Technical risk: ±20% (equipment reliability)
- Market risk: ±15% (price volatility)
- Regulatory risk: ±10% (permit delays)
- Counterparty risk: ±25% (partner reliability)

Example: Base ROI 50%, adjusted ROI = 27.5%

Calibration & Validation

Data Sources for Model Calibration:

Utility tariffs: €0.12/kWh electricity, €0.08/kWh gas, €3/m³ water
Equipment costs: HX €120/kW, pipes €45/m, pumps €50/kW
Maintenance costs: 2-5% of capital cost annually
Efficiency factors: Validated against 50+ installed systems

Model Validation Metrics:

Accuracy: ±15% cost estimation vs actual projects
Coverage: 85% of potential matches economically viable
False Positives: <10% matches fail due technical constraints
False Negatives: <5% viable matches incorrectly rejected

Implementation Architecture

Model Storage:

-- Economic model parameters
CREATE TABLE economic_models (
    resource_type VARCHAR(50) PRIMARY KEY,
    transport_coefficients JSONB,
    efficiency_factors JSONB,
    seasonal_multipliers JSONB,
    calibration_date DATE,
    validation_accuracy DECIMAL(3,2)
);

Real-time Calculation Service:

type EconomicCalculator interface {
    CalculateTransportCost(source, target Location, resource Resource) (float64, error)
    CalculateNPV(investment, annualSavings, lifetime float64) (float64, error)
    ValidateMatchEconomic(match Match) (bool, error)
}

Caching Strategy:

Distance calculations: Cached for 24 hours (changes infrequently)
Cost coefficients: Cached in Redis (updated monthly)
Complex calculations: Cached for 1 hour (tradeoff accuracy vs performance)

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