wastewater-optimization

What this skill does

# Wastewater Treatment Optimization Skill

Biological and physical-chemical wastewater treatment process optimization for municipal and industrial applications.

## Purpose

This skill provides comprehensive capabilities for optimizing wastewater treatment processes, including activated sludge modeling, nutrient removal optimization, aeration efficiency analysis, and energy consumption minimization.

## Capabilities

### Activated Sludge Process Modeling
- ASM1, ASM2d, ASM3 model implementation
- Stoichiometric and kinetic parameter estimation
- Model calibration with plant data
- Steady-state and dynamic simulation
- Sensitivity analysis for key parameters

### BioWin and GPS-X Integration
- Model input file generation
- Simulation scenario configuration
- Results parsing and analysis
- Automated optimization runs
- Comparative scenario analysis

### Nutrient Removal Optimization
- Biological Nutrient Removal (BNR) process design
- Enhanced Biological Phosphorus Removal (EBPR) optimization
- Nitrification/denitrification kinetics
- Carbon source requirements
- Recycle ratio optimization

### Aeration Efficiency Analysis
- Oxygen transfer efficiency (OTE) calculation
- Alpha and beta factor determination
- Diffuser performance assessment
- Blower energy optimization
- DO control strategy evaluation

### Sludge Age and F/M Ratio Optimization
- Solids Retention Time (SRT) optimization
- Food-to-Microorganism (F/M) ratio analysis
- MLSS concentration control
- Sludge yield prediction
- WAS flow optimization

### Energy Consumption Minimization
- Energy audit methodology
- Aeration energy optimization
- Pumping efficiency analysis
- Process control optimization
- Energy benchmarking

### Chemical Dosing Optimization
- Coagulant dose optimization
- Polymer selection and dosing
- pH adjustment requirements
- Phosphorus precipitation
- Chemical cost minimization

### Secondary Clarifier Modeling
- Settling velocity analysis
- Solids flux theory application
- State point analysis
- Clarifier capacity evaluation
- Return sludge optimization

## Prerequisites

### Installation
```bash
pip install numpy scipy pandas matplotlib
```

### Optional Dependencies
```bash
# For process simulation
pip install python-control

# For optimization
pip install scipy pymoo

# For data visualization
pip install plotly seaborn
```

## Usage Patterns

### Activated Sludge Mass Balance
```python
import numpy as np
from dataclasses import dataclass
from typing import Dict, Optional

@dataclass
class WastewaterCharacteristics:
    """Influent wastewater characteristics"""
    flow_mgd: float  # Million gallons per day
    bod5_mg_l: float  # 5-day BOD
    tss_mg_l: float  # Total suspended solids
    tkn_mg_l: float  # Total Kjeldahl Nitrogen
    tp_mg_l: float  # Total phosphorus
    temperature_c: float = 20.0

@dataclass
class ActivatedSludgeParameters:
    """Kinetic and stoichiometric parameters"""
    Y: float = 0.6  # Yield coefficient (g VSS/g BOD)
    kd: float = 0.06  # Endogenous decay rate (1/day)
    mu_max: float = 6.0  # Maximum specific growth rate (1/day)
    Ks: float = 60.0  # Half-saturation constant (mg/L BOD)
    theta_Y: float = 1.0  # Temperature coefficient for Y
    theta_kd: float = 1.04  # Temperature coefficient for kd

class ActivatedSludgeModel:
    """Simplified activated sludge process model"""

    def __init__(self, influent: WastewaterCharacteristics,
                 params: ActivatedSludgeParameters):
        self.influent = influent
        self.params = params

    def calculate_srt_for_effluent(self, target_bod_eff: float) -> float:
        """Calculate required SRT for target effluent BOD"""
        S = target_bod_eff
        S0 = self.influent.bod5_mg_l
        Y = self.params.Y
        kd = self.params.kd
        mu_max = self.params.mu_max
        Ks = self.params.Ks

        # Specific growth rate at effluent concentration
        mu = mu_max * S / (Ks + S)

        # Minimum SRT (washout)
        srt_min = 1 / (mu - kd)

        return srt_min * 1.5  # Safety factor

    def calculate_oxygen_requirement(self, srt_days: float, target_bod_eff: float) -> Dict:
        """Calculate oxygen requirements"""
        Q = self.influent.flow_mgd * 3.785  # Convert to m3/day * 1000 L/m3
        S0 = self.influent.bod5_mg_l
        S = target_bod_eff
        Y = self.params.Y
        kd = self.params.kd

        # BOD removal
        bod_removed = (S0 - S) * Q / 1000  # kg/day

        # Biomass production
        Px = Y * bod_removed / (1 + kd * srt_days)

        # Oxygen for BOD oxidation
        O2_bod = bod_removed - 1.42 * Px

        # Oxygen for endogenous respiration
        O2_endo = 1.42 * kd * Px * srt_days

        return {
            'bod_removed_kg_day': bod_removed,
            'biomass_produced_kg_day': Px,
            'O2_for_bod_kg_day': O2_bod,
            'O2_for_endogenous_kg_day': O2_endo,
            'total_O2_kg_day': O2_bod + O2_endo
        }

    def calculate_aeration_basin_volume(self, srt_days: float,
                                        mlss_mg_l: float) -> float:
        """Calculate required aeration basin volume"""
        Q = self.influent.flow_mgd * 3785.41  # m3/day
        S0 = self.influent.bod5_mg_l
        S = 10  # Target effluent BOD
        Y = self.params.Y
        kd = self.params.kd

        # Calculate biomass
        Px = Y * (S0 - S) / (1 + kd * srt_days)  # mg VSS/L influent

        # Assume VSS/TSS ratio
        vss_tss_ratio = 0.8
        mlvss = mlss_mg_l * vss_tss_ratio

        # Volume calculation
        volume_m3 = (Q * Px * srt_days) / mlvss

        return volume_m3

# Example usage
influent = WastewaterCharacteristics(
    flow_mgd=10.0,
    bod5_mg_l=200,
    tss_mg_l=220,
    tkn_mg_l=40,
    tp_mg_l=8,
    temperature_c=18
)

params = ActivatedSludgeParameters()
model = ActivatedSludgeModel(influent, params)

srt = model.calculate_srt_for_effluent(target_bod_eff=10)
print(f"Required SRT: {srt:.1f} days")

o2_req = model.calculate_oxygen_requirement(srt, target_bod_eff=10)
print(f"Oxygen requirement: {o2_req['total_O2_kg_day']:.0f} kg/day")

volume = model.calculate_aeration_basin_volume(srt, mlss_mg_l=3000)
print(f"Aeration basin volume: {volume:.0f} m3")
```

### Aeration Efficiency Optimization
```python
import numpy as np

class AerationAnalysis:
    """Aeration system efficiency analysis"""

    def __init__(self, basin_depth_m: float, diffuser_type: str = 'fine_bubble'):
        self.basin_depth = basin_depth_m
        self.diffuser_type = diffuser_type

        # Standard oxygen transfer efficiencies
        self.sote_per_m = {
            'fine_bubble': 0.06,  # 6% per meter depth
            'coarse_bubble': 0.02,  # 2% per meter depth
            'surface_aerator': 0.015  # per meter equivalent
        }

    def calculate_sote(self) -> float:
        """Calculate Standard Oxygen Transfer Efficiency"""
        base_sote = self.sote_per_m.get(self.diffuser_type, 0.04)
        return base_sote * self.basin_depth

    def calculate_aote(self, temperature_c: float, do_mg_l: float,
                       alpha: float = 0.5, beta: float = 0.95,
                       altitude_m: float = 0) -> float:
        """Calculate Actual Oxygen Transfer Efficiency"""
        # Saturation DO at standard conditions
        Cs_20 = 9.09  # mg/L at 20C, sea level

        # Temperature correction for DO saturation
        Cs_T = 14.62 - 0.3898 * temperature_c + 0.006969 * temperature_c**2

        # Altitude correction
        P_ratio = np.exp(-altitude_m / 8500)

        # Theta factor for temperature
        theta = 1.024

        # Calculate AOTE
        sote = self.calculate_sote()
        aote = sote * alpha * ((beta * Cs_T * P_ratio - do_mg_l) / Cs_20) * \
               theta ** (temperature_c - 20)

        return aote

    def calculate_air_flow(self, o2_required_kg_hr: float,
                           aote: float) -> float:
        """Calculate required air flow rate"""