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

pip install numpy scipy pandas matplotlib

Optional Dependencies

# 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

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

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"""
        # Air is ~23% oxygen by mass
        # Standard air density ~1.2 kg/m3
        o2_fraction = 0.23
        air_density = 1.2  # kg/m3

        # O2 in air = 1.2 * 0.23 = 0.276 kg O2/m3 air
        o2_per_m3_air = air_density * o2_fraction

        # Air flow required
        air_flow_m3_hr = o2_required_kg_hr / (o2_per_m3_air * aote)

        return air_flow_m3_hr

    def calculate_blower_power(self, air_flow_m3_hr: float,
                               inlet_pressure_kpa: float = 101.325,
                               discharge_pressure_kpa: float = 150,
                               efficiency: float = 0.70) -> float:
        """Calculate blower power requirement"""
        # Adiabatic compression
        gamma = 1.4  # Air specific heat ratio

        # Pressure ratio
        p_ratio = discharge_pressure_kpa / inlet_pressure_kpa

        # Convert to m3/s
        Q = air_flow_m3_hr / 3600

        # Adiabatic head calculation
        head_kj_kg = (gamma / (gamma - 1)) * (inlet_pressure_kpa / 1.2) * \
                     ((p_ratio ** ((gamma - 1) / gamma)) - 1)

        # Power in kW
        power_kw = (Q * 1.2 * head_kj_kg) / efficiency

        return power_kw

# Example usage
aeration = AerationAnalysis(basin_depth_m=5.0, diffuser_type='fine_bubble')

sote = aeration.calculate_sote()
print(f"SOTE: {sote*100:.1f}%")

aote = aeration.calculate_aote(temperature_c=18, do_mg_l=2.0, alpha=0.5)
print(f"AOTE: {aote*100:.1f}%")

air_flow = aeration.calculate_air_flow(o2_required_kg_hr=500, aote=aote)
print(f"Air flow required: {air_flow:.0f} m3/hr")

power = aeration.calculate_blower_power(air_flow_m3_hr=air_flow)
print(f"Blower power: {power:.0f} kW")

Nutrient Removal Analysis

class NutrientRemoval:
    """Nutrient removal process analysis"""

    def __init__(self):
        self.nitrification_rate_20c = 0.08  # g N/(g VSS*day) at 20C
        self.denitrification_rate_20c = 0.1  # g N/(g VSS*day) at 20C

    def calculate_nitrification_srt(self, temperature_c: float,
                                    safety_factor: float = 2.0) -> float:
        """Calculate minimum SRT for nitrification"""
        # Nitrifier parameters
        mu_max_20 = 0.8  # 1/day at 20C
        kd_n = 0.04  # 1/day
        theta = 1.07

        # Temperature correction
        mu_max = mu_max_20 * theta ** (temperature_c - 20)

        # Minimum SRT
        srt_min = 1 / (mu_max - kd_n)

        return srt_min * safety_factor

    def calculate_carbon_for_denitrification(self, no3_to_remove_mg_l: float,
                                             flow_mgd: float,
                                             carbon_source: str = 'methanol') -> Dict:
        """Calculate external carbon requirement for denitrification"""
        # Carbon requirements (g COD/g N removed)
        carbon_ratios = {
            'methanol': 3.0,
            'ethanol': 4.0,
            'acetic_acid': 3.5,
            'raw_wastewater': 4.5
        }

        ratio = carbon_ratios.get(carbon_source, 4.0)

        # Convert flow
        flow_m3_day = flow_mgd * 3785.41

        # N mass to remove
        n_mass_kg_day = no3_to_remove_mg_l * flow_m3_day / 1000

        # COD required
        cod_kg_day = n_mass_kg_day * ratio

        return {
            'nitrate_removed_kg_day': n_mass_kg_day,
            'cod_required_kg_day': cod_kg_day,
            'carbon_source': carbon_source,
            'ratio_used': ratio
        }

    def calculate_ebpr_capacity(self, vfa_mg_l: float, flow_mgd: float) -> Dict:
        """Estimate EBPR phosphorus removal capacity"""
        # VFA uptake ratio: ~10-15 mg VFA-COD per mg P removed
        vfa_p_ratio = 12  # mg VFA-COD / mg P

        # Convert flow
        flow_m3_day = flow_mgd * 3785.41

        # VFA mass available
        vfa_mass_kg_day = vfa_mg_l * flow_m3_day / 1000

        # P removal potential
        p_removal_kg_day = vfa_mass_kg_day / (vfa_p_ratio / 1000)

        return {
            'vfa_available_kg_day': vfa_mass_kg_day,
            'p_removal_potential_kg_day': p_removal_kg_day,
            'p_removal_concentration_mg_l': (p_removal_kg_day * 1000) / flow_m3_day
        }

# Example usage
nutrient = NutrientRemoval()

srt = nutrient.calculate_nitrification_srt(temperature_c=15)
print(f"Minimum SRT for nitrification at 15C: {srt:.1f} days")

carbon = nutrient.calculate_carbon_for_denitrification(
    no3_to_remove_mg_l=20,
    flow_mgd=10,
    carbon_source='methanol'
)
print(f"Methanol COD required: {carbon['cod_required_kg_day']:.0f} kg/day")

ebpr = nutrient.calculate_ebpr_capacity(vfa_mg_l=50, flow_mgd=10)
print(f"EBPR P removal potential: {ebpr['p_removal_concentration_mg_l']:.1f} mg/L")

Usage Guidelines

When to Use This Skill

• Wastewater treatment process design and optimization
• Energy efficiency improvement projects
• Nutrient removal system upgrades
• Process troubleshooting and capacity analysis
• Chemical dosing optimization

Best Practices

1. Calibrate models with plant-specific data
2. Consider seasonal variations in temperature and loading
3. Account for diurnal variations in flow and load
4. Verify model predictions with plant performance data
5. Include safety factors for critical processes like nitrification
6. Optimize holistically considering interactions between processes

Process Integration

• WW-002: Wastewater Process Optimization (all phases)
• WW-001: Water Treatment Plant Design (optimization phases)

Dependencies

• numpy: Numerical calculations
• scipy: Optimization routines
• pandas: Data analysis

References

• Metcalf & Eddy, "Wastewater Engineering: Treatment and Resource Recovery"
• Water Environment Federation, "Nutrient Removal" (MOP 34)
• Henze et al., "Activated Sludge Models ASM1, ASM2, ASM2d and ASM3"