wastewater-optimization

name	wastewater-optimization
description	Specialized skill for biological and physical-chemical wastewater treatment process optimization with activated sludge modeling, nutrient removal, aeration efficiency, and energy minimization.
allowed-tools	Read, Grep, Write, Bash, Edit, Glob
category	Water and Wastewater Treatment
backlog-id	SK-002
metadata	{"author":"babysitter-sdk","version":"1.0.0"}

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:
    

     ():
        .influent = influent
        .params = params

     () -> :
        
        S = target_bod_eff
        S0 = .influent.bod5_mg_l
        Y = .params.Y
        kd = .params.kd
        mu_max = .params.mu_max
        Ks = .params.Ks

        
        mu = mu_max * S / (Ks + S)

        
        srt_min =  / (mu - kd)

         srt_min *   

     () -> :
        
        Q = .influent.flow_mgd *   
        S0 = .influent.bod5_mg_l
        S = target_bod_eff
        Y = .params.Y
        kd = .params.kd

        
        bod_removed = (S0 - S) * Q /   

        
        Px = Y * bod_removed / ( + kd * srt_days)

        
        O2_bod = bod_removed -  * Px

        
        O2_endo =  * kd * Px * srt_days

         {
            : bod_removed,
            : Px,
            : O2_bod,
            : O2_endo,
            : O2_bod + O2_endo
        }

     () -> :
        
        Q = .influent.flow_mgd *   
        S0 = .influent.bod5_mg_l
        S =   
        Y = .params.Y
        kd = .params.kd

        
        Px = Y * (S0 - S) / ( + kd * srt_days)  

        
        vss_tss_ratio = 
        mlvss = mlss_mg_l * vss_tss_ratio

        
        volume_m3 = (Q * Px * srt_days) / mlvss

         volume_m3


influent = WastewaterCharacteristics(
    flow_mgd=,
    bod5_mg_l=,
    tss_mg_l=,
    tkn_mg_l=,
    tp_mg_l=,
    temperature_c=
)

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

srt = model.calculate_srt_for_effluent(target_bod_eff=)
()

o2_req = model.calculate_oxygen_requirement(srt, target_bod_eff=)
()

volume = model.calculate_aeration_basin_volume(srt, mlss_mg_l=)
()

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:
        
        
        Cs_20 =   

        
        Cs_T =  -  * temperature_c +  * temperature_c**

        
        P_ratio = np.exp(-altitude_m / )

        
        theta = 

        
        sote = .calculate_sote()
        aote = sote * alpha * ((beta * Cs_T * P_ratio - do_mg_l) / Cs_20) * \
               theta ** (temperature_c - )

         aote

     () -> :
        
        
        
        o2_fraction = 
        air_density =   

        
        o2_per_m3_air = air_density * o2_fraction

        
        air_flow_m3_hr = o2_required_kg_hr / (o2_per_m3_air * aote)

         air_flow_m3_hr

     () -> :
        
        
        gamma =   

        
        p_ratio = discharge_pressure_kpa / inlet_pressure_kpa

        
        Q = air_flow_m3_hr / 

        
        head_kj_kg = (gamma / (gamma - )) * (inlet_pressure_kpa / ) * \
                     ((p_ratio ** ((gamma - ) / gamma)) - )

        
        power_kw = (Q *  * head_kj_kg) / efficiency

         power_kw


aeration = AerationAnalysis(basin_depth_m=, diffuser_type=)

sote = aeration.calculate_sote()
()

aote = aeration.calculate_aote(temperature_c=, do_mg_l=, alpha=)
()

air_flow = aeration.calculate_air_flow(o2_required_kg_hr=, aote=aote)
()

power = aeration.calculate_blower_power(air_flow_m3_hr=air_flow)
()

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': ,
            : ,
            : ,
            : 
        }

        ratio = carbon_ratios.get(carbon_source, )

        
        flow_m3_day = flow_mgd * 

        
        n_mass_kg_day = no3_to_remove_mg_l * flow_m3_day / 

        
        cod_kg_day = n_mass_kg_day * ratio

         {
            : n_mass_kg_day,
            : cod_kg_day,
            : carbon_source,
            : ratio
        }

     () -> :
        
        
        vfa_p_ratio =   

        
        flow_m3_day = flow_mgd * 

        
        vfa_mass_kg_day = vfa_mg_l * flow_m3_day / 

        
        p_removal_kg_day = vfa_mass_kg_day / (vfa_p_ratio / )

         {
            : vfa_mass_kg_day,
            : p_removal_kg_day,
            : (p_removal_kg_day * ) / flow_m3_day
        }


nutrient = NutrientRemoval()

srt = nutrient.calculate_nitrification_srt(temperature_c=)
()

carbon = nutrient.calculate_carbon_for_denitrification(
    no3_to_remove_mg_l=,
    flow_mgd=,
    carbon_source=
)
()

ebpr = nutrient.calculate_ebpr_capacity(vfa_mg_l=, flow_mgd=)
()

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"