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Specialized skill for biological and physical-chemical wastewater treatment process optimization with activated sludge modeling, nutrient removal, aeration efficiency, and energy minimization.
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Reference for querying the Atlas knowledge graph through its MCP tools — the SECONDARY enrichment/comparison layer that adds best-practice context to systems you have ALREADY scanned from your real sources (`az`, repos, dirs). Use when you need to look up nodes, edges, kinds, clusters, stats, or wiki pages in Atlas to compare against your real inventory. (atlas graph, query atlas, atlas mcp, search the graph, graph neighbors, atlas record, atlas kinds, enrichment layer)
Atlas turns your STATED NEED into a real systems atlas by SCANNING your actual sources (Azure via `az`, git repos, local dirs) and process/data mining them, THEN enriching against the Atlas knowledge graph. Use this skill when asked to inventory/map your real systems, scan your cloud + repos + directories, mine the real processes or data they contain, or collect their real constraints/gotchas. (atlas, scan my systems, inventory our azure account, map my repos, real systems atlas, process mining, data mining, collect nuances, system discovery)
This skill should be used when the user asks to "find skills in the wild", "assimilate popular workflows", "discover SKILL.md files in repos", "research external skills", "find workflow patterns", "survey the skill landscape", "what skills exist out there", or wants to investigate public repositories for extractable processes, babysitter plugins, and reusable procedural insights. Searches GitHub for SKILL.md files, classifies repos by archetype, and maintains structured research under docs/reference-repos/.
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SKILL.md 표시 중
| 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"} |
| graph | {"domains":["domain:environmental-engineering"],"skillAreas":["skill-area:data-analysis","skill-area:statistical-analysis","skill-area:geospatial-data-analysis"],"workflows":["workflow:experiment-design"],"roles":["role:research-engineer"]} |
Biological and physical-chemical wastewater treatment process optimization for municipal and industrial applications.
This skill provides comprehensive capabilities for optimizing wastewater treatment processes, including activated sludge modeling, nutrient removal optimization, aeration efficiency analysis, and energy consumption minimization.
pip install numpy scipy pandas matplotlib
# For process simulation
pip install python-control
# For optimization
pip install scipy pymoo
# For data visualization
pip install plotly seaborn
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")
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")
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")