AI-Aquatica

⚖️ Usage – Ion Balance (AI-Aquatica)

This guide demonstrates how to calculate ion balance, identify potential chemical inconsistencies, and perform corrections using the ion_balance module.

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1. 📦 Importing

from ai_aquatica.ion_balance import (
    calculate_ion_balance,
    identify_potential_errors,
    correct_ion_discrepancies
)

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2. 💧 Sample dataset

import pandas as pd

data = pd.DataFrame({
    'Ca2+': [2.1, 1.9, 2.3],
    'Mg2+': [1.1, 0.8, 1.0],
    'Na+': [0.7, 0.6, 0.9],
    'Cl-': [1.8, 1.7, 1.9],
    'SO4--': [1.5, 1.3, 1.4],
    'HCO3-': [1.0, 0.9, 1.2]
})

cations = ['Ca2+', 'Mg2+', 'Na+']
anions = ['Cl-', 'SO4--', 'HCO3-']

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3. ⚖️ Calculate ion balance

balanced_data = calculate_ion_balance(data, cations, anions)
print(balanced_data[['Cations_Sum', 'Anions_Sum', 'Ion_Balance']])

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4. 🚨 Identify potential analytical errors

flagged_data = identify_potential_errors(balanced_data, threshold=5.0)
print(flagged_data[['Ion_Balance', 'Potential_Error']])

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5. 🛠️ Correct discrepancies

corrected_data = correct_ion_discrepancies(balanced_data, cations, anions)

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🧠 Notes

• Ion balance is reported as a percentage: (cations - anions)/(cations + anions) * 100
• A balance error > 5% typically indicates problems with sample or measurement.
• Corrections are done proportionally across ions.

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