AI for Water Treatment, Quality Monitoring, and Leak Detection

Module: Sustainable Tech | Difficulty: Premium

Water Quality Index

Coagulation Model

Comparison

Python Implementation

import numpy as np
from scipy.optimize import minimize_scalar

class WaterTreatmentOptimizer:
    def coagulation_optimization(self, jar_test_data):
        doses = jar_test_data['dose']
        removals = jar_test_data['removal']
        def objective(dose):
            return -np.interp(dose, doses, removals)
        return minimize_scalar(objective, bounds=(doses.min(), doses.max()), method='bounded')

    def chlorine_residual(self, C0, t, k=0.1):
        return C0 * np.exp(-k * t)

    def detect_leak(self, flow_data, pressure_data, threshold=0.15):
        flow_anomaly = np.abs(flow_data - np.mean(flow_data)) / np.std(flow_data)
        pressure_anomaly = np.abs(pressure_data - np.mean(pressure_data)) / np.std(pressure_data)
        return (flow_anomaly + pressure_anomaly) / 2 > threshold

Research Insight: IoT sensor networks combined with LSTM models can detect water quality anomalies within 5 minutes of occurrence.