How AI Optimizes Water Supply: Pumps, Leaks, and Quality Control

We develop AI systems for water supply that reduce costs and improve reliability. Water supply is one of the most energy-intensive municipal systems—typical non-revenue water (NRW) in Russian cities ranges from 20–40%, and pumps consume up to 80% of the entire water utility's electricity. Traditional control methods—manual operation or PID controllers—do not account for tariff schedules or demand forecasts. We use AI to cut these costs: we optimize pump operation considering tariffs, detect leaks without excavation, and monitor water quality in real time. Our engineers have experience with over 10 projects in municipal utilities, guaranteeing a 15–25% reduction in electricity costs, often translating to over $50,000 in annual savings for a typical utility.

How AI Reduces Electricity Costs

Pumps are the main energy consumers. Optimizing their schedule with respect to tariff rates is a classic linear programming problem. We solve it over a 24-hour horizon: shifting pump operation to nighttime hours when tariffs are lowest, while maintaining pressure and reservoir levels.

import numpy as np
from scipy.optimize import minimize
import pandas as pd

class PumpScheduleOptimizer:
    """Pump schedule optimization considering tariff schedule"""

    def __init__(self, n_pumps, reservoir_capacity_m3):
        self.n_pumps = n_pumps
        self.V_max = reservoir_capacity_m3
        self.V_min = reservoir_capacity_m3 * 0.2  # min 20% capacity

    def optimize_24h(self, demand_forecast, tariff_schedule, pump_specs, V_init):
        """
        demand_forecast: hourly water demand [m³/h]
        tariff_schedule: hourly electricity tariff [rub/kWh]
        pump_specs: [{flow_m3h, power_kw, min_run_time_h}]
        V_init: initial reservoir level [m³]
        """
        T = 24  # 24-hour horizon

        from pulp import LpProblem, LpMinimize, LpVariable, lpSum, LpBinary

        prob = LpProblem("pump_schedule", LpMinimize)

        # Binary variables: pump i on at hour t
        pump_on = [[LpVariable(f"pump_{i}_{t}", cat='Binary')
                    for t in range(T)] for i in range(self.n_pumps)]

        # Reservoir level variable
        V = [LpVariable(f"V_{t}", lowBound=self.V_min, upBound=self.V_max)
             for t in range(T+1)]
        V[0].setInitialValue(V_init)

        # Objective: minimize electricity cost
        prob += lpSum(
            pump_specs[i]['power_kw'] * pump_on[i][t] * tariff_schedule[t]
            for i in range(self.n_pumps) for t in range(T)
        )

        # Reservoir balance
        for t in range(T):
            inflow = lpSum(pump_specs[i]['flow_m3h'] * pump_on[i][t]
                          for i in range(self.n_pumps))
            prob += V[t+1] == V[t] + inflow - demand_forecast[t]

        prob.solve()
        return [[pump_on[i][t].value() for t in range(T)] for i in range(self.n_pumps)]

Optimization effect: By shifting pump operation to nighttime hours (cheap tariff), savings amount to 15–25% of electricity costs for the same water volume. Unlike replacing pumps with more efficient ones (which requires capital investment), AI optimization uses existing equipment and pays back in 3–6 months.

How to Detect Leaks Without Excavation

We combine three methods:

• Balance analysis: compare supplied and consumed water volumes to identify zones with abnormally high NRW.
• Pressure transient analysis: LSTM on pressure time series detects bursts and slow leaks.
• Correlation method: two acoustic sensors with time difference of arrival determine leak distance with accuracy of 50–100 m.

Comparison with traditional methods: AI approaches detect leaks 3–5 times faster and require 2 times fewer crew dispatches. Traditional methods like correlators and thermal imagers require prior assumptions about leak location and provide accuracy no better than 200 m.

Water Quality Control Using ML

IoT sensors measure chlorine, turbidity, pH, and temperature in real time. We build a hydraulic model using EPANET and predict chlorine concentration at any point in the network. This allows maintaining SanPiN standards (>0.05 mg/L) with minimal dosing, saving up to 30% reagent.

Contamination detection: anomalous parameter combinations (e.g., turbidity + pH) trigger an ML classifier that indicates the likely source and spread zone.

Sewer Network Management

For sewer networks, we predict wastewater inflow during storms using LSTM on rain gauge data and collector levels. A 1–2 hour forecast enables proactive gate adjustments to avoid overflow. Optimization of sewer pumping stations smooths peaks, preventing overload at treatment plants.

Why AI Optimization Is Better Than Pump Replacement

Replacing pumps with more efficient ones requires capital expenditure and prolonged downtime. AI optimization uses existing equipment, adapting its modes to current conditions. Comparison:

Development Process and Timelines

A typical project takes 4–7 months and includes the following stages:

1. Data audit (2–3 weeks): collection and validation of historical data.
2. Modeling (4–6 weeks): hydraulic network model in EPANET, ML model training.
3. Integration (3–5 weeks): connection to SCADA, IoT platform, dashboard setup.
4. Testing (2–3 weeks): A/B test on real data, validation of savings.
5. Deployment and training (2–4 weeks): container installation, staff training.

What Is Included in the Work

• Analytical report with current losses and savings potential.
• Hydraulic network model (EPANET).
• ML models (pump optimization, leak detection, water quality).
• Integration with existing systems (SCADA, IoT platform).
• Dashboards for dispatchers and reports for management.
• Staff training and documentation.
• 6-month warranty support.

Why Choose Us

We have developed AI systems for 5 water utilities (over 10 projects). We guarantee a 15–25% reduction in electricity costs and a 10–30% reduction in water loss. We use only proven stacks: PyTorch, LSTM, LP-solvers. We provide post-support and adaptation to your specific network.

Want to assess the potential of your system? Contact us for a 1-week audit and a proposal. Order a pilot project: results are visible within 2 months of start.

SanPiN 2.1.3684-21 — drinking water quality standards