Application of Artificial Intelligence for Enhancing Water and Wastewater Treatment Processes

By Rajendra Kumar V. Saraf

Environmental Engineer & Director, WASH RAG

rvsarafrotary@gmail.com

Introduction and Background

Water is the only chemical compound on Earth that occurs naturally in all three physical states: liquid, vapour (gas), and ice (solid). These three states play a crucial role in the hydrological cycle. Water is used by humans for various purposes in daily life, agriculture, and industry. Liquid water is used for drinking, cooking, sanitation, and industrial processes; vapour is used for power generation and in industries; and ice is utilized for cooling.

Historically, water was used in rural and urban areas of India as available. Traditional storage in copper vessels provided a degree of disinfection. Wells, rivers, and lakes were the main sources. Although 1990 was celebrated as the "Water Decade," a large population in India still faces difficulties in obtaining clean and potable water today.

Historically, most local governments and municipal authorities established traditional water treatment plants (WTPs) and sewage treatment plants (STPs). These systems typically pump water from reservoirs to ground-level or elevated storage tanks, supplying it after simple physical and chemical processes or disinfection with bleaching powder.

The pace of urbanization has accelerated massively globally. Small villages are rapidly transforming into large metropolises.
Water Pollution and Challenges Facing Traditional Systems

Due to escalating industrialization and urbanization, large volumes of chemical and biological wastewater are being discharged into water bodies or onto open land today. The use of man-made chemicals has contaminated surface and groundwater reserves.

Surface water quality primarily depends on the watershed, while groundwater quality relies on subsurface hydrogeochemistry. Unfortunately, due to untreated wastewater being discharged into rivers, most rivers in India & many parts of the world have today transformed into wastewater-carrying drains.

Pollution in many rivers has escalated to the point where citizens along the banks cannot use this water for any domestic purpose, and its adverse effects are visible. These waters contain numerous insoluble and soluble man-made hazardous components distinct from natural elements.

Limitations of Traditional Water Purification Systems:

• Static Feedback Systems: Traditional plants operate on aggregated data and feedback-controlled systems, which cannot respond immediately if there are sudden changes in water flow or quality.
• Delays in Laboratory Testing: By the time chemical separation and testing results are obtained from a laboratory, a large volume of water has already passed through the process.
• Inefficiency: This results in the overuse or underuse of chemicals, wasted electricity, and the risk of supplying impure water.

The Rise of Artificial Intelligence and Digital Technology

Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), and the Industrial Internet of Things (IIoT) are revolutionizing the water purification sector.

Functions of Data Aggregation and Artificial Intelligence:

Sensors installed at various stages in the plant continuously collect data across three main parameters:

• Physical: Water flow rate, temperature, total dissolved/suspended solids, turbidity.
• Chemical: pH, Dissolved Oxygen (DO), Oxidation-Reduction Potential (ORP), Chemical Oxygen Demand (COD), Total Organic Carbon (TOC).
• Biological/Nutrients: Biochemical Oxygen Demand (BOD), ammonia, nitrate, phosphorus.

This aggregated data goes directly to Programmable Logic Controllers (PLCs) and Supervisory Control and Data Acquisition (SCADA) systems. AI algorithms analyse this data to automatically control VFD pump speeds and dosing pump quantities in real time.

Key Stages of Water Purification and AI's Role

1. Reservoir Water Extraction for Water Treatment Plants

• Data Flow Streamlining: Utilizing feed-forward control, AI matches reservoir water supply with storage levels and water demand in the treatment plant, while forecasting water quality for optimal chemical dosing.
• Energy Optimization: Minimizes energy consumption by shifting pumping schedules to peak and off-peak electricity rate hours and running parallel pumps at their best efficiency points.
• Predictive Maintenance: Detects pump vibration, temperature, premature wear, cavitation risks, and erosion caused by sand and similar materials, thereby preventing unexpected failures.
• Efficient Data Collection Management: Automates real-time sensor tracking and trash-rack cleaning cycles, while providing early warnings for seasonal algae growth or water contamination.
• Digital Twins: Uses virtual hydrological models to simulate pipeline conditions, identify bio-fouling or sediment accumulation, and streamline emergency response planning for extreme weather events.

In this way, traditional water extraction transforms into a capable, predictive, and energy-efficient process.

2. Coagulation Dosing in Drinking Water

Colloidal particles in water carry a negative charge, preventing them from settling easily. Chemicals like alum, polyaluminum chloride, or ferric chloride are mixed to coagulate these particles. Occasionally, plant-based (drumstick seed powder, guar gum) or polymer-based coagulant aids are used.

• The Problem: Overuse of alum poses a risk of causing diseases like Alzheimer's in humans. Furthermore, if the water's pH is acidic, alum fails to work without adequate alkalinity, necessitating lime or sodium carbonate. If water turbidity changes suddenly during the monsoon, human operators often make incorrect chemical dosing errors.
• AI's Role: AI sensors measure the exact amount of chemicals required based on water pH, turbidity, and temperature. This stops chemical wastage and saves costs.

3. Sedimentation / Clarification Process

Following flocculation, 90% to 95% of suspended particles settle to the bottom, known as sludge. This sludge contains microorganisms and harmful chemicals.

• The Problem: Excessive sludge accumulation at the bottom causes it to mix back into the clean water above. However, keeping sludge scraper blades running continuously wastes massive amounts of electricity. Moreover, 2% to 3% of water is wasted along with the sludge.
• AI's Role: AI cameras and underwater sensors measure the thickness of the sludge blanket. AI activates scrapers and dewatering units only when the sludge reaches a specific threshold. This saves electricity and enables water recovery from sludge for reuse in the process.

4. Filtration Process

The turbidity of water exiting clarifiers is typically 20–50 NTU. To remove these fine particles, water is passed through layers of sand, gravel, or membrane filters, which can be Rapid Sand Filters or Pressure Sand Filters.

• The Problem: Dirt gradually clogging sand spaces slows down water flow, a condition known as "head loss." Backwashing restores filtration flow, but wastes 2% to 5% of clean water.
• AI's Role: AI continuously monitors head loss, uses Long Short-Term Memory (LSTM) models to accurately predict when filtration flow will decline, and triggers automated backwashing precisely at the right time.

5. Disinfection

Chlorine, ozone, or ultraviolet (UV) light is used to kill bacteria and viruses. Chlorine leaves residual chlorine in the water, preventing new bacteria growth during transit through pipelines.

• The Problem: Insufficient chlorine leaves bacteria alive; excessive chlorine imparts a strange odour and generates harmful chemical byproducts.
• AI's Role: By measuring the length of the city's water distribution network and water transit time, AI dynamically adjusts chlorine dosing.

6. Water Distribution Network

AI optimizes water networks by shifting from reactive management to predictive control. Real-time pressure and flow sensor analysis identifies anomalies early, significantly reducing Non-Revenue Water (NRW) losses from leaks and theft. Machine learning evaluates equipment health and operating conditions to prevent future failures and extend asset life. By aligning schedules with changing electricity tariffs and adjusting pressure zones, AI optimizes pump operations to support efficient energy management. Predictive models forecast demand alongside external factors like weather and seasonality to balance supply and storage, ensuring proper water planning. Compiling chemical and physical parameter data enables an early warning system for poor water quality.

Comparative Analysis: Traditional vs. AI-Based Processing

Challenges in AI Implementation and Safety Measures

1. Data Quality, Legacy Systems, and Sensor Reliability

AI systems rely on continuous, high-quality data. Water and wastewater negatively impact equipment; biofilms, chemical scales, and solid debris severely affect sensors, preventing accurate parameter measurement. Inaccurate data leads to flawed predictions and poor control performance. Integrating new AI systems into legacy SCADA requires careful bridging.

2. The Black Box Problem (Why AI Needs to Explain Itself)

• The Problem: Deep AI models function as "black boxes," providing answers without showing how decisions were made. Plant managers and regulators cannot rely on such logic for public water safety without auditability.
• Solution: Tools like SHAP and LIME act as windows into the intelligence, highlighting which factors (such as water temperature, pH, or turbidity) drove a recommendation, giving operators full information to verify decisions before taking action.

3. Operational Cyber security (Protecting Physical Controls)

• The Problem: Connecting physical water equipment (pumps, chemical valves) to internet servers creates cybersecurity vulnerabilities. A compromised system allows hackers to disrupt water treatment and endanger public health.
• Solution: Systems can be protected using three key security measures:
  • Zero Trust and Encryption: Every user and device must continuously verify their identity, and all transmitted data must remain secure.
  • Data Diodes (Unidirectional Gateways): Hardware that allows monitoring data to flow outward while physically blocking external hacker signals from entering.
  • Air-Gapped Safety Fallbacks: Independent, offline safety systems that instantly override AI commands if chemical levels exceed safe thresholds.

Climate Change Adaptation and the Future Way Forward

Climate change is driving severe challenges like heavy rainfall, flooding, and droughts. AI helps water treatment plants adapt to these shifts.

• Physics-Informed AI: These models do not rely solely on historical data; they incorporate physical laws governing water flow, quality, and energy use, preventing impossible or incorrect decisions even under changing climates.
• Plant Operator Assistant AI: All plant operational manuals are fed into an AI assistant. During emergencies, operators can ask direct questions—such as "Why did pH spike at 3 AM?"—and the AI instantly explains the cause and remedy.
• Eradicating Micro-Pollutants and Persistent Chemicals (PFAS): AI precisely schedules advanced oxidation processes to destroy complex elements like pharmaceutical residues, microplastics, and persistent chemicals.
• AI Contribution in Climate Crises:
  • Heavy Rains and Floods: Weather forecasts allow AI to anticipate turbid water 5–6 hours in advance and prepare chemical dosing.
  • Droughts and Water Shortages: Early detection of toxic algae blooms allows membrane systems to be controlled for recycled water reuse.
  • Excess Energy Usage: Energy-intensive tasks are scheduled during low-demand periods to avoid overloading the power grid.

Conclusion

Artificial Intelligence is transforming the water treatment sector. Replacing reactive troubleshooting methods with autonomous systems that predict and resolve issues before they occur is now possible through AI.

Integrating AI across coagulation, sedimentation, filtration, and disinfection stages conserves chemicals, reduces energy costs by up to 30%, and protects the environment. In the coming years, AI-driven water projects will serve as the cornerstone of sustainable, safe, and climate-resilient water management worldwide. AI replaces traditional workflows with predictive automation, ensuring an uninterrupted supply of safe water and energy savings even during extreme weather events. As smart AI tools advance, AI-driven automation will form the backbone of sustainable, resilient, and secure water systems globally.

The Well at Katanga

When Adile was seven, her mornings began before the sun. She would wake in the gray dark to the sound of her mother gathering the jerrycans, and together they would walk the two hours to the nearest water point that still had water in it — a muddy seasonal stream shared with cattle and, some mornings, with the fevered bodies of children too sick to be carried any farther.

Adile did not go to school that year, or the next. There was no time between fetching water, caring for her younger brothers when the fever came, and helping her mother grind what little maize the family could spare from the garden. Malaria moved through their home like a second family member, always present, always taking something — a week here, a season there, once, when Adile was nine, her brother.

She remembers the year the drilling rig came to Katanga village. She remembers it the way people remember weather events — the day the rains changed. Men in orange vests measured and dug, and one afternoon, water came up clean and cold from deep in the earth, and the whole village gathered around the borehole as though it were a birth.

Her mother was elected to the new Water User Committee. Adile remembers being embarrassed by how proudly her mother carried the little cash box after collections, how she stood at village meetings and read out numbers — money in, money spent — in a voice that did not used to exist in her.

The whole effort, Adile came to understand only as she got older, had been carried by the Rotary Club of Yumbe — a small club of local men and women who had spent years walking the same paths the villagers walked, listening more than they spoke, before any drill ever touched the ground. It was the club's women, in particular, who Adile's mother trusted first. They met weekly, in a borrowed room or under whatever tree gave the best shade, going over ledgers and plans and the small, stubborn details of getting a borehole from an idea to clean water — who would carry the message to the district office, who would check on the gravel pack, who would make sure the youngest mothers were not left off the committee rolls. They were not outsiders arriving with answers. They were neighbors who had organized themselves to ask better questions, and to keep asking them long after the excitement of the drilling rig had worn off.

That same year, small paper-wrapped devices appeared in the corner of their sleeping room, and a Village Health Team volunteer showed her mother how to fix them to the wall, one for the big room, and explained why the family should keep sleeping under their nets even so. Adile did not understand the chemistry of it. She only understood that the fever came less that year. And the year after, it came almost not at all.

With the hours no longer spent walking to the stream, and with her own health no longer a coin flip each rainy season, Adile went to school for the first time at eleven, sitting among children years younger, her legs too long for the bench. She was ashamed until she wasn't — until the numbers began to make sense to her, and the letters stopped swimming, and a teacher trained in a new way of teaching sang the alphabet with the whole class in a call-and-response that made even the shyest children answer.

Her mother, meanwhile, had joined the savings group that met under the mango tree on Thursdays. Small coins became a small loan became three goats became, eventually, a stall at the trading center selling dried fish and soap. The household that had once measured its year in fevers began to measure it in small surpluses — a second set of school uniforms, a tin roof to replace the thatch, seed for a second planting.

Adile grew up inside that slow unwinding. She finished primary school. She became, herself, a member of the Water User Committee her mother had once served on, one of the women whose names were now written into the record book without anyone treating it as strange. She married a man from a neighboring village who had grown up under his own borehole, in his own version of this same quiet transformation, and they built a house within walking distance — not carrying distance, walking distance — of clean water.

Her children do not know what it is to wake before dawn for water that might not be there. They do not carry the particular exhaustion of a mother who has spent a childhood being sick. They go to school because school is simply what children do, not because someone spent years insisting it should be so. Adile watches her daughter do her letters at the kitchen table by the light of a solar lamp, unhurried, uninterrupted by fever, and she thinks, without quite having the words for it, that the good years did not arrive by accident. They arrived because a well was dug, and a room was treated, and a woman — her mother — was handed a cash box and trusted to hold it.

It is a small story, hers. It will not appear in any report. But multiplied by sixteen villages, by three thousand households, by seventeen thousand people waking up on a given morning without walking two hours for water that might make them sick — it is also, quietly, the whole story.

What Adile could not have seen, from inside her own small life at Katanga, was how far the shape of it would travel. The district water office kept the records. Other clubs visited, asked questions, copied the ledgers and the committee structures, adjusted them for their own soils and their own rivers. What had worked in Yumbe was, item for item, inexpensive — a handpump, a length of pipe, a cheap insecticide device, a notebook, a savings box — and it was that cheapness, joined to how well it worked, that made it spread. NGOs took notice first, then UNICEF teams evaluating water and health programs across the region, then ministries in neighboring countries looking for something that did not require a large budget to try. Across Uganda, and then into parts of Kenya, Tanzania, and beyond, the same quiet formula reappeared under different names: a borehole paired with a women-led committee, a health team paired with an affordable disease-prevention tool, a savings group paired with basic literacy, all of it owned and run by the community itself once the first hard year was over.

None of it replaced Adile's own memory of the day the water came up clean. But she would live to see, in the accounts of visitors and in the occasional radio broadcast, that her mother's cash box and her own place on the committee rolls had become, without either of them intending it, a kind of pattern — one small women's group meeting weekly under a tree, multiplied a thousand times over, teaching a continent something it had perhaps always known and simply needed to see proven: that the most durable change is the kind that costs little, asks permission, and hands the ledger to the women who will actually keep it.

Water Footprint: Concepts, Assessment Methods, and Applications

Rtn Rajendrakumar V Saraf Director WASH RAG

Abstract

The Water Footprint (WFP) is an analytical framework—analogous to the Carbon Footprint—used to quantify direct and indirect freshwater consumption and pollution across the Raw Material–Process–Product–Consumer supply chain. Categorized into Green, Blue, and Grey components, WFP evaluates direct rainwater utilization, finite surface/groundwater depletion, and effluent-driven pollution loads. This paper examines WFP calculation methodologies, component dynamics, and domain-specific applications to inform sustainable water resource management and policy frameworks.

Introduction

Rapid economic expansion, demographic shifts, and industrialization have placed unprecedented pressure on global freshwater systems. Traditional water resource assessments often focus solely on direct extraction at the user level, ignoring the extensive indirect water usage embedded within supply chains.

The Water Footprint (WFP) addresses this limitation by measuring the total cumulative volume of freshwater utilized directly and indirectly across the entire lifecycle of a product, process, or service. WFP is categorized into three mutually exclusive components:

Green Water Footprint: The volume of rainwater stored in the soil mantle as soil moisture (green water) that is consumed through evapotranspiration or incorporated by vegetation during growth.

Blue Water Footprint: The volume of surface water or groundwater (lakes, rivers, aquifers) consumed—defined as water evaporated, incorporated into a product, or abstracted without returning to the same catchment area within the same cycle.

Grey Water Footprint: An indicator of freshwater pollution, calculated as the volume of ambient receiving water required to dilute pollutant loads to concentrations below prevailing water quality standards.

Component Analysis and Mathematical Formulations

Green and Blue Water Dynamics

Agricultural systems are heavily reliant on rainfall However, the expansion of intensive crop production and shifting precipitation patterns have driven an increasing reliance on surface and groundwater extraction.

This transition from green to blue water dependency creates systemic risks:

• Accelerated depletion of unconfined and confined aquifer systems.
• Interstate and inter-boundary water allocation disputes.
• Industrial downtime and supply chain halts during prolonged drought events (e.g., the 2013 operational shutdown of the Parali Thermal Power Station due to acute river basin depletion).

To ensure environmental sustainability, resource management strategies must focus on maximizing green water productivity through soil moisture preservation and rainwater harvesting, while simultaneously minimizing blue water abstractions.

Grey Water Footprint Assessment

The Grey Water Footprint quantifies the assimilative capacity of water bodies needed to absorb point and non-point source pollution. It is mathematically formulated as below

The simplest way of calculation of Grey Water Footprint is as below,

 Product Water Footprint Framework

Product Water Footprint Framework

Indirect & direct Consumption: Accounting for upstream and downstream water requirements across the entire supply chain gives the Blue Water footprint. When the Grey Water Footprint at each stage is added it becomes the total Water Footprint.

Water Footprint of energy: It depends on the type of the Power Plant. In the case of a Hydropower plant, the Water Footprint of the energy produced will be almost negligible. Water Foot Print of energy generated from a Thermal Power Plant will be large as it requires water for Soft & DM water generation, coal handling, steam generation, Ash handling, Cooling tower & domestic activities etc. The summation of all these, plus the Grey Water Footprint, will yield the Water Footprint of energy.  Once it is known Water Foot Print of energy consumed in the process can be calculated

Water Footprint of Water: The water is pumped from the source to the Water Treatment Plant (WTP). From the WTP, it goes to the consumer's tap through the water distribution system. Water is lost in pipeline leakages, as drain out from the sedimentation tank, and as backwash & rinse water from the filter. Leakages must also be accounted for in the distribution system. Leakage varies from 0% to 20% depending on the location. 1000 liters of water delivered accounts for 1250 liters of Blue water. Thus, leak detection and control must be taken seriously to reduce the Water Footprint of Water.

Water Foot Print of Product

Milk:  In milk production, indirect Water Foot Print incorporates water requirement of feed crop cultivation, livestock care, transport fuel energy footprints, processing, refrigeration, and consumer activities & grey water foot print at each stage.  It is shown in figure below.

Water Footprints of some products are given in the figure below.

Specific Footprint Applications

Policy Applications and Policy Recommendations

1. Virtual Water Trade Policy: Regions facing severe blue water scarcity can align import/export policies to import high-WFP commodities (e.g., water-intensive agricultural grains) from water-abundant regions, effectively preserving local groundwater reserves.
2. Standardized Eco-Labeling: Establishing standardized WFP metrics enables consumer-facing eco-labels. This incentivizes manufacturers to systematically reduce water footprints across supply chains to maintain competitive advantage.
3. Industrial Closed-Loop Integration: Utilizing Grey WFP metrics as a primary sustainability benchmark drives industries toward Zero Liquid Discharge (ZLD) systems, drastically reducing raw water intake and ambient receiving body contamination.
4. Urban Rainwater Harvesting: Capturing rainwater directly at the point of use reduces the demand on municipal surface storage dams and minimizes the energy/chemical footprints of centralized water purification plants.

Conclusion: Integrating Green, Blue, and Grey Water Footprint assessment standards into national regulatory frameworks is a critical imperative for resolving acute water scarcity. Beyond localized water balance metrics, WFP provides a holistic framework to audit cross-sectoral supply chains, optimize virtual water trade, direct corporate ESG policies, and implement resource-efficient management practices across industry and agriculture.