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February 3, 2021

In response to COVID-19, Dr Anthony Fauci, American physician and immunologist, outlined two major courses of action for the future. One is to build a response system to better prepare mankind for future outbreaks, and the second is to fix the current limitations of natural resources data management systems to deliver evidence in real-time. One such proactive response in the wastewater industry is scientists running ‘poop analytics’ to identify SARS-CoV-2 genetic material in wastewater. It determines if members of a community are infected, tracks the source of infection, and estimates the number of people affected. The future potential of such data is immense, and the use of analytics together with other digital technologies will help organizations unlock their true potential.

Traditionally, a non-lucrative business, wastewater management is a neglected area in the water industry; and yet, it is one that needs attention both from public authorities and private industry bodies. Urban planning departments, especially in densely populated cities globally, are faced with a complex set of challenges when it comes to wastewater management. For instance, according to data collated by the World Resources Institute, nearly 40 million liters of wastewater enter India’s rivers and other water bodies every day. Only a fraction of this wastewater is adequately treated, emphasizing the need for a long-term approach to wastewater management in large cities. Sadly, investments made in this area are primarily driven by regulatory norms. A quick search showcases ample cases of hefty fines issued by regulators, like the example of Southern Water in the UK, which was fined in 2019 over its failure to adequately manage sewage treatment sites and to treat wastewater.

Existing wastewater treatment methods

Currently, wastewater treatment is done by traditional methods in treatment plants, where waste materials along with unnecessary and harmful chemicals are removed and clean water is released into rivers and other water bodies. Sewage treatment plants employ conventional methods such as aeration, sedimentation, filtration, and the use of microbes to convert wastewater into a clean effluent. Although these methods are highly effective, the process is considerably slow and requires optimization at various levels.

While stringent regulatory requirements have compelled companies to deploy digital technologies to upgrade their wastewater treatment process for the long-term, COVID-19 has pushed firms to invest more. With increasing cost pressures, companies are now forced to invest in digital technologies like internet of things (IoT), low-cost sensors, and more, which will help in wastewater analysis.

Leveraging new-age technologies in wastewater management

In the past, data sets generated from machine-enabled sensors, instruments, supervisory control and data acquisition (SCADA) or traditional legacy systems were mostly used for operations and maintenance. However, today, the wastewater and water industries collect huge amounts of data related to energy consumption, chemical consumption, asset operation condition, sludge production, sludge transport, sewer flows, storm events, smart meters, and more. pH analyzers, biochemical oxygen demand (BOD) sensors, and turbidity analyzers are some instruments installed across various sites that measure and produce data on the level of chemicals and other physical characteristics of wastewater.

With these significant investments in data acquisition, companies can explore using artificial intelligence (AI), machine learning (ML), and automation for exponential value realization. For example, traditionally, rule-based triggers are set for machine temperature with actions, like a trip is activated if a certain temperature is reached. In its place, an AI/ ML algorithm can be trained on historical data to identify patterns of machine temperature during operations, which in turn can identify potential hazards regardless of the control thresholds. This could improve system availability, productivity, and throughput.

To become digitally reimagined enterprises, wastewater companies must put data at the center of their digital journeys. Such data capabilities lie at the heart of a wastewater heartbeat management system (WHMS).

                                               Figure 1: The wastewater heartbeat system

Such a system monitors the condition of equipment for effluents, spills, etc. in real time and also has many use cases:

  • Predictive maintenance of network

  • Root cause analysis of various events

  • Prediction of spills

  • Sewage blockages

  • Anomalies in networks

  • Early warning mechanisms for emerging outbreaks

  • Energy optimization

  • Workforce and parts management

  • Autonomous chatbots for customer connect

A WHMS enhances the reliability of wastewater processes by detecting emerging issues early on, predicts remaining equipment life, and helps firms plan and prioritize decisions based on available resources. This improves regulatory compliance, increases customer satisfaction, and reduces operational expenses.

The WHMS can act as an early warning system by tracking potential public health threats by leveraging data and digital technologies. This can reduce laborious and expensive testing of individuals in targeted problematic areas. This, in turn, can minimize the impact of strict lockdowns on people’s daily lives in the event of new outbreaks and speed up the process by which diseases are detected and controlled, ultimately creating healthier communities.

Anirban De is a Business Consultant for Manufacturing and Utilities, TCS. He has nine years of cross-functional experience in consulting and IT across manufacturing and utilities. Anirban has worked extensively in the areas of data monetization, data analytics, AI/ML application, data platform, and product management. He has been guiding customers to adapt to emerging business models

Omar Sharif is a Consulting Partner for Manufacturing and Utilities, TCS. With a specialization in creating value from data, he has extensive experience in analytics strategy, digital transformation, data science, AI/ML, product innovation, and data monetization. Omar has helped customers in manufacturing and utilities become insights-driven organizations.

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