Challenges

Some of the major challenges that Water utility companies face include:

  1. Need to plug Revenue leakages:
    Approximately 5-10% of retail utility water is unaccounted and unbilled for. These losses can be in the form of metering inaccuracies, data capturing and reporting errors, unbilled meters, undetected leaks and unauthorized consumption. Utilities can benefit from recovering this hidden revenue, through efficient real-time monitoring and detection.
  2. Increasing pressure to meet conservation targets:
    With the rising global water stress, industries that employ water-intensive processes such as food, beverage, power, pulp and paper, are facing the heat. In addition to this, there are regulatory requirements that encourage best practices and innovative solutions for protecting ecosystems, conserving water supplies, and improving water quality. Utilities can play a major part in industrial water conservation by helping industries monitor their water usage, avoid wastage and promote recycling through less expensive solutions like Zero Liquid Discharge (ZLD) processes.
  3. Lack of clarity in scenario and operational planning:
    Many utilities do not understand revenue profiles, strategy planning, and rate structures. Utilities currently do not have the capabilities to include factors like climatic conditions, economic scenarios or historic rate revision impacts in their scenario planning.
  4. Improving operational effectiveness:
    Utilities typically need their assets (e.g. pumps) to operate at 70% or more capacity consistently. More often, degradation of performance goes unnoticed, and this leads to money wasted on unplanned downtimes, maintenance cost over-runs and increased energy costs due to the sub-optimal operating conditions of assets.

Solution

Figure 1 depicts our Solution Overview wherein we have engineered an Industrial Platform using IoT, Blockchain and AI technologies to achieve this transformation.

Our clients in textile dyeing industry, as well as power, oil & gas, pharma, pulp & paper, chemical, petrochemicals etc., generate large volumes of waste water with high salinity/ TDS that need management. In most cases, these wastewaters are discharged via a plant outlet to a surface water body, an evaporation pond, or in some cases, in deep wells. Discharge of saline but treated wastewater pollutes ground and surface waters. There are growing environmental concerns regarding such discharge practices, which have resulted in the development of Zero Liquid Discharge (ZLD) processes. ZLD promotes water reuse by the industries themselves, which is achieved through industrial effluent treatment, sewage reuse and desalination.

Monitoring of water meters at inlet and outlet points of Effluent treatment unit would monitor the flow rate and the total volume of water that is being used in the entire process. For example, if the value of outlet flow meter indicates “0”, then it infers that there has been Zero discharge.

The flow meter data gets transmitted to the blockchain through an IoT Gateway device, which in turn, is transmitted to the Blockchain Provenance backbone. The Regulator and Industry operator are the peers registered on the blockchain network. Any deviation in the flow meter reading (values greater than 0), indicate the discharge of water from outlet to water bodies, in violation of environmental regulations. Also, the blockchain helps in providing security for the IoT Gateway devices, through the prevention of installation of malicious software on the gateway devices.

The data would then be maintained in an Enterprise Data lake. Once real-time data is available, cleansed and aggregated, Artificial Intelligence techniques and feedback optimized models are executed:

  1. Exploratory Statistical Data Analysis to identify median trends of water consumption at the consumer / county (or) industry/ industry segment level and provide dashboards to indicate water-consumption against average.
  2. Customer Segmentation Analytics to segment customers into distinct water usage groups.
  3. Correlation Analysis to identify trends related to water consumption across consumer segments (e.g. impact due to number of people in household/ weather patterns etc).
  4. Predictive machine learning models using Regression/ Random Forest, for identifying water losses across loss categories, defined as per AWWA standards.
  5. Simulation Models using Monte-Carlo that will help in Pricing Simulation and Optimization. This will have features to set rate parameters including climatic factors, economic scenarios and effect of surcharge changes.
  6. Predictive deep learning models using TensorFlow to calculate residual life of assets (e.g. Impellers) based on various parameters like pump speed, flow rate, pressure, supply reservoir levels etc.

Other problem statements to address would be- Urban Water Management, using treatment plants and an efficient use of Urban Water distribution. If the average consumption for a city is pegged at a certain level, and if a household’s consumption is drastically higher, it holds ground for an investigation pertaining to leakages, or even awareness campaigns on saving water.

Benefits and ROI

Industries have benefited immensely from our Solution implementation. Some of the benefits are listed herewith:

Increased Revenue

o Prioritized meter replacement schedules

o Increased Revenue by eliminating meter discrepancies

 Conservation & Compliance targets adherence

o Reduced Compliance Risk — Comply with Bill 555, Water Stewardship Act 2010, and similar regulations

 Pricing Optimization

o Improved scenario planning by helping CXOs visualize the impact of new rate structures before ever bringing them to customers

o Adjust customer classes and rate structures for increased revenues

Improved Operational Effectiveness

o Better visibility to plant operators by way of real-time alerts (e.g threshold exceedance) and by helping reduce downtime and maintenance costs

o Savings in energy costs

Contributors : Sreesankar, CIO Knowledge Lens, Cariappa, CBO Knowledge Lens


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