Data is all around us, for years even the water sector has been collecting data reams. Agencies, utilities, consultants and vendors have awakened to activate the latent potential of data resources to manage maintenance and predict water flow only in the last few years. The switch to leverage water information digitally has been advantageous with systems, coupled with historical datasets and additional sensor data to create personalized digital dashboards and water agency applications. This digitalization provides a better awareness of their services, linear asset dynamics and system behavior for water/wastewater utilities to operate more efficiently by reducing loads and pressures.
With the boost in omnipresent computing devices, lower-cost sensors collecting and transmitting data, new analytical tools, and cost-effective data storage options, utilities can now catch more data in real time at a lower cost to enhance the field and plant efficiency. In an industry typically resilient to the introduction of innovation, a few key factors drive and enable the shift to information-based value creation, such as making accurate predictions of the future with increasing data, system and device functionality, appreciation of data scientists ' value, and preparing efforts to handle the transition efficiently.
Although there is an availability of huge data from a typical wastewater aeration pumping system, it is usually not used to obtain valuable information to foresee the performance of its assets. To pinpoint useful patterns and designs using statistical and computational intelligence versed algorithms, an information-driven approach is necessary. The important challenge is to interpret data and put it to work to benefit the operation.
The data science platform also offers a visual representation of pumping efficiency aspects that help to build an accurate image of performance and reliability anticipated. The effort is also predicted to eradicate the need for additional supply and deployment of smart instrumentation, hardware and software typically needed to collect additional data points to accomplish the desired results due to the advanced prognostic models in use.
This transition of digital and machine learning will greatly improve the way water/wastewater is collected, shipped, treated and used as opportunities for improving infrastructure capital, improving public health, predicting asset failures and improving water management. In order to make this profitable, collaboration between large groups of stakeholders is needed and rewards must be given to achieve goals.
As it works to make sure universal access to safe drinking water, water for business economic growth development, the water sector will reap the benefit of other sectors in embracing new solutions
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