The complexity of managing water supply, while always difficult, is increasing. The rapid growth of urban populations and more extreme weather events, both drought and flooding, often associated with climate change, are leading to an increased focus on the sustainability of our raw water from rain, wells, rivers and lakes.
Meanwhile, water utilities continue to struggle to ensure they have the budgets needed just to keep their long-lived assets well maintained and operational. Water has historically been undervalued, with societal expectations that it is a fundamental right, not a commodity to be paid for. As a result, under-investment over the life of these assets is not unusual, which raises issues around water quality and availability. It is increasingly common to see incidents such as sewage overflows that potentially pose acute water and wastewater hazards and impact public health and the environment.
Having a consolidated and unified end-to-end operational view of the performance and health of utility assets is one of the biggest challenges facing utilities. This is largely a legacy of the fragmented way in which systems have grown over time. Sub-systems are often purpose-built solutions optimized for specific use cases, which must be managed separately. They feature proprietary operational systems with their own way of discovering devices, maintaining security and running diagnostics and analytics, if these capabilities are even supported. Add to this proprietary communication protocols, and it makes it difficult to integrate an end-to-end solution at all, let alone deploy it at scale.
The complexity has grown over time as the result of incremental innovations, each one creating its own efficiencies; nonetheless, the net result today could be much more efficient if properly integrated as a whole. Digital transformation of utility systems is all about the integration of the sub-systems and the communication and sharing of data between them, enabling greater insights. Paired with artificial intelligence (AI) and machine learning, the end-to-end management of our water infrastructure will have huge benefits for meeting the business and social goals of sustainable water management.
Real-time awareness from management to consumers
The dream of many utility managers is to have fingertip access to all the metrics they need for their entire operational network. This would give them awareness of their above and underground inventories, the condition of all their assets and overall efficiency, and would help them to make better decisions based on full situational awareness. Ideally, it would also help them to predict problems and failures before they occurred and to react faster to faults, enabling them to launch crews with a full readout on the situation they were being deployed to fix.
Similarly, imagine the same kind of data being made available to the consumer. The household would become much more informed about their daily consumption patterns with the availability of more real-time granular data providing greater insights, empowering customers to change their consumption behavior to enable a more sustainable future. It could monitor appliances and help to conserve water, as well as identifying potential leaks, and manage recycling and storage.
The key to these capabilities and services lies in digital sensors, wireless communications and the integration and analysis of data using open data models and systems. There is enormous potential for innovations in a wide variety of use cases, from operations to end user consumption. But getting the most of digital’s potential without creating even more complexity will require more than a little forethought and planning about digital architectures.
Open data standards
The first requirement is to ensure that the equipment you choose is based on widely adopted and, if possible, open standards. The days of standalone, proprietary systems are over. Communications systems such as your low-power wide area (LPWA) field network need to be IP-based because this will ensure that communications between devices and the network will support the widest range of possible applications into the future.
At the software level, one of the most difficult tasks will be to reconcile the data being generated by the various legacy systems. For this, you need to adopt higher-level data standards based on open-source data sets to abstract the different asset and device types, and the protocols supported, providing normalization of data so it can be effectively utilized. Although there are many different open source and proprietary solutions to choose from, there is real interest in the water utility industry to develop interoperable, open-source systems, since this will enable greater innovation through community collaboration.
Having an interoperable way to capture and model the data from your various assets then opens the possibility of creating a digital twin of your end-to-end system. This model functions as a data platform enabling the rapid development and deployment of new software-based applications and innovative business models. It provides a holistic awareness that can be used to optimize applications and sub-systems in ways that don’t pass on inefficiencies to other parts of the system.
This openness should extend to sensing and operational device data, which should be vendor-agnostic. All utilities should be able to use common data management and analytics systems with any device, and pressure needs to be put on device vendors to commit themselves to these common standards.
These recommendations are based on the experience in other fields where the move to digital transformation is more advanced, such as in smart cities, manufacturing and transportation. They reflect a larger technological revolution, often referred to as Industry 4.0, which is driving the intelligent digitization and interconnection of all industrial and smart city infrastructure. It will transform how people, systems and devices interact.
IoT and wireless communications
One of the key technologies that is making digital transformation possible is digital sensing of physical processes. Industrial internet of things (IIoT) is the bridge between a utility’s physical and digital realms. It literally transforms the physical into the digital, which then makes it possible to model the physical process as a digital twin. Then AI and machine learning analyze the massive amounts of data being collected and find patterns, draw conclusions and recommend specific actions to be taken.
One of the challenges is how to connect the thousands of sensors and devices and have them communicate with the utility edge cloud where much of the local processing will be done. The logistics of wiring sensors is daunting, thus there is much interest in new wireless technologies.
Standards-based LPWA communications are enabling low-cost, ubiquitous access to low-power IIoT sensors and their data. Cellular technologies based on 3GPP open standards, including private wireless, with support for narrowband IoT and 5G, will be able to support massive numbers of sensors, offer improved coverage, scalability and support for low-power sensors. The advantages of cellular technologies are their reliability, security and the possibility to leverage them for other utility communications such as push-to-talk radio, drone operations, video surveillance, augmented reality and machine-to-machine communications.
Sustainable lifecycle management
The goal of digital transformation is sustainable lifecycle water management. In pursuit of this, digital collection, capture and processing can be leveraged to automate many of the processes that are currently done manually. This could be specific device management to optimized system performance, or alerting staff to potential failures, automatically adjusting water reserves to meet forecasted usage surges or responding to cybersecurity threats systematically. There are a host of other possible use cases such as leakage control, pressure management, water efficiency, water re-use (from grey water to rainwater and effluent), water conservation and demand management.
Along with automation, digital transformation is also focused on augmenting the intelligence and decision capabilities of system managers and staff. Visual, contextual presentations of system-related data can be powerful tools for understanding how the system is operating. The software analytics can be set up to make estimates of the next best action, which can help personnel to narrow down the problem and improve the quality of the decisions made.
The results of digital transformation are already exciting, but its full implementation will be nothing short of total operational transformation. By connecting water resource management, operations and citizens with supply, distribution, wastewater, and irrigation systems, water supply managers can optimize the whole lifecycle of water. This will enable more intelligent management of assets and a more holistic management approach that will improve water quality, while providing a more environmentally sustainable system.
