As the water industry progresses its capital investment programme to upgrade and manage its ageing asset base, the conventional view of asset management is being replaced by a more holistic approach that brings together data, people and process and takes advantage of digital technologies. Digital twins form an important part of this approach, and their use will help to drive forward the most efficient and effective way to manage assets and deliver greater value.
Understanding the impact of a decision beyond traditional physical asset bases will become increasingly essential, from social value and natural capital to financial impact.
DATA-DRIVEN DECISIONS
Digital twins have been established for a few years and are typically referred to as a virtual representation of a physical object, process or system that will enable a user to understand and predict behaviour, plan interventions, and optimise performance in a safe, information-rich environment. However, it’s important to recognise that a digital twin is not just technology: it is a transformational project, enabled by technology. Ultimately, this is an integration of data and information and not necessarily a single product, so an agnostic approach is needed.
Delivery of a digital twin itself can be split into three main phases: the initial discovery phase focuses on whether it is feasible to use a digital twin and what its value and benefits will be. Following this, the next phase - implementation - develops and delivers the digital twin through to benefits realisation. The final lifecycle management phase covers ongoing development and management to ensure an enduring capability, adaptable to changes in technology and business challenges.
The overall approach is focusing on where most value can be released, aligned to the priorities of a specific project. The typical digital deliverables generated at this stage can include investment planning, operational optimisation, engineering design and delivery and maintenance strategy.
To understand the value of a digital twin, a comprehensive benefits analysis must be undertaken, informing both vision and key objectives. This identifies quantifiable business benefits, grouped into strategic-level areas. These are then mapped against the user and their requirements and capabilities, as well as the work packages and scope, giving a line of sight from any item through to its quantified benefits.
A detailed cost-benefit analysis (CBA) can then take place, which is powerful when it comes to justifying the investment or understanding the impact of changes to a programme. It can also identify indicative timescales for the first release of value or benefits and can be used to estimate return on investment timescales for a given project and make up part of a phased implementation plan, which includes establishing the minimal viable product (MVP).
BECKTON SEWAGE TREATMENT WORKS
Atkins worked with Thames Water and Explore AI to develop a digital twin for one of the largest sewage treatment assets in Europe, Beckton Sewage Treatment Works (pictured above and left), with the aim of enabling optimal investment decisions and improving ongoing operational costs despite the changing drivers challenging the industry.
The ASP2 effluent stream was identified as the MVP asset in scope for the project. The stream takes around 20% of the site flow and offered early benefits realisation when assessed alongside operational user stories.
Defining the MVP enabled identification of key capabilities to be delivered: at Beckton, these were a simulation model, performance dashboards and a 3D representation.
Performance dashboards allow visualisation of both the predicted performance from simulations, and the actual measured performance, by integrating several existing sources of data to build a single holistic view of historical, current and predicted site data for the first time, generating extensive and rich information to inform decisions.
3D representation is a useful and powerful element of digital twins and is often created using drone surveys and raw data. Details from the performance dashboard and simulations are linked to objects in the 3D model, providing a more interactive way for the user to view information.
Model simulation allows the user to simulate a huge number of different scenarios covering different flow and load levels, varying tide levels, and assets in and out of service. These can be modelled over any time domain, with predicted outcomes available for visualisation.
At Beckton, the simulation model brought three engineering models together for the first time and integrated them in the cloud. It uses robots to manage the complex interfaces and interacts automatically between the models, enabling operational scenarios that determine compliance and outcome of a particular set of circumstances.
SHARING IS CARING
It is essential to bring users along on the journey. At Beckton, operations were the focus and selected as test users for the product: its feedback is used to update and improve the product, meaning planning is based on real scenarios, ensuring accuracy in planning, scope and delivery.
At Beckton, the digital twin was used to identify opportunities and calculate benefits around several benefits cases: energy optimisation of the process, blower efficiency optimisation, maintenance interventions and future investment planning. The twin identified operations expenditure and capital expenditure benefits associated with each case while predicting the impact on compliance risk.
The project will also inform Thames Water’s digital twin roadmap and the digital transformation requirements needed in its business plans for the upcoming PR24, the regulatory framework in which water companies set out strategies and costs for a five-year period.
This solution-focused approach has created a transferable blueprint for the region and the water sector more broadly. It is also the first working digital twin for a sewage treatment works that addresses a wide range of user stories and capabilities within a single solution, allowing allow future integration with other twins.
This kind of development of engineering models will benefit the industry going forward, giving digitally integrated products with functionality and granularity than before.
BOX: CUTTING CARBON EMISSIONS
Atkins is also using a digital twin in a UK rail project, with the specific aim of reducing energy use. Network Rail is using Reading railway station as a pilot project to see if these techniques can significantly reduce energy use; savings of around 20% have been predicted. Sensors will capture real-time data on energy usage at points throughout the station, and this data will be fed into the Computational Urban Sustainability Platform (CUSP) developed by Cardiff University. Once baseline figures have been created, CUSP can predict the impact of specific efficiency measures on energy savings (for example dimming lights in parts of the station that are not in use), and also how they affect customer safety and the experience of being in the station.