Water & Wastewater Treatment Magazine
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www.wwtonline.co.uk | WWT | DECEMBER 2019 | 27 l Data — Dynamic asset data is then ag- gregated and combined with data from other systems across the water utility, such as cost, workforce management, asset specifications and hydraulic data. Connections to external data feeds, such as weather and social media from customers, will be included. l Integration — Sensors and external data feeds are interoperable between the real world and the digital twin via integration tools - ultimately giving the digital twin supervisory and control capability. l Analytics — Machine learning and wid- er artificial intelligence will provide us- ers with the ability to scenario options across the asset lifecycle - assisting in complex decisions on multiple complex systems giving actionable insights. l Visualisation — Analytical insights will be produced in a way that any user can visualise them in a contextual, consist- ent and intuitive form. l Action — If an intervention is required in the real world based on analysis, the digital twin will initiate the action by way of control logic, which may require user approval, that will ultimately make a change on the physical asset or system. The approach to digital twin implementa- tion will be iterative and use agile meth- odologies with the following key phases: l Agree the Vision – what are the out- comes we want a digital twin to meet and for whom. l Discovery – User research, discover and establish data requirements and define analytical and visualisation requirements. l Proof of concept – to establish and demonstrate value in a time-bound and controlled environment. l Enterprise implementation – a phased rollout prioritised on cost, risk and benefit. Requirements of the digital twin will be integrated from design en- suring maximum value can be obtained from a digital twin over the life of the installed asset base. A water utility digital twin will have aims aligned with business outcomes across operational, tactical and strategic time horizons. These aims are in recognition that the digital twin will iterate over time in terms of applicability and functionality. Achieving these aims will ensure the digi- tal twin offers tangible benefits across the asset lifecycle from acquisition to disposal in a cyclical form. The aims can be segmented into two functional business groups, below, recog- nising that a function may reside in one or both groups: Enabling business function group l Align where practicable to business benefit and any relevant industry stand- ards and methodologies as and when they emerge. l Comply and help facilitate any relevant internal and external governance and audit processes including clear owner- ship and accountability of policies and procedures. l Provide by business function and role a defined means of interacting with analysis outputs that meets user re- quirements utilising available technol- ogy, so†ware and data. l Inform and validate through quantita- tive and qualitative means descriptive, predictive and prescriptive analysis of performance and efficiency across assets, people, treatment processes, business processes and projects. l Enable existing business systems and/ or their outputs dependant on business need to be interoperable by means of master data management method- ologies and application programme interfaces. l Provide a representation of business- wide data sources - either directly or in- directly - that meet defined data quality performance measures of completeness, accuracy, reliability and timeliness. l Facilitate an initial environment of greater data sharing, visibility and accessibility and provide an insight into longer-term data sharing oppor- tunities with external organisations as the development of digital twin asset management grows. Consuming business function group l Single point of truth for a defined set of outputs by business function and role giving holistic coverage of the organisa- tion. l Provide a level of detail suitable for the decision or insight being sought. l Ability to undertake various analysis methods including scenario model- ling against a variable set of options encompassing assets, people, treatment processes, business processes and projects. l Access to internal and external datasets to enable informed evidenced based decisions including forecasting of asset and treatment performance, efficiency and events. l Ability to share and interact analysis with internal and external stakeholders via multiple mediums and formats in an intuitive way. l Facilitate evolving requirements over time giving flexibility to existing and emerging business functions. Data quality is key to understanding and managing the corporate risk process. As such data is an essential and fundamen- tal component to any successful digital driven organisation seeking to optimise the use of technology to manage an asset base which continues to grow in complex- ity and optimisation opportunities. To add structure around data quality for a digital twin a Data Quality Framework/ strategy is necessary for understanding current data quality/issues and how data enhancement will be made over defined time periods. An essential component of the Data Quality Framework is the development of a data quality scoring process and mechanism. A digital twin roadmap is designed to bring the concept to fruition through iter- ation and learning at each stage, starting small in scale in areas that are deemed to offer best value for the effort applied, then increasing this scale as the digital twin matures and develops to enterprise level. Through implementation of the roadmap we will be aiming to provide benefits as early as possible to business functions and their users.