PTQ Q2 2023 Issue

Figure 4 Kits support data visualisation

Extendibility The kits are designed so that coding and configuration, which support the basic kit structure and tools, such as the GapToPotential map, are common to multiple kits installed on the same server. It is thus simple and quick to add further kits for different process units or for additional sites over time. A navigation panel allows users to easily switch between view - ing the performance of different units and/or sites. The kit’s analytics need to be reasonably generic, as the level of sophistication built into them can be limiting. At some point, it may be worth investing in higher fidelity ana - lytics, perhaps in the form of a process digital twin (DT). The kits can be extended to support DT visualisation (see Figure 4 ) and provide a quick-to-install, out-of-the-box set of dash - boards to help with simulation model and refinery linear pro - gram (LP) model assurance, along with enhanced process monitoring and opportunity identification. Future development Initial feedback on this Value Kit approach has identified a need to make the knowledge base (which has been built with a degree of customisability) extendable by the business owner so it can identify scenarios specific to the unit and/or site in question. The knowledge base could be customised to include, for example, information on the operating enve - lope of a unit and to issue relevant advice when the opera - tion moves outside that envelope. The analytics required to do this are typically not challenging. More challenging is automating a way to extend the knowledge base without the need for an IT professional and implementing the appro - priate level of security to ensure only authorised personnel make the changes. KBC has seen most refiners preferring to keep data on-premise or in their company cloud. However, this kit- based approach to data analytics and visualisation is just

as appropriate for cloud-based solutions as for on-premise systems. Currently, KBC is developing a solution for remotely monitoring green hydrogen production facilities where data are sent to the KBC/Yokogawa cloud (via remote telemetry) from where the client will be able to access the Value Kit solution via a portal. The same advantages of scalability, cost and time to implement, and embedded value-added analyt - ics exist for a cloud-based kit as for an on-premise one. Conclusion The best way to evaluate the cost-benefit of process moni - toring solutions in terms of the level of analytics used and the amount of data analysed will always be a source of debate among industry professionals. Using a kit-based approach, which focuses on the key data and incorporates analytics and impactful graphics in a stan - dardised package, is a pragmatic starting point to improve decision-making and add value to process operations. These kits address many industry challenges, both in making better and faster decisions and simplifying and accelerating solu - tion delivery. There will always be a need for bespoke solutions. For cer - tain situations, custom analytics may be required to address a specific issue. For larger organisations that can benefit from economies of scale and have sufficient resources to design and implement, such a solution may cost no more to implement in the long term. However, even in these cases, a kit-based solution can be considered as a pragmatic, quick- to-deploy option that generates benefits in the short-term while a long-term solution is considered. Philippa Hayward is a Pre-Sales Manager in Digital Technology at KBC (A Yokogawa Company). She has over 30 years’ industry experience and works with clients to select and develop solutions to help leverage their data to make better decisions and optimise their assets’ value.

PTQ Q2 2023