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• Scope 1 covers emissions from sources that an organisation owns directly – for example from burning fuel in a cracking furnace .

• Scope 2 are emissions that a company causes indirectly when the energy it purchases and uses is produced . For example , the emissions from the generation of the electrical power used to power the industrial plant would fall into this category .

• Scope 3 covers emissions that are not produced by the company itself , but by those that it ’ s indirectly responsible for , up and down its value chain . information to drive carbon intelligence . The combination of data fed into models and analytics – displayed through simple self-serve systems – allows your company to improve operational set points and automate power source selections , reducing emissions and energy consumption .

Digital twin for operations

The digital twin runs a parallel operation to your plant in the digital environment . When it is combined with real time operational data it is able to predict behaviour with accuracy through first principle models and artificial intelligence , AI . These insights then give your teams a window into ways to improve your processes and optimise operations .

Stephen Reynolds , Industry Principal Chemicals , AVEVA

As complex it can be to monitor and reduce GHG emissions , you can start simple and get some immediate results . For example , by using digital technology to break down information silos , it is possible to covert raw plant data into contextualised information to enable the quick calculation of GHG emissions , energy consumption and carbon intensity and have some quick wins .

Through partnering data with different advanced applications , you can enable digital engineering and operations to positively transform your plant efficiency and turn those initial quick wins in significant medium and long term benefits .

A possible approach is energy and emissions management through rigorous modeling to calculate your emissions reduction potential to stabilise and optimise operations . The ‘ reduction potential ’ informs your teams of the true actionable gap that can be addressed and the limits that can be reached .

Improving the energy mix to maximise your use of clean energy is desirable but not every plant has this option . If your plant has this flexibility , you can use optimisation models to automatically maximise the use of clean energy depending on your plant operating conditions .

Improving reliability

Leveraging the power of the digital twin

Embarking on a digitisation strategy allows you to boost operations , increase asset reliability and futureproof your facilities . A digital twin , which replicates your company ’ s assets in the cloud , empowers you to make informed decisions on how to best optimise the engineering and design of low carbon processes and plants .

Ultimately , your company is empowered to rigorously contextualise data to help modify your plant ’ s carbon footprint and manage carbon off-sets and credits – now and into the future .

A Lifecycle Digital Twin tech stack runs your plant in the cloud , converting all its raw data into valuable

The next step is to improve asset reliability to minimise excursions . Many industrial processes generate more GHG emissions during startup and shutdown procedures , not to mention during accidents , so minimising or eliminating unplanned downtime is critical for keeping your carbon footprint under control .

Artificial intelligence , AI and machine learning , ML models enable early warning notifications that can prompt your staff to act in time of preventing unplanned shutdown and accidents . To take a step further , you can add inferences from rigorous models and calculate the RULE , Remaining Useful Life Estimation , adding predictions accuracy and anticipating notifications even further . This approach combined with an asset strategy optimisation can ensure maximum reliability at minimal costs .

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