Energy

QUAISR APPLICATION

De-risk scenario planning and identify trading bottlenecks

Integrate isolated component models to enable end-to-end forecasting

Challenge.: Resilient scenario generation, planning and scheduling is required to meet ambitious carbon reduction targets. Top-down strategic planning often fails to account for short-term scheduling and pricing constraints due to siloed modelling systems, coordination overheads and manual feedback loops.
Solution.: Use Quaisr to integrate existing supply, demand and pricing systems and establish end-to-end planning that can be updated daily automatically, to replace manual synchronisation once per year. Empower robust and resilient scenario planning using real-time scheduling and pricing data. Identify system bottlenecks that are typically missed without integrated simulations and sensitivity analysis.
Impact.: Make effective CAPEX decisions under uncertainty.

QUAISR APPLICATION

Cost-effective structural engineering with multi-objective optimisation.

Challenge.: Robust modelling for offshore-wind installation is challenging as multiple structural engineering and financial modelling tools must be integrated.
Solution.: Use Quaisr to automate thousands of structural simulations for statistical analysis and uncertainty quantification, accounting for environmental conditions and rare events.
Impact.: Optimise offshore foundation designs by integrating structural and financial simulations to increase structural performance while minimising material use.

QUAISR APPLICATION

Accelerate feasibility studies for CO₂ injection

Challenge.: Candidate reservoirs for CO₂ sequestration are assessed using high-performance computing (HPC) simulations and multiple models in the pipeline (e.g. geomechanical models, reservoir models, thermal models). Each detailed simulation generates large amounts of characterisation data that can take days of engineer time to process.
Solution.: Use Quaisr to connect automate and orchestrate simulations for faster scenario generation. Faster processing of results, including text summarisation and alerts for outputs that are outside of normal ranges. Apply active learning techniques to select the next input parameters to explore, and run simulation campaigns automatically from one integration platform.
Impact.: Accelerate carbon reduction targets by speeding up model driven decision making.

Additional solutions

Aerospace: Maximise fleet uptime.
Reduce energy consumption.
Reduce physical testing.; Learn more
Chemicals: Reduce physical experimentation.
Supply chain carbon footprint.
Virtual laboratory.; Learn more
Consumer Goods: Reduce physical experimentation.
Increase product shelf life.
Reduce line breakdowns.; Learn more
Defence: Verification and validation.; Learn more
Pharmaceuticals: Process selection.
Sustainability compliance.
Protocol design.; Learn more