Faster process optimisation using ML enabled workflows
Real processes have numerous objectives. Being able to locate the optimal trade-off point where all targets are met is no easy task. Machine Learning enabled workflows make this easier than ever before.
Use previous experimental results to create models that can select the next most important experiment:
Utilise online analytics / PAT
Faster optimisation and no time waiting for lab results, by coupling the ML process with real-time analytics
Exploit machine learning and self-optimisation to automatically guide a process towards the optimum
Processes can be left unattended to find their own optimum settings, freeing up scientists and engineers for more important tasks
Use true multi-objective optimisation to establish the trade off between CQAs and environmental / economic targets
Use this data to build control models, moving from optimised to controlled in a single step. Integrate with mechanistic or hybrid process models:
Optimise process parameters in a digital twin before wasting materials on a live process
Carry out in-silico experimental campaigns to hone process parameters before making the move to a live process. Increasing confidence that the process will succeed
Discover critical variables and eliminate unimportant ones prior to an experimental campaign
Exploratory experimentation on a live process is expensive and time consuming, eliminate this burden using hybrid process modelling
If you want to discuss your project or process,
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Selection of our clients and key partners we work with to improve process efficiency
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