From limited data and interacting variables to validated operating conditions
Research conducted by BINARIS's founder before BINARIS was established.
Illustrative multivariable optimisation landscape.
Complex processes involve interacting variables, limited data and costly experiments — making it difficult to find the right conditions through traditional trial-and-error approaches.
Variables influence each other in complex ways.
Each experiment consumes time and resources.
Small datasets make exhaustive search impractical.
Machine learning and Bayesian optimisation were used to model an experimental biosensor process and identify promising operating conditions.
Collect data from designed experiments.
Learn patterns and interactions.
Explore intelligently to find promising conditions.
Suggest optimal or near-optimal settings.
Test recommendations in the laboratory.
In one evaluated biosensor optimisation task, compared with the best condition in the original experimental dataset.
Reveal relationships between variables.
Focus testing on promising settings.
Target quality, yield, cost, energy, accuracy or time.
Confirm settings through experiments, simulations, pilot trials or operational testing.
Applicable to manufacturing, engineering design, materials and formulations, energy systems, operations, sensor development and experimental R&D.
This work is presented as evidence of the founder's relevant technical experience, not as a BINARIS client project.
Use data-driven modelling and intelligent optimisation to identify better settings with fewer costly trials.