Combining a comprehensive suite of empirical modelling tools and a simplified user interface, the platform enables users to quickly start making sense of their data.
Both linear and non-linear models are available, utilising state-of-art model identification algorithms. Models are validated using standard performance measures, sensitivity analysis and cross validation metrics. Any models may be interchanged between process monitoring, control and optimization within the same environment, to quickly develop the optimum improvement strategy.
Key features include:
RLS, PLS regression algorithms
Sensitivity analysis for Steady State, Transfer Function and Time Series format
Radial Basis Function Neural Network for Non-linear Modelling
Integration with PSE gPROMS mechanistic modelling
Linking with Python Libraries provides a user-friendly interface with NumPy and SciPy
Our Clients & Partners
Selection of our clients and key partners we work with to improve process efficiency
Throughout development to manufacturing, data is key to enable rapid response to market demand. The knowledge to enable intelligent decisions and the wisdom to optimise the complete product lifecycle is now at your fingertips. The latest release of PerceptiveAPC v8.0 delivers the next
PD2M May 18-20 2021 Virtual This is the place to explore what pharmaceutical manufacturing will look like in 2030, sharing discussions on new ways to develop medicines. How will Industry 4.0 enable us to respond to market demand, delivering flexible, modular manufacturing and bringing
18-19 May 2021 Virtual Perceptive Engineering - an Applied Materials company - is looking forward to participating in the Bionow Pharma Manufacturing Conference 2021. We will be joining AstraZeneca and Bristol Meyers Squibb in session 2, discussing the ‘Digitisation of Pharma