Professor Julian Morris

CPACT related research Interests @ the University of Newcastle

(Morris and Jie Zhang)

On-going R&D at Newcastle:

• Process Performance Monitoring and Multivariate Statistical Process Control:
• Dynamic process performance monitoring
• Model-based performance monitoring
• The integration of spectral and process data
• Intensified Processing and Digital Bioproduction – a new large European project researching bio-intensified bio-polymer and bio-surfactant production
• Rapid Prototyping in Biopharmaceuticals for Fermentation Process Development, Optimisation and Production, with Strathclyde Biosciences (Brain McNeil), miniaturised sensors (David Littlejohn), UCL Biochemical Engineering (Frank Baganz) and Newcastle (Julian Morris) – industrial consortium being put together if anyone is interested
• PAT and the extraction of maximum information from messy spectroscopic data (Dr Zengping Chen)
• Advanced Chemometrics for the extraction and elucidation of chemical and biological information from spectroscopic measurements contaminated by spectral variations, instrument variations and variations caused by fluctuations in both external process variables and physical properties of the materials being measured (Dr Zengping Chen)
• Knowledge Transfer Partnership with Nexia Solutions and the British Nuclear Fuels Group on Predictive Modelling and Performance Monitoring
• Knowledge Transfer Partnership with GSK of Process Analytics (Strathclyde and Newcastle)
• Advanced Data Mining – Commercialisation of an advanced multivariate data mining toolbox with AJM Consulting (MS2)
• Neural networks for process modelling and control
• Data based nonlinear process modelling
• Soft-sensor and inferential estimation
• Modelling and control of batch processes
• Iterative learning control of batch processes
• Batch to batch control based on recursively updated nonlinear PLS models
• Reliable optimisation incorporating model prediction confidence bounds
• Inferential feedback control
• Monitoring of processes with multiple operation modes using principal angle and multiple PCA/PLS models
• Hybrid modelling of chemical process by combining simplified first principle models with neural network models.
• Recursive nonlinear PLS
• Process monitoring using nonlinear principal component analysis

(Suresh Thennadil)

Spectroscopic Analysis and Monitoring of Powders, Liquids and Suspensions (SAMPLS)

Research Programme: Work at the SAMPLS Laboratory is aimed at developing a measurement-interpretation platform that can lead to breakthrough technologies for a range of problems in online process monitoring of particulate systems.

Research Expertise:

• Optical property measurements using Integrating Sphere:
• Total Diffuse Transmittance, Total diffuse Reflectance, Collimated Transmittance in conjunction with the inverse Adding-Doubling method to extract bulk absorption and scattering properties.
• Physical information (refractive index, particle size, shape and microstructure): Using Mie theory for spherical and T-matrix method or Raleigh-Gans approximation for non-spherical particles.
• On-line implementation using a flow through system is being planned.
• Optical property measurements using spatially resolved system (reflectance at multiple source to detector distances). A spatially resolved spectrometer system has been built in-house – capable of up to 9 simultaneous measurements.
• Faster measurements than the integrating sphere method.
• Easier to implement online/inline/in-vivo measurement system.
• Novel semi-empirical methodologies for scatter correction in order to improve the performance of calibration models for estimating chemical properties.
• Application of these methodologies to emulsions and emulsion polymerisation, Fermentation reactions, powder mixtures.