Intelligent Decision Support & Control Technologies for Continuous Manufacturing & Crystallisation of Pharmaceuticals & Fine Chemicals (ICT CMAC)

The ICT CMAC project is a five-year programme funded by the EPSRC in collaboration with a number of industrial co-creators including AstraZeneca and GSK, with the aim of creating a comprehensive Intelligent Decision Support and Control platform using state-of-the-art data acquisition, signal processing, analysis and control mechanisms, integrated within an extensive Electronic Laboratory Notebook environment that promotes best practices in data management.

The goal is to enable the migration from the established batch to continuous production of pharmaceutical products, releasing a compelling range of impacts:

  • improved flexibility, agility and sustainability
  • plant footprint reductions
  • a reduction in CAPEX of between 25% to 60%
  • a reduction in energy consumption of between 40% to 70%

A solution that combines real-time data from in-line and at-line sensors to extract quantitative information on particle shape, size and form during the crystallisation process – essential for effective monitoring and control of a continuous process - does not exist and has been the subject of extensive research by both the industrial and academic communities for many years.

The evolution from batch processing to the continuous manufacture of particulate products still remains one of the major challenges the industry is facing and is key to sustaining and growing what is a key sector for the UK economy.

The project has created a unique and complementary mix of expertise and innovation culture across two universities (Strathclyde and Loughborough), multiple departments and with harnessing the input of major industrial organisations operating across the supply chain, represent the multi-disciplinary expertise in data analysis and processing, intelligent decision support, crystallisation and particle engineering, ultrasonics and mathematical modelling required for an integrated platform solution that provides critical product quality attributes to be monitored, analysed and in turn controlled in real time.

The methodology to develop data-driven solutions established and validated during the project is also highly relevant to other industrial manufacturing sectors in their pursuit of Industry 4.0 principles. Recent successes have been the demonstration of the applicability of Hyperspectral Imaging (HSI) to distinguishing different substances in solution, the use of image analysis for determining fouling/encrustation, mathematical models to predict and evaluate particle morphology and industrially-relevant crystalliser models using population balance methods.

Who’s involved?

Academic & related

Ivan Andonovic (Electronic & Electrical Engineering), Jan Sefcik (Chemical & Process Engineering), Robert Atkinson (Electronic & Electrical Engineering), Alison Cleary (Electronic & Electrical Engineering), Alastair Florence (Continuous Manufacturing & Crystallisation), Tony Gachagan (Electronic & Electrical Engineering), Andrea Johnston (Continuous Manufacturing & Crystallisation), Blair Johnston (Continuous Manufacturing & Crystallisation, Strathclyde Institute of Pharmacy & Biomedical Sciences), Craig Johnston (Continuous Manufacturing & Crystallisation), Steve Marshall (Electronic & Electrical Engineering), Craig Michie (Electronic & Electrical Engineering), Tony Mulholland (Maths), Zoltan Nagy (Loughborough and Purdue Universities), Alison Nordon (Chemistry), Chris Rielly (Loughborough University), Christos Tachtatzis (Electronic & Electrical Engineering), Massimiliano Vasile (Mechanical & Aerospace Engineering)

Research & related

Okpeafoh Stephen Agimelen (Chemical & Process Engineering), Akos Borsos (Loughborough), Javier Cardona (Electronic & Electrical Engineering), Jerzy Dziewierz (Electronic & Electrical Engineering), John Robertson (Continuous Manufacturing & Crystallisation, Strathclyde Institute of Pharmacy & Biomedical Sciences).