Intelligent Decision Support and Control Technologies for Continuous Manufacturing of Pharmaceuticals and Fine Chemicals
Lead Research Organisation:
University of Strathclyde
Department Name: Electronic and Electrical Engineering
Abstract
Although continuous crystallisation provides significant benefits to innovative manufacture, the key challenge of real time, robust monitoring of quantitative attributes (form, shape, size) of particulate products still remains a massive challenge. While particle attributes are crucial for downstream processing of products, no current solution allows the processing of data from in-line sensors to reliably extract these attributes in real time across multiple manufacturing steps and the subsequent use of this knowledge for IDS and control of processes. The development of solutions for the sector requires expertise across many technologies driven by end user requirements. Industrial co-creators will provide the requirements, the range of expertise within the applicants ensuring that the goals of the programme are met. The grant will enable the establishment of a process test-bed which as the project matures, will be made available to a range of national and international user and application communities. This activity will support the creation of a requirement and technology roadmap, in so doing informing both the research and commercial communities on future opportunities. The project will also yield the following added value to the community:
- the cross-disciplinary nature of the project and participating teams will stimulate new solutions and promote creativity through sharing best practice in executing research from different perspectives
- the PDRAs will be applying their know-how to joint development tasks, allowing them to gain comprehensive knowledge and expertise across a range of field and in so doing provide trained, talented engineers that will fuel the deployment of these innovative solutions
- the project addresses the integration of a number of distinct architectural layers to transform a physical infrastructure into a flexible platform which supports a range of applications whilst accessible to users
- the cross-disciplinary nature of the project and participating teams will stimulate new solutions and promote creativity through sharing best practice in executing research from different perspectives
- the PDRAs will be applying their know-how to joint development tasks, allowing them to gain comprehensive knowledge and expertise across a range of field and in so doing provide trained, talented engineers that will fuel the deployment of these innovative solutions
- the project addresses the integration of a number of distinct architectural layers to transform a physical infrastructure into a flexible platform which supports a range of applications whilst accessible to users
Planned Impact
The project contains the following elements of a strategic impact strategy;
- fostering the global economic competitiveness of the UK. As the industrial co-creators will have first access to the technology, and since dissemination will be carried out initially within the UK, the project will enhance economic competitiveness.
- increasing the effectiveness of UK business by engaging with industries across the supply chain, in so doing promoting future commercial partnerships aimed at capturing the massive global market opportunities for these solutions.
- increasing practical engagement - most notably through the pilot process test-bed - with key researchers in academia who can evaluate new concepts within a controlled environment
and contains the following key people-based activities;
- collaboration with industrial partners
- exchange/sustaining/exploration of knowledge
- cross-disciplinary collaborations/cross-sector learning
- engagement with wider community of beneficiaries (public demonstrations)
- new collaborations/research activities to enhance exploitation
In order to reach the widest possible impact and outreach, the plan is focussed on cross-disciplinary learning based on strong engagement with a wide range of beneficiaries including ICT hardware and software suppliers, ICT exploitation providers and users involved in pharmaceutical and fine chemicals manufacturing.
The project combines a range of disciplines in academia and the private sectors and consequently the outputs of the project will bring benefit to all communities and the economy. The industry co-creators contributing to the activities range from end users, to data management providers through to SMEs offering technology solutions to the sector. The Academic institutions have established strong knowledge exchange programmes to transmit the relevance of their research to the public. The knowledge exchange within the project between the disciplines will enhance the project and the partners, particularly between the pharmaceutical specialists and engineers.The project will deliver highly skilled people to a key sector whilst meeting the needs of industry for researchers to address their environmental challenges that have increased over the recent past.
- fostering the global economic competitiveness of the UK. As the industrial co-creators will have first access to the technology, and since dissemination will be carried out initially within the UK, the project will enhance economic competitiveness.
- increasing the effectiveness of UK business by engaging with industries across the supply chain, in so doing promoting future commercial partnerships aimed at capturing the massive global market opportunities for these solutions.
- increasing practical engagement - most notably through the pilot process test-bed - with key researchers in academia who can evaluate new concepts within a controlled environment
and contains the following key people-based activities;
- collaboration with industrial partners
- exchange/sustaining/exploration of knowledge
- cross-disciplinary collaborations/cross-sector learning
- engagement with wider community of beneficiaries (public demonstrations)
- new collaborations/research activities to enhance exploitation
In order to reach the widest possible impact and outreach, the plan is focussed on cross-disciplinary learning based on strong engagement with a wide range of beneficiaries including ICT hardware and software suppliers, ICT exploitation providers and users involved in pharmaceutical and fine chemicals manufacturing.
The project combines a range of disciplines in academia and the private sectors and consequently the outputs of the project will bring benefit to all communities and the economy. The industry co-creators contributing to the activities range from end users, to data management providers through to SMEs offering technology solutions to the sector. The Academic institutions have established strong knowledge exchange programmes to transmit the relevance of their research to the public. The knowledge exchange within the project between the disciplines will enhance the project and the partners, particularly between the pharmaceutical specialists and engineers.The project will deliver highly skilled people to a key sector whilst meeting the needs of industry for researchers to address their environmental challenges that have increased over the recent past.
Organisations
- University of Strathclyde (Lead Research Organisation)
- LOUGHBOROUGH UNIVERSITY (Collaboration)
- Siemens (United Kingdom) (Collaboration)
- Mettler Toledo Safeline Ltd (Collaboration)
- AstraZeneca (Collaboration)
- Perceptive Engineering Ltd (Collaboration)
- GlaxoSmithKline (GSK) (Collaboration)
- Dassault Systèmes (United Kingdom) (Project Partner)
- Intelligence Business Solutions UK (Project Partner)
- GlaxoSmithKline (United Kingdom) (Project Partner)
- Process Systems Enterprise (United Kingdom) (Project Partner)
- Mettler-Toledo (United Kingdom) (Project Partner)
- Gilden Photonics (United Kingdom) (Project Partner)
- Sympatec (Project Partner)
- AstraZeneca (United Kingdom) (Project Partner)
- GSE Systems Ltd (Project Partner)
- Perceptive Engineering (United Kingdom) (Project Partner)
- Honeywell (United Kingdom) (Project Partner)
Publications
Agimelen O
(2018)
Multi-sensor inline measurements of crystal size and shape distributions during high shear wet milling of crystal slurries
in Advanced Powder Technology
Su Q
(2015)
Pharmaceutical crystallisation processes from batch to continuous operation using MSMPR stages: Modelling, design, and control
in Chemical Engineering and Processing: Process Intensification
Li W
(2024)
A framework for systematic crystal shape tuning - Case of Lovastatin's needle-shaped crystals
in Chemical Engineering Research and Design
Cardona J
(2018)
Image analysis framework with focus evaluation for in situ characterisation of particle size and shape attributes
in Chemical Engineering Science
Agimelen O
(2016)
Integration of in situ imaging and chord length distribution measurements for estimation of particle size and shape
in Chemical Engineering Science
Agimelen O
(2015)
Estimation of particle size distribution and aspect ratio of non-spherical particles from chord length distribution
in Chemical Engineering Science
Sheridan R
(2022)
Effect of oscillatory flow conditions on crystalliser fouling investigated through non-invasive imaging
in Chemical Engineering Science
Tachtatzis C
(2015)
Image-based monitoring for early detection of fouling in crystallisation processes
in Chemical Engineering Science
Agimelen O
(2017)
Modelling of artefacts in estimations of particle size of needle-like particles from laser diffraction measurements
in Chemical Engineering Science
Neugebauer P
(2018)
Crystal Shape Modification via Cycles of Growth and Dissolution in a Tubular Crystallizer.
in Crystal growth & design
Ahmed B
(2019)
Engineering of acetaminophen particle attributes using a wet milling crystallisation platform.
in International journal of pharmaceutics
Su Q
(2015)
Mathematical Modeling, Design, and Optimization of a Multisegment Multiaddition Plug-Flow Crystallizer for Antisolvent Crystallizations
in Organic Process Research & Development
Ferreira C
(2020)
Quantification of particle size and concentration using in-line techniques and multivariate analysis
in Powder Technology
Description | Development and validation of multi-sensor models for variable particle shapes. Successfully completed: Algorithm to estimate particle size distribution (PSD) and aspect ratio from chord length distribution (CLD) data. Algorithm to incorporate aspect ratio estimate from images in PSD estimation from CLD data. Algorithm to estimate PSD from laser diffraction data. Algorithm to estimate PSD from combined laser diffraction and CLD data. Implementation of robust inversion of multi-sensor data to identify particle size and shape distributions. Successfully completed: Implementation of CLD inversion algorithm in a wet milling process. Software package for near real-time CLD inversion. Implementation of CLD forward model in gCRYSTAL. Implementation of CLD inversion algorithm on industrial data. Implementation of combined CLD and Laser diffraction inversion algorithm on data from different particle suspension. Algorithm to detect fouling and determine nucleation induction times in crystallisation processes Algorithm for particle size and shape characterisation from in-line imaging (PVM) Validation on standard systems Evaluation on a range of applications Implementation in real time monitoring and control Engagement with CMAC academic and industrial communities through demonstrations and dissemination of tools (ImagingApp) Various mathematical model have been developed for crystallization: single- and multi-stage MSMPR crystallization, antisolvent crystallization, tubular-loop crystallization, COBC; Mathematical models for screen- and dry-milling; Model-based predictive control for anti-solvent crystallization; and polymorphic transformation; Model for particle breakage by using novel moment closure technique; Experimental investigation and model validation of crystallization including polymorphic transformation; Fully automated control system (including data transfer) by using multi-sensor PAT. |
Exploitation Route | Given the project had a spectrum of industrial partners, that grouping represents a viable route to commercilisation of the software developed under the project. In addition the close working relationship with CMAC and its body of researchers also guaranteed ready adoption of these outputs. |
Sectors | Digital/Communication/Information Technologies (including Software) Pharmaceuticals and Medical Biotechnology |
Description | Findings have been implemented in commercial software after publication. These include both SMEs and large multinational organisations. The possibilities of new types of process measurement have also been demonstrated to project partners. The foundation knowledge acquired during the project, enhanced through a successful partnership with an existing pharma-led project led by another Department within the University - EPSRC-funded 'Continuous Manufacturing and Advanced Crystallisation (CMAC)' - who subsequently adopted the outputs and methodologies generated under EP/K014250/1 ('Intelligent Decision Support and Control Technologies for Continuous Manufacturing of Pharmaceuticals and Fine Chemicals') to enhance existing research scope and a core technology spine facilitating securing further significant funding to establish both the 'Continuous Manufacturing and Advanced Crystallisation Future Manufacturing Research Hub (www.cmac.ac.uk)' and an industry-championed 'Medicines Manufacturing Innovation Centre' funded by CPI-UK (https://www.uk-cpi.com/about/national-centres/medicines-manufacturing-innovation-centre). Both programmes have migrated to the use of digital data-driven practices which were proven under EP/K014250/1 as the optimisation of the control to enable continuous manufacturing processes is heavily reliant on acquiring and analysing measurement data throughout the stages. The project outputs also informed the strategic direction of the National Manufacturing Institute for Scotland (NMIS), a £70M investment by Government to create an innovation environment to promote the development of Industry 4.0 through academic-industry co-development. One of the spines of the capability is a test-bed that accelerates the understanding of the impact of data on traditional manufacturing processes with the techniques developed throughout the project embedded within the environment. |
First Year Of Impact | 2017 |
Sector | Aerospace, Defence and Marine,Agriculture, Food and Drink,Digital/Communication/Information Technologies (including Software),Pharmaceuticals and Medical Biotechnology |
Impact Types | Economic |
Description | Imapct Accelerator Account / Direct Industrial Funding |
Amount | £31,592 (GBP) |
Organisation | University of Strathclyde |
Sector | Academic/University |
Country | United Kingdom |
Start | 06/2016 |
End | 03/2017 |
Description | Impact Acceleration Account |
Amount | £14,000 (GBP) |
Organisation | University of Strathclyde |
Sector | Academic/University |
Country | United Kingdom |
Start | 03/2016 |
End | 07/2016 |
Title | CLD Inversion Software |
Description | "This is the software for converting a measured chord length distribution (CLD) data to particle size distribution (PSD). The CLD is measured with the Mettler Toledo focused beam reflectance measurement (FBRM) sensor immersed in a particle suspension, for example, crystalline particles in a crystallisation process. The software then performs the necessary calculations to estimate the PSD and the aspect ratio for the particles. Details on the use of the software are contained in the user guides. The software was developed under the EPSRC funded ICT-CMAC project (grant number EP/K014250/1). Details of the workings of the algorithm behind the software are given in the publications: [1] O. S. Agimelen et al., "Estimation of particle size distribution and aspect ratio of non-spherical particles from chord length distribution", Chemical Engineering Science, 123, pp. 629-640, 2015. [2] O. S. Agimelen et al., "Integration of in situ Imaging and Chord Length Distribution for Estimation of Particle Size and Shape", Chemical Engineering Science 144, pp. 87-100, 2016. We would greatly appreciate your feedback on your experience using the software. For further details or help using the software please contact the software developer Stephen Agimelen at the email address okpeafoh.agimelen@strath.ac.uk. " |
Type Of Material | Database/Collection of data |
Year Produced | 2018 |
Provided To Others? | Yes |
Impact | Unknown |
Title | Data for "Monitoring crystal breakage in wet milling processes using inline imaging and chord length distribution measurements" |
Description | This is the dataset associated with the article titled 'Monitoring crystal breakage in wet milling processes using inline imaging and chord length distribution measurements'. The dataset consists of images obtained with the Mettler Toledo Particle Vision and Measurement (V819) sensor, as well the Malvern Morphologi G3 instrument. It also contains the chord length distribution data obtained with the Mettler Toledo focused beam reflectance measurement (G400) sensor. Various particle statistics obtained from the Morphologi G3 instrument are also contained in the dataset. The data were collected at various stages of different wet milling processes using the following crystalline materials: 1. Benzoic acid 2. Paracetamol 3. Metformin hydrochloride The data from the Morphologi G3 instrument were obtained before and at the end of each wet milling process. The MagicLab rotor stator wet mill was employed in all the processes. The file 'Read me' contains more details of this dataset. |
Type Of Material | Database/Collection of data |
Year Produced | 2017 |
Provided To Others? | Yes |
Impact | Unknown |
Title | Data for: "Engineering of Acetaminophen Particle Attributes Using a Wet Milling Crystallisation Platform" |
Description | "This dataset is for the article titled 'Engineering of Acetaminophen Particle Attributes Using a Wet Milling Crystallisation Platform'. Mettler Toledo focused beam reflectance measurement (G400) and particle vision and measurement (V819) sensors were used for capturing inline counts, chord length distributions and images. Chord lengths were further inverted for an estimation of inline particle size distributions through a CLD-inversion app. A Malvern Morphologi G3 instrument was also used which provided various particle size and shape statistics and distributions from particles undergone a wet milling process. In addition, an inverse gas chromatography measured various surface properties such as surface areas and surface energy distributions. This is all shown in the dataset. The data collected was for acetaminophen. Various wet milling configurations were used which produced particles exhibiting different attributes and features. " |
Type Of Material | Database/Collection of data |
Year Produced | 2018 |
Provided To Others? | No |
Impact | . |
Title | Data for: "Fusion of laser diffraction and chord length distribution data for estimation of particle size distribution using multi-objective optimisation" |
Description | "This is the dataset for the articl titled ""Fusion of laser diffraction and chord length distribution data for estimation of particle size distribution using multi-objective optimisation"". The preprint of the article is available at: arXiv:1804.05633. Full details of the materials and measurement proceedure can be found in the preprint." |
Type Of Material | Database/Collection of data |
Year Produced | 2018 |
Provided To Others? | Yes |
Impact | . |
Title | Data for: "In situ characterisation of particle size and shape attributes through image-based analysis" |
Description | This dataset contains a set of in-line microscopy images analysed in the manuscript ""In situ characterisation of particle size and shape attributes through image-based analysis"". The Particle Vision and Measurement (PVM) images were captured by the PVM V819 probe for two different materials: - standard polystyrene microspheres of 150, 300, 400 and 500 microns at solid loadings ranging from 1 to 5 wt.%. - standard silicon particles of elongated shape of 20x20x160, 20x20x60, and 20x20x20 microns. This dataset is freely available, however due to its size access should be requested using the contact email provided on this page. |
Type Of Material | Database/Collection of data |
Year Produced | 2018 |
Provided To Others? | No |
Impact | Unknown |
Title | Data for: "Integration of in situ imaging and chord length distribution measurements for estimation of particle size and shape" |
Description | This is the dataset for the article titled Â"Integration of in situ imaging and chord length distribution measurements for estimation of particle size and shapeÂ" [Chemical Engineering Science 144 (2016) pp. 87Â-100]. The dataset was produced with the focused beam reflectance measurement (FBRM) and particle vision and measurement (PVM) sensors both from Mettler Toledo Ltd. Details of measurement procedure are given in [Chemical Engineering Science 144 (2016) pp. 87Â-100]. More details of the dataset are contained in the PDF file included. |
Type Of Material | Database/Collection of data |
Year Produced | 2018 |
Provided To Others? | Yes |
Impact | . |
Title | ImagingApp: image analysis framework for particle size and shape characterisation |
Description | "This is a software to extract particle size and shape distributions from image analysis. The implementation of a focus classifier allows the user to apply this tool to both off-line and inline imaging. For more details on this image analysis framework, please visit the following publications: [1] J. Cardona et al., ""Image analysis framework with focus evaluation for in situ characterisation of particle size and shape attributes"", Chemical Engineering Science, 191, pp. 208-231, 2018. [2] O. S. Agimelen et al., Â"Integration of in situ Imaging and Chord Length Distribution for Estimation of Particle Size and ShapeÂ", Chemical Engineering Science, 144, pp. 87-100, 2016. We would greatly appreciate your feedback on your experience using the software. For further details or help using the software please contact the data creators using the contact details contained within the user guides provided." |
Type Of Material | Database/Collection of data |
Year Produced | 2016 |
Provided To Others? | Yes |
Impact | . |
Title | Supplementary information files for A framework for systematic crystal shape tuning - case of Lovastatin's needle-shaped crystals |
Description | © the authors, CC-BY 4.0Supplementary files for article A framework for systematic crystal shape tuning - case of Lovastatin's needle-shaped crystalsOne of the most important challenges in the pharmaceutical industry is to produce crystals with desired size and shape distributions, to enhance the critical quality attributes of the drug product, such as efficacy, and to improve manufacturability during downstream processing, such as filtration, drying and granulation. The paper provides a framework for effective crystal shape and size tuning, based on a systematic exploration of standard techniques, such as the linear cooling and supersaturation control (SSC), and novel methods based on the systematic combination of several techniques, namely direct nucleation control (DNC), wet milling, SSC and shape modification additives. The crystallization of lovastatin, which is notorious for its challenging needle-shaped crystals, with an extremely high aspect ratio, was used as a case study, and polypropylene glycol (PPG-4000), at different concentrations, was used as an effective shape modifier from small-scale tests studied previously. The proposed techniques were implemented in the case of seeded and unseeded systems. It was demonstrated that the combination of temperature cycling and polymer additive enhances greatly the control over the aspect ratio and crystal size distribution, compared to conventional linear cooling and SSC strategies. The implementation of wet milling at the beginning of the process, or the introduction of seeds, enhances even further the control of the critical quality attributes of the crystalline product. |
Type Of Material | Database/Collection of data |
Year Produced | 2024 |
Provided To Others? | Yes |
URL | https://repository.lboro.ac.uk/articles/dataset/Supplementary_information_files_for_A_framework_for_... |
Description | Consultancy with AZ |
Organisation | AstraZeneca |
Country | United Kingdom |
Sector | Private |
PI Contribution | The consultancy was to provide specialist analysis on a specific project for which the need had been identified within the company. |
Collaborator Contribution | The partners provided access to data and experienced personnel to facilitate understanding of the data and the problem to be solved. |
Impact | The output was a short consultancy report. |
Start Year | 2015 |
Description | Joint research with Loughborough University |
Organisation | Loughborough University |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | University of Strathclyde researchers worked on this project with researchers from Loughborough University |
Start Year | 2013 |
Description | Mettler Toledo project partnership |
Organisation | Mettler Toledo Safeline Ltd |
Country | United Kingdom |
Sector | Private |
PI Contribution | Mettler Toledo Ltd worked with the research team and assisted/contributed to the project outcomes |
Collaborator Contribution | Mettler Toledo provide technical information and in-kind support. |
Impact | Working with Mettler Toledo has accelerated understanding of the subject area and therefore accelerated research. This is a multi-disciplinary collaboration, covering crystallisation, imaging, data acqusition and signal processing. |
Start Year | 2014 |
Description | Project partnership with Astra Zeneca |
Organisation | AstraZeneca |
Country | United Kingdom |
Sector | Private |
PI Contribution | Astra Zeneca worked with the research team and assisted/contributed to the project outcomes |
Start Year | 2013 |
Description | Project partnership with Glaxo Smithkline (UK) |
Organisation | GlaxoSmithKline (GSK) |
Country | Global |
Sector | Private |
PI Contribution | Glaxo Smithkline (UK) worked with the research team and assisted/contributed to the project outcomes |
Start Year | 2013 |
Description | Project partnership with Perceptive Engineering Ltd |
Organisation | Perceptive Engineering Ltd |
Country | United Kingdom |
Sector | Private |
PI Contribution | Perceptive Engineering Ltd worked with the research team and assisted/contributed to the project outcomes |
Start Year | 2013 |
Description | Project partnership with Process Systems Enterprise (PSE) |
Organisation | Siemens Process Systems Engineering Ltd |
Country | United Kingdom |
Sector | Private |
PI Contribution | Two of the five Work Packages work closely with PSE to develop models using their software, and to integrate other models to PSE software |
Collaborator Contribution | Expertise and software licenses. |
Impact | Publications arising from using the collaboration are: DOI: 10.1021/acs.oprd.5b00110 10.1016/j.cep.2015.01.001 10.1109/ACC.2015.7172001 |
Start Year | 2013 |
Description | CMAC Summer School - Particle Sizing Workshop |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Other audiences |
Results and Impact | CMAC Summer School - Particle Sizing Workshop |
Year(s) Of Engagement Activity | 2017 |
Description | Engage with Strathclyde: CMAC Tier 2 Partners event |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Industry/Business |
Results and Impact | The ICT CMAC project was represented at the CMAC Tier 2 partners event through a flash presentation. Discussions during the day led to a number of new contacts. |
Year(s) Of Engagement Activity | 2016 |
URL | http://www.strath.ac.uk/workwithus/engage/ |
Description | Engage with Strathclyde: ICT CMAC and Hyperspectral Imaging |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Industry/Business |
Results and Impact | Industrial visitors to Engage with Strathclyde Week attended short workshop sessions explaining the purpose of the ICT CMAC project and also the use of Hyperspectral Imaging in the context of crystallisation and mixing processes. The event led to a number of follow up activities and new industrial links. |
Year(s) Of Engagement Activity | 2016 |
URL | http://www.strath.ac.uk/workwithus/engage/ |
Description | Invited presentation and poster at 'Embracing Industrie 4.0' workshop at University of Nottingham |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Professional Practitioners |
Results and Impact | The event was targetted at senior policymakers and practitioners, academics and professionals involved in Industrie 4.0 and Digital Manufacturing. Presentations were invited from EPSRC grant holders in the area. |
Year(s) Of Engagement Activity | 2015 |
URL | https://www.nottingham.ac.uk/conference/fac-eng/dtmi2015/index.aspx |