Abstracts will be added as available and instructions for your own poster abstract here.
“Feel the pulse of the factory” – To be competitive UK industry must embrace digital technologies to improve productivity through data analytics
Professor David Brown, University of Portsmouth
Professor David Brown talks about data collection right though to HPC processing and decision making. Connectivity using 4G/5G/satellite/the cloud allows for factory to factory data comparisons and allows the optimisation of production linked to strategic business models – something that has been explored in many funded projects linked to data analytics and the manufacturing sector. Some of these projects will be given as a case study in this talk.
The Connected Everything network plus, which is funded by the Engineering and Physical Sciences Research Council (EPSRC), links academics across a number of disciplines to address the question “how do we support the future of manufacturing in the UK?” One of its central activities has been the development of reports on six key thematic areas, enabling ‘state of the art’ in each area to be identified and shared. One of these thematic areas involved data analytics and decision making and its report calls on industry to embrace digital technologies to improve their productivity and business gains through connected data analytics.
Combining mechanistic models and (big) data to arrive at the next level in terms of identifying, quantifying and addressing process robustness and product quality issues: Reviewing the QbD definition in the light of digitalisation opportunities and the needs of continuous manufacture
Sean Bermingham, PSE Formulated Products, Director of the ADDoPT project
This paper will first argue that whilst current data driven QbD approaches have shown significant promise, only new approaches that put significantly more emphasis on the “sound science” and “process understanding” elements of the QbD definition can routinely deliver on that promise.
We will next review a new mechanistic model-based approach that:
- offers a structured, process understanding based manner to determine the true critical parameters (as opposed to heuristics based reasoning and sparse DoEs); and
- allows a significant reduction in data requirements for model calibration thereby making the approach suitable for routine application (as opposed to traditional data driven QbD approaches).
A mechanistic model-based QbD approach is of particular interest to continuous manufacturing processes where the number of factors (operating conditions, material attributes and disturbances) renders a traditional DoE practically infeasible. Thanks to advances in computing it is now feasible to use calibrated mechanistic models to investigate in the order of 10 factors on a pc/laptop, and in the order of 20 factors on HPC clusters.
Where mechanistic understanding is insufficient, (big) data based approaches can be used to create hybrid models that deliver the best of both worlds (scientific understanding and data driven). Examples of this include the prediction of blend properties.
The presentation will conclude with three industrial case studies that will illustrate abovementioned benefits of a new QbD approach:
- The first one involves continuous crystallization and will illustrate the significant reduction in experiments required to calibrate mechanistic models resulting in a fourfold decrease of the process development effort.
- The second case study focuses on continuous direct compression and demonstrates the ability for a step increase in control strategy testing.
- The final case study links manufacturing and product performance (oral absorption behaviour) and illustrates how process parameters that may appear to be CPPs from a unit operation perspective many not be CPPs when considering the integrated manufacturing and product performance system.
CAFE4DM - The Centre in Advanced Fluid Engineering for Digital Manufacturing
Professor Philip Martin, The University of Manchester
Jo Cook, Unilever
The Centre in Advanced Fluid Engineering for Digital Manufacturing (CAFE4DM), is a large collaborative research programme, set-up between the University of Manchester and Unilever that addresses the challenges in understanding, creating and scaling up manufacturing processes for formulated products in fast moving consumer goods (home/personal care and food products).
CAFE4DM aims to improve the sustainability of formulated products and increase capacity in factories. Although there have been impressive strides in engineering models for formulated product manufacture, many of the underpinning academic and industrial practices build on traditional chemical engineering approaches rather than revolutionise them. CAFE4DM aims to revolutionise the approach through the combination of state of the art digital approaches with new mechanistic understanding (Industry 4.0).
The learning that is occurring during the evolution of this revolution will be exemplified from both the academic and industrial viewpoints.
Design of Experiment and Model Development on the PROSPECT CL - Scale Up Facility
Dr Katharina Roettger, CPI @ Birmingham University
PROSPECT CL is a pilot scale research facility for liquid scale up established by the Centre for Process Innovation (CPI). It is part of the foundational capability of the National Formulation Centre (NFC) in a joint project with the universities of Birmingham, Edinburgh and Leeds and Perceptive Engineering Ltd.
The state-of-the-art facility will enable us to develop, prototype and scale up the next generation of formulated products and processes and has two key objectives:
- Understanding the universal principles of manufacturing formulations at different scales and enabling the predictive scale-up of batch formulation processes.
- Developing, validating and utilizing new Process Analytics Technologies (PAT) and process analytics capabilities.
The hardware control and data fusion were provided by Perceptive Engineering’s software package which allowed us to implement advanced process control models for real time prediction of process parameters such as particle size and viscosity, and the detection of process abnormalities at different scales.
To prove the potential of this approach, we combined a classic design of experiments with step-change experiments and PRBS to understand and optimize the process from bench scale to pilot plant scale. We will present first results on optimizing a model formulation process to demonstrate the capability of this facility.
The Use of 4IR Techniques to Address the Challenges of Agile Formulation Manufacturing
Simon Mazier / Matthew McEwan, Perceptive Engineering
A recent trend across many areas of manufacturing is the move towards more agile manufacturing. This is where rather than concentrating on bulk manufacturing, a broader range of different products are made on process equipment which may or may not be reconfigurable. Agile manufacturing means that batch sizes are smaller – ultimately down to the level of personalised medicines/products - and the range of products made is both widely varying and constantly changing. Arguably the formulation industry does ‘Agile’ a lot better than many other process industries, however there are still challenges. Some of these are associated with process scale-up and the introduction of new process variants, while other challenges are associated with closing the gap between making a product that is merely ‘acceptable’, towards making a product which is ‘optimum’ and right first time every time.
The new techniques encapsulated by the term “4IR” offer the ability to both collect and aggregate process information in better ways, and to also better manage (and act upon) the meta-data required to achieve optimum performance. The talk will focus on the new techniques that Perceptive Engineering are introducing (and piloting on CPI facilities). In particular the concepts of cloud-based data and model management, and unsupervised machine learning for model development and updating.
Implementing Aspects of the 4IR Toolbox to Improve Powder Formulation Processes
Dr Dave Berry, National Formulation Centre, CPI
The talk will primarily address the implementation of a models based control strategy on a highly instrumented continuous wet granulation powder formulation process; to enable superior quality products and reduced waste.
The talk will also address the broader implementation of a Process Analytical Tool (PAT) infrastructure within the National Formulation Centre’s Complex Powder lab.
This will highlight the uneven distribution of sensor implementation and control across the breadth of the formulation industry and the benefits that can be seen by incremental progress towards 4IR implementation. It will also address some of the challenges that such implementation presents.
Statistics for Digital Formulation
Richard Boddy - Statistics for Industry (s4i)
The presentation examines the methods of statistical analysis for relatively large data sets from formulation trials. This shows the limitations of poorly designed trials or happenstance data and how to abstract the sometimes very limited information from enormous amounts of ill-conditioned data and its use in model building. We then look at good designs, including factorial and saturated designs, to obtain well-conditioned data leading to clear conclusions. Lastly we consider a successful application for formulations of design and analysis which has led to capturing a high percentage of a world market.
Industry 4.0 - Enabling Business Growth
Mo Chowdhury, AkzoNobel
When businesses investigate Industry 4.0 and what it can do for them, there tends to be a bias towards increasing efficiency with business operations.
Mo works in AkzoNobel’s Innovation Incubator where they work on transformative innovation to decouple value from volume.
He will present examples of how utilising existing data and sensors can enable new business models to drive revenue from within a traditional industry for business growth.
CALL FOR ABSTRACTS
Abstract submission for consideration for a Poster presentation is 7th December 2018.
No abstracts sent by other means (fax, post) will be accepted.
Instructions must be followed strictly and those not conforming to the required format will be disqualified.
The personal data of the presenting author must be filled in the submission form.
Abstracts should contain a sentence stating the study’s objective, a brief statement of methods, a summary of the results obtained and a statement of the conclusions. Please note that it will not be satisfactory to say ‘the results will be discussed’. Use a short and specific title. Capitalize initial letters of trade names.
Abstracts must be written in English and can not exceed 300 words.
Type your abstract using any word processor and using the template provided. Tables, graphics, figures and images can be included in the abstract. Ensure you include the title and authors in the abstract submission.
Industry 4.0 is a developing area and so we are keen to hear about a wide range of different ways in which science and technology are advanced formulation from design to consumption. Please ensure that your poster highlights the impact of Industry 4.0 on formulation and shows how your poster is either enabling Industry 4.0 or shows the impact of Industry 4.0 on business as usual.
All presenting authors must register for the meeting and pay the appropriate delegate fees.
Once the abstract has been submitted, the presenting author will receive an e-mail message within 3 days as confirmation of receipt stating the submission reference number. Abstracts will be reviewed following the final submission date.
Details of abstracts will appear in the program exactly as submitted. Authors are requested to follow the rules and guidelines strictly and use the template provided. Authors are solely responsible for the correctness and accuracy of the data submitted.
Abstracts will be published in the program only on receipt of registration payment in full.
If you wish to withdraw or cancel your abstract submission then this must be done via email to Dr Helen Ryder by 5th December 2018. Abstracts not withdrawn by 5th December 2018 may appear in the program.
Files for download:
Abstract guidelines -
Abstract template -