Institute of Chemical Engineers
Advances in Process Automation and Control
18 - 20 November, Manchester, UK
Mihai Rascu - Perceptive Engineering
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Best practice and emerging technologies
in process automation and control
Spend three days with leading figures from industry and academia as you consider best practice and emerging technologies in process automation and control.
Advances in Process Automation and Control will give attendees a forum to share learning and experience and to create their own effective and sustainable professional networks.
The conference will provide a focus for promoting learning and practice in the application of IoT technologies, big data analytics and machine learning in the process manufacturing sector and an opportunity to develop professional networks for new entrants into the field.
Attendees will go home equipped to deliver the most effective use of automation technologies at their workplace and to take advantage of the potential and promise of 'smart manufacturing' in the process sector..
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Abstract
The paper from Perceptive Engineering will describe a digital design approach for Model Predictive Control (MPC) of a pharmaceutical twin screw wet granulation (TSWG) process. A workflow involving PharmaMV software, is developed to interact with the parameterised TSWG digital twin, as a virtual plant, implemented in gPROMS Formulated Product software.
The workflow involves first running open-loop step tests on the critical process parameters (CPP), including the Active Pharmaceutical Ingredient (API) and excipient feeders as well as the binder pump, to characterise their response on the critical quality attributes (CQA) including production rate, concentration and particle size distribution (PSD) D50 at the outlet of the TSWG.
The next step is to run an online model adaption routine to automatically identify an MPC control model between the CPPs and CQAs. Then, this MPC controller is tested on the virtual plant by making (simultaneous) setpoint changes on the Concentration, PSD D50 and the Production rate. Results demonstrate that the MPC controller is able to deliver robust setpoint tracking for all the CQAs by efficiently manipulating the CPPs within their respective constraint limits. This is followed by commissioning the designed MPC controller on the actual TSWG process.
The advantages of the proposed scheme include enhanced process understanding, rapid development of the advanced control scheme as well as reduced API consumption and experimental effort. Furthermore, the proposed scheme can be used to investigate what-if scenarios and scale-up effects on the process as well as use as an online soft sensor for any hard-to-measure CQAs.