(284e) Digital Design and Operation of Continuous Crystallization Processes Via Mechanistic Modelling
AIChE Annual Meeting
Tuesday, November 12, 2019 - 9:27am to 9:48am
John Mack Normal Niall Mitchell 2 2 2019-04-12T17:01:00Z 2019-04-12T17:01:00Z 2 571 3256 27 7 3820 16.00
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design and operation of continuous crystallization processes via mechanistic
modelling text-align:center">Niall Mitchell1,
John Mack2, Furqan Tahir2, Cameron Brown3, Tariq
Islam3, John Robertson3, Eduardo Lopez-Montero2,
Alastair Florence3 text-align:center">1 mso-bidi-font-size:11.0pt;line-height:107%">Process Systems Enterprise (PSE)
Ltd. London, UK text-align:center">2 mso-bidi-font-size:11.0pt;line-height:107%"> Perceptive Engineering Ltd,
Daresbury, UK text-align:center">3 mso-bidi-font-size:11.0pt;line-height:107%">CMAC, University of Strathclyde,
models are becoming more commonly applied for Research and Development in the
pharmaceutical sector to gain process understanding, enable process design and
operation. Traditionally, the output from this activity is a validated
mechanistic model, which is capable of quantitatively predicting the behaviour
of the various Critical Quality Attributes (CQA) for typical batch or
continuous pharmaceutical processes for a wide range of Critical Process
Parameters (CPP). However, these tools are almost exclusively employed in an
offline manner currently to enable digital design efforts, primarily aimed at
assessing process robustness and variability, with very little subsequent
online application of the mechanistic model to enable control or soft sensing.
Predictive Control (MPC) is an established technology in the process
industries. It uses a statistical model of the process to capture the dynamic
relationships between the inputs (CPPs) and outputs (CQAs) of the process.
Using this statistical model, it predicts impact of known disturbances on
operation and controls the process through co-ordinated moves on multiple
inputs. The MPC exploits all opportunities to reduce variability in the CQAs
whilst compensating for measured and unmeasured disturbances.
statisitical process model is built from process response test data at
production scale using techniques such as Pseudo-Random Binary Sequence (PRBS)
or step-tests. The PRBS test is a relatively non-invasive technique compared to
traditional step-tests as it allows the product to remain within specification
whilst generating statistically rich information for modelling. Although PRBS
testing is suitable for many industries it cannot be used in the Pharmaceutical
sector as product generated during testing cannot be utilised for clinical or
commercial supply. Consequently, the cost to generate the statistical control
model would be significant, presenting a barrier to uptake of the technology.
This drawback can be overcome by the integration of mechanistic models,
developed using laboratory scale data with MPC system, such as Perceptive
Engineerings PharmaMV platform via a digital design approach.
this work we outline, the application of an advanced process modelling tool,
namely gPROMS FormulatedProducts, to describe a number of pharmaceutical
crystallization processes. The mechanistic process model and the mechanistic
model kinetic parameters were validated using process data gathered from the
literature and from lab-based experiments. The lab-based mechanistic model was
subsequently used to predict the behaviour of the full scale production scale.
validated mechanistic model was subsequently integrated with PharmaMV to
develop and tune the MPC against the mechanistic simulation of the process, by
using the mechanistic model as a Digital Twin or Virtual Plant as follows:
- gPROMS: Build mechanistic model
- gPROMS: Small scale parameterisation experiments & mechanistic model validation
- gPROMS + PharmaMV: Validate/check mech model against full scale data
- gPROMS + PharmaMV: Build MPC using mechanistic model as a digital twin
- PharmaMV: Transfer MPC to live process and test
this approach, the MPC derived from the mechanistic model was utilized to
accurately control the defined CQAs, such as final particle attributes (PSD,
yield) for continuous crystallization processes, with reduced material wastage
at the production scale
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