(13e) Interval Type-2 Fuzzy Predictive Modelling for a High Shear Granulation Process

interval type-2 fuzzy predictive
modelling for a high shear granulation process

Wafa’ H. Alalaween1, Mahdi Mahfouf1 and Agba D. Salman2

¹
Department of Automatic Control and Systems Engineering, University of
Sheffield, UK
² Department of Chemical and Biological Engineering, University of
Sheffield, UK

Email: whalalaween1@sheffield.ac.uk

Nowadays, there is a
strong need for a further step to be taken towards the development of a
cost-effective product that meets the stringent regulations that are being
imposed on most industries. Such a step is very important but represents a
non-trivial task, particularly for the pharmaceutical industry and those
industries where, for instance granulation is defined as being a unit operation
of the production process; this being due to the complex nature of the
granulation process itself. Consequently, developing a fast, more accurate,
transparent and cost-effective predictive model is a target that researchers in
both industry and academia strive to achieve. Moreover in the pharmaceutical
industry, such a target, if it is successfully achieved, may significantly decrease
the time required for new drug development. Since the granulation process is
considered to be the unit operation that determines the properties of the final
drug, various approaches have hitherto been utilized to model this process such
as population balance or neural networks based models [1-2]. However, these
modelling approaches cannot effectively deal with the uncertainties present in
both the inputs and the outputs of the granulation process systematically. Such
uncertainties may be due to measurement errors or constraints, or simply to the
heterogeneous distribution of both porosity and binder content during the
granulation process. In this research, a type-2 fuzzy modelling paradigm has
been implemented to predict the properties of the granules produced by a high
shear granulation process and also to absorb any uncertainties that surround
the process. This choice was motivated by the fact that fuzzy logic can deal
with uncertainties more naturally and is considered to be a universal
approximator. In addition, such a model has been utilized to extract meaningful
information to describe the process linguistically in a simple way that can be
understood by users. In order to take account of any unmodelled stochastic
behaviour of the process, and also to improve the prediction performance, a
Gaussian mixture model has been used. Experimental results show that the
proposed predictive model was successful in predicting the properties of the
granules accurately, as shown in Figure 1, with the added advantage of the
modelling framework being interpretable.

Figure 1. The modelling framework: The
predicted (^o) and the experimental (*)
size distribution (using impeller type II, speed=6000rpm, L/S ratio (w/w)=15%
and granulation time=15min).

References

[1] C. Sanders, A. Willemse, A.
Salman, M. Hounslow, Development of a predictive agglomeration model, Powder Technology,
138 (2003) 18-24.

[2] W.H. AlAlaween, M. Mahfouf, A.D. Salman,
Predictive modelling of the granulation process using a systems-engineering
approach, Powder Technology, 302 (2016) 265–274.