Page 10 - Industrial Technology February 2020

MACHINE BUILDING

DEEP LEARNING

CONDITION MONITORING

MARTIN GADSBY, DIRECTOR AT OPTIMAL INDUSTRIAL TECHNOLOGIES,
LOOKS AT HOW DEEP LEARNING-POWERED PROCESS ANALYTICAL
TECHNOLOGY CAN BOOST MANUFACTURING PROCESSES
eep learning has the potential to revolutionise a relevant information from the in-line PAT-led measurements
broad range of industries by offering increasingly on the product’s chemical and physical make-up.
accurate predictive capabilities with little to no When deep learning is applied to PAT, it is not only
Dhuman supervision. These can have a tremendous possible to simplify the creation of a predictive algorithm, as
impact on the manufacturing sector by supporting the no coding is required, but the resulting model could also
application of process analytical technology (PAT) and its improve as manufacturing plants develop more and more
concomitant increase in process performance and product products. As larger volumes of process and quality data are
quality. generated, the system can use them to determine additional,
Deep learning is a highly flexible and adaptive artificial less obvious connections between data. As a result, industries
intelligence tool that, when exposed to new datasets, can can build a futureproof processing unit that continuously
increase its ability to identify patterns and classify upgrades process efficiency and product quality without the
relationships between data. This means that the larger the need to re-programme the modelling algorithm.
volume of data fed into a deep learning-generated predictive
model, the higher the probability that the system will create Process orchestrators to stay in control
more accurate and precise forecasts. Furthermore, the As this technology becomes established, PAT knowledge
evolution of the model is automatic, ie no programming or management platforms take on a more important role. As
other actions from human operators are required. larger volumes of data are being generated and ANNs analyse
These unique capabilities are enabled by artificial neural them without offering any insight into their prediction
network (ANN) architectures that mimic the human brain. generation process, it is important for manufacturers to have
ANNs are collections of interconnected artificial neurons or a clear overview of what is happening on the factory floor,
nodes organised in layers. Each neuron receives an input with what the real-time multi- and uni-variate data looks like, and
data to analyse and automatically performs different how the ANN models are evolving.
computations on it without the need for any rule-based Therefore, by implementing a PAT knowledge manager, it
programming. The resulting output is then sent to another is possible to monitor and respond quickly to the presence of
node for further processing. Every time an input is fed to the anomalies or when the predictive model is ceasing to
ANN, the system may be able to notice new correlations represent the input data, ie ANNs have identified correlations
between data and implement them into its predictive model. that are not relevant or unrealistic.
An extremely advanced ANN may even be able to find out One of the most advanced PAT knowledge management
interdependencies that are not known to human experts, thus platforms on the market is Optimal’s synTQ, its efficacy having
delivering forecasts with unprecedented accuracy. been proven worldwide by several of the world’s largest
pharmaceutical and life-science organisations. By choosing a
Adopting quality by design strategies software solution like this, manufacturers can rely on a
Deep learning’s abilities make the technology a potentially platform that is able to interact with cutting-edge technologies
powerful ally for manufacturing industries that adopted and methods, such as deep learning, as soon as they are
Quality by Design (QbD) strategies and PAT. These two available. In addition, synTQ offers a robust and user-friendly
operational process methodologies rely heavily on in-depth interface to keep both product development and scale-up
process understanding in order to maximise the efficiency of production organised at all times. In this way, manufacturers
the overall production process. In fact, knowing how critical can remain in control as they improve plant efficiency, product
process parameters (CPPs) affect products’ critical quality quality and consistency.
attributes (CQAs) is essential in order to control the different As deep learning applications gain popularity in quality
processes in real-time and obtain products that meet elevated prediction, offering a unique tool to boost competitiveness,
quality standards. PAT knowledge management software products like synTQ
The relationships between CPPs and CQAs are assessed by can provide the key to successfully implementing these
means of multivariate analysis (MVA) and chemometrics, ie by strategies and driving productivity.
using mathematical and statistical procedures to extract MORE INFORMATION: www.optimal-ltd.co.uk

INDUSTRIAL TECHNOLOGY • February 2020