Use cases of data science in the manufacturing of chemicals

1. 36 www.aiche.org/cep March 2016 CEP
Special Section: Big Data Analytics
B
ig data in the process industries has many of the
characteristics represented by the four Vs — volume,
variety, veracity, and velocity. However, process
data can be distinguished from big data in other industries
by the complexity of the questions we are trying to answer
with process data. Not only do we want to find and interpret
patterns in the data and use them for predictive purposes, but
we also want to extract meaningful relationships that can be
used to improve and optimize a process.
Process data are also often characterized by the pres-
ence of large numbers of variables from different sources,
something that is generally much more difficult to handle
than just large numbers of observations. Because of the
multisource nature of process data, engineers conducting a
process investigation must work closely with the IT depart-
ment that provides the necessary infrastructure to put these
data sets together in a contextually correct way.
This article presents several success stories from dif-
ferent industries where big data has been used to answer
complex questions. Because most of these studies involve
the use of latent variable (LV) methods such as principal
component analysis (PCA) (1) and projection to latent
structures (PLS) (2, 3), the article first provides a brief
overview of those methods and explains the reasons such
methods are particularly suitable for big data analysis.
Latent variable methods
Historical process data generally consist of measure-
ments of many highly correlated variables (often hundreds
to thousands), but the true statistical rank of the process, i.e.,
the number of underlying significant dimensions in which the
process is actually moving, is often very small (about two to
ten). This situation arises because only a few dominant events
are driving the process under normal operations (e.g., raw
material variations, environmental effects). In addition, more
sophisticated online analyzers such as spectrometers and
imaging systems are being used to generate large numbers of
highly correlated measurements on each sample, which also
require lower-rank models.
Latent variable methods are uniquely suited for the
analysis and interpretation of such data because they are
based on the critical assumption that the data sets are of
low statistical rank. They provide low-dimension latent
variable models that capture the lower-rank spaces of
the process variable (X) and the response (Y) data with-
out over-fitting the data. This low-dimensional space is
defined by a small number of statistically significant latent
variables (t1, t2, …), which are linear combinations of the
measured variables. Such variables can be used to con-
struct simple score and loading plots, which provide a way
to visualize and interpret the data.
Big data holds much potential for optimizing
and improving processes. See how it has
already been used in a range of industries,
from pharmaceuticals to pulp and paper.
Salvador García Muñoz
Eli Lilly and Co.
John F. MacGregor
ProSensus, Inc.
BIG DATA
Success Stories in the
Process Industries
Copyright © 2016 American Institute of Chemical Engineers (AIChE)

2. CEP March 2016 www.aiche.org/cep 37
The scores can be thought of as scaled weighted aver-
ages of the original variables, using the loadings as the
weights for calculating the weighted averages. A score plot
is a graph of the data in the latent variable space. The load-
ings are the coefficients that reveal the groups of original
variables that belong to the same latent variable, with one
loading vector (W*) for each latent variable. A loading
plot provides a graphical representation of the clustering of
variables, revealing the identified correlations among them.
The uniqueness of latent variable models is that they
simultaneously model the low dimensional X and Y
spaces, whereas classical regression methods assume that
there is independent variation in all X and Y variables
(which is referred to as full rank). Latent variable models
show the relationships between combinations of variables
and changes in operating conditions — thereby allowing
us to gain insight and optimize processes based on such
historical data.
The remainder of the article presents several industrial
applications of big data for:
• the analysis and interpretation of historical data and
troubleshooting process problems
• optimizing processes and product performance
• monitoring and controlling processes
• integrating data from multivariate online analyzers
and imaging sensors.
Learning from process data
A data set containing about 200,000 measurements was
collected from a batch process for drying an agrochemical
material — the final step in the manufacturing process. The
unit is used to evaporate and collect the solvent contained in
the initial charge and to dry the product to a target residual
solvent level.
The objective was to determine the operating conditions
responsible for the overall low yields when off-specification
product is rejected. The problem is highly complex because
it requires the analysis of 11 initial raw material conditions,
10 time trajectories of process variables (trends in the evolu-
tion of process variables), and the impact of the process
variables on 11 physical properties of the final product.
The available data were arranged in three blocks:
• the time trajectories measured through the batch,
which were characterized by milestone events (e.g., slope,
total time for stage of operation), comprised Block X
• Block Z contained measurements of the chemistry of
the incoming materials
• Block Y consisted of the 11 physical properties of the
final product.
A multiblock PLS (MBPLS) model was fitted to the three
data blocks. The results were used to construct score plots
(Figure 1), which show the batch-to-batch product quality
variation, and the companion loading plots (Figure 2), which
show the regressor variables (in X and Z) that were most
highly correlated with such variability.
Contrary to the initial hypothesis that the chemistry vari-
ables (Z) were responsible for the off-spec product, the analy-
sis isolated the time-varying process variables as a plausible
cause for the product quality differences (Figure 1, red) (4).
This was determined by observing the direction in which the
product quality changes (arrow in Figure 1) and identifying
the variables that line up in this direction of change (Figure 2).
Variables z1–z11 line up in a direction that is close to perpen-
dicular to the direction of quality change.
Z1
Z3
Z10
Z5 Z4
Z8
Z2
Z7
Z11
Z6
Level1
Temp1
Time2
Time4 Temp2
Time1
Time3
TempSlope
0.7
0.6
0.5
0.4
0.3
0.2
0.71
0
–0.1
–0.2
–0.3
–0.4 –0.3 –0.2 –0.1 0 0.1 0.2 0.3 0.4
W* [1]
W*[2]
Z9
Weight Wet
Cake
p Figure 1. A score plot of two latent variables shows lots clustered by
product quality. Source: (4).
p Figure 2. A companion loading plot reveals the process parameters that
were aligned with the direction of change in the score plot. Source: (4).
6
4
2
0
–2
–4
–6
–6 –4 –2 0 2 4 6
t2
t1
On-Spec (High Residual Solvent)
On-Spec
Off-Spec
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Special Section: Big Data Analytics
Optimizing process operations
The manufacture of formulated products (such as
pharmaceutical tablets) generates a complex data set that
extends beyond process conditions to also include informa-
tion about the raw materials used in the manufacture of each
lot of final product, and the physical properties of the raw
materials. This case study can be represented by multiple
blocks of data: the final quality of the product of interest
(Y), the weighted average for the physical properties of the
raw materials used in each lot (RXI), and the process and
environmental conditions at which each lot was manufac-
tured (Z). These blocks of data were used to build a MBPLS
model that was later embedded within a mixed-integer non-
linear programming (MINLP) optimization framework. The
physical properties of the lots of material available in inven-
tory are represented by data block XA and the properties of
the lots of material used to manufacture the final product are
represented by data block X.
The objective for the optimization routine was to deter-
mine the materials available in inventory that should be
combined and the ratios (r) of those that should be blended
to obtain the best next lot of finished product. The square of
the difference between the predicted and the target quality of
the product was used to choose the lots and blending ratios.
The underlying calculations reduce the problem to the
score space, where the differences in quality — in this case
tablet dissolution — correspond to different locations on
the score plot (Figure 3). The MINLP optimization routine
identified the candidate materials available in inventory
that should be blended together to make the final product
so that the score for the next lot lands in the score space
corresponding to the desired quality (i.e., target dissolu-
tion). Implementing this optimization routine in real time
significantly improved the quality of the product produced
in this manufacturing process (Figure 4).
Selecting the materials from inventory to be used in
manufacturing a product is not as simple as choosing those
that will produce the best lot of product. If you choose
materials aiming to produce the best next lot, you will
inevitably consume the best materials very fast; this may
be acceptable for a low-volume product. For high-volume
products, however, using this same calculation will lead to
an undesired situation where the best materials have been
depleted and the less-desirable raw materials are left. In
this case, it is better to perform the optimization routine
for the best next campaign (a series of lots), which will
account for the fact that more than one acceptable lot of
product is being manufactured. The optimization calcula-
tion in this latter case will then balance the use of inventory
and enable a better management of desireable vs. less-
desirable raw materials for the entire campaign of manu-
factured product.
The MINLP objective function must be tailored to the
material management needs for the given product so that
it adequately considers operational constraints, such as
the maximum number of lots of the same material to
blend (5, 6).
Monitoring processes
Perhaps the most well-known application of principal
components analysis in the chemical process industries
(CPI) is its use as a monitoring tool, enabling true multi-
variate statistical process control (MSPC) (7, 8). In this
example, a PCA model was used to describe the normal
variability in the operation of a closed spray drying system
in a pharmaceutical manufacturing process (9). The system
Slow Dissolution
Fast Dissolution
Target Dissolution
1.5
1
0.5
0
–0.5
–1
–1.5
–2 –1.5 –1 –0.5 0 0.5 1 1.5 2
t3
t1
p Figure 3. The dissolution speed of a pharmaceutical tablet is identified
on a score plot of the latent variables. Source: (5).
Historical
Data
Best-Next-Lot Approach
Best-Next-Campaign Approach
{{
Quality Problems
Dissolution,%
USL
LSL
Target
45
40
35
30
25
20
15
Lots of Finished Goods
p Figure 4. A control chart of the degree of dissolution of a pharmaceuti-
cal tablet reveals the onset of quality problems. Quality problems are
reduced by the implementation of a best-next-lot solution, then eliminated
by the best-next-campaign approach. Source: (6).
Copyright © 2016 American Institute of Chemical Engineers (AIChE)

4. CEP March 2016 www.aiche.org/cep 39
(Figure 5) includes measurements of 16 process variables,
which can be projected by a PCA model into two principal
components (t1 and t2), each of which describes a differ-
ent source of variability in the process. A score plot that
updates in real time can then be used as a graphical tool
to determine when the process is exhibiting abnormal
behavior. This is illustrated in Figure 6, where the red dots
indicate the current state of the process, which is clearly
outside of the normal operating conditions (gray markers).
It is important to emphasize that this model could be
used to effectively monitor product quality without the
need to add online sensors to measure product properties.
Building an effective monitoring system requires a good
data set that is representative of the normal operating con-
ditions of the process.
Control of batch processes
Multivariate PLS models built from process data that
relate the initial conditions of the batch (Z), the time-varying
process trajectories (X), and the final quality attributes (Y)
(10) provide an effective way to control product quality and
productivity of batch processes. Those models can be used
online to collect evolving data of any new batch (first the
initial data in Z and then the evolving data in X), which are
then used to update the predictions of final product qual-
ity (Y) at every time interval during the batch process. At
certain critical decision points (usually each batch has one
or two), a multivariate optimization routine is run to identify
control actions that will drive the final quality into a desired
target region and maximize productivity while respecting all
operating constraints (11–13).
Figure 7 displays one quality attribute of a high-value
food product before and after this advanced process
6
4
2
0
–2
–4
–6
–10 –8 –6 –4 –2 0 2 4 6 8 10
t1
t2
Abnormal
Operating
Conditions
Normal
Operating
Conditions
p Figure 6. A score plot of the two principal components describing
the closed-loop spray drying system (Figure 5) shows that the process is
operating under abnormal conditions. Source: (9).
p Figure 5. A closed-loop spray drying system in a pharmaceutical manufacturing facility is being monitored by the measurement of 16 variables that a PCA
model projects into two principal components. Source: (9).
With Control
No Control
0.08
0.07
0.06
0.05
0.04
0.03
0.02
0.01
0
–0.4 –0.2 0 0.2 0.4 0.6 0.8 1 1.2 1.4
Deviation from Target
FinalProductQualityAttribute
p Figure 7. Advanced control eliminated the variation in the final product
quality attribute of a food product. Source: (9).
Feed
Pump
Drying
Chamber
T
P
FS
T
P
W
Cyclone
Baghouse
Process
Heater
HEPA
Filter
Thermal
Mass Flow
Sensor
FS
Supply
Fan
Condenser
Exhaust
Fan
HEPA
Filter
Data Logging
of Product
Collection
Weight
Exhaust Pressure
Controlled by Exhaust
Fan Speed
Drying Gas Flowrate
Controlled by Supply
Fan Speed
Exhaust Pressure
Transducer
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5. 40 www.aiche.org/cep March 2016 CEP
Special Section: Big Data Analytics
control method was implemented over many thousands of
batches. The process control method reduced the root-
mean-square deviation from the target for all final product
quality attributes by 50–70% and increased batch produc-
tivity by 20%.
Analyzing information from
advanced analyzers and imaging sensors
The use of more-sophisticated online analyzers (e.g.,
online spectrometers) and image-based sensors for online
process monitoring is becoming more prevalent in the
CPI. With that comes the need for more powerful meth-
ods to handle and extract information from the large and
diverse data blocks acquired from such sophisticated
online monitors. Latent variable methods provide an effec-
tive approach (14).
Consider a soft sensor (i.e., virtual sensor software that
processes several measurements together) application for
predicting the quality of product exiting a lime kiln at a pulp
and paper mill. Real-time measurements on many process
variables were combined with images from a color camera
capturing the combustion region of the kiln. The information
extracted from the combustion zone images and data from
the process data blocks were combined using the online
multivariate model to assess combustion stability and make
2-hr-ahead predictions of the exit lime quality.
Concluding remarks
Contextually correct historical data is a critical asset
that a corporation can take advantage of to expedite asser-
tive decisions (3). A potential pitfall in the analysis of big
data is assuming that the data will contain information
just because there is an abundance of data. Data contain
information if they are organized in a contextually cor-
rect manner; the practitioner should not underestimate the
effort and investment necessary to organize data such that
information can be extracted from them.
Multivariate latent variable methods are effective tools
for extracting information from big data. These methods
reduce the size and complexity of the problem to simple
and manageable diagnostics and plots that are accessible to
all consumers of the information, from the process design-
ers and line engineers to the operations personnel. CEP
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Multivariate latent variable methods
reduce a problem to manageable
diagnostics and simple plots.
Copyright © 2016 American Institute of Chemical Engineers (AIChE)