Hydrocarbon Processing - March 2022 - 12

Business Trends
ventional tools are not enough for the successful
utilization of big data. Integrating
big data requires a specialized approach, an
understanding of programming languages
and advanced analytic skills. It is very important
for engineers to make the right decisions
at the right time, so they must have
the right skillsets to accomplish this. The
following are some workforce developments
required in the chemical industry:
* Programming language. It is not
sufficient to hire chemical engineers
and simply ask them to integrate
big data into their operations. It is
necessary to invest in programming
and development areas. There is a
need for big data developers who
can understand the function of big
data technologies, and to use their
knowledge to address chemical
engineering issues. Necessary
programming language skills
include Apache Spark, Hadoop with
Python and Java.
When engineers have
an in-depth competency of
programming, they can easily
divert it into a wider scope
demanded by big data. Having
strong programming language and
domain competencies can save
the industry from the problems
created by an enormous volume
of big data. While these challenges
are difficult to tackle with the
restricted amount of funds available
to the industry, investments in
programming languages are needed.
For the implementation of big data
in the chemical industry, engineers
must integrate big data tools into
their legacy software. Normally, the
respective programming language
provides a library facility that makes
it easier for engineers to perform
their required big data analytics.
* Quantitative aptitude and
statistics. The competency of
quantitative aptitude is necessary to
understand big data and interpret
it in the desired situation. It is
necessary for the company to
invest in the quantitative aptitude
of the people handling big data.
In addition to having the required
technology, it is equally important to
have a command over linear algebra
and statistics. Big data analytics
is heavily dependent on statistics,
12 MARCH 2022 | HydrocarbonProcessing.com
and this demand can increase
from simple to complex statistical
methods, depending on the project.
This statistics literacy is necessary
because the algorithms used to make
the programming language are based
on statistical rules and fundamentals.
Therefore, having a command over
statistics, other than just domain
knowledge, is equally important for
the integration of big data.
Companies in the chemical
industry often create teams of
chemical engineers and statisticians
to meet these needs, but these
two jobs can be done by just one
person to reduce the cost of big data
integration. For big data integration,
data scientists, statisticians and
chemical engineers are working
together to avoid security problems
in the long run. Today, chemical
engineers need to have data science
application skills, which are not
presently taught in universities, and
companies can provide these skills
through a comprehensive employee
education program.
* Machine learning (ML). For
chemical engineers, the need to
invest in programming cannot
be mixed with the significance
of ML-and its subset known as
deep learning. Most companies
outsource the job of ML. However,
the integration and application of
big data becomes easy and effective
when a company has integrated
ML techniques and chemical
engineering competencies. The
complex multivariate diagnostic and
prediction problems that chemical
engineers face is simplified by ML,
which includes models such as
regression, decision trees and neural
networks. Chemical engineers
provide timely, accurate and precise
solutions to maximize profit. ML
techniques enhance this by making
the path to achieving the required
solution more efficient. If engineers
have ML knowledge, which is a
part of data science, then they
can perform operations at a safe
level and, consequently, maximize
profit values.
* Process monitoring. Chemical
engineering processes are sometimes
complex and long, requiring a fulltime
person to monitor and observe
them. This task can be done with
big data tools. Chemical engineering
demands predictive, descriptive,
diagnostic and prescriptive activities
when monitoring these processes.
When companies invest in the skill
of data science for their chemical
engineers, these engineers will be
able to provide exploratory data
analysis (EDA), and this analysis will
revolve around making conclusions
and extracting valuable insights from
the data. It will automatically cover
statistical literacy. Engineers will
also be capable of using descriptive
and visualization statistics as a tool
for big data analytics. EDA will help
companies identify solutions that
will have the biggest contributions
to profitability.
Takeaway. For the successful integration
of big data, it is necessary to have the right
big data scalability and professional competencies.
By making investments in big data
scalability factors and acquiring big data
competencies, engineers working in the
chemical industry will be able to capitalize
on the enormous amount of data and to
utilize this data for solution development.
The chemical industry needs tools that
can collect the desired information in a
single platform from multiple sources and
provide a facility to arrange it. After having
the desired skill set and architecture, the
chemical industry must invest in big data
tools that meet industry requirements.
The chemical industry should drive the
switch from conventional analytical methods
to big data-driven approaches.
ASHIFF KHAN is a Lead Engineer for Saudi Aramco,
handling process engineering, projects and master
plans. He has more than 19 yr of experience in the
chemical industry, and has three filed patents in the
U.S. In addition to multiple advanced degrees, postgraduate
diplomas, international certifications, and
doctorates, including an MS degree in data science
from Liverpool John Moores University in the UK, Dr.
Khan also conducts research in business management
as part of his doctoral studies at the SP Jain School of
Global Management in Australia.
MOHAMMAD MANZOOR is a Lead Engineer for the
utilities, WIP and cogeneration (COGEN) plants at
the Saudi Aramco Khurais central processing facility
in Saudi Arabia. He is a chartered chemical engineer
with more than 18 yr of experience working as a
process engineer. Mr. Manzoor earned a BEng degree
in chemical engineering and a post-graduate degree
in computing from the University of Bradford in the
UK. He is also working toward an MS degree in data
science at Liverpool John Moores University in the UK.
http://www.HydrocarbonProcessing.com
Hydrocarbon Processing - March 2022

Table of Contents for the Digital Edition of Hydrocarbon Processing - March 2022
