Hydrocarbon Processing - March 2022 - 9

A. KHAN and M. MANZOOR,
Saudi Aramco, Dhahran, Saudi Arabia
Business Trends
The big data tools and related professional
competencies required in the chemical industry
Big data is making tremendous changes
in various sectors of the economy, and
chemical industries can now leverage
these new advancements. Companies in
the chemical industry are starting to realize
how they can create additional value
from the vast amounts of data they have,
and how this data can be efficiently used
to make more effective and strategic decisions.
However, to leverage this data,
there is a need for timely investments in
big data projects, including investing in
big data competency training and in tools
for scalability.
Engineers must understand data science,
and how to take command over
coding and develop a strong quantitative
aptitude. Various big data tools are available
on the market that can make big data
integration easy by providing a unified
platform for the collection and arrangement
of information extracted from big
data. This article will focus on the competencies
and tools necessary to make the
investment for big data adoption. It will
also review present and future big data
adoption challenges, and highlight how
entities (such as government, academia
and industry) must collaboratively work
together to overcome them.
What is big data? Big data is a vast
amount of data of a dissimilar nature that
is produced through various sensors in the
chemical industry, and this data requires a
large volume of storage space and specialized
tools for processing. Big data can enable
engineers to enhance their analyzing
capabilities by providing them with vast
amounts of accurate and real-time information
from many sources. This also requires
visualization and storage tools that
can provide them the facility to store their
data, and to analyze and forecast the outcomes
of their processes. Big data is especially
helpful for engineers in the chemical
industry, as their work is highly complex
and deals with a huge amount of data beyond
human capabilities. Engineers are
continuously in need of a source that provides
them with access to a high volume of
data to perform necessary analyses. More
data is generated following these analyses;
hence, it is important for engineers to
store this data to keep a record of simulations
and modeling for further optimization
and forecasting.
Big data in the chemical industry.
The chemical industry requires hardware
that can process complex algorithms for
multivariate analyses to support decision
making. The industry also needs to invest
in big data tools and related professional
competencies. In the past, traditional engineering
did not include such skills and
advanced technologies as part of a highereducation
curriculum. This is now changing,
as these competencies have become
more relevant in the modern big data era.
Traditionally, engineers manually make
the correlations, identify the relationships
in variables, and design hypotheses for limited
variables. The challenge is to shift engineers
from these conventional methods
to the big data era so that all analyses can
be done through automated systems with
higher accuracies for multivariate analysis.
In the past, it was difficult for the chemical
industry to successfully implement the
latest technologies in its operations because
of the complexity of the domain and the
additional prerequisites for the necessary
technology integration. However, big data
is different from conventional advancements
because of the increased awareness
and available tools in the market. Through
its industry-wide adoption and integration,
big data can increase the competitive capabilities
of the chemical industry.
Big data scalability. Scalability in the
chemical industry is the ability of available
systems to grow in response to
changing demands for big data adoption.
For example, this may include computing
and networking, in addition to human resource
requirements.
Big data is easily scalable, which allows
companies in the chemical industry
to adjust the data according to their
changing demands. This means that big
data contains a large volume of data that
is reliable and verified through various
platforms that have already been implemented.
This includes data from various
systems, such as process and management
information systems.
Data may contain different versions,
including text and video content, in addition
to several forms of numerical data
from sensors. Obviously, engineers cannot
use all this data, as they rely on accurate
data that is relevant to their field and
processes. If scalability is not considered,
then the outcome of the company's big
data utilization, along with the benefits
that it can provide, could be diverted from
the engineers' expectations.
For the successful utilization of big
data in industry, it is necessary for engineers
to have strong analytical tools.
Quality will suffer if the scalability is not
correctly adjusted. This can raise questions
on the competitive capability and
on the overall stakeholder satisfaction for
these tools, and can risk their profitability.
Scalability is not as simple as it appears,
and requires careful consideration of the
following factors:
* Random access memory (RAM)
status. This is the most critical
component often ignored by
companies in the adaptation of big
data. It is linked with the capacity
of the servers and computers
Hydrocarbon Processing | MARCH 2022 9
Hydrocarbon Processing - March 2022

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