Hydrocarbon Processing - March 2022 - 10

Business Trends
to accept and continue the data
communication. For the processing
of analytics used by chemical
engineers, it is necessary for
engineers to have servers that do
not disrupt the information flow
of the application. Companies can
easily upgrade RAM memory to
reduce challenges.
* Disk storage capacity. Disk storage
space is also an important factor
and is required for analytics by
big data tools. Systems must have
sufficient disk storage to both install
and operate the required big data
tools without disrupting operations.
Big data tools use algorithms that
require a lot of disk space to run
many iterations and models to
arrive at the optimum solutions.
* Central processing unit (CPU)
consumption. Big data tools can be
CPU processor hungry, especially
when running several solutions
simultaneously, which can slow
down processing. There can be
times when the highest CPU usage
is required for several solutions,
thus impacting other processes
that require CPU usage. Therefore,
in the chemical industry, it is
important for big data tools to be
applied to systems with sufficient
CPU processing power.
Scalability can be beneficial, but it is
equally important to understand the necessity
for it. Despite its significance, each
company has a specific situation and need
for scalability. Scaling is required when
the company is facing problems of downtime
and slow performance. Scaling is a
better choice (rather than allowing the
problem to become a major hurdle) for
adapting new technologies related to big
data. High latency can be the signal for
the right time for scalability. There are
scaling-up systems that can be adjusted
based on criteria required for data.
Scalability is not only limited to adjusting
the volume of information by setting
a criterion, but it also includes the initiatives
related to big data, such as big data
analytics and infrastructure. All the elements
and factors related to big data are
needed for the system to be scalable, so
they can easily reach the desired target
without wasting resources. The big data
tools available in the market enable companies
to scale the infrastructure in both
10 MARCH 2022 | HydrocarbonProcessing.com
horizontal and vertical directions. Adopting
big data tools and replacing conventional
methods may enable a facility to
scale its existing analytical tools.
Big data tools enable a systematic approach
for engineers to work together.
There will be a point when the need to
scale the number of workers will be felt.
Here, scalability will help to scale human
resource requirements efficiently and effectively.
Investing time in, and paying attention
to, the scalability of the analytics
team to determine the team size that is
capable of meeting demands can provide
long-term benefits to companies in the
chemical industry.
Cloud computing platform. The major
reason for big data technology evaluation
is to enable engineers to solve complex
problems. They are expected to consistently
obtain reliable, repeatable, highly
accurate and precise solutions for complex
problems, which requires a high degree
of computational power.
Investing in a cloud-based computing
platform can help address these challenges.
This type of platform will contain
a collection of servers that are able to support
big data analytics. Cloud computing
is a powerful technology that provides
remote access to the database and analytics
through the internet. It is beneficial
because it solves the problem of data storage
and CPU limitations when utilizing
big data tools. This is especially true for
engineers that will use big data tools for
complex problem-solving algorithms that
require higher computational power.
Another benefit of utilizing a cloud
platform is cost savings. This is because
the service provider will be liable for the
security and maintenance of the platform.
Users only have to pay for the service provided
by the company. The cloud service
provider also ensures the availability of
virtual resources. There is no need to purchase,
maintain, expand or replace hardware
when a cloud-based service can be
purchased on a pay-as-you-go basis.
The mining algorithms are easily processed
by virtual resources. Also, many
vendors and contractors can be given access
to the cloud for real-time feedback.
Normally, computer systems available to
engineers are insufficient to process the
amount of data and to manipulate the size
of algorithms. Therefore, there is a need
for cloud-computing technologies that
have easily scalable architecture specifically
designed for big data. Such systems
that can meet these industry requirements
are still in development.
Data specialization. In the complex
chemical industry, the role of data scientists
and big data specialists cannot be
ignored. They play an important part in
the successful integration of big data in
the chemical industry. The data scientist
can understand paradigms of information
processing and can shift the conventional
model to a big data-driven approach.
Another critical role for data scientists
is data mining. Data scientists can assist
in collecting data and extracting the
required logic from it. Data collection,
analysis, interpretation, management and
arrangement are equally important. It is
mandatory for data scientists to convert
the data into synchronized patterns, so
that the data has a visible trend and can
prove a hypothesis.
The chemical industry cannot deny the
importance of big data specialists. A big
data specialist is a data scientist with domain
knowledge and experience specific to
the chemical industry. There can be situations
when data scientists have specialized
knowledge of big data and the know-how
about its integration. However, this is not
always the situation. A big data specialist
is needed for interrogating, understanding
and handling large groups of data. There
is a significant difference between the job
of a data scientist and a big data specialist.
The nature of a data scientist is to understand
the given data and to use it in a favorable
form, while the big data specialist
has chemical industry domain knowledge
and is familiar with the pattern of big data,
making it available for the data scientist.
Therefore, it is necessary for companies to
invest in both big data specialists and data
scientists to successfully integrate big data
in chemical engineering.
The best way to overcome these challenges
is to scale chemical engineers as
data scientists and big data specialists
through university courses and/or professional
training.
Big data professional competency.
Despite the latest big data tools and availability,
the chemical industry cannot deny
the need for workforce development for
the successful integration of big data. Having
traditional knowledge and using con
http://www.HydrocarbonProcessing.com
Hydrocarbon Processing - March 2022

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