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Today, raw data on any industry is widely available. With the help
of artificial intelligence (AI) and machine learning (ML), this
data can be used to gain meaningful insights. In addition, as data
is the new raw material for today's world, AI and ML will be
applied in every industrial sector. Industry 4.0 mainly focuses on
the automation of things. From that perspective, the oil and gas
industry is one of the largest industries in terms of economy and
energy. Applications of Artificial Intelligence (AI) and Machine
Learning (ML) in the Petroleum Industry analyzes the use of AI and
ML in the oil and gas industry across all three sectors, namely
upstream, midstream, and downstream. It covers every aspect of the
petroleum industry as related to the application of AI and ML,
ranging from exploration, data management, extraction, processing,
real-time data analysis, monitoring, cloud-based connectivity
system, and conditions analysis, to the final delivery of the
product to the end customer, while taking into account the
incorporation of the safety measures for a better operation and the
efficient and effective execution of operations. This book explores
the variety of applications that can be integrated to support the
existing petroleum and adjacent sectors to solve industry problems.
It will serve as a useful guide for professionals working in the
petroleum industry, industrial engineers, AI and ML experts and
researchers, as well as students.
- presents in-depth insights regarding fundamentals associated with
big data technologies involved in petroleum streams. - builds on
earlier works of researchers and inventors, which is essential
source material for students in this area of study. - discusses
essential processes and methodologies in petroleum streams that
will direct researchers to pursue a practical approach to the
field. - sheds light on challenges and problems of individual
streams and inert-relation issues, while asking the reader to
innovate and ideate upon those issues. - Offers an analysis of the
financial aspects and business perspective on the processes to help
the reader make constructive and practical decision in the field.
This book discusses major issues in the field of agriculture are
crop diseases, lack of storage management, pesticide control, weed
management, lack of irrigation and water management and their
effective resolution via automation including IoT, wireless
communications, machine learning, artificial intelligence, and deep
learning. It further discusses the sterile insect technique which
is a replacement of conventional pesticide and fertilizer
techniques. Hydroponics and Vertical Farming, two of the top ranked
agricultural engineering accomplishments of the past century, are
also treated. Features: • Offers in-depth insights regarding the
fundamentals of technologies associated with the agriculture
sector. • Synthesizes earlier works of researchers and inventors
in this field • Sheds light on the challenges and problems of
supply and demand worldwide. • Encourages the reader to innovate
and ideate upon those issues. • Analyses the wide array of
services provided by companies worldwide and discusses recent
breakthrough in agriculture automation. This book is aimed at the
work of many researchers to obtain a concise overview of the
current implementation of automation in agriculture and derive
important insight into its upcoming challenges.
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