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.