Despite the OMS eLearning Academy having started operations in June 2021, our media presence is still in the process of developing. However, comprehensive information regarding our media presence can be found on both our official website and LinkedIn profile. A comprehensive account of our organization can be obtained from our official website and LinkedIn profile. Furthermore, engagement in industry events and publications would provide further perspectives.

As our academy continues to grow and establish a strong foothold in the field of downstream refining training, its visibility in the media will increase.

Academy News

Evolution of HW, SW, and OS for Fuel Blending

In our last article, we traced the evolution of fuel blending from manual methods to modern analyzer-less systems. However, to fully appreciate this evolution, it's crucial to explore the development of various components like hardware, software, and operating systems. This narrative is particularly significant for our Academy Director and CEO, Dr. Suresh Agrawal, who has been pivotal in deploying blending automation projects since the early 1980s, involving systems from mainframes to workstations and software such as IBM’s ACS-RTPMIS and Windows affecting both servers and PCs. Let us walk together to learn how blending systems have developed since 1950's.
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Evolution of Gasoline Blending From Manual to Analyzer-Less Systems

We’re thrilled to share some exciting news with you! Over the past year, we’ve successfully conducted a series of 12 Monthly Master Class Webinars, each focusing on different aspects of Refinery Offsite Operations. Our goal has been to provide you with practical strategies to tackle refinery operation issues, explore the latest automation technologies, dive into informative case studies, and much more.
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Evolution of Gasoline Blending From Manual to Analyzer-Less Systems

The petroleum sector has greatly advanced in gasoline blending technology, enhancing efficiency, precision, and sustainability. From manual methods, the industry adopted First Principles Blend Models (FPBM) for accurate simulations, then integrated AI and machine learning to improve processes and predict blend characteristics. The latest innovation, analyzer-less systems, eliminates the need for physical analyzers, using advanced algorithms and spectroscopy to boost blending accuracy and cost-effectiveness.
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