[V7N1] – Can AI Alone Boost Refinery’s Efficient Operations And Profitability? Part I: Myth, Hype Or Reality?

The refining industry faces significant challenges in sustaining profitability due to the unpredictable fluctuations in the global crude oil market, which are influenced by supply and demand. A surge in crude oil prices results in increased fuel costs, reduced travel by consumers, and, subsequently, a negative impact on the profitability of refineries.

Refineries endeavour to enhance their operational efficiency to economize one additional cent. Conversely, a decline in crude oil prices induces a reorientation towards complacency, wherein streamlined operations are disregarded until the subsequent surge in crude oil prices.

In this series of articles, we will review various aspects of Artificial Intelligence (AI), Machine learning (ML), Neural Network (NN) or Deep Learning (DL) technologies and how they are or can be applied in the downstream refining industry.

In the first part of the series, we define the distinction between AI, ML, and NN as they are often lumped together or interchanged by us all. They all don’t mean the same thing. However, the idea may be the same.

Let us understand: AI or any of its derivatives can not solve our life’s problems. The human mind has created AI and hence remains an obedient servant of the human mind. AI can extrapolate wisdom only by interpolating existing Human or physical process knowledge. It truly emulates the saying “Garbage In and Garbage Out,” or GIGO in short.

Artificial Intelligence (AI) has the broadest concept to think and act like humans to recognize, solve problems, learn and perceive.

Machine Learning (ML) is a subset of AI and must have data to learn and train itself to create rules based on past events. It interpolates past events to extrapolate future events.

Neural Network (NN) or Deep Learning is a subset of ML and works as closely to human brains with interconnected neurons. It is structured and learns complex interactions between patterns and classifications.


So, let us ask ourselves:

Is AI and its derivatives a myth?

The answer is not really. But it has some misconceived myths.

  • Myth #1: Machine Learning is more intelligent than humans. Not True
  • Myth #2: Machine Learning Can Be Used Anywhere. Not True
  • Myth #3: Machine Learning will take over jobs. Not True
  • Myth #4: Machine Learning never changes. Not True
  • Myth #5: Machine Learning requires more data to get reliable results. Not True
  • Myth #6: Machine Learning can predict the future. Partly True
  • Myth #7: There’s no difference between Artificial Intelligence & Machine Learning. Not True
  • Myth #8: Machine Learning can work independently without Human Intervention. Not True
  • Myth #9: Machine Learning Platform is easy to build, and anyone can do it. Not True
  • Myth #10: Machine Learning is the Future-Only. Partially True

(🔗 Reference: https://www.linkedin.com/pulse/some-myths-machine-learning-artificial-intelligence-ranjan-sinha/ )

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Now you have AI myths, let us ask another question – What is the hype about AI?

Google’s Gemini answers about itself:

I have genuine capability with problem-solving power, automated and efficient, personalized feedback, and offer innovation beyond human innovation. I look forward to future enhancements such as Artificial General Intelligence (AGI) and enhanced human capabilities. But I am hyped with misconceptions such that I am superintelligent, which I am not. Humans are still my masters. And then there is a singular myth that advanced AI can fuel more hype and unrealistic expectations.”

Is AI for real?

Yes, It has strengths and limitations.

AI Strengths are:

  • Narrow AI Excels At Specific Tasks
  • Powerful Data Processing
  • Automation & Efficiency
  • Personalized Experiences

AI Limitations are:

  • No General Intelligence
  • Data Dependence
  • Explainability Challenges
  • Limited Physical Interaction

The reality of AI is one of immense potential but with clear limitations. It’s a powerful tool, not a magic bullet, and its impact depends on responsible development and use. Understanding its strengths and weaknesses is crucial for navigating the hype and harnessing AI for good.

Disclaimer: OMS eLearning Academy and Google Gemini collaborated as Humans and AI to generate this article for you.

In Part II,

We will discuss the Success and Failure of the First Principle Models For Refining Processes, which is close to our eLearning business for the downstream refining professionals.

Stay tuned for more groundbreaking publications and enrich your expertise with OMS Academy. 🌐✨

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