Refineries need machine learning to improve operations

Machine learning and other advanced technologies are embedded in advanced analytics software to empower engineers and other experts.

By Michael Risse May 30, 2019

In the book Thinking, Fast and Slow by Daniel Kahneman, machine learning is described as “the science of getting computers to learn and act like humans do, and improve their learning over time in autonomous fashion, by feeding them data and information in the form of observations and real-world interactions.” Commonalities between the human mind and machine learning technology include:

  • Drawing on compiled patterns
  • Executing responses and generating intuition
  • Requiring opportunities to learn regularities through practice
  • Needing to experience variability while learning
  • Approximating a large number of items
  • Understanding the reasoning behind the intuition
  • Seeing patterns in randomness.

The challenge is refineries need transparent algorithms so their subject matter experts (SMEs) can understand and explain the causal reasoning behind their conclusions. Machine learning algorithms do not always arrive at answers for the same reasons people do, which typically means one can’t inherently understand the outputs of machine learning systems, or directly trace their relationship back to the inputs. These algorithms rely on recognition, not reasoning, so it’s unlikely a random output from a complicated “black box” technique can be explained to any decent level of satisfaction or relied upon without further inspection.

Therefore, it is necessary to use advanced analytics software to leverage machine learning techniques in the context of SME expertise. The Sidebar figure shows just how this can take place. Machine learning models can make predictions and recommendations by finding patterns in data at a scale much greater than humans are capable of handling, but if there is no transparency, an SME often is not able to explain the how or the why.

It is clear then that machine learning must be encapsulated in advanced analytics software to leverage human intuition and reasoning. This allows the best use of human decision-making and judgement, which operates under two interlocking systems, Systems 1 and 2.

System 1 is our rapid and largely unconscious mode of operation, drawing on association and metaphor—a “gut reaction” way of thinking. System 2 is our unrushed, analytical, deliberate and conscious mode of reasoning—a “critical” way of thinking.

System 1 operates well in many cases, but there are times when it can introduce errors of bias since it seeks to create a coherent and plausible story by relying on assumptions, memories and pattern-matching. When the thought process uses only existing evidence and ignores absent evidence, System 1 can create a believable story, but often not a complete solution. System 1 limitations often lead to cognitive biases, or unconscious errors of reasoning. A better path requires the application of Systems 1 and 2 together.

Because machine learning algorithms rely only on recognition without reasoning, and because the human brain has its own challenges regarding bias, the best solution requires:

  • Understanding the power and limitations of machine learning algorithms
  • Optimizing an advanced analytics solution to promote effective use of human intuition and reasoning
  • Providing the solution with a user-friendly interface to take advantage of both machine learning and human reasoning.

Further, the approach of leveraging teamwork within an advanced analytics solution helps proactively identify and eliminate many human biases. In conjunction with machine learning algorithms, the impact from this broader human engagement results in a better outcome.

Original content can be found at Oil and Gas Engineering.

Author Bio: Michael Risse is the CMO and vice president at Seeq Corporation, a company building advanced analytics applications for engineers and analysts that accelerate insights into industrial process data. He was formerly a consultant with big data platform and application companies, and prior to that worked with Microsoft for 20 years. Michael is a graduate of the University of Wisconsin at Madison, and he lives in Seattle.