Process Industry

Artificial intelligence proposes a radically different approach to the traditional scientific method. It wants to automate the learning processes of that experienced operator. Because although humans are smarter, computers are faster. And luckily humans are smart enough to make computers mimic the human learning process.

These are AI algorithms.

Towards fully automated plants and enterprises

Traditionally, the road from discovery to production starts with scientists developing theories about causal relations and the use of mathematics to describe these cause-and-effects. Then after a series of experiments at increasing scales, engineers apply the science to design and operate processes at large industrial plants.

Having the need to know the underlying mechanics in order to develop better mathematical models is slowing the whole process down significantly. Much more valuable is that experience-based expert operator’s knowledge… Isn’t it?

Talk to your Faktion AI Consultant to learn how we can accelerate this whole process and what the benefits are.
1990
Low impact
Basic digital adoption
Selective advanced analytics adoption
Cross-discipline optimization
Autonomous plant
Autonomous enterprise
2030+
High impact

An experience-based expert
operator’s knowledge is not enough

They accumulated the knowledge
over years-to-decades of experience.

They are not always available,
only during their shifts.

They cannot know what has never occurred in operations or experiments.

.

An AI toolbox that provides a shortcut to classical mathematical models

ArtificiaI Intelligence looks at the experiments and measurements and derives the model from that data. That’s why many AI algorithms are called ‘learning’ algorithms. They learn the solution without any prior paradigm, just from the data.

Technically, it is important to understand that this happens very much like human experienced-based learning: by repeated trial and error. AI algorithms provide the procedure to try something out, learn from it, adapt the model intelligently for the next time and then try it again.

Classic method

Limited complexity
Very general and broad
Scientific principles

Artificial Intelligence

Fast training, development
Very specific and precise
Based on correlations

The goal should always be business improvement​

The potential for business value creation is especially large in the process industry, given the complexity and high value of the industry.

Today, processing plants have already made substantial
investments in process monitoring with mostly traditional and some AI-powered sensor tech. These sensors ‘measure’ the process and the resulting state of the process.


However, operators must still rely on their experience, intuition, and judgment for process analysis and control. They are expected to monitor a multitude of information and adjust the process settings as required.

At the same time, they must troubleshoot and run tests and trials. Thus, many operators take shortcuts and prioritize urgent activities that don’t necessarily add value.

.

What are the drivers behind an
AI implementation in this industry?​

There is so much more to come and we are here to help

We are aware that the value for companies is not in the theory, but in seeing potential applications.

For the implementation, everything is there, expert AI service companies exist, and the basic theory is mature enough to be implemented in a controlled and targeted fashion.

At the same time, the AI framework is disruptive enough for the results to be not less than breakthroughs.

Cherry-pick the AI solutions that deliver
the most value to your business​

Business Value
When the problem is solved and implemented, the solution will yield significant and recurring value. As explained in the second chapter, the goal is always business value and may never be technological advancement as such.
Urgency
The problem defined is recognized as a priority across the business, by all stakeholders at business and plant level. Resources are made available to solve the problem.


Data Availability & Quality
It should be clear by now that, an AI solution starts from the data. Proper quality & availability of data is determined by: Data accuracy, consistency, timeliness, completeness & frequency.


Problem Complexity
Is the problem complex enough that traditional methods cannot solve it? At Faktion, we don’t want to make things more complex than needed. Hence, conventional methods should first be deemed insufficient.

Implementation Potential
Once solved, the solution can be implemented to deliver the impact with low CAPEX, on a short time frame and on a large scale.




AI solutions for Process Industry ​

Technological improvements are merely a means to an end. It sounds obvious but it’s often confused. The potential for business value creation is especially large in the process industry, given the complexity and high value of the industry.​
Get your introduction to the benefits
of AI in your business today.


An extended whitepaper will be available soon. Follow us on LinkedIn for updates.

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AI in the Process Industry