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22/06/2022

AI-PROFICIENT Partners Interviews: INEOS

In this exclusive interview with Christophe Van Loock, Manufacturing Excellence Manager at INEOS (Pilot User – Use Case provider), you will learn more about the company’s point of view on:

  • How ΑΙ technology benefits the manufacturing industry
  • Its role within AI-PROFICIENT and what more or different will INEOS try to encapsulate within the project
  • What the company expects to gain from participating in the project

1. Can you tell us about your company?

Ineos is one of the largest chemical companies with activities across the globe. Our products touch every aspect of modern day life. You’ll find our pipe-products when drinking water networks are being built, our medical products in syringes at the hospital, our impact resistant polymers in cars, and so on.

Lately, Ineos has moved beyond petrochemical activities and plays a role in automotive, sports and fashion industries. You might know us from the F1 Mercedes team, the French Ligue 1 team Nice and the Ineos Grenadiers cycling team. Grenadier by the way is the name of the car we are launching this year.

Contributing to a more sustainable world is one of the core Company values, fully embedded in all we do. It’s inspiring and fun to actually make a difference not just in your company but also in the world. In fact, in Antwerp Belgium we are building the biggest petrochemical installation in decades, with a carbon footprint far less than today’s frontrunner installations, a true game changer.

2. What is your company’s role in the AI-PROFICIENT project?

Ineos is one of the two industrial partners in this exciting project. We provide three different use cases spread over two European assets, one in Belgium and one in Germany. We support the technical partners in deploying the developments in a real life industrial environment. Vice versa, the technical partners assist us in solving very complex use cases.

Two of our use cases are quite similar, they both relate to the drift of key process parameters in our continuous processes. We want to have an additional lever, which can help us in keeping these parameters at the desired level, and hope AI technology / machine learning / advanced analytics can help us here. Our third use case relates to reducing and ideally even eliminating human error rate during a routine operator activity. Advanced image recognition should be of support here.

3. What does your organization expect to gain from participating in the AI-PROFICIENT project?

Our teams are used to solving complex technical problems, it’s what they do and what they are good at. Over the years, our processes became more and more efficient, though some complex matters have not found a final solution to drive improvement even further. By cooperating with best in class partners, we expect to enlarge our toolkit to solve problems, and get a breakthrough in the selected use cases.

This breakthrough can be significant. It can increase our annual prime product production capacity in a structural way with a financial impact of well above 1M€ per year, just for these use cases. Imagine if we could translate this technology into all our production assets…

4. From your point of view, what are the most important benefits that AI technology can offer to the manufacturing industry?

We see AI as a key extra toolkit in support of our workers. Throughout the years, our employees have used the best available tools supporting them in their jobs. We see AI as a potential step change giving people this additional supporting tool to drive improvement further. Also for new workers, this can be of great help to give them more confidence whilst learning the job.

It’s something new for us, for sure, but we want to get acquainted with this and fully assess what it could mean for our plants and our industry.

5. Nowadays AI in manufacturing involves using technology to automate complex tasks and unearthing previously unknown patters in manufacturing processes or workflows. What more or different will you try to encapsulate within the AI-PROFICIENT project?

Good question. Our focus is not really on automating complex tasks, it’s indeed more on unearthing previously unknown patterns in our processes which will help our workers in doing their jobs. It gives them an extra insight in the process, allowing them to be more in control to excel in their job execution. AI is not yet fully deployed in our industry, with this project we would like to contribute to changing this.

20/06/2022

AI-PROFICIENT Partners Interviews: Continental France
27/07/2022

2022 Manufacturing Industry Outlook: Growth despite Turbulence

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      How does Machine Learning relate to AI?

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      Machine Learning is a subset of AI
      Machine learning is a subset of AI. The idea is that machines will “learn” and improve at tasks over time rather than having humans continually input parameters. Machine learning is a practical application of AI.
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      What is Neural Networks?

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      Is a method in artificial intelligence that teaches computers to process data in a way that is inspired by the human brain​
      Neural networks are a class of machine learning algorithms. The neuron part of the neural is the computational component, and the network part is how the neurons are connected. Neural networks pass data among themselves, gathering more and more meaning as the data moves along. Because the networks are interconnected, more complex data can be processed more efficiently.​
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      What is Artificial intelligence?​

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      Making a Machine intelligent​
      Artificial intelligence (AI) is intelligence—perceiving, synthesizing, and inferring information—demonstrated by machines, as opposed to intelligence displayed by non-human animals and humans​
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      The characteristics of the computer system capable of thinking, reasoning and learning is known as

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      artificial intelligence​
      Artificial intelligence (AI) is the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The term is frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience.​
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      The first AI programming language was called:​

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      IPL
      Information Processing Language (IPL) is a programming language created by Allen Newell, Cliff Shaw, and Herbert A. Simon at RAND Corporation and the Carnegie Institute of Technology about 1956. This is the first AI programming Language known.​
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      An AI technique that allows computers to understand associations and relationships between objects and events is called:​

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      pattern matching​
      Pattern matching is a technique used in AI which allows a computer program to identify a specific pattern that is present in a given data set and take appropriate actions based on the results of the analysis.​
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      Artificial Intelligence has its expansion in the following application.

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      All of the above​
      From manufacturing to marketing, businesses across multiple industries are leveraging artificial intelligence to optimize their processes and services.
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      Who is the father of artificial intelligence?​

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      John McCarthy​
      John McCarthy (September 4, 1927 – October 24, 2011) was an American computer scientist and cognitive scientist. He was one of the founders of the discipline of artificial intelligence.​
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      When did the term "artificial intelligence" first appear?​

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      In 1956​
      In 1956, at a conference at Dartmouth College, in Hanover, New Hampshire, the term "artificial intelligence" was coined​
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      Which is not the commonly used programming language for AI?​

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      Perl​
      Perl is not the commonly used programming language for AI. ​
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      Great, now that you understand the basics of Artificial Intelligence, we suggest that you glance at our project documents highlighting the improvements that AI services introduced in the manufacturing industry.
      Within the AI-PROFICIENT project, we piloted seven different use cases run across different manufacturing sites of INEOS and Continental. Our solutions aim at improving manufacturing efficiency while keeping humans in control.
      The following questions are based on the outcomes of AI-PROFICIENT use cases and should give you an idea of the practical advantages of AI for manufacturing. Click next to get started.
      Answer
      Great, now that you understand the basics of Artificial Intelligence, we suggest that you glance at our project documents highlighting the improvements that AI services introduced in the manufacturing industry.
      Within the AI-PROFICIENT project, we piloted seven different use cases run across different manufacturing sites of INEOS and Continental. Our solutions aim at improving manufacturing efficiency while keeping humans in control.
      The following questions are based on the outcomes of AI-PROFICIENT use cases and should give you an idea of the practical advantages of AI for manufacturing. Click next to get started.
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      By how much percent AI implementation decreases raw material waste in tyres production?​

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      8%
      Combining Explainable AI algorithms with the global optimisation algorithms in the rubber extrusion allows continuous suggestion of the operator's optimal machinery parameters. This reduces the time when the rubber extrusion process is set and thus reduces the time when the suboptimal rubber outcome that cannot be used in further tire production.
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      Which of the following is a polymer extrusion process:​

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      A chemico-mechanical process​
      Chemically, the process involves the melting and flow of a polymer material under heat, which can result in chemical reactions such as degradation or cross-linking of the polymer chains. The chemical properties of the polymer material and the extrusion conditions, such as temperature, pressure, and residence time, can affect the final properties of the extruded product.​

      Mechanically, the process involves the deformation of the molten polymer material under pressure as it is forced through a die orifice to form a continuous shape. The mechanical properties of the polymer material, such as its elasticity, viscosity, and melt strength, determine its ability to withstand the stresses and strains imposed during the extrusion process.​
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      Which are the key elements of an optimization problem:​

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      The objective function, the decision variables and the constraints​
      The objective function defines the goal of the optimization problem, whether it is to maximize or minimize a certain value. The decision variables are the variables that can be adjusted or controlled to achieve the optimal solution to the objective function. The constraints are the limitations or restrictions that must be satisfied during the optimization process.​

      The other two options mentioned in the question are not related to the key elements of an optimization problem. Hyperparameters, validation schemes, and accuracy metrics are more related to machine learning or data science problems. The differential hypothesis is not a common term used in optimization problems. ​
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      When the blade of the cutting system starts to wear out but it can keep cutting:​

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      It might work well but chances are eventually it will compromise the quality of the final product.​
      As the blade of the cutting system starts to wear out, it will gradually lose its sharpness and efficiency, which can affect the quality of the cut and increase the cutting forces. If the blade is allowed to continue cutting even after it has worn out, it can lead to several negative consequences.
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      According to the literature, which are the three main sources of variation when trying to model a cutting process in an industrial setup:​

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      Workpiece factor, feed factors and saw blade factors​
      In a well-controlled manufacturing environment, the cutting process should be designed to be robust and less sensitive to operator variation. Regarding to the environmental factors, they are less significant compared to other sources of variation.
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      Developing the AI service which will improve tread alignment in part of the Combiline depends upon marking pictures of the tread pieces, which will then be used to train the AI. How should the developers and industrial partner approach this picture marking task for AI training?​

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      The developers should clearly specify how many pictures need to be taken, how long the process will take, and discuss with the industrial partner who will do it.​
      An ethical approach to this task involves clearly specifying how many pictures, and how much added work time it involves. On that basis the developer can discuss with the industrial partner who should do the task, e.g. whether the developers themselves are better placed to do it, or whether the industrial partner’s operators should do it. If the latter, it should be made clear to the operators whether they can refuse to do it, or it is an obligation which makes up part of their work. ​
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      What is explainable AI and how it can help in manufacturing?​

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      XAI stands for "explainable artificial intelligence" and it can help in manufacturing by improving process control, enabling predictive maintenance, enhancing quality control, and improving safety.​
      XAI stands for explainable artificial intelligence and provides insights into decision-making processes, identifies patterns and anomalies in equipment data, and detects defects in real-time. This allows manufacturers to make better decisions, reduce downtime and maintenance costs, and address problems quickly.​
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      How should the developers engage the operators when developing the AI service and related apps and HMIs, with the goal of simplifying the job of confirming correct chemical product bag placement before production? ​

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      The developers should work closely with the operators to design the services, choose the HMIs and adapt the apps, according to the actual work context. ​
      To implement an ethical by design user centered approach, the developers should work closely with the operators who will actually use the service. They should take care to understand the specific work conditions (dust, noise, time for tasks, etc.), discuss the planned solutions with the operator users, design initial mockups and discuss these with the operator users regularly in an iterative process as they develop the service. ​