How Artificial Intelligence is Creating Better Chemical Processes – Spotchemy Blog

How Artificial Intelligence is Creating Better Chemical Processes - Spotchemy Blog

Since the 1970s, the speed of computer processing has almost doubled every two years. Moore’s Law predicts that this rate will continue meaning that the ‘brain power’ of most CPUs will soon outshine the human mind.

Taking into account other factors such as cloud computing, quantum computing, Internet of Things, data shared via Robotic Process Automation (RPA), ever-smaller transistors (even as small as an atom). ) and the possibility of the invention of artificial intelligence. That computer will play an increasingly bigger role in everything.

While this may remind us of all the Terminator movies and the idea of ​​asking Skynet to run your defense system, we must totally remember that this is science fiction. Science fact shows that practical use for industry will be enormous and highly profitable for those who understand the opportunity.

nowhere would it be more true chemical industry, With its high-tech manufacturing processes, complex logistics, current high use of computers and advanced R&D programs, chemical industry There will be clear beneficiaries of advanced computers and AI.

but is it the future or the present, because many times chemical companies AI is already making an impact.

One such advocate of investment in artificial intelligence is a polymer scientist Dr. Ata Zadi, As the founder of the Canadian Plastics Company exopolymer He is convinced of the competitive edge that his business has through the implementation of AI. In a recent interview with Industry Magazine, he clarified an opinion Canadian Plastics”Every plastics manufacturer has the potential to become more competitive by integrating machine learning into their operations and gaining predictive insights into production,” he said. “The core technologies of machine learning align well with the complex problems that manufacturers face daily, especially large manufacturers with the greatest amounts of raw data.”

Sandeep SreekumarThe global head of adhesive digital operations at Henkel also sees benefits. “We use AI to drive efficient analytics of complex data arrays to achieve high production performance, accelerated product innovation and scaleup for our self-adjusting production systems,” he explained. “Our focus is not only on collecting internal manufacturing data, but also on actively working with customers on data collection opportunities during product use and accommodating to changing customer needs.”

Pushing the frontiers of AI in the chemical industry even further

in japan, sdk (a Toyko-based chemical and technology business) and hi-tech manufacturer Hitachi has contributed to the development of AI-Assisted Predictive-Maintenance Platform,

online industry magazine chemical Engineering Explains, “SDK’s Oita Complex ethylene plant served as a test facility to demonstrate the commercial viability of the new AI service, which uses adaptive resonance theory (ART) to analyze and classify plant operational data in real time.” and identifies anomalies that could lead to equipment failure. In tests at the Oita plant, the technology successfully predicted the occurrence of coking. According to Hitachi, this method detects those patterns and abnormalities. Now, the SDK plans to roll out the technology to additional plants, while further refining the AI ​​model to determine different coking mechanisms.

Meanwhile, in Huelva, Spain, energy and chemical producer sepsahas employed AI in its phenol production plant resulting in a 2.5% increase in production (an additional 5,500 metric tonnes per annum).

This was achieved through machine learning and real-time predictive models that offer plant personnel recommendations every 15 minutes to improve production. The analysis involves AI, considering over 3,000 process variables. It also considers the weather.

And in Madrid, energy and chemical producer Repsol Has collaborated with Google Cloud to apply AI and advanced data analytics to optimize resources at its 186,000 bbl/d petroleum refinery in Tarragona.

as a company Press release Notes, the process will manage, “…about 400 variables, which demand high levels of computational capacity and large amounts of data control.” This is something that represents “an unprecedented challenge in the refining world”. [because] So far, the highest number of digitally integrated functions in an industrial plant is about 30 variables.

Importantly, the company also highlights the economic benefits in using such advanced computers to analyze its refining process, stating that, “the project has the potential to add 30 cents on the dollar to Repsol’s refined barrel margins.” That could translate to up to $20 million annually for the Tarragona refinery, with significant growth if all optimization objectives are achieved.”

Obviously, the presence of AI computing chemical industry just goes beyond capacity. Leading manufacturers are already implementing machine learning and real-time ‘big data’ analysis.

While predicting the future has always been foolish, it would be foolish not to believe that AI has a future. chemical industry Because it already exists.

If nothing else, its potential is truly astounding, like what was said about the Internet in the 1990s, when no one fully understood it, now people talk about AI the same way.

as an online journal chemical Engineering notes, “While advanced technologies such as quantum computing are still very new chemical processing industryNew applications will certainly continue to arise as more users begin to understand the capabilities of AI.”

If you want to know more about AI and its impact on the chemical industry, then you may enjoy reading ‘How Artificial Intelligence Is Making Smarter Chemical Products’. Or check out other articles on the SPOTCHEMI blog page.

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