ArcInsight Partners’ new report examines analytics innovations from three companies, bringing some field experiences solving domain-specific problems using IIoT analytics. Oil & Gas midstream refining and chemical process/batch manufacturing often throw the hardest multivariate problems for optimization of processes. Two are early technology companies with a track record combining domain specific models with AI to solve client process issues. The third, a business unit of a large global conglomerate. The three companies exemplify the use of deep-domain expertise with operating experiences in upstream & midstream of oil &, gas and downstream aromatics process and chemicals industries. One is based in Europe, the other in Asia, the third headquartered in the United States.
Repsol, the Spanish energy major, plans to deploy big data and artificial intelligence tools to optimize performance of its Tarragona integrated refining complex in eastern Spain, and ultimately across its five other refineries with a total capacity of 896,000 b/d. The Tarragona Industrial Complex processes 9.5 million tons of raw materials a year and has the capacity to distill 186,000 barrels of oil a day. The company already uses Tools such as Siclos which Repsol’s refinery control-panel operators use to forecast in real time, the economic impacts of operating decisions; or Nepxus, which increases planning, analysis, and agility in decision-making in the control rooms at these sites.
As part of Repsol’s broader digital transformation program, this new initiative will leverage Google’s ML Cloud to to analyze hundreds of variables that measure pressure, temperature, flows and processing rates among other functions for each unit at Tarragona. Repsol’s developers will build and deploy machine-learning models for refinery production. Ultimately, the company plans to redesign more than 300 functions from its traditional operation to a digital version.
OUR LATEST THOUGHTS
Here are a few typical questions Rudina Seseri (Founder and Managing Partner, Glasswing Ventures) would pose to any AI startup.
What are the founders passionate about?
Are you solving a real-world problem?
Is AI core to your strategy?
What market are you going after?
How talented is your AI talent?
The bottom line: VCs who know the segment will quickly be able to see, or see-through, the depth of your AI knowledge. To ensure your credibility and be taken seriously as an artificial intelligence startup, if you say you’re doing AI, show it.