{"id":17154,"date":"2020-12-12T20:48:51","date_gmt":"2020-12-12T19:48:51","guid":{"rendered":"https:\/\/www.predictland.com\/casos-de-exito\/energy-optimization-and-emission-minimization\/"},"modified":"2025-02-17T16:15:58","modified_gmt":"2025-02-17T15:15:58","slug":"energy-optimization-and-emission-minimization","status":"publish","type":"caso-de-exito","link":"https:\/\/www.predictland.com\/en\/case-studies\/energy-optimization-and-emission-minimization\/","title":{"rendered":"Energy Optimization and Emission Minimization"},"content":{"rendered":"\n
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PredictLand designs a solution for energy optimization and emissions reduction in a highly complex chemical process for a major multinational Oil & Gas company.<\/p>\n\n\n\n

Customer<\/h2>\n\n\n\n

KBC<\/a>, a Yokogawa Company, is a multinational consulting services and technology provider to the Refining, Petrochemical, Chemical and Energy sectors. The Yokogawa group has a turnover in excess of 2.9 billion euros. <\/p>\n<\/div>\n\n\n\n

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\"\"<\/figure>\n<\/div>\n<\/div>\n\n

Challenge<\/h2>\n\n

KBC aimed to improve the performance of its Petro-SIM\u2122 process simulation software through AI and Big Data-based technologies.<\/p>\n\n

The first application of this improvement has been carried out at a refinery of one of its Clients. The objective was to maintain one of its crude oil processing units at the optimal energy point, meeting and potentially optimizing production plans, as well as minimizing emissions levels to help reduce the Company’s carbon footprint. <\/p>\n\n

Solution<\/h2>\n\n

PredictLand has addressed this challenge by designing an Artificial Intelligence solution that has learned from the operation of the refining process that provides relevant information to the station operator, thus optimizing energy consumption and reducing emissions.<\/p>\n\n

This solution contributes to a significant increase in refinery OEE efficiency, as well as greater autonomy for the station operator. In addition, new insights have been gained from the station, thanks to data cleaning and modeling. In this way, it is possible to know which factors influence the energy consumption of the refining process, as well as to define much more efficient strategies. <\/p>\n\n

Testimonial<\/h2>\n\n

Toni Gim\u00e9nez<\/strong>Digital Solutions Executive at KBC, a Yokogawa Company<\/p>\n\n

“Working with PredictLand is a pleasure, their team combines a deep knowledge of Machine Learning modeling and software development with a total customer and results orientation. From the beginning of the project the integration between our teams flowed naturally. <\/em><\/p>\n\n

They have demonstrated a remarkable ability to work and adapt to a new highly complex environment.<\/em><\/p>\n\n

It is also important to emphasize that all of the above transpires in their company culture and is perceived as such by our client.”<\/em><\/p>\n\n

\n\n

Pablo Jim\u00e9nez-Asenjo<\/strong>Visual Mesa Global Business Line Leader at KBC, a Yokogawa Company<\/p>\n\n

“Together with PredictLand, we continue to advance the integration of traditional simulation technology with Artificial Intelligence tools.<\/em><\/p>\n\n

In addition to contributing its technical expertise, PredictLand brings value to the team right from the start.”<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

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