Sulzer deploys artificial intelligence solution at two Atlantica solar plants
Solarkraftwerk Mojave. (Image source: Sulzer Ltd.)
Located 100 miles northeast of Los Angeles, US, the Mojave 280 MW solar plant generates clean electricity to power approximately 90’000 households, preventing the emission of 350’000 tons of CO2 annually. KaXu Solar One in South Africa has a capacity of 100 MW and saves emissions to the amount of 315’000 tons annually.
Sulzer’s BLUE BOX advanced analytics solution on the boiler feedwater and heat transfer fluid pumps will strengthen Atlantica’s own anomaly detection capabilities and thus increase uptime, improve reliability and eliminate operational risks of their renewable energy plants. After successful completion, Atlantica aims to implement Sulzer’s cloud-based technology in all its assets worldwide as a part of their enterprise-level digitalization efforts.
The latest version of Sulzer’s BLUE BOX is now capable of enhancing operational performance of pumping systems in both the short- and long-term. This includes automated flagging of anomalous operations detected and estimated remaining lifetimes as well as pro-active recommendations on actions by Sulzer experts aiming for a more targeted maintenance. Atlantica will benefit from a new BLUE BOX with artificial intelligence features, strengthening their own machine learning capabilities. With Sulzer’s help, Atlantica expects to generate significant cost savings and higher revenue. In addition, BLUE BOX helps understand the operational performance of the pumps, therefore providing the possibility for proposed efficiency or output gains in the future.
的不w version of BLUE BOX makes use of artificial intelligence and combines statistical with physical models based on Sulzer’s expertise. By analyzing real-time and historical operational data, Sulzer’s pump experts will proactively support Atlantica to make better-informed business decisions.
The award-winning BLUE BOX by Sulzer is a set of digital enabled services, leveraging the Internet of Things for asset optimization and real-time predictive maintenance. It uses existing pump data and machine learning to cut costs in pump operation and maintenance considerably.
Source:Sulzer Ltd.
