Industry Case

Our Client has production plants in Asia, America, Africa where their products are sold in dozens of countries and regions around the world.
Our team used AI to establish a boiler combustion optimization model, which in turn adjusted key parameters to increase the amount of coal burnt.

Asia’s leading cement company has two cement clinker production lines with a daily output of 5,000 tons, and its leading products are sold in China, the United States, the Middle East, Africa, Southeast Asia and other countries and regions.
We helped to integrate with data intelligence technology and cement production industrial technology, energy-saving and consumption reduction for cement clinker production is achieved.

Our client is a large rubber company, one of the top ten tire companies in the world. It owns more than 38,000 stores and is exported to 160 countries and regions. The challenge is the high rubber defective rate.
We optimise the supply chain and deployed AI algorithms to recommend the best production parameters to help customers improve the yield rate.

Our client is one of the top three steel and vanadium manufacturers in the world, their products are widely used in metallurgy, petroleum, railway, chemical, shipbuilding, construction, machinery manufacturing, home appliances and other industries.
Our team used AI technology to model and analyze the steelmaking operation process and key parameters, which recommended the optimal parameters, control the consumption of raw materials to improve efficiency.