In-depth scenarios, deep integration of technology and application

The key to realizing the potential of AI technology is to promote the integration and innovation of AI technology and practical application scenarios. As a “practitioner” and “enabler” of AI scenario-based applications, Schneider Electric is committed to deeply integrating AI technology with a series of vertical industry scenarios to enable production to improve quality and efficiency:

IS200TBCIH1BBC • Process optimization: Schneider Electric developed intelligent control strategies with AI algorithms to provide a disruptive production line optimization solution for a beer manufacturer. By gathering and analyzing the full production data, keenly monitoring the working conditions, and predicting and fine-tuning the optimal control strategy, we helped customers achieve 20% material saving and 15% production efficiency improvement while producing safely and high-quality.
Industrial process carbon reduction: In a chemical company application example, Schneider Electric deployed a customized machine learning model to monitor six carbon emission sources in a vacuum distillation unit. The model uses the AVEVA PI System to operate a big data management platform to analyze data streams every 5 minutes, so as to generate timely feedback on potential deviations in CO2 emissions. This enables operators to react quickly, investigate root causes, and make targeted adjustments to optimize processes and minimize CO2 emissions. The model is not only suitable for vacuum distillation units, but also can be transferred to different industrial processes.
• Refined energy consumption management: The ice machine cooling capacity prediction solution provided by Schneider Electric for a semiconductor company is based on AI algorithm and accurately predicts the cooling capacity of the demand side according to the historical data of the ice machine operation. Through more accurate control of energy consumption demand, to achieve fine management of energy consumption. The measured data show that the energy saving effect of the scheme is 3-5%, and the comprehensive energy saving of 5-10% can be realized if the hardware is reformed.
• Air compressor performance improvement: Schneider Electric realizes optimal control and intelligent management of air compressor stations through AI intelligent algorithms, helping enterprises significantly improve energy efficiency. In the station building management system project of a new energy vehicle enterprise, through data collection, modeling and analysis, the optimal operation parameters are provided for the control system and HVAC control system of the comprehensive station building of the factory, so as to achieve control logic optimization and energy saving and efficiency improvement, so that the enterprise can get twice the result with half the effort on the road of building an efficient and energy-saving modern and green chemical plant.
IS200TBCIH1BBC • Dynamic cooling efficiency: In the HVAC energy-saving renovation project of a data center, Schneider Electric injected AI modeling and data analysis algorithm into the traditional PID closed-loop control, and optimized the terminal precision air conditioning in the machine room through four steps of modeling and data acquisition, accurate prediction, optimization and solution, and strategy output, so that it can perform dynamic cooling output according to actual needs. At the same time, the global optimization of the cold station control system is carried out to achieve 31% power saving of the terminal air conditioning system, and the cooling efficiency of the cold station is expected to increase by 20%.
Predictive maintenance: The equipment fault prediction and diagnosis system based on vibration mechanism + mathematical model, combined with the process mathematical model fault diagnosis tool, can not only help users diagnose mechanical aging and wear problems, but also diagnose electrical faults or equipment failures caused by process changes. Schneider Electric Xiamen plant deployed an AI-based predictive maintenance solution for vacuum furnace equipment, realized 24/7 real-time data monitoring equipment conditions throughout the year, and planned equipment maintenance according to the forecast curve, saving about 1.2 million yuan in maintenance costs per year.