369-LO-R-M-0-D-0-E In the process of transformation from traditional industry to new industry, creating real value with cutting-edge technology is the only way – Schneider Electric is further breaking the barriers between IT and OT, going deep into the full life cycle of enterprises from design, construction to operation and maintenance, and putting the iterative driving force of AI technology for productivity into practice.
In the early stage of research and development design, Schneider Electric is innovating the traditional development methods of software with AI technology, such as assisting the generation of basic code through large models and helping to check the integrity of the code, saving engineers a lot of repetitive work, and injecting more vitality into the development of new technologies and new functions. In key production and manufacturing links, AI technology helps factories improve quality and efficiency, such as helping to coordinate multiple factors and develop precise production plans through AI intelligent decision-making; Through AI visual inspection, we can efficiently identify product defects and improve product quality. In the operation and maintenance management process, Schneider Electric is using AI algorithms and machine learning to help enterprises efficiently manage assets and equipment, improve operational efficiency, optimize energy use, and help enterprises improve the efficiency and resilience of operations and maintenance.
It can be seen that whether it is visual recognition, machine learning, large language models, or generative AI, it has been effectively permeated into all aspects of industrial production processes. So what is the key to maximizing the value of AI scenarios?
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:
Process optimization: Schneider Electric developed intelligent control strategies with AI alg369-LO-R-M-0-D-0-E orithms 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 enable it 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.
Energy consumption refined management: Schneider Electric provides a semiconductor company with a cold capacity prediction solution for the ice machine, based on AI algorithm, according to the historical data of the ice machine operation, the cooling capacity of the demand side is accurately predicted. 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 369-LO-R-M-0-D-0-E 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.
Dynamic refrigeration 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: Equipment fault prediction and diagnosis system based on vibration mechanism + mathematical model, combined with 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.
