Analysis of Intelligent Construction and Energy Saving Renovation Policies in European and American Laboratories
25-09-09
Major policies and strategic initiatives in Europe and the United States
In recent years, various parties in Europe and the United States have introduced a series of policies and strategic initiatives aimed at promoting the intelligent upgrading and energy-saving transformation of laboratories, reflecting their high-level attention to the modernization and sustainable development of scientific research infrastructure.
| Area | Policy/Plan Name | Issuing Authority | Core Goal | key Field |
| USA | NSF PCL Test Bed | NSF | Invest 100 million US dollars to establish an AI-driven automated laboratory network, accelerating scientific discoveries | Biotechnology, Materials Science |
| EU | European Strategy for AI in Science | EC | Coordinate the resources of all member countries, establish the RAISE platform, and promote the responsible application of AI in scientific research. | Climate change, health, clean technology |
| AI-on-Demand platform | A one-stop service platform that provides reliable AI tools, datasets and educational resources | All fields of science | ||
| Mutual Learning Exercise | Promote the exchange of best practices among member states, and release reports on infrastructure and human resources | High-performance computing, scientific data, talent cultivation | ||
| Germany | Practices of Sustainable Development in University Laboratories | Many universities have joined together | By using a mixed research approach to systematically evaluate the sustainable performance of the laboratory, propose improvement paths | Material consumption, equipment management, procurement process |
Key Support Areas and Typical Measures
Integration of artificial intelligence and automation
1、AI-driven cloud laboratories: The Programmable Cloud Laboratories (PCL) network funded by the US NSF enables researchers to remotely access and run custom AI-driven experimental procedures, aiming to enhance experimental efficiency and accuracy, and reduce trial-and-error costs. Its initial focus is on data-intensive fields such as biotechnology and materials science.
2、"AI Scientists" and Autonomous Discovery Systems: The EU report regards "AI Scientists" (AI Scientists), "robotic scientists", and "autonomous laboratories" as the cutting-edge of scientific AI. These systems are integrated with laboratory automation to achieve closed-loop automation in scientific research. The EU calls for the establishment of an "European Distributed AI Science Institute" (similar to the organization CERN) to promote its development.
Energy-saving renovations and sustainable development
1、High energy consumption challenge: Data shows that the average energy consumption of university laboratories in Germany is 3 to 5 times that of office buildings, and the annual carbon emissions of researchers are 1.6 tons higher than those of the German population. The energy consumption of medical laboratories is even 3 to 6 times that of ordinary office buildings.
2、Energy-saving focus: The major energy-consuming entities in the laboratory include:
（1）HVAC system (heating, ventilation, and air conditioning): It accounts for 40% to 60% of the total energy consumption of the laboratory.
（2）Continuous operation of precision equipment: such as chromatographs (operating 75% of the time on a 24/7 basis) and ultra-low temperature refrigerators (100% continuously operating in the biological laboratory).
（3）Lighting system: It can account for up to 15% of energy consumption.
4、Energy-saving measures and standards:
（1）Adopting energy management system standards, such as ISO 50001. The case shows that through heat recovery devices (such as recovering waste heat from centrifuges), the annual reduction of CO₂ emissions can reach 42 tons.
（2）Equipment optimization and sharing: It is recommended to dynamically adjust the temperature of the ultra-low temperature refrigerator (for example, from -80°C to -70°C, which can reduce energy consumption by 30%), and establish a device sharing mechanism.
（3）Circulation design: For instance, a closed-loop water circulation system is adopted (such as using the wastewater from the pure water system for cooling the laser), achieving the recycling of water resources.
（4）Intelligent Monitoring and AI Prediction: Utilize AI to predict sample flow and optimize equipment operation status; or through the centralized demand control ventilation (DCV) system, adjust the laboratory ventilation volume in real time based on pollutant concentrations, significantly reducing energy consumption.
Data infrastructure and open science
1、The European Union is building the European Open Science Cloud (EOSC), aiming to provide researchers with an open multi-disciplinary environment for the publication, search, and reuse of data, tools and services.
2、The United States emphasizes the establishment of world-class scientific datasets and the development of privacy-enhancing technologies (PETs), such as differential privacy and federated learning, in order to facilitate data sharing and collaboration while protecting privacy and security.
Personnel training and skill enhancement
1、The policies of the European Union emphasize the need to provide AI skills qualification certifications for researchers in various fields through education and training programs.
2、It is suggested to introduce interdisciplinary master's and doctoral programs that integrate AI and high-performance computing, and to establish formal training programs for researchers and industry professionals.