The vast majority of safety accidents in university laboratories occur when instructors, students, or researchers perform experimental procedures. Process safety management (PSM) has become a weak link in the laboratory safety governance system of higher education institutions, and no systematic and effective management paradigm has yet been established. This paper adopts a comparative research method based on investigation and survey. On the basis of systematically sorting out the framework and core elements of the PSM system used in industry, this study conducts an in-depth investigation into the practical exploration and existing problems of PSM in domestic university laboratories. Combined with the characteristics of teaching and research activities in university laboratories, a dynamic process safety management (DPSM) model for university laboratories is developed. This model takes hazard identification as the starting point, risk assessment, grading and classification as the core, targeted control measures as the main body, and emergency response and personal protection as the bottom-line safeguards. Preliminary application practices have also been carried out. The results show that the DPSM model effectively reduces inherent laboratory risks, improves the level of refinement and dynamism in process safety control, and provides theoretical references and practical approaches for improving and implementing laboratory safety management systems in higher education institutions.

This paper adopts an analytical comparison method. First, it systematically investigates mature PSM systems used in industry, as well as the preliminary research and exploratory achievements of universities in planning laboratory process safety management systems. Second, it compares and analyzes these practical experiences, examining the similarities and differences among the 20 elements of industrial chemical PSM in domestic and international practice. It also systematically analyzes the characteristics, advantages, and disadvantages of the RAMP risk prevention and control system developed by domestic and foreign universities, as well as scientific and technological societies.

In terms of inherent safety, this paper systematically plans all system elements related to hazard identification, risk assessment, and risk control in laboratory process management, establishes a DPSM system for university laboratories, and demonstrates its implementation methods and pathways through practical applications.

DPSM is a dynamic management system based on risk identification and assessment. Through the scientific identification of all hazard sources in university laboratories, it evaluates hazards using appropriate assessment methods and technical approaches, and formulates targeted control measures and emergency disposal procedures based on the assessment results. This paper also systematically expounds the relationships among process management elements, such as hazard identification, risk assessment, the establishment of control measures, and emergency disposal. The elements of the PSM system form an organic whole, in which hazard identification is the foundation of process management, and risk assessment is the core. To cultivate high-level talent, universities can establish laboratory PSM systems suited to their characteristics by adopting advanced concepts and practical experience from enterprise PSM. This approach not only improves the laboratory safety management model but also prepares high-quality professionals with strong safety literacy for employment in enterprises. Meanwhile, transforming the laboratory safety hazard management model toward pre-event prevention, while strengthening the identification and assessment of safety risks and the targeted rectification of key safety risks at the source, will continuously improve laboratory safety risk control capabilities, fundamentally preventing and eliminating potential safety accidents.

This study investigates the transformative potential of big data technology in revolutionizing experimental materials management throughout its entire lifecycle in higher education institutions, using Soochow University’s innovative practices as a case study. The research systematically examines the limitations of conventional management approaches, including data fragmentation, lack of procurement transparency, and passive inventory control, and develops a comprehensive data-driven framework to overcome these challenges. By implementing this integrated system, the study demonstrates significant improvements in operational efficiency and cost reduction while establishing a reliable foundation for sustainable scientific research amid growing experimental complexities and increasing funding pressures.

This study employs a mixed-methods approach grounded in Soochow University’s experimental material management practices, combining case analysis, predictive modeling, and iterative process optimization. Leveraging big data analytics, the research integrates multi-source data from procurement records, IoT sensors, and laboratory monitoring systems to establish a unified management platform. Machine learning algorithms were applied to analyze historical procurement patterns and predict demand fluctuations, while API-based interoperability protocols enabled seamless integration with external supplier systems. The PDCA (Plan-Do-Check-Act) cycle was rigorously implemented to ensure data quality and refine operational workflows, with real-time feedback from stakeholders informing system improvements. Cross-disciplinary collaboration between laboratory managers, data scientists, and supply chain experts further enhanced model accuracy and adaptability. Additionally, blockchain frameworks were pilot-tested to validate data immutability and traceability across procurement stages. These methodologies collectively supported the transition from fragmented management to a holistic, data-driven ecosystem.

The implementation of big data-driven strategies at Soochow University has yielded significant improvements. The intelligent procurement platform streamlined workflows, drastically reducing administrative burdens in financial reimbursement while achieving widespread adoption across departments. By integrating IoT sensors, real-time monitoring of material consumption and environmental conditions enabled adaptive inventory adjustments, enhancing procurement efficiency and responsiveness. The adoption of a “zero-inventory” model, supported by predictive analytics and seamless supplier collaboration, eliminated traditional warehousing costs while ensuring uninterrupted material availability. AI-powered anomaly detection modules improved supply chain resilience by proactively identifying demand fluctuations and operational risks, enabling dynamic resource allocation. Blockchain technology further strengthened traceability and compliance across procurement cycles, fostering transparency in auditing processes. Additionally, cross-system interoperability between procurement, safety protocols, and financial platforms minimized operational redundancies and elevated risk management capabilities. Collectively, these advancements established a robust, data-driven framework that optimized resource utilization, reduced operational costs, and strengthened support for high-impact scientific research.

The study concludes that the critical role of big in revolutionizing university experimental material management, transitioning from fragmented practices to an interconnected, proactive ecosystem. Soochow University’s “Internet Plus” model demonstrates how integrating IoT, AI, and blockchain can optimize procurement precision, reduce costs, and align with sustainability goals. Key achievements include the unification of siloed systems into a centralized platform, predictive analytics for demand-driven supply chains, and blockchain-enabled accountability. Future directions should prioritize scaling AI models for real-time adaptive decision-making, expanding blockchain applications to cross-institutional collaborations, and embedding circular economy principles (e.g., material recycling) into procurement frameworks. Additionally, fostering partnerships with tech providers and policymakers will be vital to address challenges like data security and ethical AI use. Ultimately, the research underscores the importance of tailoring big data management systems to the unique characteristics and needs of each university to ensure effective experimental material management and support high-quality development in higher education institutions.

With the further improvement in the level of scientific research in universities, the annual increase in the use of chemical reagents in various disciplines not only brings more challenges to laboratory safety but also runs counter to the current “dual carbon” goal of pursuing high-quality development with low-carbon environmental protection. In recent years, laboratory accidents involving hazardous chemicals have occurred occasionally. These accidents not only seriously affect the normal teaching and research orders at universities but also cause considerable harm to the personal safety of teachers and students and the property of the country. At the same time, we observe that the extensive use of chemical reagents in university laboratories has exerted great pressure on safety management. Furthermore, the use of numerous chemicals has an increasingly serious impact on the environment. Through various measures and approaches, controlling and reducing the use of chemical reagents significantly improves the essential safety of chemical laboratories.

Chemical research under the “dual carbon” goal is a new discipline born in the context of sustainable development. Starting from the concept of essential safety management in chemical laboratories, the use of chemical reagents can be controlled and reduced through the following approaches: ① controlling the amount of chemical reagents used at the source, ② recycling chemical reagents to achieve “zero waste and zero pollution,” ③ completely utilizing new technological processes such as new chemical synthesis, analysis, and characterization methods and the application of artificial intelligence technology, and ④ continuously improving environmental governance capabilities.

Suzhou University’s practices regarding the essential safety of chemical laboratories mainly focus on the following aspects: ① strengthening the source management of hazardous chemicals through virtual simulation experiments, chemical theoretical calculations, and other means to achieve the elimination and reduction of chemical reagents. ② Strengthening the entire supervision process of hazardous chemicals and combining laboratory procurement and inventory management to avoid potential risks caused by the excessive accumulation of chemical reagents in laboratories. ③ Improving the ability to manage hazardous waste. Since 2020, Suzhou University has been comprehensively implementing the third-party “environmental stewardship” service for laboratory hazardous waste. Through door-to-door collection, classification guidance, and hidden danger investigation in each laboratory, this service ensures the standardization of hazardous waste classification and packaging, reducing potential safety risks that may arise from the mixing of hazardous waste by teachers and students. ④ Exploring intelligent laboratory safety management based on digital technology. Suzhou University has made certain explorations and pilot constructions to apply new digital technologies to perform intelligent laboratory management. Using big data and artificial intelligence technology, Suzhou University analyzes historical and experimental process data of chemical laboratories, identifies potential safety hazards, and provides corresponding safety management suggestions and early warning measures. To achieve essential safety in chemical laboratories, it is necessary to establish a new chemical research project approval and scientific research achievement evaluation system, actively guide universities in the direction of chemical and material science research to adopt new methods and technologies, and gradually shift to simpler experimental schemes with shorter cycles, better energy efficiency, and lesser risk. Moreover, this emphasizes the need to reform and innovate existing experimental methods, change conventional experiments to microchemical experiments and use tandem reactions, solvent-free synthesis, and other means to optimize experimental methods, gradually developing a new chemical research system.