Core Viewpoint - The integration of AI into the chemical industry, particularly in safety management, is in its early stages but shows promising growth potential as companies explore various applications and models [1][2][3]. Group 1: Current State of AI in the Chemical Industry - AI is being actively embraced in the chemical sector, with many companies and parks initiating smart upgrades and exploring AI applications [1][2]. - The application of AI technology in the chemical industry is still in the exploratory and pilot phase, with large-scale implementation requiring more time [2][3]. - Key challenges include the immaturity of cost-effective AI models, the lack of high-quality datasets, and concerns over data privacy [3][4]. Group 2: Challenges and Opportunities - The high cost and long-term investment required for developing private AI models hinder widespread adoption among typical chemical companies [3]. - The absence of quality datasets is primarily due to companies' reluctance to share data for privacy reasons, which limits the effectiveness of AI models in addressing industry-specific issues [3]. - Concerns about data privacy and security are significant barriers to the adoption of AI technologies, necessitating the development of secure private deployment methods [4]. Group 3: Future Prospects and Applications - The integration of AI into chemical safety management is seen as a critical area for development, with potential applications in risk monitoring, predictive maintenance, and operational efficiency [5][8]. - The Chinese government is promoting the integration of AI, big data, and IoT technologies into safety production, indicating a supportive regulatory environment for AI adoption in the chemical sector [5][8]. - Successful case studies and pilot projects in AI applications are expected to drive broader industry adoption and demonstrate the technology's effectiveness [12][15]. Group 4: Practical Applications of AI - AI technologies are already yielding results in risk warning and detection, as well as in maintenance and inspection processes [13]. - Specific applications include the use of AI for predictive maintenance by analyzing historical data to identify patterns and anomalies [13][14]. - The development of modular AI models tailored to specific applications within the chemical industry is recognized as a promising approach for effective implementation [10].