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Intelligent transformation in the petrochemical industry gains further support According to reports from Sinochem New Network, eight departments including the Ministry of Industry and Information Technology recently issued the \"Implementation Guidelines for the ‘Artificial Intelligence + Manufacturing’ Initiative\" (hereinafter referred to as the \"Guidelines\"). These guidelines are accompanied by two annexes: the \"Guidelines for Using Artificial Intelligence to Drive Transformation in Key Industries in Manufacturing\" (hereinafter referred to as the \"Transformation Guidelines\") and the \"Guidelines for the Application of Artificial Intelligence in Manufacturing Enterprises\" (hereinafter referred to as the \"Application Guidelines\"). These documents provide pathways and methodological guidance for the intelligent upgrading of industries such as chemicals and other raw material sectors. The Guidelines state that by 2027, China will ensure a secure and reliable supply of key core artificial intelligence technologies, with its industry scale and level of innovation remaining among the highest in the world. Promote the in-depth application of 3 to 5 general-purpose large models in the manufacturing sector, introduce 1,000 high-quality industrial agents, create 100 high-quality datasets for industrial applications, and promote 500 typical use cases. Cultivate 2–3 ecosystem-leading enterprises with global influence and a number of specialized, innovative small and medium-sized enterprises; develop a group of application service providers who have expertise in intelligence technologies and are familiar with the relevant industries; and identify 1,000 model enterprises. The Guidelines focus on seven key tasks: laying a foundation through innovation, enhancing capabilities through intelligence, achieving breakthroughs in products, fostering relevant entities, strengthening the ecosystem, ensuring security, and promoting international cooperation. These efforts aim to facilitate a two-way enhancement between artificial intelligence technologies and their application in the manufacturing sector, thereby accelerating the intelligent, sustainable, and integrated development of manufacturing. This in turn provides strong support for building a strong manufacturing nation, a strong cyber nation, and a digital China. The reporter noted that, as a refinement of the \"Opinions,\" the specific measures mentioned in the two annexes are closely related to the petrochemical industry. The Guidelines call for promoting improved quality and efficiency in the petrochemical industry. For example, comprehensive use of large models and digital twin technology can break through the paradigms in oil and gas exploration and development, as well as in the research and development of new chemical materials. By deeply integrating the operational mechanisms of oil and gas production, pipeline transportation and storage, chemical processing, expert knowledge, and production data, a large-scale model for the petrochemical industry is developed. The integration of large and small models is promoted to enable functions such as safety monitoring and early warning in oilfield operations and chemical manufacturing, predictive maintenance of equipment, adaptive optimization of chemical processes, and prediction of product quality. Build data infrastructure such as high-quality industry datasets and data resource nodes to support the training and development of industry-wide large models and agents, thereby improving the level of artificial intelligence applications in complex scenarios. The Guidelines provide implementation paths and methodological guidance for enterprises to achieve intelligent transformation and upgrading. “Since 2023, key enterprises in the industry have actively explored applications of artificial intelligence, giving rise to a series of specialized large models such as intelligent chemical engineering large models, TPT time-series large models, and Kunlun large models. However, challenges remain, including slow progress in the development of high-quality datasets, insufficient reliability, and weak underlying infrastructure. ”Li Yuanyuan, secretary-general of the Digital Transformation Promotion Center for the petrochemical industry under the Ministry of Industry and Information Technology, explained that these guidelines specifically focus on improving the quality and efficiency of the petrochemical industry. They also provide preliminary guidance on how to put these ideas into practice, suggesting that enterprises strengthen their data infrastructure and explore high-value applications of artificial intelligence in order to achieve intelligent upgrades. Li Yuanyuan stated that as a typical process industry, the petrochemical industry features complex production processes that are characterized by cross-scale effects, non-linearity, and strong coupling. The traditional approach of \"mathematical modeling + experimental verification\" results in a gap between the mechanism model and the real system due to dimensionality reduction modeling. Artificial intelligence can enable the computational modeling of complex chemical systems by fitting the mapping relationships between long-distance data and multimodal data, thus showing great potential for application in the petrochemical industry. Meanwhile, the industry’s solid foundation in automation along with its large volume of data provide ample opportunities for the development of artificial intelligence applications.