The refining and chemical production process is difficult to be described using strict principles of chemical kinetics as it is complex and includes multiple processes such as reaction, separation, back mixing, and refining of various materials.
Most of the materials in the refining and chemical production process are mixtures which are complex in nature. Therefore, it is difficult to obtain accurate physical properties by conventional analytical and inspection methods.
The efficiency of the production installation depends on the overall balance of raw materials, processing process, catalyst consumption, energy consumption and product quality, and this needs to be optimized from multiple dimensions.
For the problems of processes, equipment, energy consumption, HSE(health, safety and environment) existed in petrochemical production plant, AI techniques were used to carries out deep analysis & mining on the historical data of the production plant obtained using big data technology, based on the enterprise's application system data, such as DCS, real-time database, MES, LIMS, HSE, corrosion management, etc., to discover the problem, locate factors related to the problem, and find the cause of the problem, based on which to establish solutions and form production operation guidance and risk assessment technology that can be used for promotion, and finally to create a new way of using big data technology to solve petrochemical plant production problems.
By applying Lenovo Big Data, IoT (Internet of Things), and AI (Artificial Intelligence) in the petroleum and petrochemical industry, the traditional business can be deeply analyzed from the macro perspective of petroleum and petrochemical production to find out the potential benefit points and multi-unit and multi–service horizontal correlation which could not be found by manual experience in the past. On this basis, the data analysis capability of Lenovo big data platform is used to form several business application models, including process optimization, equipment early warning, energy optimization, optimization of spare parts inventory, safety assessment and early warning, and distribution of hazardous chemicals, in order to improve the operation efficiency of traditional business, reduce the production risk in the petrochemical industry and increase the economic efficiency of enterprises.