【Ten Years of Rapid Development in Chemical Equipment】The first AI optimization model for methanol distillation units in China was put into operation at Yulin Energy Chemical Complex in Shaanxi’s Future Energy sector, between 2027 and 2025

Thread Content

Let’s give praise and support to the achievements made in China’s chemical engineering technology and equipment; your participation in discussions is the greatest encouragement. **********************【Ten Years of Progress in Chemical Engineering Equipment】Regular updates and summaries are available – feel free to join the discussions: https://bbs.hcbbs.com/thread-3576046-1-1.html ***************** China’s first AI optimization model for methanol distillation units has been put into operation. Recently, the intelligent optimization project for the methanol distillation units at Shaanxi Future Energy Yulin Energy Chemical Company was successfully completed after its initial testing phase, and the system is now in operation. To date, it has operated steadily for 150 hours straight, achieving both the goals of eliminating manual intervention and reducing energy consumption. This significant breakthrough marks the first implementation of \"AI + chemicals\" in the chemical sector of Shandong Energy Group. It is also the first instance of artificial intelligence being used to optimize methanol distillation units in the coal chemical industry, providing a replicable and scalable model for the technological transformation of the coal-to-methanol sector. By addressing the key pain points, AI + APC is used to create intelligent solutions. The methanol distillation unit in Yulin Energy Chemicals adopts the common \"five-tower, three-effect process\" used in the industry; during long-term operation, it faces three major production challenges: high energy consumption, high sensitivity of product quality, and frequent fluctuations in operating conditions. To overcome these challenges, Yunding Technology, in collaboration with Yulin Energy Chemical, has developed an \"Intelligent Optimization System for Methanol Distillation Processes\". This system innovatively integrates \"AI models + APC (Advanced Process Control) systems\", making extensive use of advanced artificial intelligence technologies to develop two core capabilities: on one hand, it enables accurate prediction of product quality, eliminating the latency associated with traditional quality monitoring and ensuring stable product quality ； On the other hand, optimize steam usage to reduce energy consumption at the source. This technical approach holds significant value for industry adoption and market application. Through \"data-driven + intelligent control\", it achieves optimization across the entire process in four steps. This intelligent system is based on the core principle of \"data-driven + intelligent control\", and it utilizes a four-step closed-loop process to optimize the entire chain, thereby enabling more precise and efficient operation of the devices. Data collection and integration. The system captures in real time key process parameters such as the temperature, pressure, liquid level, feed flow rate, reflux flow rate, and steam flow rate of the distillation tower. It also integrates quality inspection and testing data, thereby creating a \"data pool\" that covers all stages of production and providing a foundation for intelligent analysis ； Model construction and training. By leveraging big data analysis and machine learning algorithms, an intelligent prediction model for the process of methanol distillation units is developed. Through training using historical data and iterative updates with real-time data, the model’s ability to adapt to complex operating conditions and its prediction accuracy are continuously improved ； Generation and distribution of optimal parameters. Based on the real-time production data from the equipment, the model dynamically predicts and automatically generates the \"optimal operating parameters\", which are then sent to the APC control system in real time ； Closed-loop control and implementation of results. The APC control system precisely adjusts the relevant control points based on the received \"optimal parameters\", thereby achieving the goals of optimizing the production process, stabilizing product quality, reducing steam consumption, lowering the methanol content in wastewater, and improving economic efficiency. By making efforts in three areas – carbon reduction, efficiency improvement, and quality enhancement – significant comprehensive benefits have been achieved. Once the system was put into use, breakthroughs were made in all three of these areas, bringing tangible improvements to the enterprise. Carbon reduction: Solid progress being made in low-carbon production. In terms of reducing overall energy consumption, through the integration of \"AI-guided optimization + APC-based execution control,\" the system makes full use of the precision offered by artificial intelligence and the technical advantages of advanced control methods to enable real-time prediction and closed-loop control of the distillation process. Operational data show that the steam consumption per ton of methanol has decreased by 2%, which translates to annual savings of 2 million yuan in steam costs ； In terms of reducing wastewater pollution, the methanol content in the wastewater was approximately 0.45% before the system was put into use; after its implementation, this value decreased by 0.15 percentage points. It is estimated that over 200 tons of methanol can be recovered throughout the year, which not only improves resource utilization but also reduces the environmental burden associated with wastewater treatment for enterprises, thereby providing strong support for low-carbon production. Improved efficiency: Both operational efficiency and management standards are enhanced; “zero manual intervention” has become a key highlight of the project. With \"fully autonomous operation of the methanol distillation process\" as its core goal, the system integrates functions for quality prediction, anomaly detection, and automatic parameter adjustment, thereby significantly reducing the need for manual intervention. During the trial operation phase, the large artificial intelligence model automatically acquires system parameters, performs calculations, and issues adjustment commands, eliminating the need for any human intervention. This not only reduces the workload but also decreases reliance on highly skilled operators ； At the same time, the operational stability of the device has been significantly improved. By comparing the data before and after the implementation of the system, it was observed that the fluctuations in various key parameters of the distillation unit were significantly reduced. This minimized the time wasted on dealing with abnormalities caused by fluctuations in operating conditions, thereby improving production continuity and overall operational efficiency. Ren Peilong, a field technician at Yulin Energy Chemicals, said, “Things are much easier now; we just need to check the computer screen occasionally. AI adjusts the parameters dynamically, enabling automation of the entire process with almost no manual intervention required.” ” Quality improvement: Enhanced product competitiveness. This system utilizes artificial intelligence to conduct real-time analysis of various process parameters, thereby creating accurate quality prediction models. It enables real-time forecasting of product quality, reduces testing costs and time spent, and ensures that products meet the required quality standards ； At the same time, the optimization of process parameters reduces the methanol content in wastewater, further improving the product yield. After the deployment of the large AI model, the purity of the product remains stable at over 99.95%. Based on an annual recovery volume of 200 tons of methanol, this can generate an additional economic benefit of 400,000 yuan. Coupled with the cost reductions resulting from lower energy consumption per ton of methanol produced, these factors together enhance the product’s competitiveness in the market. The person in charge of the project said, “The successful deployment of this system not only provides technical support for the efficient, stable, and energy-efficient operation of methanol distillation units, but it also sets a benchmark for the intelligent upgrading and process optimization in the coal-to-methanol industry. It offers a clear path for companies in this sector to overcome technical barriers and achieve transformative development.” ” Next, the project team at Cloud Ding Technology will continuously refine the methanol distillation model based on operational performance, while simultaneously working on process optimization for units such as gasification coal blending, coal gasification, low-temperature methanol washing, and coal-to-oil conversion.