First, the core concept and value of digital twin factory

The digital twin factory is a virtual factory mirror image built through technologies such as Internet of Things, big data and artificial intelligence, which realizes real-time mapping and two-way interaction between physical entities and digital models. This technology breaks through the limitations of traditional manufacturing, enabling enterprises to optimize production processes, predict equipment failures and improve resource utilization through virtual simulation, and finally realize the intelligent transformation from "experience-driven" to "data-driven".

In the era of intelligent manufacturing, digital twin factories are not only technological upgrading, but also the core elements of enterprise competitiveness. It builds a high-precision virtual model by collecting equipment operation data, process parameters and environmental information in real time, which provides scientific basis for production decision. For example, by analyzing the operation data of the equipment, the system can predict the NOx emission concentration of the ethylene cracking furnace 15 minutes in advance, and give the optimization scheme to realize the source emission reduction.

Second, the key technical architecture of the digital twin factory

1. Multi-dimensional data acquisition and processing

Rely on the industrial Internet of Things (IIoT) to deploy sensor networks, collect data such as equipment status, energy consumption and quality in real time, and realize data cleaning and preprocessing through the edge computing platform. For example, Zhongke Refinery Ethylene Factory supports the analysis of more than 120 industrial agreements through edge computing to ensure the real-time and accuracy of data.

2. Construction of high-precision digital twin model

Using hybrid modeling technology (physical model+data-driven), a multi-disciplinary virtual model covering technology, equipment and safety is constructed. For example, the mechanical stress distribution is simulated by finite element analysis, and the model parameters are modified by combining historical data to realize the accurate reproduction of the equipment state.

3. Intelligent analysis and optimization

The machine learning algorithm is used to deeply mine the data to realize the optimization of production process and fault prediction. For example, based on the cracking reaction model independently developed, the diene yield of ethylene plant in Zhongke Refining and Chemical Company increased by 0.315 percentage points, and the annual efficiency increased by over 3.6 million yuan.

4. Virtual-real interaction and remote operation and maintenance

Combined with 3D visualization technology and AR/VR interaction, technicians can remotely monitor the equipment status and simulate the operation process, greatly reducing the maintenance cost. For example, virtual drills can improve the efficiency of emergency response and reduce the loss of unplanned downtime.

Third, the implementation path of digital twin factory

1. Demand analysis and goal setting

Identify business pain points (such as high equipment failure rate and low production efficiency), set quantifiable targets (such as increasing equipment utilization rate by 20% and reducing energy consumption by 15%), and coordinate cross-departmental resources.

2. System architecture design

Modular design is used to integrate data acquisition, model simulation, visualization and other functional modules to ensure the scalability and security of the system. For example, the modules are loosely coupled through the micro-service architecture to support the subsequent function upgrade.

3. Data-driven model construction

Deploy sensor networks in stages and gradually improve the data acquisition system. The accuracy of the model is verified by historical data, and combined with the dynamic correction of real-time data, the virtual model and the physical entity are finally highly synchronized.

4. Intelligent application deployment

From single equipment optimization to intelligent management and control of the whole process, for example, predictive maintenance of equipment is realized first, and then intelligent scheduling of production planning is promoted. Through the phased pilot verification effect, reduce the implementation risk.

V. Future trends of digital twin factories

With the integration of 5G, AI and blockchain technologies, digital twin factories will develop to a higher level:

-Real-time: realize millisecond data interaction through the low latency of 5G, and support real-time optimization and remote control.

-Autonomy: The combination of digital twinning and digital thread technology can realize dynamic reconfiguration and independent decision-making of the factory to meet the requirements of flexible production.

-Greening: Help enterprises realize low-carbon transformation through intelligent carbon footprint accounting and energy optimization technology. For example, the ethylene plant of Zhongke Refining & Chemical Co., Ltd. reduced waste alkali emission by 19% through process optimization, and the annual cost savings exceeded 800,000 yuan.