Page 50 - 《橡塑技术与装备》英文版2026年3期

HINA R&P TECHNOLOGY AND EQUIPMENT

supervised or unsupervised learning algorithms to predict the 3.3.7 Continuous learning and optimization
type, quantity, and quality of pyrolysis products, and optimize Online learning: Leveraging AI's online learning
pyrolysis process parameters. capabilities, continuously optimize models and control
Deep learning model: By utilizing techniques such strategies based on real-time data, thereby enhancing system
as deep neural networks, it achieves modeling of complex performance.
nonlinear relationships and enhances prediction accuracy. Feedback loop: Establish a closed-loop system, adjust
3.3.3 Process control and intelligence model parameters based on actual operational feedback, and
AI-driven PID controller: Integrates intelligent PID achieve adaptive optimization of the system.
control algorithms to dynamically adjust key parameters of 3.3.8 Safety and compliance
the pyrolysis process (such as temperature, pressure, residence Safety protection system: Integrates AI safety protection
time, etc.) based on the output of the AI model. mechanism to ensure the safe operation of the system and
Intelligent execution system: Through the integrated prevent misoperations and accidents.
control module, it achieves automated operation of the Compliance inspection: Utilize AI technology to conduct
pyrolysis equipment, enhancing production efficiency and compliance inspection to ensure that the pyrolysis process
stability. complies with relevant regulations and standards.
3.3.4 Resource recycling and energy
utilization 4 Application case and analysis of AI-
AI-optimized product separation: Utilizing AI algorithms based pyrolysis of waste plastics
to optimize the product separation process, thereby enhancing AI-assisted pyrolysis of waste plastics not only enhances
the purity and value of the recovered materials. the intelligence level of waste plastics pyrolysis technology but
Energy management system: Integrating AI energy also provides technical support for achieving sustainable plastic
consumption prediction and optimization technology to recycling. These application cases demonstrate the potential
achieve efficient energy utilization during the pyrolysis process of AI-assisted pyrolysis of waste plastics in improving the
and reduce energy consumption. efficiency, environmental friendliness, economic benefits, and
3.3.5 Environmental monitoring and energy reducing environmental impacts of the waste plastics pyrolysis
conservation and emission reduction process.
Pollutant emission monitoring: Integrate AI models to 4.1 Case and analysis of optimizing process
conduct real-time monitoring of pollutant emissions, ensuring parameters for AI waste plastic pyrolysis
compliance with environmental standards. Data-driven pyrolysis process optimization is one of the
Energy conservation and emission reduction strategy: important applications of intelligent technology in the field of
Through AI analysis, propose specific strategies for energy waste plastic pyrolysis. By training machine learning models
conservation and emission reduction, such as optimizing on historical data, the efficiency, oil yield, and properties of by-
process parameters and improving equipment energy efficiency. products during the pyrolysis process under different operating
3.3.6 System integration and management conditions (such as temperature, pressure, residence time, etc.)
platform can be predicted. A research team has analyzed the impact of
Internet of Things (IoT) Platform: Build a unified IoT factors such as temperature, pressure, and residence time during
platform that integrates all devices and data, enabling remote the pyrolysis process on oil yield and by-product characteristics
monitoring and management. using machine learning models (such as neural networks or
Decision support system: Develop a decision support support vector machines). Through training the model, they
system that provides scientific decision-making basis based are able to predict the optimal output of the pyrolysis process
on AI analysis results, optimizing pyrolysis processes and under different parameter settings, thereby optimizing the
resource allocation. pyrolysis process and improving resource recovery efficiency.

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