Page 51 - 《橡塑技术与装备》英文版2026年3期

SPECIAL AND COMPREHENSIVE REVIEW

Research institutions and enterprises in Germany may have Companies in the United States, such as Ensyn Technologies,
conducted in-depth research on optimizing pyrolysis process are developing AI-driven pyrolysis technology. By integrating
parameters using AI technology. By analyzing a large sensor data and AI algorithms, they achieve automated control
amount of data through AI models, they predict the efficiency and optimization of the pyrolysis process, thereby improving
and product characteristics of the pyrolysis process under energy recovery efficiency.
different operating conditions, achieving process parameter This case study demonstrates that AI-assisted pyrolysis of
optimization, improving resource recovery rates, and reducing waste plastics can enhance the efficiency and quality of waste
environmental impacts. Research institutions and enterprises in plastic processing, reduce manual intervention, and improve
France are exploring how to utilize AI algorithms to optimize the controllability of the overall pyrolysis process quality.
the pyrolysis process, especially for specific types of plastics 4.3 Case study and analysis of AI-assisted
(such as polypropylene and polyethylene), to increase oil yield pyrolysis of waste plastics to promote the
and reduce environmental pollution. development of green circular economy
The case study in this section demonstrates that the AI-driven waste plastic pyrolysis technology plays a
pyrolysis model can assist engineers in real-time adjustment pivotal role in advancing the development of a green circular
and intelligent optimization of process parameters, thereby economy. By enhancing the efficiency of waste plastic
maximizing production efficiency and optimizing product processing, mitigating environmental pollution, and generating
value. economic value, it facilitates efficient resource utilization and
4.2 AI-enabled intelligent, efficient, and high- sustainable development. A specific company has developed
quality pyrolysis case studies and analysis an AI-driven intelligent pyrolysis system that integrates
By adopting advanced sensor technology and real-time deep learning algorithms and real-time data monitoring to
data processing algorithms, the intelligent control system can optimize key parameters during the pyrolysis process, such as
automatically adjust operating parameters such as heating rate temperature, pressure, and residence time, thereby enhancing
and ventilation volume based on preset process parameters the efficiency of converting waste plastics into valuable
and real-time feedback information, ensuring efficient and products. The system is also equipped with a fault prediction
stable pyrolysis process and maximizing the quality and yield model that can identify potential equipment issues in advance,
of products. Furthermore, by integrating machine learning reducing downtime and boosting overall operational efficiency.
models, the system can continuously learn and adapt to the This case study demonstrates that the introduction of AI
characteristics of different types of waste plastics, optimize technology has significantly enhanced the intelligence level
pyrolysis process conditions, and further enhance resource of the pyrolysis process, not only improving production
recovery and energy conversion efficiency. This intelligent efficiency and minimizing human operational errors but also
control system not only significantly improves production reducing operational costs and equipment failure rates through
efficiency and product quality but also reduces human predictive maintenance.
operational errors, providing reliable technical support for the This case study demonstrates that AI intelligent systems
widespread application of waste plastic pyrolysis technology. can help maximize resource utilization and reduce waste
Germany's robust industrial foundation and technological generation.
prowess position it at the forefront of intelligent waste plastic 4.4 Case study and analysis of by-product
pyrolysis. A research team from Purdue University has classification and prediction
developed a technology that utilizes AI and machine learning Utilizing machine learning for the classification and
to convert waste plastics into fuel. This study, published prediction of by-products (such as fuel oil, carbon black,
in the Journal of American Chemical Society Sustainable metals, etc.) enhances the automation level of the recycling
Chemistry & Engineering, demonstrates the potential of AI in process. By predicting the characteristics of by-products,
enhancing pyrolysis efficiency and optimizing product quality.

Vol.52，2026 ·5·