Page 52 - 《橡塑技术与装备》英文版2026年2期

HINA R&P TECHNOLOGY AND EQUIPMENT

learns how to take actions in a given environment to maximize propose optimization suggestions, which may include code
a certain cumulative reward through interaction with the optimization, algorithm adjustment, resource scheduling,
environment. It emphasizes trial-and-error learning and self- system architecture improvement, etc., and retest to verify the
improvement through reward and punishment signals, making optimization effect. Systematically evaluate and enhance the
it particularly suitable for complex systems that need to adapt performance of technical products to ensure their reliability
to constantly changing conditions. Deep learning, especially and efficiency in practical applications.
convolutional neural networks (CNN) and recurrent neural 2.7 Main functional analysis and key
networks (RNN), can identify complex patterns and time series technical implementation strategies of data
changes in the pyrolysis process, and is particularly effective preprocessing and feature engineering
for predicting the relationship between key parameters such as models
temperature and residence time and product characteristics. Main functions: When developing a machine learning
Key Technology Implementation Strategy: Machine model for the pyrolysis process of waste plastics using AI,
learning, reinforcement learning, and deep learning are data preprocessing and feature engineering are crucial steps.
crucial components in the field of artificial intelligence, each By utilizing data augmentation techniques to generate more
with its unique characteristics and application scenarios. The training samples, the generalization ability of the model can be
selection of each model should be based on the specific needs improved.
of the waste plastic pyrolysis process, data characteristics, Data preprocessing and feature engineering are crucial
computational resources, as well as the interpretability steps in constructing an effective model, ensuring the quality
and generalization ability of the model, to ensure optimal and applicability of data, and enhancing the performance of AI
predictive accuracy and decision support. When implementing models in the pyrolysis process of waste plastics.
any machine learning technology, it is essential to pay Key technology implementation strategy: Identify and
attention to data quality and the model's generalization ability, handle missing and outlier values in the data. This can be done
ensuring that the model performs well on unseen data. By through deletion, filling, or interpolation methods. Remove
combining the strengths of machine learning, reinforcement noise and interference information from the data to enhance its
learning, and deep learning, hybrid models can be designed purity.
for specific problems, such as using deep learning for feature 2.8 Main functional analysis and key
extraction and then using reinforcement learning for strategy technical implementation strategies of data
optimization. collection and cleaning model
2.6 Main functional analysis and key Main functions: Obtain high-quality datasets and perform
technical implementation strategies of the necessary preprocessing to eliminate noise, missing values,
performance evaluation model and outliers. Improve data accessibility. Through meticulous
Main functions: During the pyrolysis process of waste data collection and cleaning efforts, a solid foundation can be
plastics, AI performance evaluation primarily focuses on provided for the subsequent construction of machine learning
predicting the yield and quality of pyrolysis products, as well models, thereby enhancing the accuracy and practicality of
as assessing the accuracy, stability, generalization ability, and model predictions for waste plastic pyrolysis processes.
practical feasibility of optimizing pyrolysis process parameters. Key technology implementation strategy: During
Key Technology Implementation Strategy: Set specific the data collection process, it is essential to ensure the
performance metrics based on evaluation objectives, comprehensiveness and representativeness of the data,
such as response time, throughput, concurrent user count, encompassing different types of waste plastics, various
resource utilization, latency, etc. Conduct tests according pyrolysis conditions, and diverse factors that may influence
to the predetermined test plan, record data, and observe the the pyrolysis process. Data cleaning involves identifying and
dynamic behavior of the system. Based on the test results, addressing inconsistencies, errors, or incomplete information

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