Page 51 - 《橡塑技术与装备》英文版2026年2期

SPECIAL AND COMPREHENSIVE REVIEW

parameters in the pyrolysis process individually or collectively technologies of neural network models
affect the final result. Random forests, on the other hand, Main functions: Applied to various scenarios such
improve prediction accuracy by integrating multiple decision as image recognition, natural language processing, and
trees to predict key parameters of the pyrolysis process, such recommendation systems, it is used to simulate complex
as temperature and pressure, in order to optimize pyrolysis pyrolysis chemical reaction pathways, predict the generation
efficiency and product quality. Models such as decision trees probability of various by-products during the pyrolysis process,
and random forests are used to identify key factors affecting and provide a basis for process optimization. It can handle
pyrolysis efficiency, such as the type, composition, and complex nonlinear relationships, learn feature representations
impurity content of waste plastics, as well as the characteristics through multi-layer structures, and is suitable for large-scale
of pyrolysis equipment, thereby guiding the design of more and complex datasets.
efficient pyrolysis systems. Neural networks encompass feedforward neural
Key technology implementation strategy: networks, recurrent neural networks (RNNs), and long short-
Select a technical implementation strategy for the decision term memory networks (LSTMs), which are utilized to capture
tree that involves choosing features with strong influence on intricate relationships and patterns.
the target variable. Use classic decision tree algorithms such as Key technical implementation strategy: Select an
ID3, C4.5, and CART, or utilize the DecisionTreeClassifier or appropriate neural network architecture. For image data,
DecisionTreeRegressor from the sklearn library. Set parameters operations such as rotation, scaling, and flipping can be
such as the depth of the decision tree, minimum sample size, performed to increase the diversity of training data. Apply data
and splitting criteria (such as information gain and Gini index) standardization/normalization techniques to ensure that all
to optimize model performance. Train the model, evaluate features are on a similar scale, which is beneficial for model
its generalization ability through cross-validation, avoid training. Select an appropriate optimizer for model training, set
overfitting, and assess model performance. an appropriate loss function, use a learning rate decay strategy,
Technical implementation strategy of Random Forest. apply regularization techniques, choose an appropriate batch
Utilize an ensemble learning framework, such as the size, and balance training speed and model performance.
RandomForestClassifier or RandomForestRegressor from the Monitor model performance in real-time, adjust model
scikit-learn library. Adjust parameters, including the number parameters or retrain the model according to actual conditions.
of decision trees, maximum depth of each tree, and feature Pay attention to the latest developments in the field of neural
subset size. Train the Random Forest model using training set networks and introduce new technologies in a timely manner.
data. Random Forest enhances model stability by integrating 2.5 Analysis of the main functions
multiple decision trees. Optimize model performance by and implementation strategies of key
adjusting Random Forest parameters, such as increasing the technologies for machine learning,
number of trees and adjusting the feature subset size. Train reinforcement learning, and deep learning
the model, evaluate its generalization ability through cross- models
validation, avoid overfitting, and assess model performance. Main functions: Machine learning is a kind of algorithm
Decision trees can be used to understand how different that enables computers to automatically learn patterns from
parameters in the pyrolysis process individually or collectively data and use these patterns for prediction or decision-making.
affect the final result. Random Forest, on the other hand, It improves the efficiency and product quality of the pyrolysis
provides more stable predictions and enhances prediction process by constructing predictive models, handling complex
accuracy by integrating multiple decision trees. data relationships, capturing nonlinear and high-dimensional
2.4 Analysis of main functions and features during the pyrolysis process, and achieving in-depth
i mpl emen tatio n strateg ies fo r key understanding and optimization of the pyrolysis reaction

mechanism. Reinforcement learning is an algorithm that

Vol.52，2026 ·7·