Prediction of Strip Width in Finishing-Mill Group Based on PCA-PSO-LightGBM DOI
Jialiang Wang,

Runyun Yao,

Jingcheng Wang

et al.

Published: Sept. 23, 2023

Aiming to improve the strip free width hitting rate in finishing rolling, this paper proposes a Light Gradient Boosting Machine (LightGBM) prediction algorithm based on particle Swarm Optimization (PSO) with Principal Component Analysis (PCA). First, raw data are effectively dimensionally reduced using PCA after preprocessing, and input into LightGBM establish model, followed by optimization of key parameters PSO. Finally, design experiments simulations carried out actual production line steel plant Shanghai, results show that PCA-PSO-LightGBM model proposed has good accuracy robustness, meets requirements practical applications.

Language: Английский

High-Precision machining energy consumption prediction based on multi-sensor data fusion and Ns-Transformer network DOI
Meihang Zhang, Hua Zhang, Wei Yan

et al.

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 126903 - 126903

Published: Feb. 1, 2025

Language: Английский

Citations

0
An interpretable and reliable framework for alloy discovery in thermomechanical processing DOI Creative Commons
Sushant Sinha,

Xiaoping Ma,

Kashif Rehman

et al.

Materials Today Communications, Journal Year: 2025, Volume and Issue: unknown, P. 112134 - 112134

Published: March 1, 2025

Language: Английский

Citations

0
Multi-objective optimization enabling CFRP energy-efficient milling based on deep reinforcement learning DOI
Meihang Zhang, Hua Zhang, Wei Yan

et al.

Applied Intelligence, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 18, 2024

Language: Английский

Citations

2
Dynamic Operation Optimization of Complex Industries Based on a Data-Driven Strategy DOI Open Access
Huixin Tian,

Chenning Zhao,

Jueping Xie

et al.

Processes, Journal Year: 2024, Volume and Issue: 12(1), P. 189 - 189

Published: Jan. 15, 2024

As industrial practices continue to evolve, complex process industries often exhibit characteristics such as multivariate correlation, dynamism, and nonlinearity, making traditional mechanism modeling inadequate in terms of addressing the intricacies problems. In recent years, with advancements control theory practices, there has been a substantial increase volume data. Data-driven dynamic operation optimization techniques have emerged effective solutions for handling processes. By responding environmental changes utilizing advanced algorithms, it is possible achieve operational processes, thereby reducing costs emissions, improving efficiency, increasing productivity. This correlates nicely goals set forth by conventional theories. Nowadays, this dynamic, data-driven strategy shown significant potential characterized correlations nonlinear behavior. paper approaches subject from perspective establishing models reviewing state-of-the-art time series forecasting cope changing objective functions over time. Meanwhile, aiming at problem concept drift series, summarizes new detection methods introduces model update solve challenge. solving multi-objective problems, reviews developments change response while summarizing commonly used well latest performance measures conclusion, discussion research progress challenges relevant domains undertaken, followed proposal directions future research. review will help deeply understand importance application prospects fields.

Language: Английский

Citations

1
Prediction of Strip Width in Finishing-Mill Group Based on PCA-PSO-LightGBM DOI
Jialiang Wang,

Runyun Yao,

Jingcheng Wang

et al.

Published: Sept. 23, 2023

Aiming to improve the strip free width hitting rate in finishing rolling, this paper proposes a Light Gradient Boosting Machine (LightGBM) prediction algorithm based on particle Swarm Optimization (PSO) with Principal Component Analysis (PCA). First, raw data are effectively dimensionally reduced using PCA after preprocessing, and input into LightGBM establish model, followed by optimization of key parameters PSO. Finally, design experiments simulations carried out actual production line steel plant Shanghai, results show that PCA-PSO-LightGBM model proposed has good accuracy robustness, meets requirements practical applications.

Language: Английский

Citations

0