Linjin Sun
Zhejiang University
8 Papers
Linjin Sun is an academic researcher from Zhejiang University. The author has contributed to research in topics: Computer science & Process (computing). The author has co-authored 1 publications.
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Papers
Process knowledge-based random forest regression for model predictive control on a nonlinear production process with multiple working conditions
TL;DR: In this article , a predictive controller with a control process knowledge-based random forest (RF) model is proposed, where working data are clustered at first to handle diverse working conditions.
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Collaborative Optimization of the Battery Capacity and Sailing Speed Considering Multiple Operation Factors for a Battery-Powered Ship
TL;DR: In this paper , a joint optimization method of the sailing speed and battery capacity, which considers the interaction between battery size and sailing speed as well as multiple operation factors, such as freight demand and battery life, and port electricity price, is proposed to fully exploit the battery-powered ships' application potential.
A clustering-based energy consumption evaluation method for process industries with multiple energy consumption patterns
Linjin Sun,Yangjian Ji,Z. Sun,Qixuan Li,Yingjie Jin +4 more
TL;DR: In this paper , a two-stage clustering-based energy consumption evaluation method is proposed for process industries in which a novel structure of the fuzzy clustering method is designed with a mixture of unsupervised and semi-supervised learning stages that leverages the weighted information to independently address energy consumption patterns.
3
A process knowledge-based hybrid method for univariate time series prediction with uncertain inputs in process industry
Linjin Sun,Yangjian Ji,Qixuan Li,Tiannuo Yang +3 more
TL;DR: A process knowledge-based hybrid method, KE-DKN, is proposed for univariate time series prediction in process industry with uncertain inputs, achieving higher accuracy and robustness than baseline methods in both stable and unstable working conditions.
1
Chronicle knowledge-based multi-level response prediction for predictive control by forest models in process industry
Linjin Sun,Yangjian Ji,Zheren Zhu,Xiaoyu Jiang,Xiaoyang Zhu,Nian Zhang +5 more
TL;DR: This study proposes a hybrid predictive control method combining chronicle knowledge and data-driven models, leveraging intrinsic knowledge of controlled variables to improve predictive performance and accuracy in process industries.
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