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Optimal design of hot rolling process for C-Mn steel by combining industrial data-driven model and multi-objective optimization algorithm

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Journal of Iron and Steel Research International 2018年第7期25卷 700-705页

作者： Si-wei Wu Xiao-guang Zhou Jia-kuang Ren Guang-ming Cao Zhen-yu Liu Nai-an Shi State Key Laboratory of Rolling and Automation Northeastern University Shenyang 110819 Liaoning China Continuous Rolling Plant of No. 3 Steelmaking of Angang Co. Ltd. Anshan 114021 Liaoning China

A successful mechanical property data-driven prediction model is the core of the optimal design of hot rolling process for hot-rolled strips. However, the original industrial data, usually unbalanced, are inevitably mixed with fluctuant and abnormal values. Models established on the basis of the data without data processing can cause misleading results, which cannot be used for the optimal design of hot rolling process. Thus, a method of industrial data processing of C-Mn steel was proposed based on the data analysis. The Bayesian neural network was employed to establish the reliable mechanical property prediction models for the optimal design of hot rolling process. By using the multi-objective optimization algorithm and considering the individual requirements of costumers and the constraints of the equipment, the optimal design of hot rolling process was successfully applied to the rolling process design for Q345B steel with 0.017% Nb and 0.046% Ti content removed. The optimal process design results were in good agreement with the industrial trials results, which verify the effectiveness of the optimal design of hot rolling process.

关键词： industrial data data processing - Mechanical property Optimal design Hot rolling process C-Mn steel

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Efficient and High-quality Recommendations via Momentum-incorporated Parallel Stochastic Gradient Descent-Based Learning

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IEEE/CAA Journal of Automatica Sinica 2021年第2期8卷 402-411页

作者： Xin Luo Wen Qin Ani Dong Khaled Sedraoui MengChu Zhou the School of Computer Science and Technology Dongguan University of TechnologyDongguan 523808 Hengrui(Chongqing)Artificial Intelligence Research Center Department of Big Data Analyses TechniquesCloudwalkChongqing 401331China the School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing 400065 Chongqing Engineering Research Center of Big data Application for Smart Cities Chongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714China the Department of Computer and Information Science City College of Dongguan University of TechnologyDongguan 523419China the Department of Electrical and Computer Engineering Faculty of Engineeringand Center of Research Excellence in Renewable Energy and Power SystemsKing Abdulaziz UniversityJeddah 21481Saudi Arabia the Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA the Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz UniversityJeddah 21481Saudi Arabia

A recommender system(RS)relying on latent factor analysis usually adopts stochastic gradient descent(SGD)as its learning ***,owing to its serial mechanism,an SGD algorithm suffers from low efficiency and scalability when handling large-scale industrial *** at addressing this issue,this study proposes a momentum-incorporated parallel stochastic gradient descent(MPSGD)algorithm,whose main idea is two-fold:a)implementing parallelization via a novel datasplitting strategy,and b)accelerating convergence rate by integrating momentum effects into its training *** it,an MPSGD-based latent factor(MLF)model is achieved,which is capable of performing efficient and high-quality *** results on four high-dimensional and sparse matrices generated by industrial RS indicate that owing to an MPSGD algorithm,an MLF model outperforms the existing state-of-the-art ones in both computational efficiency and scalability.

关键词： Big data industrial application industrial data latent factor analysis machine learning parallel algorithm recommender system(RS) stochastic gradient descent(SGD)

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Modeling of a Demethanizer Tower Using Statistical Tools

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Journal of Environmental Science and Engineering(A) 2015年第3期4卷 124-130页

作者： Fernando D'Almeida Carlos Pires Chemical Reaction Engineering Laboratory Polytechnic School UFBA (Federal University of Bahia) Salvador-BA 40210-630Brazil

Monitoring of industrial plant performance and detection on flaws is important to the successful operation on industrial production units. Malfunctioning equipment can greatly impact plant performance by reducing the efficiency and increasing the production cost. Phenomenological equations cannot properly describe industrial processes. Thus, it is necessary to develop new equations for model industrial operations. The purpose of this study is to develop an empirical model for industrial demethanizer tower which is malfunctioning due to an error in the design in one of its plates. A nonlinear statistical model was designed to predict the pressure variation in the column, and consequently, the flooding conditions. This model was validated using industrial data to predict the maximum loads in the column.

关键词： Demethanizer tower planning experiments industrial data empirical model.

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