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online Self-Learning-Based Raw Material Proportioning for Rotary Hearth Furnace and Intelligent Batching System Development

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APPLIED SCIENCES-BASEL 2024年第19期14卷 9126页

作者： Zhang, Xianxia Wang, Lufeng Tang, Shengjie Zhao, Chang Yao, Jun Shanghai Univ Sch Mechatron & Automat Engn Shanghai 200444 Peoples R China Shanghai Huawei Technol Co Ltd Shanghai 201210 Peoples R China

With the increasing awareness of environmental protection, the rotary hearth furnace system has emerged as a key technology that facilitates a win-win situation for both environmental protection and enterprise economic benefits. This is attributed to its high flexibility in raw material utilization, capability of directly supplying blast furnaces, low energy consumption, and high zinc removal rate. However, the complexity of the raw material proportioning process coupled with the rotary hearth furnace system's reliance on human labor results in a time-consuming and inefficient process. This paper innovatively introduces an intelligent formula method for proportioning raw materials based on online clustering algorithms and develops an intelligent batching system for rotary hearth furnaces. Firstly, the ingredients of raw materials undergo data preprocessing, which involves using the local outlier factor (LOF) method to detect any abnormal values, using Kalman filtering to smooth the data, and performing one-hot encoding to represent the different kinds of raw materials. Afterwards, the affinity propagation (AP) clustering method is used to evaluate past data on the ingredients of raw materials and their ratios. This analysis aims to extract information based on human experience with ratios and create a library of machine learning formulas. The incremental AP clustering algorithm is utilized to learn new ratio data and continuously update the machine learning formula library. To ensure that the formula meets the actual production performance requirements of the rotary hearth furnace, the machine learning formula is fine-tuned based on expert experience. The integration of machine learning and expert experience demonstrates good flexibility and satisfactory performance in the practical application of intelligent formulas for rotary hearth furnaces. An intelligent batching system is developed and executed at a steel plant in China. It shows an excellent user interface a

关键词： intelligent formula of raw materials rotary hearth furnace online clustering algorithm intelligent system

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A diversity-driven migration strategy for distributed evolutionary algorithms

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SWARM AND EVOLUTIONARY COMPUTATION 2023年 82卷

作者： Araujo, Jean N. R. Batista, Lucas S. Univ Fed Minas Gerais Grad Program Elect Engn Operat Res & Complex Syst Lab Av Antonio Carlos 6627 BR-31270901 Belo Horizonte MG Brazil Univ Fed Minas Gerais Dept Engn Elect Av Antonio Carlos 6627 BR-31270901 Belo Horizonte MG Brazil

The increasing complexity of real-world problems raises new challenges to evolutionary computation. Distributed models have been successfully employed by many evolutionary algorithms (EAs) to deal with these challenges. In particular, distributed models provide a means to enable collaboration between multiple subpopulations, thus allowing the design of strategies to deal with premature convergence and loss of diversity, which are common problems in traditional evolutionary algorithms. Through introducing periodic migrations, many Distributed Evolutionary algorithms (DEAs) have been proposed to improve the balance between exploration and exploitation. However, most of them focus on performing migrations at fixed or probabilistic intervals. In this work, we present a mechanism to estimate the moment of executing the migrations by assessing the loss of diversity of the subpopulations. Another relevant issue is that most studies choose to migrate the best or a random individual. We report a strategy that identifies a migrant individual capable of generating diversity that helps a given subpopulation explore non-visited regions without harming its health. The proposed approach uses an online clustering algorithm to create clouds of good fitness individuals that have been previously migrated. The solution to be migrated must be extracted from a cloud whose population distribution is sufficiently different from the population distribution of the original subpopulation. We called this approach a Diversity-driven Migration Strategy (DDMS). The efficiency of DDMS is experimentally compared against traditional migration strategies (fixed and probabilistic) on the CEC'2014 test suite. Considering the average error values for the objective function, the proposed approach is specially better in 50D and 100D (dimensional) instances. Regarding the diversity, the proposed strategy is better in 100% and about 96% of the test functions in 50D and 100D scenarios, respectively. In gener

关键词： Distributed evolutionary algorithm Exploration and exploitation Migratory policy Migratory frequency online clustering algorithm TEDA

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