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<title cf:type="text"><![CDATA[ -->Industrial AI and Intelligent Technology]]></title>
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<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Industrial AI Technology to Promote Energy Saving and Carbon Reduction in the Paper Industry: Discussion and Practice Based on Large-scale System Thinking]]></title>
<link><![CDATA[http://zgzz.ijournals.cn/zgzzen/ch/reader/view_abstract.aspx?file_no=202502001&flag=1]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[As a resource and energy intensive industry， the paper industry is facing increasingly strict carbon reduction pressure and environmental protection requirements， with enormous transformation pressure. Industrial AI technology provide new possibilities for low-carbon development. The paper industry as the research object， based on the large-scale system thinking and concept of “closed-loop system of digital- real physical worlds fusion”， the paper explored the application of industrial AI technology in carbon reduction. From the data valuation， process AI technology to the practice of large-scale system thinking， the effects on production efficiency， resource utilization and carbon emission were analyzed， and the optimization path were put forward. The results showed that the combination of large-scale system thinking and industrial AI technology could significantly improve the resource utilization efficiency of the industry， reduce carbon emission， and help the paper industry transform to high-quality development.]]></description>
<pubDate>2025/2/25 13:04:24</pubDate>
<category><![CDATA[Industrial AI and Intelligent Technology]]></category>
<author><![CDATA[LIU Huanbin,LI Jigeng]]></author>
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<atom:name>LIU Huanbin,LI Jigeng</atom:name>
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<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Design and Application of Solvers for Nonlinear and Multidimensional Problems in Paper Production Systems]]></title>
<link><![CDATA[http://zgzz.ijournals.cn/zgzzen/ch/reader/view_abstract.aspx?file_no=202502002&flag=1]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[The intelligent transformation of the paper industry involves solving dynamic and real-time problems in numerous high-dimensional mathematical models. Due to the nonlinearity， multidimensionality， and uncertainty inherent in paper production systems， the mathematical models describing these processes often consist of extensive sets of equations. Furthermore， frequent production fluctuations and transitions necessitate efficient and frequent resolution of these complex model sets to meet the demands of dynamic production optimization. Developing solvers tailored to the challenges of paper production models is key to addressing this issue. This study focused on the characteristics of nonlinear， multidimensional solutions in paper production models and designed a global optimization solver for nonlinear， multidimensional paper production systems based on the trust-region interior-point method and TikTak multi-start optimization algorithm. The proposed solver achieves efficient resolution of complex production constraints and uncertain initial conditions. The results showed that the solver achieved a 100% success rate in finding the global optimum in a paper drying section optimization case， with an average solving time of 0.81 per instance， exhibiting high robustness. Additionally， in a paper energy system optimization case， the solver successfully reduced computational resource usage by 59.7% and computation time by 9.29%.]]></description>
<pubDate>2025/2/25 13:04:25</pubDate>
<category><![CDATA[Industrial AI and Intelligent Technology]]></category>
<author><![CDATA[LI Kanghao,CHEN Haozhou,ZHANG Jie,HAN Yulin,MAN Yi]]></author>
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<atom:name>LI Kanghao,CHEN Haozhou,ZHANG Jie,HAN Yulin,MAN Yi</atom:name>
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<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Intelligent Control System and Optimization Method for Alkali Recovery Based on Dual Network Dual Server Architecture]]></title>
<link><![CDATA[http://zgzz.ijournals.cn/zgzzen/ch/reader/view_abstract.aspx?file_no=202502003&flag=1]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[Currently， most paper mills have poor emission reduction and decarbonization effects， and their level of informatization is relatively low. The alkali recovery section was taken as an example in this study， and an intelligent control system for alkali recovery based on a dual network and dual server architecture was propesed. The system was based on a dual ring Ethernet dual redundant server architecture， with Siemens S7-400 series PLC controller selected as the lower computer. Hardware such as CPU and I/O modules were designed with redundancy， providing stable and reliable decentralized control for alkali recovery， evaporation， combustion， and causticization sections. The upper computer was equipped with Web server， enterprise office internet， and remote service channel， which could not only enhance the information sharing capability within the system， but also realize remote diagnosis and maintenance of the system. Finally， advanced control algorithms were used to optimize and control the important parameters of each section. The practical application results showed that the system could not only effectively improve the treatment efficiency of black liquor， but also reduce energy loss in the production process. It also provided the basis for the intelligent and information-based transformation and upgrading of the alkali recovery section.]]></description>
<pubDate>2025/2/25 13:04:26</pubDate>
<category><![CDATA[Industrial AI and Intelligent Technology]]></category>
<author><![CDATA[TANG Wei,ZHENG Xiaohu,WANG Mengxiao,WANG Qilin,ZHOU Guoqing,GAO Qifan]]></author>
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<atom:name>TANG Wei,ZHENG Xiaohu,WANG Mengxiao,WANG Qilin,ZHOU Guoqing,GAO Qifan</atom:name>
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<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Multi-objective Optimization of Distributed Flexible Flow Workshop Scheduling Based on the Total Carbon Emissions of Trucks and Total Cost]]></title>
<link><![CDATA[http://zgzz.ijournals.cn/zgzzen/ch/reader/view_abstract.aspx?file_no=202502004&flag=1]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[In this study， a model of distributed flexible flow workshop scheduling and logistics collaborative optimization was constructed， with total cost and total carbon emissions of trucks as the optimization objectives. The multi-objective particle swarm optimization based framework was used to improve the global leader selection strategy and maintenance plan for the global leader profile. The simulation experiments were conducted， based on the real data of a tissue paper manufacturing enterprises， and multiple sets of examples were generated for testing the performance of the algorithm. The results showed that the above two improved strategies could effectively enhance the ability of multi-objective particle swarm optimization algorithm to find the optimal solution. Compared with other particle swarm optimization algorithm in 10 cases， the improved multi-objective particle swarm optimization reduced the average total cost by 3.29% and the average total carbon emissions of trucks by 11.1%.]]></description>
<pubDate>2025/2/25 13:04:27</pubDate>
<category><![CDATA[Industrial AI and Intelligent Technology]]></category>
<author><![CDATA[LIANG Wenyi,ZENG Zhiqiang,HONG Zhiyong]]></author>
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<atom:name>LIANG Wenyi,ZENG Zhiqiang,HONG Zhiyong</atom:name>
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