Gansu Zhang, Hongyang Li, Zhiqiang Li, Shuxian Su, Xuan Xu, Liang Dong, Wei Dai, Qinglai Wei
{"title":"使用带剪枝的动态模式分解对密集分离流化床进行进化识别","authors":"Gansu Zhang, Hongyang Li, Zhiqiang Li, Shuxian Su, Xuan Xu, Liang Dong, Wei Dai, Qinglai Wei","doi":"10.1016/j.cej.2024.157477","DOIUrl":null,"url":null,"abstract":"Evolutionary identification of hydrodynamics from pressure signals is crucial for advancing the precise control of dry coal separation. Dynamic Mode Decomposition (DMD) is the key method to construct the data-driven control framework. Pressure signals rather than snapshots are investigated for industrial applications, bringing challenges to the implementation of DMD. The techniques of time delay embedding and optimal amplitude are introduced to make DMD work better for pressure signals. Comprehensive parameter tests of stack dimension <span><span style=\"\"></span><span data-mathml='<math xmlns=\"http://www.w3.org/1998/Math/MathML\"><mi is=\"true\">s</mi></math>' role=\"presentation\" style=\"font-size: 90%; display: inline-block; position: relative;\" tabindex=\"0\"><svg aria-hidden=\"true\" focusable=\"false\" height=\"1.394ex\" role=\"img\" style=\"vertical-align: -0.235ex;\" viewbox=\"0 -498.8 469.5 600.2\" width=\"1.09ex\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><g fill=\"currentColor\" stroke=\"currentColor\" stroke-width=\"0\" transform=\"matrix(1 0 0 -1 0 0)\"><g is=\"true\"><use xlink:href=\"#MJMATHI-73\"></use></g></g></svg><span role=\"presentation\"><math xmlns=\"http://www.w3.org/1998/Math/MathML\"><mi is=\"true\">s</mi></math></span></span><script type=\"math/mml\"><math><mi is=\"true\">s</mi></math></script></span> and truncation order <span><span style=\"\"></span><span data-mathml='<math xmlns=\"http://www.w3.org/1998/Math/MathML\"><mi is=\"true\">r</mi></math>' role=\"presentation\" style=\"font-size: 90%; display: inline-block; position: relative;\" tabindex=\"0\"><svg aria-hidden=\"true\" focusable=\"false\" height=\"1.394ex\" role=\"img\" style=\"vertical-align: -0.235ex;\" viewbox=\"0 -498.8 451.5 600.2\" width=\"1.049ex\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><g fill=\"currentColor\" stroke=\"currentColor\" stroke-width=\"0\" transform=\"matrix(1 0 0 -1 0 0)\"><g is=\"true\"><use xlink:href=\"#MJMATHI-72\"></use></g></g></svg><span role=\"presentation\"><math xmlns=\"http://www.w3.org/1998/Math/MathML\"><mi is=\"true\">r</mi></math></span></span><script type=\"math/mml\"><math><mi is=\"true\">r</mi></math></script></span> are carried out to seek for optimal identification performance. Due to parameter sensitivity, the qualification verification by sliding windows is performed to determine the robustness of parameter pairs. Spatiotemporal coherent structures are extracted to guide the regulation of separation process. In order to avoid the inefficiency of control, a heuristic sparsity promoting method using pruning is proposed to obtain a reduced order model. The original modes more than 100 can be reduced to approximately 35 primary modes. Furthermore, the Prune dominant frequency is defined, which can perceive the subtle fluctuations of temporal evolution than FFT and DMD for the long-term time. Present study provides the insight of hydrodynamics of dense gas-solid fluidized bed, establishing the foundation for future control studies of dry coal separation.","PeriodicalId":270,"journal":{"name":"Chemical Engineering Journal","volume":"25 1","pages":""},"PeriodicalIF":13.3000,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Evolutionary identification in dense separation fluidized beds using dynamic mode decomposition with pruning\",\"authors\":\"Gansu Zhang, Hongyang Li, Zhiqiang Li, Shuxian Su, Xuan Xu, Liang Dong, Wei Dai, Qinglai Wei\",\"doi\":\"10.1016/j.cej.2024.157477\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Evolutionary identification of hydrodynamics from pressure signals is crucial for advancing the precise control of dry coal separation. Dynamic Mode Decomposition (DMD) is the key method to construct the data-driven control framework. Pressure signals rather than snapshots are investigated for industrial applications, bringing challenges to the implementation of DMD. The techniques of time delay embedding and optimal amplitude are introduced to make DMD work better for pressure signals. Comprehensive parameter tests of stack dimension <span><span style=\\\"\\\"></span><span data-mathml='<math xmlns=\\\"http://www.w3.org/1998/Math/MathML\\\"><mi is=\\\"true\\\">s</mi></math>' role=\\\"presentation\\\" style=\\\"font-size: 90%; display: inline-block; position: relative;\\\" tabindex=\\\"0\\\"><svg aria-hidden=\\\"true\\\" focusable=\\\"false\\\" height=\\\"1.394ex\\\" role=\\\"img\\\" style=\\\"vertical-align: -0.235ex;\\\" viewbox=\\\"0 -498.8 469.5 600.2\\\" width=\\\"1.09ex\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\"><g fill=\\\"currentColor\\\" stroke=\\\"currentColor\\\" stroke-width=\\\"0\\\" transform=\\\"matrix(1 0 0 -1 0 0)\\\"><g is=\\\"true\\\"><use xlink:href=\\\"#MJMATHI-73\\\"></use></g></g></svg><span role=\\\"presentation\\\"><math xmlns=\\\"http://www.w3.org/1998/Math/MathML\\\"><mi is=\\\"true\\\">s</mi></math></span></span><script type=\\\"math/mml\\\"><math><mi is=\\\"true\\\">s</mi></math></script></span> and truncation order <span><span style=\\\"\\\"></span><span data-mathml='<math xmlns=\\\"http://www.w3.org/1998/Math/MathML\\\"><mi is=\\\"true\\\">r</mi></math>' role=\\\"presentation\\\" style=\\\"font-size: 90%; display: inline-block; position: relative;\\\" tabindex=\\\"0\\\"><svg aria-hidden=\\\"true\\\" focusable=\\\"false\\\" height=\\\"1.394ex\\\" role=\\\"img\\\" style=\\\"vertical-align: -0.235ex;\\\" viewbox=\\\"0 -498.8 451.5 600.2\\\" width=\\\"1.049ex\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\"><g fill=\\\"currentColor\\\" stroke=\\\"currentColor\\\" stroke-width=\\\"0\\\" transform=\\\"matrix(1 0 0 -1 0 0)\\\"><g is=\\\"true\\\"><use xlink:href=\\\"#MJMATHI-72\\\"></use></g></g></svg><span role=\\\"presentation\\\"><math xmlns=\\\"http://www.w3.org/1998/Math/MathML\\\"><mi is=\\\"true\\\">r</mi></math></span></span><script type=\\\"math/mml\\\"><math><mi is=\\\"true\\\">r</mi></math></script></span> are carried out to seek for optimal identification performance. Due to parameter sensitivity, the qualification verification by sliding windows is performed to determine the robustness of parameter pairs. Spatiotemporal coherent structures are extracted to guide the regulation of separation process. In order to avoid the inefficiency of control, a heuristic sparsity promoting method using pruning is proposed to obtain a reduced order model. The original modes more than 100 can be reduced to approximately 35 primary modes. Furthermore, the Prune dominant frequency is defined, which can perceive the subtle fluctuations of temporal evolution than FFT and DMD for the long-term time. 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Evolutionary identification in dense separation fluidized beds using dynamic mode decomposition with pruning
Evolutionary identification of hydrodynamics from pressure signals is crucial for advancing the precise control of dry coal separation. Dynamic Mode Decomposition (DMD) is the key method to construct the data-driven control framework. Pressure signals rather than snapshots are investigated for industrial applications, bringing challenges to the implementation of DMD. The techniques of time delay embedding and optimal amplitude are introduced to make DMD work better for pressure signals. Comprehensive parameter tests of stack dimension and truncation order are carried out to seek for optimal identification performance. Due to parameter sensitivity, the qualification verification by sliding windows is performed to determine the robustness of parameter pairs. Spatiotemporal coherent structures are extracted to guide the regulation of separation process. In order to avoid the inefficiency of control, a heuristic sparsity promoting method using pruning is proposed to obtain a reduced order model. The original modes more than 100 can be reduced to approximately 35 primary modes. Furthermore, the Prune dominant frequency is defined, which can perceive the subtle fluctuations of temporal evolution than FFT and DMD for the long-term time. Present study provides the insight of hydrodynamics of dense gas-solid fluidized bed, establishing the foundation for future control studies of dry coal separation.
期刊介绍:
The Chemical Engineering Journal is an international research journal that invites contributions of original and novel fundamental research. It aims to provide an international platform for presenting original fundamental research, interpretative reviews, and discussions on new developments in chemical engineering. The journal welcomes papers that describe novel theory and its practical application, as well as those that demonstrate the transfer of techniques from other disciplines. It also welcomes reports on carefully conducted experimental work that is soundly interpreted. The main focus of the journal is on original and rigorous research results that have broad significance. The Catalysis section within the Chemical Engineering Journal focuses specifically on Experimental and Theoretical studies in the fields of heterogeneous catalysis, molecular catalysis, and biocatalysis. These studies have industrial impact on various sectors such as chemicals, energy, materials, foods, healthcare, and environmental protection.