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001476082 001__ 1476082
001476082 003__ OCoLC
001476082 005__ 20231003174631.0
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001476082 019__ $$a1393913613$$a1394117180
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001476082 0247_ $$a10.1007/978-981-99-4550-4$$2doi
001476082 035__ $$aSP(OCoLC)1394866534
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001476082 049__ $$aISEA
001476082 050_4 $$aHD9510.5
001476082 08204 $$a338.4/76691$$223/eng/20230821
001476082 1001_ $$aWu, Dinghui,$$eauthor.
001476082 24510 $$aCollaborative optimization of complex energy systems :$$bapplications in iron and steel industry /$$cDinghui Wu, Junyan Fan, Shenxin Lu, Jing Wang, Yong Zhu, Hongtao Hu.
001476082 264_1 $$aSingapore :$$bSpringer,$$c2023.
001476082 300__ $$a1 online resource (xiii, 145 pages) :$$billustrations (some color).
001476082 336__ $$atext$$btxt$$2rdacontent
001476082 337__ $$acomputer$$bc$$2rdamedia
001476082 338__ $$aonline resource$$bcr$$2rdacarrier
001476082 4901_ $$aEngineering applications of computational methods,$$x2662-3374 ;$$vvolume 17
001476082 504__ $$aIncludes bibliographical references.
001476082 5050_ $$aIntro -- Preface -- Contents -- Acronyms -- 1 Introduction -- 1.1 Background and Significance -- 1.1.1 Background -- 1.1.2 Significance -- 1.2 Current Research Status -- 1.2.1 Energy Plan Optimization -- 1.2.2 Prediction Methods and Models -- 1.2.3 Energy Distribution Optimization -- 1.3 Main Content -- References -- 2 Energy System Analysis and Modeling -- 2.1 Mathematical Model of Multi-medium Energy -- 2.2 Energy Analysis -- 2.2.1 By-product Gas -- 2.2.2 Steam -- 2.2.3 Electricity -- 2.3 Energy System Unit Operation Model -- 2.3.1 Model of Energy Conversion Unit
001476082 5058_ $$a2.3.2 Model of Energy Storage Unit -- 2.3.3 Model of Waste Heat Recovery Unit -- 2.4 Energy System Operating Condition Analysis -- 2.4.1 Main Process Working Conditions Analysis -- 2.4.2 Working Condition Analysis of Energy Conversion Equipment -- 2.5 Conclusion -- References -- 3 Energy Efficiency Evaluation and Optimization Methods -- 3.1 Evaluation Index System of Energy Efficiency in Steel Enterprises -- 3.2 Comprehensive Evaluation Method Based on Combination Weighting -- 3.2.1 Determination of Subjective Weight -- 3.2.2 Determination of Objective Weight
001476082 5058_ $$a3.2.3 Determination of Combination Weight -- 3.2.4 Fuzzy Comprehensive Evaluation -- 3.3 Example Analysis -- 3.3.1 Calculation of Index Weight -- 3.3.2 Energy Efficiency Evaluation of Iron and Steel Enterprises -- 3.4 Conclusion -- References -- 4 Energy Planning Optimization of Iron and Steel Enterprises -- 4.1 Energy Planning Optimization Under Ordinary Working Conditions -- 4.1.1 Mathematical Model for Energy Planning Optimization -- 4.1.2 An Improved MOEA/D Optimization Method Based on the Degree of Population Evolution -- 4.1.3 Simulation Analysis
001476082 5058_ $$a4.2 Energy Planning Optimization Under Multiple Working Conditions -- 4.2.1 Multiple Working Conditions Factors -- 4.2.2 Energy System Model -- 4.2.3 Improvement of Optimization Algorithm -- 4.2.4 Simulation Analysis -- 4.2.5 Optimal Results -- 4.3 Simulation Analysis -- References -- 5 Prediction of Production and Consumption of BFG -- 5.1 Feature Extraction and Weight Distribution of Production and Consumption -- 5.1.1 Statistical Characteristics of Time Series -- 5.1.2 Characteristics of Multi-scale Sample Entropy -- 5.1.3 Feature Weight Allocation on Account of CRITIC
001476082 5058_ $$a5.2 SOM-K-means Double Clustering Algorithm -- 5.2.1 K-means Algorithm -- 5.2.2 SOM Neural Network -- 5.2.3 Clustering Evaluation Index -- 5.2.4 Implementation of SOM-K-means Double Clustering -- 5.3 XGBoost Classification-Regression Prediction Model -- 5.3.1 The Principle of XGBoost Algorithm -- 5.3.2 XGBoost Classification-Regression Prediction -- 5.4 Simulation Example -- 5.4.1 Description of the Data Set -- 5.4.2 Construction of Prediction Model Based on Feature Clustering and XGBoost -- 5.4.3 Analysis of Simulation Results -- 5.5 Conclusion -- References
001476082 506__ $$aAccess limited to authorized users.
001476082 520__ $$aThis book mainly focuses on the multi-media energy prediction technology and optimization methods of iron and steel enterprises. The technical methods adopted include swarm intelligence algorithm, neural network, reinforcement learning, and so on. Energy saving and consumption reduction in iron and steel enterprises have always been a research hotspot in the field of process control. This book considers the multi-media energy balance problem from the perspective of system, studies the energy flow and material flow in iron and steel enterprises, and provides energy optimization methods that can be used for planning, prediction, and scheduling under different production scenes. The main audience of this book is scholars and graduate students in the fields of control theory, applied mathematics, energy optimization, etc.
001476082 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed August 21, 2023).
001476082 650_0 $$aIron industry and trade$$xEnergy consumption$$xMathematical models.
001476082 650_0 $$aSteel industry and trade$$xEnergy consumption$$xMathematical models.
001476082 650_0 $$aEnergy conservation.
001476082 655_0 $$aElectronic books.
001476082 7001_ $$aFan, Junyan,$$eauthor.
001476082 7001_ $$aLu, Shenxin,$$eauthor.
001476082 7001_ $$aWang, Jing,$$eauthor.
001476082 7001_ $$aZhu, Yong,$$eauthor.
001476082 7001_ $$aHu, Hongtao,$$eauthor.
001476082 77608 $$iPrint version:$$aWu, Dinghui$$tCollaborative Optimization of Complex Energy Systems$$dSingapore : Springer,c2023$$z9789819945498
001476082 830_0 $$aEngineering applications of computational methods ;$$vv. 17.$$x2662-3374
001476082 852__ $$bebk
001476082 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-99-4550-4$$zOnline Access$$91397441.1
001476082 909CO $$ooai:library.usi.edu:1476082$$pGLOBAL_SET
001476082 980__ $$aBIB
001476082 980__ $$aEBOOK
001476082 982__ $$aEbook
001476082 983__ $$aOnline
001476082 994__ $$a92$$bISE