پیشرفت‌های هوش مصنوعی در محیط انرژی سبز

Advances of Artificial Intelligence in a Green Energy Environment reviews the new technologies in intelligent computing and AI that are reducing the dimension of data coverage worldwide. This handbook describes intelligent optimization algorithms that can be applied in various branches of energy engineering where uncertainty is a major concern. Including AI methodologies and applying advanced evolutionary algorithms to real-world application problems for everyday life applications, this book considers distributed energy systems, hybrid renewable energy systems using AI methods, and new opportunities in blockchain technology in smart energy. Covering state-of-the-art developments in a fast-moving technology, this reference is useful for engineering students and researchers interested and working in the AI industry. Looks at new techniques in artificial intelligence (AI) reducing the dimension of data coverage worldwide Chapters include AI methodologies using enhanced hybrid swarm-based optimization algorithms Includes flowchart diagrams for exampling optimizing techniques Front Cover Advances of Artificial Intelligence in a Green Energy Environment Advances of Artificial Intelligence in a Green Energy Environment Copyright Contents Contributors About the editors Preface Acknowledgments 1 - Application of some ways to intensify the process of anaerobic bioconversion of organic matter 1.1 Introduction 1.2 Methods for improving the process of methane digestion of manure and manure runoff and their analysis 1.3 Application results 1.3.1 Temperature regime 1.3.2 Separation of the processing process into two or more stages 1.3.2.1 Aerobic–anaerobic digestion 1.3.2.2 Biohydrogen production 1.3.3 Application of biological and physicochemical methods for pretreatment of organic waste 1.3.4 Combining the processes of biological and thermochemical gasification of organic waste and intermediate products of their p ... 1.3.4.1 Thermochemical gasification 1.3.4.2 Immobilization of methane-forming microorganisms on carriers 1.3.5 Carrying out the process under various influences on the processed substrate 1.3.5.1 Increased pressure in the reactor 1.3.5.2 Electrophysical impact 1.3.5.3 Application of conductive materials for direct interspecies electron transfer 1.3.5.4 Application of increased pressure in the reactor space and conductive materials 1.3.5.5 Integrated preheating from a heat exchanger using waste heat of the effluent and heating using microwave radiation 1.3.6 Application of heat energy recovery directly and using thermal transformers 1.3.6.1 Direct heat recovery 1.3.6.2 Heat recovery using thermal transformers 1.3.6.3 Aerobic–anaerobic–aerobic treatment with heat recovery 1.3.7 Complex application of biogas plant and additional sources of thermal and electric energy based on other RES 1.4 Energy model 1.5 Conclusion References 2 - Disasters impact assessment based on socioeconomic approach 2.1 Introduction 2.2 Statistics and tendencies of natural and man-made disasters 2.3 Overview of institutions dealing with risk management 2.4 Promising approaches to decrease risks and losses caused by disasters 2.5 The issues of effective disaster countering organization 2.6 Socioeconomic approach to the estimations of risk dynamics 2.7 Conclusions Appendix A: An example of selecting the optimal list of prevention or mitigation measures References 3 - Uninterruptible power supply system of the consumer, reducing peak network loads 3.1 Introduction 3.2 Methodology of the work 3.3 Work results 3.4 Conclusions References 4 - Optimization of the anaerobic conversion of green biomass into volatile fatty acids for further production of h ... 4.1 Introduction 4.2 Experimental part and methods 4.2.1 Biocatalyst 4.2.2 Substrate 4.2.3 Methods 4.3 Results and discussion 4.3.1 Specific acid-producing activity of the biocatalyst 4.3.2 Biomass pretreatment 4.3.3 Obtaining VFAs and ethyl alcohol during the conversion of pretreated green biomass with a biocatalyst in submesophilic and ... 4.3.4 Increase in the yields of volatile fatty acids and ethanol alcohol during the transformation of pretreated green mass with ... 4.3.5 Increase in the yields of volatile fatty acids and ethyl alcohol with adding a cosubstrate of glycerin to pretreated green ... 4.3.6 The efficiency of conversion of substrates into volatile fatty acids and ethanol, depending on the temperature and time of ... 4.4 Conclusions Abbreviations References 5 - Life cycle cost and life cycle assessment: an approximation to understand the real impacts of the Electricity S ... 5.1 Importance of Electricity Supply Industry 5.2 The economics of Electricity Supply Industry 5.3 The life cycle of Electricity Supply Industry 5.4 Importance of life cycle assessment of Electricity Supply Industry 5.5 Life cycle cost of Electricity Supply Industry 5.6 Mobilizing industry for a clean and circular economy References 6 - Comparison of open access multi-objective optimization software tools for standalone hybrid renewable energy sy ... 6.1 Introduction 6.2 Literature review 6.3 Open access multi-objective optimization tools 6.3.1 HOMER 6.3.2 iHOGA 6.3.2.1 Primary algorithm 6.3.2.2 Secondary algorithm 6.3.3 Load dispatch strategies 6.3.3.1 Demand following 6.3.3.2 Cycle charging 6.3.3.3 Mixed strategy 6.3.4 HYBRID-2 6.3.4.1 Peak shaving 6.3.4.2 Renewable battery only 6.3.4.3 Renewable genset only 6.3.4.4 Customized load dispatch strategy 6.3.5 Comparison of multiobjective optimization algorithm 6.4 Case study 6.4.1 Input of load 6.4.2 Solar radiation data input 6.4.3 Wind speed data input 6.4.4 Selecting other components data 6.4.5 Selection of battery and inverter 6.4.6 Calculation time 6.5 Conclusion References 7 - Optimization of the organic waste anaerobic digestion in biogas plants through the use of a vortex layer apparatus 7.1 Introduction 7.2 Anaerobic processing of organic waste: general characteristics of fermentation 7.2.1 General characteristics of the methanogenesis 7.2.2 General characteristics of the hydrolysis process 7.2.3 The impact of external factors on the intensity of the gas formation process 7.3 Methods of preprocessing 7.4 Vortex layer apparatus 7.5 Application of the vortex layer apparatus in biogas plants 7.5.1 Pretreatment of the substrate in the vortex layer apparatus together with biomass recycling 7.5.1.1 Complex application of microwave and VLA 7.6 Conclusion References 8 - Search of regularities in data: optimality, validity, and interpretability 8.1 Introduction 8.2 Occam's razor principle for verification of parametric regression models 8.2.1 Choice between complex and simple models 8.2.2 Permutation test technique 8.2.3 Choice of simple model 8.2.4 Using Occam's razor principle to evaluate significance of piecewise linear models 8.3 Occam's razor principle for verification of regression models based on optimal partitioning 8.3.1 Optimal partitioning 8.3.2 Statistical significance 8.3.3 Multiple testing 8.4 Conclusion Acknowledgment References 9 - Artificial intelligence techniques for modeling of wind energy harvesting systems: a comparative analysis 9.1 Introduction 9.2 Review of related works 9.3 Modeling of wind energy harvesting system 9.3.1 Turbine model 9.3.2 Modeling of PMSG 9.4 Maximum power point tracking system 9.5 Load side converter control 9.6 Results and discussion 9.6.1 Case 1: step change in wind speed and fixed load 9.6.2 Case 2: continuous change in wind speed 9.7 Conclusion Acknowledgments References Further reading 10 - Human paradigm and reliability for aggregate production planning under uncertainty 10.1 Introduction 10.1.1 Human paradigm in APP 10.1.2 Reliability in APP 10.1.3 Uncertainty in APP 10.2 Literature review 10.3 Discussion and conclusion References 11 - Artificial intelligence–based intelligent geospatial analysis in disaster management 11.1 Introduction 11.2 Related work 11.3 Proposed work 11.3.1 Preparation of various thematic maps 11.3.2 Thematic maps 11.3.2.1 Lithology 11.3.3 Land use and land cover 11.3.4 Digital elevation map 11.3.5 Slope 11.3.6 Slope gradient 11.3.7 Lineaments 11.3.8 Drainage 11.3.9 Landslide susceptibility zone map preparation 11.3.10 Convolution neural networks 11.4 Performance analysis 11.5 Conclusion References 12 - Optimizing the daily use of limited solar panels in closely located rural schools in Zimbabwe 12.1 Introduction 12.2 Modeling the solar panel problem 12.2.1 The TSP model 12.2.1.1 Available methods Methods that give exact solutions Heuristics 12.2.1.2 Linear programming formulation and its weakness 12.3 TSP network features 12.3.1 Network feature 1 12.3.2 Network feature 2 12.3.3 TSP network feature 3 12.3.4 TSP network feature 4 12.3.4.1 Justification 12.3.5 TSP network feature 5 12.3.5.1 Spanning tree 12.3.5.2 Minimum spanning tree 12.3.5.3 Tour 12.3.5.4 Optimal tour 12.3.5.5 Minimum spanning tree algorithm 12.3.5.6 TSP tree 12.4 Dummies and their use in elimination of subtours 12.4.1 Dummy schools 12.4.2 Dummy point 12.4.3 Identifying dummy points 12.4.4 Subtour eliminators 12.5 Proposed algorithm for TSP 12.5.1 Proposed algorithm 12.5.2 Numerical illustration 12.5.2.1 Identifying dummy points 12.5.2.2 Introducing dummy node to the network 12.5.2.3 Linear programming formulation 12.5.2.4 Optimal solution of the formulated linear program 12.5.2.5 Adjusted optimal solution of the formulated linear program 12.6 Other applications of the traveling salesman 12.6.1 Wiring problem 12.6.2 Hospital layout 12.6.3 Dartboard design 12.6.4 Designing a typewriter keyboard 12.6.5 Production 12.6.6 Scheduling 12.7 Conclusions References Further reading 13 - Review on recent implementations of multiobjective and multilevel optimization in sustainable energy economics 13.1 Introduction 13.2 Economic load/emission dispatch 13.3 Bioenergy and biofuel supply chains 13.4 Sustainable capacity planning and optimization 13.5 Outlook References 14 - Hybrid optimization and artificial intelligence applied to energy systems: a review 14.1 Introduction 14.2 Stochastic programming 14.2.1 The general model 14.2.2 Software for stochastic programming instances 14.3 Optimization in energy systems 14.3.1 Energy system models and their optimization processes 14.3.2 A classification according to the applications 14.3.2.1 Energy, manufacturing, and production 14.3.2.2 Energy and forecast models 14.3.2.3 Energy computation and mathematical programming 14.3.2.4 Energy and construction 14.3.2.5 Management energy 14.4 Conclusions Abbreviations Acknowledgments References Further reading 15 - A brief literature review of quantitative models for sustainable supply chain management 15.1 Introduction 15.2 Theoretical foundation and literature reviews 15.2.1 Supply chain management 15.2.2 Sustainable supply chain management 15.2.3 Literature reviews 15.3 Methodology 15.3.1 Material collection 15.3.1.1 Selection of research questions 15.3.1.2 Definition of database sources 15.3.1.3 Selection of search terms 15.3.1.4 Application of practical inclusion and exclusion criteria 15.3.2 Descriptive analysis 15.3.3 Category identification 15.3.4 Material evaluation 15.4 Results 15.4.1 Material collection 15.4.2 Descriptive analysis 15.4.3 SCM dimension 15.4.4 Modeling dimension 15.4.5 Sustainability dimension 15.4.6 Research gaps and future research perspectives 15.5 Discussion 15.6 Conclusion References 16 - Optimized designing spherical void structures in 3D domains 16.1 Introduction 16.2 Problem formulation 16.3 Mathematical model 16.4 Mathematical model with balancing conditions 16.5 Numerical experiments 16.6 Conclusions Acknowledgments References 17 - Swarm-based intelligent strategies for charging plug-in hybrid electric vehicles 17.1 Introductions 17.1.1 Study objectives 17.2 Problem formulation 17.3 Swarm-based intelligence approaches 17.3.1 Particle swarm optimization 17.3.1.1 Advantages and disadvantages of PSO 17.3.2 Accelerated particle swarm optimization 17.3.2.1 Advantages and disadvantages of APSO 17.3.3 Gravitational search algorithm 17.3.3.1 Advantages and disadvantages of GSA 17.3.4 Hybrid PSOGSA algorithm 17.3.4.1 Advantages and disadvantages of PSOGSA 17.4 Results and discussions 17.4.1 Particle optimization swarm findings 17.4.2 Accelerated PSO findings 17.4.3 Gravitational search algorithm findings 17.4.4 Hybrid PSO and GSA (PSOGSA) findings 17.4.5 Comparative analysis 17.4.5.1 Convergence analysis 17.4.5.2 Fitness value 17.4.5.3 Time of computation 17.4.5.4 Robustness 17.5 Conclusions 17.5.1 Future research direction References Index A B C D E F G H I K L M N O P Q R S T U V W Back Cover