Nonlinear density wave and energy consumption investigation of traffic flow on a curved road

A new car-following model is proposed based on the full velocity difference model（FVDM） taking the influence of the friction coefficient and the road curvature into account. Through the control theory, the stability conditions are obtained,and by using nonlinear analysis, the time-dependent Ginzburg-Landau（TDGL） equation and the modified Korteweg-de Vries（mKdV） equation are derived. Furthermore, the connection between TDGL and mKdV equations is also given. The numerical simulation is consistent with the theoretical analysis. The evolution of a traffic jam and the corresponding energy consumption are explored. The numerical results show that the control scheme is effective not only to suppress the traffic jam but also to reduce the energy consumption.

Prediction of Low-Energy Building Energy Consumption Based on Genetic BP Algorithm

Combined with the energy consumption data of individual buildings in the logistics group of Yangtze University,the analysis model scheme of energy consumption of individual buildings in the university is studied by using Back Propagation(BP)neural network to solve nonlinear problems and have the ability of global approximation and generalization.By analyzing the influence of different uses,different building surfaces and different energysaving schemes on the change of building energy consumption,the grey correlation method is used to determine the main influencing factors affecting each building energy consumption,including uses,building surfaces and energy-saving schemes,which are used as the input of the model and the building energy consumption as the output of the model,so as to establish the building energy consumption analysis model based on BP neural network.However,in practical application,BP neural network has the defects of slow convergence and easy to fall into local minima.In view of this,this paper uses genetic algorithm to optimize the weight and threshold of BP neural network,completes the improvement of various building energy consumption analysis models,and realizes the qualitative analysis of building energy consumption.The model verification results show that the viscosity of the building energy consumption analysis model based on genetic algorithm improved BP neural network algorithm(GABP)in this paper is relatively high,which is more accurate than the results of the traditional BP neural network model,and the relative error of the analysis model is reduced from 11.56%to 8.13%,which proves that the GABP can be better suitable for the study of school building energy consumption analysis model,It is applied to the prediction of building energy consumption,which lays a foundation for the realization of carbon neutralization in the South expansion plan of Yangtze University.

Energy consumption hierarchical analysis based on interpretative structural model for ethylene production

Interpretative structural model(ISM) can transform a multivariate problem into several sub-variable problems to analyze a complex industrial structure in a more efficient way by building a multi-level hierarchical structure model. To build an ISM of a production system, the partial correlation coefficient method is proposed to obtain the adjacency matrix, which can be transformed to ISM. According to estimation of correlation coefficient, the result can give actual variable correlations and eliminate effects of intermediate variables. Furthermore, this paper proposes an effective approach using ISM to analyze the main factors and basic mechanisms that affect the energy consumption in an ethylene production system. The case study shows that the proposed energy consumption analysis method is valid and efficient in improvement of energy efficiency in ethylene production.

Energy Efficiency in Internet of Things:An Overview

Energy efficiency is very important for the Internet of Things(IoT),especially for front-end sensed terminal or node.It not only embodies the node’s life,but also reflects the lifetime of the network.Meanwhile,it is also a key indicator of green communications.Unfortunately,there is no article on systematic analysis and review for energy efficiency evaluation in IoT.In this paper,we systemically analyze the architecture of IoT,and point out its energy distribution,Furthermore,we summarized the energy consumption model in IoT,analyzed the pros and cons of improving energy efficiency,presented a state of the art the evaluation metrics of energy efficiency.Finally,we conclude the techniques and methods,and carry out a few open research issues and directions in this field.

Robust Electric Vehicle Routing Problem with Time Windows under Demand Uncertainty and Weight-Related Energy Consumption

Vehicle routing problem with time windows(VRPTW)is a core combinatorial optimization problem in distribution tasks.The electric vehicle routing problem with time windows under demand uncertainty and weight-related energy consumption is an extension of the VRPTW.Although some researchers have studied either the electric VRPTW with nonlinear energy consumption model or the impact of the uncertain customer demand on the conventional vehicles,the literature on the integration of uncertain demand and energy consumption of electric vehicles is still scarce.However,practically,it is usually not feasible to ignore the uncertainty of customer demand and the weight-related energy consumption of electronic vehicles(EVs)in actual operation.Hence,we propose the robust optimization model based on a route-related uncertain set to tackle this problem.Moreover,adaptive large neighbourhood search heuristic has been developed to solve the problem due to the NP-hard nature of the problem.The effectiveness of the method is verified by experiments,and the influence of uncertain demand and uncertain parameters on the solution is further explored.

An input-output model for energy accounting and analysis of industrial production processes： a case study of an integrated steel plant

To promote sustainability, it has become increasingly vital to properly account material and energy flows in industrial production processes. Therefore, a generic process-level input-output （IO） model was developed to provide an integrated energy （material） accounting and analysis approach for industrial production processes. By extending the existing processlevel IO models, the production, usage, export and loss of by-products were explicitly considered in the proposed IO model. Moreover, the by-products allocation procedures were incorporated into the proposed IO model to reflect individual contributions of products to energy consumption. Finally, the proposed model enabled calculating embodied energy of main products and total energy consumption under hierarchical accounting scope. Plant managers, energy management consultants, governmental officials and academic researchers could use this input-output model to account material and energy flows, thus calculating energy consumption indicators of a production process with their specific system boundary requirements. The accounting results could be further used for energy labeling, identifying bottlenecks of production activities, evaluating industrial symbiosis effects, improving materials and energy utilization efficiency, etc. The model could also be used as a planning tool to determine the effect that a particular change of technology and supply chains may have on the industrial production processes. The proposed model was tested and applied in a real integrated steel mill, which also provided the reference results for related researches. At last, some concepts, computational issues and limi- tations of the proposed model were discussed.