Industrial Recycling Process of Batteries for EVs

The growing number of decarbonization standards in the transportation sector has resulted in an increase in demand for electric cars.Renewable energy sources have the ability to bring the fossil fuel age to an end.Electrochemical storage devices,particularly lithium-ion batteries,are critical for this transition’s success.This is owing to a combination of favorable characteristics such as high energy density and minimal self-discharge.Given the environmental degradation caused by hazardous wastes and the scarcity of some resources,recycling used lithium-ion batteries has significant economic and practical importance.Many efforts have been undertaken in recent years to recover cathode materials(such as high-value metals like cobalt,nickel,and lithium).Regrettably,the regeneration of lower-value-added anode materials(mostly graphite)has received little attention.However,given the widespread use of carbon-based materials and the higher concentration of lithium in the anode than in the environment,anode recycling has gotten a lot of attention.As a result,this article provides the most recent research progress in the recovery of graphite anode materials from spent lithium ion batteries,analyzing the strengths and weaknesses of various recovery routes such as direct physical recovery,heat treatment recovery,hydrometallurgy recovery,heat treatment-hydrometallurgy recovery,extraction,and electrochemical methods from the perspectives of energy,environment,and economy;additionally,the reuse of recycled anode mats is discussed.Finally,the problems and future possibilities of anode recycling are discussed.To enable the green recycling of wasted lithium ion batteries,a low energy-consuming and ecologically friendly solution should be investigated.

Multi-dimensional Security Range Query for Industrial IoT

The Internet of Things(IoT)has allowed for significant advancements in applications not only in the home,business,and environment,but also in factory automation.Industrial Internet of Things(IIoT)brings all of the benefits of the IoT to industrial contexts,allowing for a wide range of applications ranging from remote sensing and actuation to decentralization and autonomy.The expansion of the IoT has been set by serious security threats and obstacles,and one of the most pressing security concerns is the secure exchange of IoT data and fine-grained access control.A privacypreserving multi-dimensional secure query technique for fog-enhanced IIoT was proposed in light of the fact that most existing range query schemes for fog-enhanced IoT cannot provide both multi-dimensional query and privacy protection.The query matrix was then decomposed using auxiliary vectors,and the auxiliary vectorwas then processed usingBGNhomomorphic encryption to create a query trapdoor.Finally,the query trapdoor may be matched to its sensor data using the homomorphic computation used by an IoT device terminal.With the application of particular auxiliary vectors,the spatial complexity might be efficiently decreased.The homomorphic encryption property might ensure the security of sensor data and safeguard the privacy of the user’s inquiry mode.The results of the experiments reveal that the computing and communication expenses are modest.

An Optimal Method for Supply Chain Logistics Management Based on Neural Network

From raw material storage through final product distribution,a cold supply chain is a technique in which all activities are managed by temperature.The expansion in the number of imported meat and other comparable commodities,as well as exported seafood has boosted the performance of cold chain logistics service providers.On the basis of the standard basicpursuit(BP)neural network,a rough BP particle swarm optimization(PSO)neural network model is constructed by combining rough set and particle swarm algorithms to aid cold chain food production enterprises in quickly picking the best cold chain logistics service providers.To reduce duplicate information in the original data and make the input index more compact,the model employs rough set.Instead of using gradient descent to train the weights of the neural network,particle swarm optimization is utilized to ensure that the output results are not readily caught in local minima and that the network’s generalization capacity is improved.Finally,an example is presented to demonstrate the model’s validity and viability.The findings reveal that the model’s prediction error is 40.94 percent lower than the BP neural network model,and the prediction result is more accurate and dependable,providing a new technique for cold chain food production companies to swiftly pick the best cold chain logistics service provider.

An Optimal Framework for SDN Based on Deep Neural Network

Software-defined networking(SDN)is a new paradigm that promises to change by breaking vertical integration,decoupling network control logic from the underlying routers and switches,promoting(logical)network control centralization,and introducing network programming.However,the controller is similarly vulnerable to a“single point of failure”,an attacker can execute a distributed denial of service(DDoS)attack that invalidates the controller and compromises the network security in SDN.To address the problem of DDoS traffic detection in SDN,a novel detection approach based on information entropy and deep neural network(DNN)is proposed.This approach contains a DNN-based DDoS traffic detection module and an information-based entropy initial inspection module.The initial inspection module detects the suspicious network traffic by computing the information entropy value of the data packet’s source and destination Internet Protocol(IP)addresses,and then identifies it using the DDoS detection module based on DNN.DDoS assaults were found when suspected irregular traffic was validated.Experiments reveal that the algorithm recognizes DDoS activity at a rate of more than 99%,with a much better accuracy rate.The false alarm rate(FAR)is much lower than that of the information entropy-based detection method.Simultaneously,the proposed framework can shorten the detection time and improve the resource utilization efficiency.