Temperature stress of waste bunker in municipal solid waste incineration power generation plant

With large number of municipal solid waste incineration power generation plants appearing, serious environmental pollution will be caused if temperature cracks appear in waste bunkers. To reveal the interaction between the surrounding soil and bunker walls under the action of temperature, a finite element model is established. Considering the surrounding soil layer, the characteristics and influence laws of the interaction between the municipal solid waste bunker and the soil under different temperature conditions are studied. The simulation results show that the existence of the surrounding soil layer will affect the stress distribution, mainly at the bottom of the bunker and the surface of the bunker wall. Due to the thermal expansion and contraction, the municipal solid waste bunker is pressed during the heating process. In the process of cooling, there will be excessive tensile stress at the bottom of the bunker. To address this problem, expansion belt is arranged at the stress concentration portion to reduce the stress concentration. This measure proves to be effective according to analysis results, which provides a reference for the design of municipal solid waste bunkers

Effect of temperature stress on main structure in waste incineration power generation plant

Waste incineration power station includes many functional structures, such as garbage discharge, storage, sorting and feeding units, boiler unit, tail gas treatment unit, leachate treatment unit, and coal storage unit. The structural forms of each part are different. Because of the particularity of garbage, structure units are required to be as close as possible and temperature joints should not be set up for the sake of possible leakage of exhaust gas or effluent liquor, so various structural units are integrated, which leads to the difficulty in structure design, and thermal stress cannot be neglected. In order to understand the effect temperature stress on main plant structures, a finite element model is established to study the distribution of thermal stress of the whole structure under three conditions: heating in summer, cooling in winter and heating in winter. It is found the influence of temperature on frame beam, column and steel space truss can be neglected, and the thermal stress on floor cannot be neglected [1]. The maximum stress is mainly distributed on both edges of floor along the longitude direction of structure. For those regions where stress concentration occurs, reinforcement bands or reinforcement mesh can be used to reduce the tensile stress. The analysis results show that this measure is effective and provides a reference for the design of the main structure of waste incineration power plant. This paper innovatively analyses the structure system of main workshop of refuse incineration power plant, which is composed of steel structure and concrete structure, and describes the skills and key points of complex system modeling. According to different seasons and heating temperature difference, the temperature stress on the surface of the structure is analyzed, which provides a reference for calculating degree stress and temperature difference of the similar structure system. The weak part of resistance to temperature stress in the structure system composed of concrete structure and steel structure is found out, and the corresponding solutions are put forward, which provides guidance for the construction of the main workshop of refuse incineration power station

Cross-Layer Active Predictive Congestion Control Protocol for Wireless Sensor Networks

In wireless sensor networks (WSNs), there are numerous factors that may cause network congestion problems, such as the many-to-one communication modes, mutual interference of wireless links, dynamic changes of network topology and the memory-restrained characteristics of nodes. All these factors result in a network being more vulnerable to congestion. In this paper, a cross-layer active predictive congestion control scheme (CL-APCC) for improving the performance of networks is proposed. Queuing theory is applied in the CL-APCC to analyze data flows of a single-node according to its memory status, combined with the analysis of the average occupied memory size of local networks. It also analyzes the current data change trends of local networks to forecast and actively adjust the sending rate of the node in the next period. In order to ensure the fairness and timeliness of the network, the IEEE 802.11 protocol is revised based on waiting time, the number of the node's neighbors and the original priority of data packets, which dynamically adjusts the sending priority of the node. The performance of CL-APCC, which is evaluated by extensive simulation experiments. is more efficient in solving the congestion in WSNs. Furthermore, it is clear that the proposed scheme has an outstanding advantage in terms of improving the fairness and lifetime of networks

Meat and bone meal stimulates microbial diversity and suppresses plant pathogens in asparagus straw composting

Meat and bone meal (MBM), as slaughterhouse waste, is a potential biostimulating agent, but its efficiency and reliability in composting are largely unknown. To access the MBM application to the composting process of asparagus straw rice, we followed the composting process for 60 days in 220-L composters and another 180 days in 20-L buckets in treatments applied with MBM or urea. The microbial succession was investigated by high-throughput sequencing. Compared with urea treatments, MBM addition stabilized pH and extended the thermophilic phase for 7 days. The germination index of MBM treatments was 24.76% higher than that of urea treatments. MBM also promoted higher microbial diversity and shifted community compositions. Organic matter and pH were the most significant factors that influence the bacterial and fungal community structure. At the genus level, MBM enriched relative abundances of organic matter-degrading bacteria (Alterococcus) and lignocellulose-degrading fungi (Trichoderma), as well as lignocellulolytic enzyme activities. Notably, MBM addition decreased sum abundances of plant pathogenic fungi of Phaeoacremonium, Acremonium, and Geosmithia from 17.27 to 0.11%. This study demonstrated the potential of MBM as an effective additive in asparagus straw composting, thus providing insights into the development of new industrial aerobic fermentation.Peer reviewe

Hierarchical Leak Detection and Localization Method in Natural Gas Pipeline Monitoring Sensor Networks

In light of the problems of low recognition efficiency, high false rates and poor localization accuracy in traditional pipeline security detection technology, this paper proposes a type of hierarchical leak detection and localization method for use in natural gas pipeline monitoring sensor networks. In the signal preprocessing phase, original monitoring signals are dealt with by wavelet transform technology to extract the single mode signals as well as characteristic parameters. In the initial recognition phase, a multi-classifier model based on SVM is constructed and characteristic parameters are sent as input vectors to the multi-classifier for initial recognition. In the final decision phase, an improved evidence combination rule is designed to integrate initial recognition results for final decisions. Furthermore, a weighted average localization algorithm based on time difference of arrival is introduced for determining the leak point’s position. Experimental results illustrate that this hierarchical pipeline leak detection and localization method could effectively improve the accuracy of the leak point localization and reduce the undetected rate as well as false alarm rate

A novel prognostic classification integrating lipid metabolism and immune co-related genes in acute myeloid leukemia

BackgroundAs a severe hematological malignancy in adults, acute myeloid leukemia (AML) is characterized by high heterogeneity and complexity. Emerging evidence highlights the importance of the tumor immune microenvironment and lipid metabolism in cancer progression. In this study, we comprehensively evaluated the expression profiles of genes related to lipid metabolism and immune modifications to develop a prognostic risk signature for AML.MethodsFirst, we extracted the mRNA expression profiles of bone marrow samples from an AML cohort from The Cancer Genome Atlas database and employed Cox regression analysis to select prognostic hub genes associated with lipid metabolism and immunity. We then constructed a prognostic signature with hub genes significantly related to survival and validated the stability and robustness of the prognostic signature using three external datasets. Gene Set Enrichment Analysis was implemented to explore the underlying biological pathways related to the risk signature. Finally, the correlation between signature, immunity, and drug sensitivity was explored.ResultsEight genes were identified from the analysis and verified in the clinical samples, including APOBEC3C, MSMO1, ATP13A2, SMPDL3B, PLA2G4A, TNFSF15, IL2RA, and HGF, to develop a risk-scoring model that effectively stratified patients with AML into low- and high-risk groups, demonstrating significant differences in survival time. The risk signature was negatively related to immune cell infiltration. Samples with AML in the low-risk group, as defined by the risk signature, were more likely to be responsive to immunotherapy, whereas those at high risk responded better to specific targeted drugs.ConclusionsThis study reveals the significant role of lipid metabolism- and immune-related genes in prognosis and demonstrated the utility of these signature genes as reliable bioinformatic indicators for predicting survival in patients with AML. The risk-scoring model based on these prognostic signature genes holds promise as a valuable tool for individualized treatment decision-making, providing valuable insights for improving patient prognosis and treatment outcomes in AML

Adding power of artificial intelligence to situational awareness of large interconnections dominated by inverter‐based resources

Large-scale power systems exhibit more complex dynamics due to the increasing integration of inverter-based resources (IBRs). Therefore, there is an urgent need to enhance the situational awareness capability for better monitoring and control of power grids dominated by IBRs. As a pioneering Wide-Area Measurement System, FNET/GridEye has developed and implemented various advanced applications based on the collected synchrophasor measurements to enhance the situational awareness capability of large-scale power grids. This study provides an overview of the latest progress of FNET/GridEye. The sensors, communication, and data servers are upgraded to handle ultra-high density synchrophasor and point-on-wave data to monitor system dynamics with more details. More importantly, several artificial intelligence (AI)-based advanced applications are introduced, including AI-based inertia estimation, AI-based disturbance size and location estimation, AI-based system stability assessment, and AI-based data authentication

Simulation and Analysis for Electric Bicycle Traffic Flow

Keywords: traffic engineering, electric bicycle flow, lane changing rule, cellular automaton model. Abstract. The electric bicycle has become the main part of non motor vehicles in small and medium-sized cities. Research on the traffic flow characteristic of the electric bicycle has important practical significance. Based on NaSch model, this paper models electric bicycle traffic flow with CA model and improves the lane changing model. Then the electric bicycle lanes change into general lane change and whistle change, and corresponding lane changing rules are set up. Simulation analysis of the model is carried out. The results show that when the traffic density is small, whistling behavior to raise the road utilization rate has some effect, but in the high density, whistle behavior can not improve road traffic capacity

Multi-Agent Based Microscopic Simulation Modeling for Urban Traffic Flow

Traffic simulation plays an important role in the evaluation of traffic decisions. The movement of vehicles essentially is the operating process of drivers, in order to reproduce the urban traffic flow from the micro-aspect on computer, this paper establishes an urban traffic flow microscopic simulation system (UTFSim) based on multi-agent. The system is seen as an intelligent virtual environment system (IVES), and the four-layer structure of it is built. The road agent, vehicle agent and signal agent are modeled. The concept of driving trajectory which is divided into LDT (Lane Driving Trajectory) and VDDT (Vehicle Dynamic Driving Trajectory) is introduced. The “Link-Node” road network model is improved. The driving behaviors including free driving, following driving, lane changing, slowing down, vehicle stop, etc. are analyzed. The results of the signal control experiments utilizing the UTFSim developed in the platform of Visual Studio. NET indicates that it plays a good performance and can be used in the evaluation of traffic management and control