垃圾焚烧发电技术研究进展及其环境效益

垃圾处理一直是城市管理面临的重要问题。传统的垃圾填埋方法对环境有着严重的污染和资源浪费,这促使了垃圾焚烧发电技术的发展。本文旨在探讨垃圾焚烧发电技术的原理、影响因素、环境效益以及未来发展方向。

海洋上大型发电站

日本通产省着手研究在海洋上建浮动的大型发电站。在海洋上浮动的金属盘上装入张开的太阳能电池配电盘,为提高整体发电效果,发电站由太阳能、风力、波力发电组合。发电设施发电量100万kW,与一所原子能发电站相当,建设成本也要求与原子能发电站相同。电力通过海底电缆或燃料电池船输送。

沼气发电在造纸废水处理中的应用

本文结合实例。介绍了度纸造纸企业在度水处理中采用沼气发电技术回收利用沼气的工艺流程、主要设备设计参数，分析了沼气发电效果，为造纸企业选择沼气发电和节能减排提供了一定的参考和借鉴。

Nanobubble generation and its application in froth flotation (part I): nanobubble generation and its effects on properties of microbubble and millimeter scale bubble solutions

A special nanobubble generation system has been developed for evaluating the effect of nanobubble on froth flotation. In this study, an eight-factor five-level Central Composite Experimental Design was conducted for investigating eight important parameters governing the median size and the volume of nanobubbles. These process parameters included surfactant concentration, dissolved oxygen (O2) content, dissolved carbon dioxide gas (CO2) content, pressure drop in cavitation tube nozzle, <50 nm hydrophobic particle concentration, <50 nm hydrophilic particle concentration, slurry temperature and the time interval after nanobubble generation. The properties, stability and uniformity of nanobubbles were investigated. The study of the produced nanobubble’s effects on the characteristics of microbubble solutions and millimeter scale bubble solutions was performed in a 50.8 mm column.

Analysis on Application of Wavelet Neural Network in Wind Electricity Power Prediction

Wind electricity power has fluctuation, and accurate and reasonable wind electricity power prediction is very important for solving wind electricity network and combination. This paper takes an analysis of a lot of actual data of a certain wind electricity field. Through wavelet neural network and time series method rolling, it can predict the overall power of wind electricity field. The result shows that for the original data of sampling time length and large sampling frequency, the model constructed by this paper has very good prediction effect. Because of the fan installation position, wind electricity fan flow effect and other random factor influence, wind electricity field overall power and single unit power distribution have difference. Through comparing with the time series parameters, it puts forward that single wind electricity unit power has smooth effect for overall power of wind electricity field. Finally, it summarizes the prediction effect and puts forward some reasonable suzestions for wind electricity network troblems.

Tempospacial energy-saving effect-based diagnosis in large coal-fired power units:Energy-saving benchmark state

The energy-saving analytics of coal-fired power units in China is confronting new challenges especially with even more complicated system structure, higher working medium parameters, time-dependent varying operation conditions and boundaries such as load rate, coal quality, ambient temperature and humidity. Compared with the traditional optimization of specific operating parameters, the idea of the energy-consumption benchmark state was proposed. The equivalent specific fuel consumption(ESFC) analytics was introduced to determine the energy-consumption benchmark state, with the minimum ESFC under varying operation boundaries. Models for the energy-consumption benchmark state were established, and the endogenous additional specific consumption(ASFC) and exogenous ASFC were calculated. By comparing the benchmark state with the actual state, the energy-saving tempospacial effect can be quantified. As a case study, the energy consumption model of a 1000 MW ultra supercritical power unit was built. The results show that system energy consumption can be mainly reduced by improving the performance of turbine subsystem. This nearly doubles the resultant by improving the boiler system. The energy saving effect of each component increases with the decrease of load and has a greater influence under a lower load rate. The heat and mass transfer process takes priority in energy saving diagnosis of related components and processes. This makes great reference for the design and operation optimization of coal-fired power units.

Evaporate prediction and compensation of intake port wall-wetting fuel film for spark ignition engines fueled with ethanol-gasoline blends

The fuel dynamic transfer process,including fuel injection,fuel film deposition and evaporation in the intake port,was analyzed for spark ignition(SI) engines with port fuel injection(PFI).The influence of wall-wetting fuel film,especially its evaporation rate,upon the air-fuel ratio of in-cylinder mixtures was also discussed.According to the similarity principle,Fick's law,the ideal gas equation and the Gilliland correlation,an evaporate prediction model of wall-wetting fuel film was set up and an evaporate prediction based dynamic fuel film compensator was designed.Through engine cold start tests,the wall-wetting temperature,which is the key input of the fuel film evaporate prediction model,was also modeled and predicted.Combined with the experimental data of the evaporation characteristics of ethanol-gasoline blends and engine calibration tests,all the parameters of the wall-wetting fuel film evaporate prediction model used in the fuel film compensator were identified.Square-wave disturbance tests of fuel injection showed that with the help of the fuel film compensator the response of the in-cylinder air-fuel ratio was significantly improved and the real air-fuel ratio always closely matched the expected ratio.The fuel film compensator was then integrated into the final air-fuel ratio controller,and the engine tests showed that the air-fuel ratio control error was less than 2% in steady-state conditions,and less than 4% in transient conditions.The fuel film compensator also showed good adaptability to different ethanol-gasoline blends.

Functional tumor imaging based on inorganic nanomaterials

Inorganic nanomaterials have attracted substantial research interest due to their unique intrinsic physicochemical properties. We highlighted recent advances in the applications of inorganic nanoparticles regarding their imaging efficacy, focusing on tumor-imaging nanomaterials such as metal-based and carbon-based nanomaterials and quantum dots. Inorganic nanoparticles gain excellent in vivo tumor-imaging functions based on their specific characteristics of strong near-infrared optical absorption and/or X-ray attenuation capability. The specific response signals from these novel nanornaterials can be captured using a series of imaging techniques, i.e., optical coherence tomography （OCT）, X-ray computed tomography （CT） imaging, two-photon luminescence （TPL）, photoacoustic tomography （PAT）, magnetic resonance imaging （MRI）, surface-enhanced Raman scattering （SERS） and positron emission tomography （PET）. In this review, we summarized the rapid development of inorganic nanomaterial applications using these analysis techniques and discussed the related safety issues of these materials.

Volatile organic compounds sensing based on Bennet doubler-inspired triboelectric nanogenerator and machine learning-assisted ion mobility analysis

Ion mobility analysis is a well-known analytical technique for identifying gas-phase compounds in fastresponse gas-monitoring systems.However,the conventional plasma discharge system is bulky,operates at a high temperature,and inappropriate for volatile organic compounds(VOCs)concentration detection.Therefore,we report a machine learning(ML)-enhanced ion mobility analyzer with a triboelectric-based ionizer,which offers good ion mobility selectivity and VOC recognition ability with a small-sized device and non-strict operating environment.Based on the charge accumulation mechanism,a multi-switched manipulation triboelectric nanogenerator(SM-TENG)can provide a direct current(DC)bias at the order of a few hundred,which can be further leveraged as the power source to obtain a unique and repeatable discharge characteristic of different VOCs,and their mixtures,with a special tip-plate electrode configuration.Aiming to tackle the grand challenge in the detection of multiple VOCs,the ML-enhanced ion mobility analysis method was successfully demonstrated by extracting specific features automatically from ion mobility spectrometry data with ML algorithms,which significantly enhance the detection ability of the SM-TENG based VOC analyzer,showing a portable real-time VOC monitoring solution with rapid response and low power consumption for future internet of things based environmental monitoring applications.