Design and optimization of new La1−xCexNi1−yFeyO3 (x,y = 0–0.4) nano catalysts in dry reforming of methane

The paper reports the production of syngas from dry reforming of methane (DRM) over La_1?xCe_xNi_1?yFe_yO₃ (x, y = 0–0.4) perovskites. A series of La_1?xCe_xNi_1?yFe_yO₃ were designed by central composite design (CCD) and synthesized by a sol–gel auto combustion method. Artificial neural network (ANN) approach was used to determine the relationship between preparation and operational parameters on the performance of the catalysts in the DRM process. Nickel mole fraction, lanthanum mole fraction, calcination temperature, and reaction temperature were considered as input variables, and conversion of methane was considered as the output variable. An ANN model with nine neurons in the hidden layer was the suitable in predicting conversion of methane. The genetic algorithm (GA) was subsequently used to determine the optimal preparation condition for enhancing the conversion of methane. La_0.6Ce_0.4Ni_0.99Fe_0.01O₃ catalyst, calcined at 756°C was obtained to be the most active catalyst owing to the optimal composition of nickel and lanthanum in the catalyst formulation.     

Numerical modeling and optimization of a power augmented S-type vertical axis wind turbine

An axial symmetry augmented vertical axis wind turbine, which is suitable for arbitrary wind directions, is proposed in this paper. In order to improve the power generation ability of the S-type vertical axis wind turbine, a set of so-called “collection-shield boards” are installed symmetrically around the rotating S-type rotor. The flow fields around this type of wind turbine are numerically simulated with the aid of CFD method. The optimized design of geometrical parameters of the rotor and collection-shield boards is conducted by using the orthogonal design method. The obtained results suggest that the power output of the optimized augmented wind turbine can reach nearly three times higher than that of the conventional S-type vertical axis wind turbine.     

胶质气体泡沫(CGA)特性优化试验研究

采用十二烷基硫酸钠(SDS)为表面活性剂制备CGA,得出CGA制备的最优条件。结果表明,表面活性剂SDS的浓度在临界胶束浓度、搅拌速度6400r/min、搅拌时间3min时制备的CGA最稳定,CGA的含气率会随着搅拌时间的延长而有所增加。     

基于MCOA布置矿井瓦斯传感器

为了预防矿井瓦斯事故,确定增设瓦斯传感器的数量和位置。通过建立监测有效 等级来确定增设瓦斯传感器的数量,然后通过分析多个猴群同步并行搜索、简易猴群算 法的初步搜索和基本和声算法的二次搜索等方法,设计出猴群优化算法的计算流程,再 根据瓦斯传感器布置的目标函数确定增设瓦斯传感器位置。以大隆矿为例进行了现场工 业试验,结果表明,增设瓦斯传感器的数量与监测有效等级之间成正比例关系,但并非 随着监测有效等级的提高而持续增加。     

基于动态网络流的区域疏散分析模型与仿真

区域疏散分析是应急管理工作的重要内容。基于CBRN事故区域疏散的特点,引入疏散亚区域的概念,并运用运筹学中图论与离散时间动态网络流的理论和方法,建立CBRN事故区域疏散优化模型。采用RELAX算法求解该模型,并结合CBRN事故时期人员疏散过程详细介绍区域疏散分析的流程。最后将所建模型应用于具体实例,程序运算结果表明群众对紧急疏散通知的反应速度越快,被疏散人数越少以及尽早通知群众疏散时,人员疏散效率明显提高。CBRN事故区域疏散优化模型可为优化和改进CBRN事故区域疏散的方法提供依据。     

Land-use planning via enhanced multi-objective evolutionary algorithms: optimizing the land value of major Greenfield initiatives

The successful implementation of major development initiatives relies on the sound allocation of land uses against critical design criteria and constraints. The discovery of optimum development plans introduces severe complexities in formulating and solving the underlying multi-objective optimization problem. Moreover, in the presence of conflicting planning criteria decision-makers should be provided with a set of alterative-yet-optimum solutions that uniformly cover the spectrum of feasible maps. The introduction of sophisticated optimization algorithms addresses this challenge by pursuing a complete approximation of the Pareto front containing all prominent spatial allocations. This study demonstrates the effectiveness of a new evolutionary algorithm (UDT-MOEA) against the results of an established multi-objective genetic algorithm (NSGA-II) when applied on a major greenfield initiative against the optimum location(s), size and shape of three new land uses. Each algorithm performs best in different areas of the feasible objective space, providing planning alternatives with distinct characteristics.     

Exploring price effects on the residential water conservation technology diffusion process: a case study of Tianjin city

Reforms of the water pricing management system and the establishment of a flexible water pricing system are significant for cities in northern China to tackle their critical water issues. The WATAP (Water conservation Technology Adoption Processes) model is developed in order to capture the water conservation technology adoption process under different price scenarios with disaggregate water demands down to the end use level. This model is explicitly characterized by the technological selection process under maximum marginal benefit assumption by different categories of households. In particular, when households need to purchase water devices in the provision market with the consideration of complex factors such as the life span, investment and operating costs of the device, as well as the regulated water price by the government. Applied to Tianjin city, four scenarios of water price evolutions for a long-term perspective (from year 2011 to 2030) are considered, including BAU (Business As Usual), SP1 (Scenario of Price increase with constant annual rate), SP2 (Scenario of Price increase every four years) and SP3 (Scenario of Price increase with affordable constraint), considering many factors such as historic trends, affordability and incentives for conservation. Results show that on aggregate 2.3%, 11.0% and 18.2% of fresh water can be saved in the residential sector in scenario SP1, SP2 and SP3, respectively, compared with the BAU scenario in the year 2030. The water price signals can change the market shares of different water appliances, as well as the water end use structure of households, and ultimately improve water use efficiency. TheWATAP model may potentially be a helpful tool to provide insights for policy makers on water conservation technology policy analysis and assessment.     

On equilibrium in resource markets with scale economies and stochastic prices

In this paper, I show the existence and the characteristics of equilibrium in a non-renewable resource market where extraction costs are non-convex and market price is subject to stochastic shocks, an empirically relevant setting. In my model firms may be motivated to hold inventories to facilitate production smoothing, which allows them to continue producing at a smooth pace at any instant when extraction ceases, e.g. when reserves are exhausted. This aspect of the model then supports a competitive equilibrium in the presence of non-convex costs. Casual empirical evidence is provided that supports the central features of my model for a variety of non-renewable resources, lending credence to the explanation for equilibrium I propose.     

一株花生根际促生菌的筛选鉴定及其特性研究

从种植于红壤的健康花生根际,筛选出7株产吲哚乙酸(IAA)菌株,以菌株L4合成IAA的能力最强,培养24 h时IAA产生量达135.67μg.mL-1,且菌株L4具有解磷能力。通过菌株形态、生理生化特征测定及16SrRNA的保守序列鉴定,初步确定菌株L4为氯酚节杆菌(Arthrobacter chlorophenolicus),其GenBank登录号为JQ277449。菌株生长和发酵条件试验结果表明,菌株L4生长和分泌IAA的最佳培养条件并不完全一致,既能促进菌株生长又能合成较多IAA的最佳培养条件是初始pH值为5～6,装液量为50 mL.(250 mL)-1,30℃摇床培养24 h;促进菌株生长的最佳碳、氮源分别是麦芽糖和酵母粉,而提高IAA产生量的最佳碳源是木糖,最佳氮源是KNO3。     

Long-term prediction model of rockburst in underground openings using heuristic algorithms and support vector machines

Rockburst possibility prediction is an important activity in many underground openings design and construction as well as mining production. Due to the complex features of rockburst hazard assessment systems, such as multivariables, strong coupling and strong interference, this study employs support vector machines (SVMs) for the determination of classification of long-term rockburst for underground openings. SVMs is firmly based on the theory of statistical learning algorithms, uses classification technique by introducing radial basis function (RBF) kernel function. The inputs of models are buried depth H, rocks’ maximum tangential stress σ_θ, rocks’ uniaxial compressive strength σ_c, rocks’ uniaxial tensile strength σ_t, stress coefficient σ_θ/σ_c, rock brittleness coefficient σ_c/σ_t and elastic energy index W_et. In order to improve predictive accuracy and generalization ability, the heuristic algorithms of genetic algorithm (GA) and particle swarm optimization algorithm (PSO) are adopted to automatically determine the optimal hyper-parameters for SVMs. The performance of hybrid models (GA + SVMs = GA-SVMs) and (PSO + SVMs = PSO-SVMs) have been compared with the grid search method of support vector machines (GSM-SVMs) model and the experimental values. It also gives variance of predicted data. A rockburst dataset, which consists of 132 samples, was employed to evaluate the current method for predicting rockburst grade, and the good results of overall success rate were obtained. The results indicated that the heuristic algorithms of GA and PSO can speed up SVMs parameter optimization search, the proposed method is robust model and might hold a high potential to become a useful tool in rockburst prediction research.