Evolving an Accurate Decision Tree-Based Model for Predicting Carbon Dioxide Solubility in Polymers

Solubility is one of the most indispensable physicochemical properties determining the compatibility of components of a blending system. Research has been focused on the solubility of carbon dioxide in polymers as a significant application of green chemistry. To replace costly and time-consuming experiments, a novel solubility prediction model based on a decision tree, called the stochastic gradient boosting algorithm, was proposed to predict CO₂ solubility in 13 different polymers, based on 515 published experimental data lines. The results indicate that the proposed ensemble model is an effective method for predicting the CO₂ solubility in various polymers, with highly satisfactory performance and high efficiency. It produces more accurate outputs than other methods such as machine learning schemes and an equation of state approach.     

Fully coupled LES-DEM of particle interaction and agglomeration in a turbulent channel flow

Coupled large eddy simulation and the discrete element method are applied to study turbulent particle–laden flows, including particle dispersion and agglomeration, in a channel. The particle–particle interaction model is based on the Hertz–Mindlin approach with Johnson–Kendall–Roberts cohesion to allow the simulation of van der Waals forces in a dry air flow. The influence of different particle surface energies, and the impact of fluid turbulence, on agglomeration behaviour are investigated. The agglomeration rate is found to be strongly influenced by the particle surface energy, with a positive relationship observed between the two. Particle agglomeration is found to be enhanced in two separate regions within the channel. First, in the near-wall region due to the high particle concentration there driven by turbophoresis, and secondly in the buffer region where the high turbulence intensity enhances particle–particle interactions.     

不同折流板结构螺旋折流板换热器传热性能的比较(英文)

Numerical simulations were performed on flow and heat transfer performances of heat exchangers having six helical baffles of different baffle shapes and assembly configurations, i.e., two trisection baffle schemes, two quadrant baffle schemes, and two continuous helical baffle schemes. The temperature contour or the pressure contour and velocity contour plots with superimposed velocity vectors on meridian, transverse and unfolded concentric hexagonal slices are presented to obtain a full angular view. For the six helix baffled heat exchangers, the different patterns of the single vortex secondary flow and the shortcut leakage flow were depicted as wel as the heat transfer properties were compared. The results show that the optimum scheme among the six configurations is a circumferential overlap trisection helix baffled heat exchanger with a baffle incline angle of 20° (20°TCO) scheme with an anti-shortcut baffle structure, which exhibits the second highest pressure dropΔpo, the highest overal heat transfer coefficient K, shel-side heat transfer coefficient ho and shel-side average comprehensive index ho/Δpo.     

综放高瓦斯矿井煤层瓦斯涌出量综合预测

为了成功预测竹林山煤矿综放高瓦斯矿井大采高工作面煤层瓦斯涌出量,以主采3号煤层为主要研究对象,针对3号煤层以往开采情况,通过布设测点测量其煤层瓦斯含量和了解相邻矿井瓦斯含量,采用分源预测法、回归法及统计法等预测方法得到了3号煤层瓦斯含量的分布规律,并绘制了3号煤层的瓦斯含量等值线图。对矿井不同生产时期的瓦斯含量进行预测,得到了生产前期、中期及后期采区的最大绝对瓦斯涌出量和最大相对瓦斯涌出量,说明了竹林山煤矿各个时期均属于高瓦斯矿井。     

摇摆运动下多环路主冷却剂系统自然循环流量的稳定性分析

海洋核动力平台因其突出的安全性已成为当今核能领域热点研究问题之一，但在海洋等非惯性条件下会使一回路系统的热工特性发生变化。针对此问题，本文对几种典型的一回路系统在摇摆条件下的自然循环流量波动特性进行计算分析。分别建立典型的双环路、三环路、四环路的一回路系统模型布置方案，并同时考虑摇摆中心的位置，根据流体动量守恒方程，得到不同状态下一回路系统内的流量变化规律。对于单堆双环路系统，摇摆中心在船上/中/下部位置时，环路流量波动幅度分别为13.2%/11.2%/9.5%，堆芯流量波动幅度分别为0.9%/0.8%/0.6%；对于单堆三环路系统，摇摆中心在船下部时，环路流量和堆芯流量波动幅度分别为9.2%和0.8%；对于单堆四环路系统，摇摆中心在船下部时，布置方案1和方案2的环路流量波动幅度分别为9.5%和9.2%，堆芯流量波动幅度分别为0.9%和0.7%。计算结果表明：采用单堆双环路的设计布置方案是最有利于系统稳定性的。     

不同含量低污染水对人工湿地中细菌的影响研究

探究水平流人工湿地(HFCW)系统处理低污染水过程中,相同COD/ρ(TN)下不同碳氮含量对细菌群落结构的影响。结果表明,进水为较高碳氮含量的HFCW(HF1)和进水为较低碳氮含量的HFCW(HF2)对COD和TN的去除效率具有一定的差异,HF1和HF2对COD的去除效率分别为48.26%和28.89%,对TN的去除率分别为79.06%和81.87%。HF1中细菌的丰富度和多样性均高于HF2,HF1中富集的优势细菌为Chloroflexaceae、Comamonadaceae和Rhodocyclaceae,均具有异养反硝化功能,HF2中富集的优势细菌为Xanthomonadaceae和Rhodocyclaceae,其中Xanthomonadaceae具有自养反硝化功能。COD、NH4^+-N和NO3^--N对HF1中细菌群落的影响大于对HF2中细菌群落的影响,HF1中COD对细菌群落的影响大于NH4^+-N和NO3^--N。     

The influence of the waveguide on the quality of measurements with ultrasonic Doppler velocimetry

The article is devoted to the study of the influence of geometric parameters of sound-conducting walls on the quality of measurement of liquid metal flow velocities by ultrasonic Doppler velocimetry. It was shown that the thickness and radius of a sound-conducting wall (waveguide) have a notable effect on the resulting velocity profiles. The flow in a round pipe, the length of which is much larger than its diameter, is considered as a reference flow. The positive effect of a stepwise waveguides with a diameter smaller than the diameter of the piezoelectric element of an ultrasonic transducer on the quality of velocity measurements was verified experimentally. It was found that the accuracy of the resulting velocity profiles largely depends on the length and the material of the waveguides, as well as the velocity of the incoming flow of liquid metal.     

A bat-neural network multi-agent system (BNNMAS) for stock price prediction: Case study of DAX stock price

Creating an intelligent system that can accurately predict stock price in a robust way has always been a subject of great interest for many investors and financial analysts. Predicting future trends of financial markets is more remarkable these days especially after the recent global financial crisis. So traders who access to a powerful engine for extracting helpful information throw raw data can meet the success. In this paper we propose a new intelligent model in a multi-agent framework called bat-neural network multi-agent system (BNNMAS) to predict stock price. The model performs in a four layer multi-agent framework to predict eight years of DAX stock price in quarterly periods. The capability of BNNMAS is evaluated by applying both on fundamental and technical DAX stock price data and comparing the outcomes with the results of other methods such as genetic algorithm neural network (GANN) and some standard models like generalized regression neural network (GRNN), etc. The model tested for predicting DAX stock price a period of time that global financial crisis was faced to economics. The results show that BNNMAS significantly performs accurate and reliable, so it can be considered as a suitable tool for predicting stock price specially in a long term periods.     

A bi-layer optimization approach for a hybrid flow shop scheduling problem involving controllable processing times in the steelmaking industry

A steelmaking-continuous casting (SCC) scheduling problem is an example of complex hybrid flow shop scheduling problem (HFSSP) with a strong industrial background. This paper investigates the SCC scheduling problem that involves controllable processing times (CPT) with multiple objectives concerning the total waiting time, earliness/tardiness and adjusting cost. The SCC scheduling problem with CPT is seldom discussed in the existing literature. This study is motivated by the practical situation of a large integrated steel company in which the just-in-time (JIT) and cost-cutting production strategy have become a significant concern. To address this complex HFSSP, the scheduling problem is decomposed into two subproblems: a parallel machine scheduling problem (PMSP) in the last stage and an HFSSP in the upstream stages. First, a hybrid differential evolution (HDE) algorithm combined with a variable neighborhood decomposition search (VNDS) is proposed for the former subproblem. Second, an iterative backward list scheduling (IBLS) algorithm is presented to solve the latter subproblem. The effectiveness of this bi-layer optimization approach is verified by computational experiments on well-designed and real-world scheduling instances. This study provides a new perspective on modeling and solving practical SCC scheduling problems.     

Power Input Prediction in Agitated Gas‐Liquid Contactors with Non‐coalescent Batch

Volumetric mass transfer coefficients, k_La, just as power input are considered as essential parameters for mechanically agitated gas‐liquid contactors in relation to their optimization and design. The knowledge of power input is crucial for the prediction of other mass transfer characteristics. A power input correlation is created for the industrial design of the process with a non‐coalescent batch that would be appropriate for a broad range of operational conditions. The recommended resulting correlation is able to predict the power input for impellers in industrial‐scale design for a significant scope of operational conditions.