Automated prediction system of household energy consumption in cities using web crawler and optimized artificial intelligence

The supply of electrical energy is critical to convenient and comfortable living. However, people consume a large amount of energy, contributing to an energy crisis and global warming, and damaging some ecological cycles. Residential electricity consumption has greater elasticity than industrial and business consumption; it therefore has high energy-saving potential. This work establishes an automated platform, which provides information about residential electricity consumption in each city in Taiwan. Machine learning was used to forecast future residential electricity demand. A nature-inspired optimization method was applied to enhance the accuracy of the best machine learner, yielding an even better hybrid ensemble model. Performance measures indicate that the resulting model is accurate and provides effective information for reference. An automatic web-based system based on the model was combined with a web crawler and scheduled to run automatically to provide information on monthly residential electricity consumption in each county and city. By providing energy consumption information across the country, power providers and government can discuss policy and set different goals for energy use. The results of this study can facilitate the early implementation of energy-saving and carbon emission-reducing in cities and aid utility companies in establishing energy conservation guidelines.     

Additional deepwater sampling of prey fish in Lake Michigan annual bottom trawl survey reveals new insights for depth distribution dynamics

Beginning in 2013, sites at the 128-m bottom depth contour were added to the sampling design of the annual Lake Michigan bottom trawl survey for prey fish, which has been conducted by the U.S. Geological Survey Great Lakes Science Center (GLSC) each fall since 1973, to better assess fish depth distributions in a changing ecosystem. The standard sampling design included bottom depths from 9 to 110 m, although the GLSC also sporadically conducted bottom trawl tows at the 128-m bottom depth contour during 1973–1988. Enactment of this new sampling design in 2013 revealed that mean biomass density of deepwater sculpins (Myoxocephalus thompsonii) at the 128-m depth exceeded the sum of mean biomass densities at shallower depths, indicating that the bulk of the deepwater sculpin population is residing in waters deeper than 110 m. Thus, our findings supported the hypothesis that the depth distribution of the deepwater sculpin population had shifted to deeper waters beginning in 2007, thereby explaining, at least in part, the marked decline in deepwater sculpin abundance since 2006 based on the standard sampling design. In contrast, our results did not support the hypothesis that the slimy sculpin (Cottus cognatus) population had shifted to deeper waters sometime after 2000. A portion of the burbot (Lota lota) population may have also shifted in depth distribution to waters deeper than 110 m after 2007, based on our results. Our findings have served as an impetus to further expand the range of depths sampled in our bottom trawl survey.     

Improved one-dimensional beam model for unbalanced composite single-lap joint

An analytical nonlinear solution was provided for unbalanced composite single-lap joint (CSLJ) using an improved one-dimensional beam model, which incorporated the effect of bending–tension coupling. The bending–tension coupling stiffness was introduced to characterize the coupling bending and tension behavior induced by the un-symmetric stacking sequence of composite laminates. The governing differential equations captured the bending–tension coupling behavior and the geometrically nonlinear features were constructed based on the displacement compatibility conditions of flexible interface. The transverse deformation in overlap region, edge moment factors and adhesive stress distributions for the unbalanced CSLJ with inflexible, intermediate flexibility and flexible adhesive can be determined by the present one-dimensional beam model. The accuracy of the present model was validated by the comparison with nonlinear finite element model. The effect of bending–tension coupling on edge moment factors and peak values of adhesive stresses was shed light on with the present model.     

Effect of Nanoparticles on Viscosity and Interfacial Tension of Aqueous Surfactant Solutions at High Salinity and High Temperature

Surfactant flooding has widely been used as one of the chemically enhanced oil recovery (EOR) techniques. Surfactants majorly influence the interfacial tension, γ, between oil and brine phase and control capillary number and relative permeability behavior and, thus, influence ultimate recovery. Additives, such as nanoparticles, are known to affect surfactant properties and are regarded as promising EOR agents. However, their detailed interactions with surfactants are not well understood. Thus, in this work, we examined the influence of silica nanoparticles on the ability of surfactants to lower γ and to increase viscosity at various temperatures and salinities. Results show that the presence of nanoparticles decreased γ between n-decane and various surfactant formulations by up to 20%. It was found that γ of nanoparticles–surfactant solutions passed through a minimum at 35 °C when salt was added. Furthermore, the viscosity of cationic surfactant solutions increased at specific salt (1.5 wt.%) and nanoparticle (0.05 wt.%) concentrations. Results illustrate that selected nanoparticles–surfactant formulations appear very promising for EOR as they can lower brine/n-decane interfacial tension and act as viscosity modifiers of the injected fluids.     

The control of paracetamol particle size and surface morphology through crystallisation in a spray dryer

The parameters governing the crystallisation of paracetamol using various conventional techniques has been extensively studied, however the factors influencing the drug crystallisation using spray drying is not as well understood. The aim of this work was to investigate the crystallisation of an active pharmaceutical ingredient through evaporative crystallisation using a spray dryer to study the physicochemical properties of the drug and to use semi-empirical equations to gain insight into the morphology and particle size of the dried powder. Paracetamol solutions were spray dried at various inlet temperatures ranging from 60 °C to 120 °C and also from a series of inlet feed solvent compositions ranging from 50/50% v/v ethanol/water to 100% ethanol and solid-state characterisation was done. The size and morphology of the dried materials were altered with a change in spray drying parameters, with an increase in inlet temperature leading to an increase in particle Sauter mean diameter (from 3.0 to 4.4 µm) and a decrease in the particle size with an increase in ethanol concentration in the feed (from 4.6 to 4.4 µm) as a result of changes in particle density and atomised droplet size. The morphology of the dried particles consisted of agglomerates of individual crystallites bound together into larger semi-spherical agglomerates with a higher tendency for particles having crystalline ridges to form at higher ethanol concentrations of the feed.     

复卷机伺服张力控制系统设计及优化

本研究通过对复卷机工艺流程进行分析,分别对纸幅张力、速度和纠偏的控制系统进行了设计及优化,采用罗克韦尔PLC作为主控制器,结合伺服驱动器和伺服电机等,实现对纸幅张力的稳定控制。该控制系统在实际生产中取得了良好的控制效果,张力调整时间由之前的10 s缩短到5 s内,并且无超调量。     

Biodegradability Study of the Biosurfactant Contained in a Crude Extract from Corn Steep Water

Over the last few years, the global biosurfactant market has raised due to the increasing awareness among consumers, for the use of biological or bio-based products. Because of their composition, it can be speculated that these are more biocompatible and more biodegradable than their chemical homologous. However, at the moment, no studies exist in the literature about the biodegradability of biosurfactants. In this work, a biosurfactant contained in a crude extract, obtained from a corn wet-milling industry stream that ferments spontaneously in the presence of lactic acid bacteria, was subjected to a biodegradation study, without addition of external microbial biomass, under different conditions of temperature (5–45 °C), biodegradation time (15–55 days), and pH (5–7). For that, a Box–Behnken factorial design was applied, which allowed to predict the percentage of biodegradation for the biosurfactant contained in the crude extract, between the range of the independent variables selected in the study, obtaining biodegradation values between 3 and 80%. The percentage of biodegradation for the biosurfactant was calculated based on the increase in the surface tension of samples of the crude extract. Furthermore, it was also possible to predict the variation in t_1/2 for the biosurfactant (time to achieve the 50% of biodegradation) under different conditions.     

Bitumen-water interfacial tension modeling by using subtractive clustering method

Calculation of interfacial tension during bitumen production is a crucial issue in heavy crude oil history. Upon variation in pressure, temperature and phases composition, interfacial tension between bitumen and water change. In this work a sophisticated method called subtractive clustering was utilized to predict dynamic interfacial tension between bitumen and water. The subtractive clustering method is composed of optimized fuzzy logic algorithm. A data bank which is collected from open-source literature, is used to create a reliable model. Then the prediction accuracy of the measured dynamic interfacial tension using subtractive clustering have been examined. Results state that the comparison of measured interfacial tension and predicted interfacial tension indicate acceptable accuracy of proposed model. Also more than 90 percent of data points have less than 3 percent absolute error.     

Neural network modelling on temperature coefficient of surface tension and its usage in melting point prediction of nanosized metal particles

Temperature coefficient of surface tension is a very important parameter to calculate phase diagrams of nanoparticle metal systems. In this paper, neural network calculation was for the first time used to evaluate the temperature coefficient. It shows that the constructed neural network can predict the temperature coefficient values for 37 metals, with the deviation from the averaged experimental measurements smaller than 25%. Furthermore, the neural network predictions were compared with the calculated values by using an empirical equation and it shows a better performance.     

时间效应在悬滴法测定表面张力中的影响

表面张力是流体重要的物理性质,测定表面张力的方法通常包括毛细管上升法、最大气泡压力法、拉环法、旋滴法和悬滴法等。目前,最普遍的表面张力的测定方法为国家标准推荐的平板法或拉环法。然而,悬滴法作为一项成熟的表面张力测定方法且具备静态表面张力及动态表面张力测定功能,目前使用该法测定的较少。文章使用悬滴法测定较低含量的表面活性剂溶液静态表面张力时发现结果与拉环法差异较大,而测定单组分液体及含量较大的表面活性剂溶液,悬滴法与拉环法的测定数据差异较小,这种情况的相关报道较少。另外,使用动态表面张力测定探索了静态测定时出现差异的原因,并对应用悬滴法进行表面张力测定的适用范围进行了总结。使用悬滴法测定表面张力时,应注意时间效应的影响。