A tool for mass transfer evaluation in packed-bed columns for chemical engineering students

This paper presents a Microsoft Excel tool to calculate liquid-gas mass transfer coefficients in packed towers to support numerical design activities in the courses of Unit Operations for Industrial Process and Sustainable Process Design for the Master’s degree in Chemical Engineering of the University of Naples Federico II (Italy).The Mass Transfer Solver Tool (MT Solver Tool) uses several available models to estimate, separately, the values of liquid and gas mass-transfer coefficients and the wet surface area for 144 random and structured packings of interest for absorption/stripping and distillation processes. In addition, a separate spreadsheet can be used in a user-defined mode, to evaluate the mass transfer coefficients with new packing types or to interpret experimental data when the geometrical and physical characteristics of the packing are known. Eventually, the tool is supplied with a data library, where packing geometry and model fitting parameters can be retrieved.The software is aimed to support students and educators in the Unit Operations for Industrial Process and Sustainable Process Design courses. In particular, this is meant to be an example on how the accuracy of design algorithms adopted in unit operation processes is affected by the use of the underpinning correlations for mass transfer rate or pressure drops. Besides, this is aimed to encourage comparison of different correlations when exact field data are not available. Besides, chemical engineers and researchers interested in packed columns design and modelling data may also benefit from the utilization of the software. The MT Solver Tool was introduced to students in a dedicated tutorial lesson after lecturers on packed column design algorithms for distillation, absorption and stripping. Most of the students of the course participated to a group training aimed to simulate the design of an absorption column supported by the MT Solver Tool providing feedback on its application.After the training, an anonymous survey was proposed to the students to monitor the approval rating of the proposed activity and the use of the MT Solver Tool software to support numerical calculations.     

Stochastic evaluation of leakages through holes in wrinkle networks of composite liners

This paper presents a study on stochastic evaluation of leakages through holes in wrinkle networks of composite liners. The statistical parameters of wrinkles are used as indexes to describe the spatial distributions of wrinkles in a wrinkle network and the wrinkle density is modeled as a random field in the proposed approach, which allows the construction of a database about how wrinkles may be distributed in different conditions and provides input parameters for leakage evaluation at the design stage when the site has not been constructed yet and the aerial image of the wrinkle network (AWN) is unavailable. Statistical analyses were performed on wrinkle geometric parameters and wrinkle density of wrinkles from three sites reported in the literature. The procedures of generating random wrinkle networks (RWNs) based on the statistical parameters of wrinkles are introduced. The proposed approach was applied to typical examples and showed sufficient accuracy when compared to the evaluated leakages based on the corresponding AWNs. Wrinkle density is recommended to be modeled as random field.     

A new method of predicting the saturation pressure of oil reservoir and its application

Saturation pressure is a vital parameter of oil reservoir which can reflect the oilfield characteristics and determine the oilfield development process, and it is determined by experiments in the laboratory in general. However, there was only one well with saturation pressure test in this target reservoir, and it is necessary to determine whether this parameter is right or not.In this work, we present a new method for quickly determining saturation pressure using machine learning algorithms, including random forest regressor (RF), support vector machine (SVM), decision trees (DT), and artificial neural network (ANN or NN). Using these approaches, saturation pressure was obtained by using the initial solution gas-oil ratio (GOR), temperature, API gravity and other reservoir-fluid data available in the oilfields. Compared with the empirical formula for saturation pressure calculation, the calculated result shows that the accuracy given from machine learning is higher than that from other formulas at home and abroad, and has a good match with the lab test. On the basis of the calculated saturation pressure, it can determine whether the reservoir enters into the stage of dissolved gas drive or not, which also provides the basis for maintaining the reservoir pressure by water injection in advance, rational development decision-making and work over measures.This approach above can provide technical guidance for predicting the saturation pressure in the development of different kinds of reservoirs, including the sandstone reservoirs and carbonate reservoirs.     

MOOC Learner’s Final Grade Prediction Based on an Improved Random Forests Method

Massive Open Online Course (MOOC) has become a popular way of online learning used across the world by millions of people. Meanwhile, a vast amount of information has been collected from the MOOC learners and institutions. Based on the educational data, a lot of researches have been investigated for the prediction of the MOOC learner’s final grade. However, there are still two problems in this research field. The first problem is how to select the most proper features to improve the prediction accuracy, and the second problem is how to use or modify the data mining algorithms for a better analysis of the MOOC data. In order to solve these two problems, an improved random forests method is proposed in this paper. First, a hybrid indicator is defined to measure the importance of the features, and a rule is further established for the feature selection; then, a Clustering-Synthetic Minority Over-sampling Technique (SMOTE) is embedded into the traditional random forests algorithm to solve the class imbalance problem. In experiment part, we verify the performance of the proposed method by using the Canvas Network Person-Course (CNPC) dataset. Furthermore, four well-known prediction methods have been applied for comparison, where the superiority of our method has been proved.     

Yield improvement and yield variability reduction in an EOQ model with planned shortages and random yield

This paper reports the results of an investigation of a set of continuous time, constant demand inventory models under the condition of yield uncertainty. Specifically, the impact of yield improvement programs on lot size, backorder level, and the resulting costs are examined. Models for improving yield rate and reducing yield variability are developed and examined through a series of numerical exercises. In addition, a model for the simultaneous improvement of yield rate and yield variability is presented for the case where there is a relationship between the mean and variance of the yield distribution. In all cases, investment programs improve the picture with respect to manufacturing yield for processes which are not necessarily under statistical control.     

Balancing exploration and exploitation in differential evolution via variable scaling factors: An application to practical problems

Some optimization problems in the field of nuclear engineering, as for example the incore nuclear fuel management and a nuclear reactor core design, are highly multimodal, requiring techniques that overcome local optima, exploring the search space and promoting the exploitation of its most promising areas. The differential evolution algorithm (DE) relies mainly on the mechanism of mutation, where an individual is perturbed using the weighted difference (with the so-called “scaling factor” F) between two randomly chosen individuals. DE's canonical version employs a constant value of F. However, this parameter should be variable in order to balance the exploration and exploitation of the search space. In this work, we test some variable scaling factors from the literature and present the novel exponential scaling factor. These methods are applied to two problems: the aforementioned core design and the turbine balancing problem, which is an NP-hard (i.e. intrinsically harder than those that can be solved in nondeterministic polynomial time) combinatorial optimization problem that can be used to assess the potential of an algorithm to be applied to fuel management optimization. DE with variable scaling factors perform well in both problems, showing potential to be used in other nuclear science and engineering optimization problems.     

Effect of thermal noise on random lasers in diffusion regime

In this paper, we study the effects of thermal noise on the time evolution of a weak light pulse (probe) in the presence of a strong light pulse (pump) within a gain medium which includes random scatterer particles. Suitable thermal noise term is added to a set of four coupled equations including three diffusion equations for energy densities and a rate equation for the upper level population in a four-level gain medium. These equations have been solved simultaneously by Crank–Nicholson numerical method. The main result is that the back-scattered output probe light is increased as the thermal noise strength is increased and simultaneously, with the same rate, the amplified spontaneous emission is decreased. Therefore, the amplified response of the random laser in diffusion regime for the input probe pulse is enhanced due to effect of the thermal noise.     

Remaining useful life prediction for degrading systems with random shocks considering measurement uncertainty

Degradation data have been widely used for the remaining useful life (RUL) prediction of systems. Most existing works apply a preset model to capture the degradation process and focus on the degradation process without shocks or constant shock effects. More generally, the actual degradation path is unobservable due to the existence of measurement uncertainty, which interferes with the determination of the degradation model. Besides, the effect of random shocks is usually fluctuating. Given these problems, a general degradation model with the random shock fluctuant effects considering the measurement uncertainty is first developed to describe the degradation process, and a two-step approach combining the arithmetic average filter and the Bayesian information criterion is adopted to identify the degradation path. Subsequently, the transfer processes of the actual degradation state and the abrupt change caused by shocks are depicted using a two-dimensional state-space model, and an expectation-maximization algorithm combined with the particle filtering is developed for parameter estimation. Furthermore, the explicit solution of RUL distribution is obtained when only considering harmful shocks, while a simulation method of RUL distribution is provided when both harmful and beneficial shocks exist. Finally, the effectiveness of the proposed method is verified by a numerical example and two practical case studies.     

Stacked autoencoder based deep random vector functional link neural network for classification

Extreme learning machine (ELM), which can be viewed as a variant of Random Vector Functional Link (RVFL) network without the input–output direct connections, has been extensively used to create multi-layer (deep) neural networks. Such networks employ randomization based autoencoders (AE) for unsupervised feature extraction followed by an ELM classifier for final decision making. Each randomization based AE acts as an independent feature extractor and a deep network is obtained by stacking several such AEs. Inspired by the better performance of RVFL over ELM, in this paper, we propose several deep RVFL variants by utilizing the framework of stacked autoencoders. Specifically, we introduce direct connections (feature reuse) from preceding layers to the fore layers of the network as in the original RVFL network. Such connections help to regularize the randomization and also reduce the model complexity. Furthermore, we also introduce denoising criterion, recovering clean inputs from their corrupted versions, in the autoencoders to achieve better higher level representations than the ordinary autoencoders. Extensive experiments on several classification datasets show that our proposed deep networks achieve overall better and faster generalization than the other relevant state-of-the-art deep neural networks.     

基于Marr小波改进的SIFT算法的遥感影像配准

〖HTH〗通讯作者〖HTSS〗:[ZK（]金〓燕(1991-)，女，山东济宁人，硕士研究生，主要从事图形图像处理，图像与视觉信息计算方面的研究。E\|mail:jyan0529@163.com。[ZK）] 〖ZW)〗〖HT〗 〖AM〗〖HT5SS〗〖MM(〗〖ZZ(S〗〖HT5”〗〖SX(B〗第34卷〓第3期〖〗2019年6月〖SX)〗[KG0.2mm]〖KG7*3〗〖HT〗〖SX(B〗遥〓感〓技〓术〓与〓应〓用〖〗〖WT5，6〗REMOTE SENSING TECHNOLOGY AND APPLICATION〖SX)〗〓〓〓〓〖KG6*2〗〖WT5”BX〗〖SX(B〗Vol.34〓No.3〖〗Jun.2019〖WT〗〖SX)〗〖ZZ)〗〖MM)〗〖HT〗 〖HT2H〗〖JZ(〗〖WTHZ〗〖STHZ〗 基于Marr小波改进的SIFT算法的遥感影像配准 〖STBZ〗〖WTBZ〗〖HT4K〗 张海涛，金〓燕，刘万军 〖HT5K〗 (辽宁工程技术大学 软件学院，辽宁 葫芦岛〓125105) 〖JZ)〗〖ＨＴ５Ｈ〗〖GK2!2〗摘要〖HTK〗: [KG(0.1mm]针对遥感图像配准方法中错误匹配点对过多、配准效率低和其他性能，提出了一种基于小波的遥感图像配准方法。首先，利用尺度空间理论下的Marr小波对参考图像和待配准图像进行特征提取，然后利用欧氏距离对参考图像和待配准图像的特征点进行初配准，再根据随机采样一致法，对初配准结果进行精配准。为了验证方法的有效性，选择无人机实时航拍图像、不同时相变化遥感图像以及遥感不同高度的遥感图像。实验结果表明：该方法与SIFT(Scale Invariant Feature Transform)算法以及其他改进SIFT算法相比可以有效剔除错误匹配点对，提高了配准精度，同时提高配准效率两倍以上。该方法可以应用于不同遥感数据源，能够有效地提高配准精度，降低配准时间。[KG)] 〖ＨＴＨ〗关〓键〓词〖ＨＴ５Ｋ〗: 遥感图像配准;Marr小波;欧氏距离;随机采样一致法 〖ＨＴＨ〗中图分类号〖HTSS〗:TP79〓〓〖HTH〗文献标志码〖HTSS〗:A〓〓〖HTH〗文章编号〖HTSS〗:1004 0323(2019)03 0622 08 〖HK〗〖HT5SS〗