A systematic methodology for optimal mixture design in an integrated biorefinery

Biomass is a sustainable source of energy which can be utilised to produce value-added products such as biochemical products and biomaterials. In order to produce a sustainable supply of such value-added products, an integrated biorefinery is required. An integrated biorefinery is a processing facility that integrates multiple biomass conversion pathways to produce value-added products. To date, various biomass conversion pathways are available to convert biomass into a wide range of products. Due to the large number of available pathways, various systematic screening tools have been developed to address the process design aspect of an integrated biorefinery. Process design however, is often inter-linked with product design as it is important to identify the optimal molecule (based on desired product properties) prior to designing its optimal production routes. In cases where the desired product properties cannot be met by a single component chemical product, a mixture of chemicals would be required. In this respect, product and process design decisions would be a challenging task for an integrated biorefinery. In this work, a novel two-stage optimisation approach is developed to identify the optimal conversion pathways in an integrated biorefinery to convert biomass into the optimal mixtures in terms of target product properties. In the first stage, the optimal mixture is designed via computer-aided molecular design (CAMD) technique. CAMD technique is a reverse engineering approach which predicts the molecules with optimal properties using property prediction models. Different classes of property models such as group contribution (GC) models and quantitative structure property relationship (QSPR) are adapted in this work. The main component of the mixture is first determined from the target product properties. This is followed by the identifying of additive components to form an optimal mixture with the main component based on the desired product properties. Once the optimal mixture is determined, the second stage identifies the optimal conversion pathways via superstructural mathematical optimisation approach. With such approach, the optimal conversion pathways can be determined based on different optimisation objectives (e.g. highest product yield, lowest environmental impact etc.). To illustrate the proposed methodology, a case study on the design of fuel additives as a mixture of different molecules from palm-based biomass is presented. With the developed methodology, optimal fuel additives are designed based on optimal target properties. Once the optimal fuel additives are designed, the optimal conversion pathways in terms of highest product yield and economic performance that convert biomass into the optimal fuel additives are identified.     

Bayesian Nonparametric Joint Mixture Model for Clustering Spatially Correlated Time Series

Abstract

We develop a Bayesian nonparametric joint mixture model for clustering spatially correlated time series based on both spatial and temporal similarities. In the temporal perspective, the pattern of a time series is flexibly modeled as a mixture of Gaussian processes, with a Dirichlet process (DP) prior over mixture components. In the spatial perspective, the spatial location is incorporated as a feature for clustering, like a time series being incorporated as a feature. Namely, we model the spatial distribution of each cluster as a DP Gaussian mixture density. For the proposed model, the number of clusters does not need to be specified in advance, but rather is automatically determined during the clustering procedure. Moreover, the spatial distribution of each cluster can be flexibly modeled with multiple modes, without determining the number of modes or specifying spatial neighborhood structures in advance. Variational inference is employed for the efficient posterior computation of the proposed model. We validate the proposed model using simulated and real-data examples. Supplementary materials for the article are available online.     

高斯混合模型结合加权似然的目标跟踪算法

鉴于高斯混合模型对背景变化快时无法精确检测出目标和目标跟踪的适应性差等瑕疵，提出了基于加权似然跟踪器来改进高斯混合模型实现运动目标跟踪算法。主要引入了自适应高斯混合模型来实时检测运动目标，然后空间加权似然来进行视频中的目标定位，引入加权似然期望值来改进高斯混合模型处理视频中的多尺度、多角度变化的目标跟踪不精准问题。通过VOT 2014 dataset对比实验结果表明提出的基于加权似然跟踪（Weighted Likelihood Tracking，WLT）和改进高斯混合模型（Improved Gaussian Mixture Model，IGMM）的目标跟踪算法较传统高斯混合模型跟踪算法在跟踪的精度有较大提高。在应对多尺度、多角度变化的目标跟踪表现出了较大的优势。     

Performance assessment of gas crossover phenomenon and water transport mechanism in high pressure PEM electrolyzer

To improve proton exchange membrane (PEM) electrolyzes’ performance the voltage loss through them should be avoided. In this work, it is intended to analyze losses including of diffusion loss, ohmic loss due to electrode, bipolar plate (BP), and membrane resistances, and gas crossover associated with the water transferring mechanisms. All of the losses are associated with water transferring mechanisms, which is created due to electro-osmoic drag, pressure differential between the anode and cathode sides, and diffusion. Furthermore, the effect of membrane thickness, cathode pressure, and operating temperature on the hydrogen crossover is examined. In addition, the contribution of ohmic loss due to electrode bipolar plate (BP), and membrane resistances is studied and, the contribution of different losses on the cell performance is discussed. Results show that raising cathode pressure from 1 to 40 bar lead to the increment of anodic hydrogen content from 1.038% to 21% at the specific current density of 10,000 A/m2. Enhancing the thickness of membrane has considerable impact on decrementing anodic hydrogen content, but the mass transfer loss rises from 0.022 to 0.027 V with enhancing membrane thickness from 50 to 300 μm, respectively. Furthermore, the contribution of voltage losses, assigned to each of losses are equal to 85%, 3%, and 12% for activation, diffusion and ohmic losses, respectively. It is found that, from the reported contribution for ohmic loss, the contribution of electrode BP, and membrane resistances are 31% and 69%, respectively.     

LTE-LAA cell selection through operator data learning and numerosity reduction

Long Term Evolution-Licensed Assisted Access (LTE-LAA) architecture is markedly different from traditional LTE HetNets. LTE-LAA deployments also have to contend with interference from coexisting Wi-Fi transmissions in the unlicensed spectrum. Hence, there is a need for innovative cell selection solutions that cater specifically to LTE-LAA. Further, the impact of cell selection on the performance of the existing LTE-LAA deployments should also be investigated through operator data analysis. This work addresses these challenges. We gather a large sample of LTE-LAA deployment data for three cellular operators, i.e., AT&T, T-Mobile, and Verizon, which is analyzed through several supervised machine learning algorithms. We study the effect of cell selection on LTE-LAA capacity and network feature relationships. Insightful inferences are drawn on the contrasting characteristics of the Licensed and Unlicensed components of an LTE-LAA system. Further, a cell-quality metric is derived from operator data and is shown to have a strong correlation with Unlicensed coexistence network performance. To validate the proposed ideas, two state-of-the-art cell association and resource allocation solutions are implemented. Validation results show that data-driven cell-selection can reduce Unlicensed association time by as much as 34.89%, and enhance Licensed network capacity by up to 90.41%. Finally, with the vision to reduce the computational overhead of data-driven cell selection in LAA and 5G New Radio Unlicensed networks, the performance of two popular numerosity reduction techniques is evaluated.     

Experimental Design Applied for Cost and Efficiency of Antioxidants in Biodiesel

Oxidation stability is a parameter of great importance for biodiesel quality control to both producers and subsequent consumers. To maintain the quality of biodiesel, currently the most effective and economical method is the addition of antioxidants that prevent or retard the biofuel oxidation reaction. In this study, efficiency and cost of synthetic antioxidants added to B100 biodiesel from soybean oil and pork fat were evaluated, using butylhydroxyanisole (BHA), butylhydroxytoluene (BHT) and tert-butylhydroquinone (TBHQ), in pure form or in mixtures, according to a simplex-centroid mixture experimental design. Results demonstrate an increased induction period (IP) in all trials when compared to the control sample, and TBHQ was the only antioxidant alone that met all the specification standards, while BHT and BHA alone met only the American standard specifications. The antioxidant mixture that presented the highest synergistic effect was that of TBHQ and BHA. Multi-response optimization indicated an optimum formulation containing 75 % TBHQ and 25 % BHA with an IP of 7.27 h at 110 °C and the antioxidant mixture cost of 31.31 USD, to be added for a ton of biodiesel. This simplex-centroid mixture experimental design shows an ability to be applied in the biodiesel, oils and fats industry to evaluate the oxidation stability and the occurrence of synergism between different mixtures of synthetic or natural antioxidants and their costs.     

村落光伏电站系统运行及使用的调研分析

本次调研通过对已建成的村落光伏系统站点进行现场考查及调查访问、下载系统运行状态数据,并对收集到的大量信息资料和数据进行统计处理、分析研究,对村落光伏电站系统的运行状况及光伏电站的机手等进行评估分析,并得到村民家庭用电情况、村落独立电力系统的负载等方面的有关结论,然后对村落光伏电站系统的设计建设及运行管理提出几点建议。     

第二类双边装配线平衡的混合粒子群算法

双边装配线广泛用于汽车、卡车等大型产品的装配过程中。为有效解决第二类双边装配线平衡问题(TALBP-II),提出了一种新的混合粒子群算法(HPSO)。该算法利用粒子群进行全局搜索,使用遗传算法中的交叉算子增强全局搜索,同时利用变邻域搜索进行局部搜索。结合TALBP-II的特点,提出一种解码方式,减少由于操作关系导致的空闲时间。提出工位时间判断策略,缩小搜索空间,提高局部搜索效率。实验结果表明所提出的混合粒子群算法能有效解决第二类双边装配线平衡问题。     

Properties of a Binary System Containing Anionic and Cationic Gemini Surfactants

Surface tension, fluorescence, and dynamic light scattering were used to investigate the properties of a binary surfactant system comprising an anionic gemini surfactant (DLMC) and cationic gemini surfactant (II‐12‐EO₂). Surface tension measurements afforded the critical micelle concentration (cmc) of the mixture and the values are all lower than those of pure constituent surfactants. For the mixtures of II‐12‐EO₂/DLMC, the micelle aggregation number decreases with the increase of II‐12‐EO₂, and the micropolarity of the micelle is lowest when the molar fraction of II‐12‐EO₂ is 0.5; the hydrodynamic radius (R_h) of the mixed micelle first increases and then decreases with the addition of II‐12‐EO₂, and larger micelles are obtained when the molar fraction of II‐12‐EO₂ is 0.5 or 0.7.