Performance improvement of microbial fuel cell through artificial intelligence

The current work introduces an enhancement in the performance of the microbial fuel cell through estimating the optimal set of controlling parameters. The maximization of both power density (PD) and the percentage of chemical oxygen demand (COD) removal were considered as the enhancement in the cell's performance. Three main parameters in terms of performance as well as commercialization are the system's inputs; the Pt which takes the range of 0.1‐0.5 mg/cm², the degree of sulphonation in sulfonated‐poly‐ether‐ether‐ketone that changes in the range of 20‐80%, and the rate of aeration of cathode which varies between 10 and 150 mL/min. From the experimental dataset, two robust adaptive neuro‐fuzzy inference system models based on the fuzzy logic technique have been constructed. The comparisons between the models' outputs and the experimental data showed well‐fitting in both training and testing datasets. The mean squared errors of the PD model, for testing and whole datasets, were found 2.575 and 0.909 while for the COD model it showed 19.242 and 6.791, respectively. Then, based on the two fuzzy models, a Particle Swarm Optimization algorithm has been used to determine the best parameters that maximize both of the PD and the COD removal of the cell. The optimization process was utilized for single and multi‐object optimization processes. In the single optimization, the resulting maximums of the PD and the COD removal were found 62.844 (mW/m²) and 99.99 (%), respectively. Whereas, in the multi‐object optimization, the values of 61.787 (mW/m²) and 96.21 (%) were reached as the maximums for the PD and COD, respectively. This implies that, in both cases of optimization processes, the adopted methodology can efficiently enhance the microbial fuel cell performances than the previous work.     

Biofuel supply chain optimal design considering economic,environmental, and societal aspects towards sustainability

Biofuel supply chain design plays a critical role in facilitating the large‐scale substitution of biofuel for traditional fossil fuels with a cost‐effective and environmentally friendly manner towards sustainability. This paper proposes a multiobjective optimization model for a 4‐layer biofuel supply chain network using mixed integer nonlinear programming while considering the benefits from economic, environmental, and societal aspects. The model can be used either to optimize an existing biofuel supply chain network or to guide the construction of a new biofuel supply chain network. The profit, the greenhouse gas emissions in transportation, and the market share of biofuel were set as targets for optimization. The selection of the participators at each layer, and the amount of the material flow between each pair of selected supplier and customer located at two adjacent layers were modeled as decision variables. The conventional weighted aggregation method was used to unify 3 objectives after normalization. Particle swarm optimization was used to solve this high‐dimension multiobjective problem to obtain a near optimal solution. A numerical case study based on the state of Missouri in the United States was implemented to verify the effectiveness of the proposed model. The results of the case study illustrate that the benefits in terms of transportation emission, profit, and market share can be achieved simultaneously. Using the equal weights configuration in conventional weighted aggregation as an example, a 21% reduction of the transportation emission, a 33% increase of the profit, and a 2% augmentation of the market share were achieved compared to the benchmark scenario.     

电路板芯片级修复在X射线荧光光谱仪维护中的应用

X射线荧光光谱仪(XRF)在国内的冶金、矿山等行业的实验室有着广泛的应用。以ARL9900系列X射线光谱仪为例,介绍了其XPS电路板上光耦合芯片和XPSI电路板上IGBT芯片损坏故障的故障现象和报警信息,以及如何利用电路原理和电子知识进行电路板上损坏元件的检查和处理。通过这两个典型的电路板芯片级故障处理方法,为实验室精密仪器的芯片级故障修复提供一种思路,帮助企业快速恢复生产,节约备件成本。     

Performance optimization of integrated gas and power within microgrids using hybrid PSO–PS algorithm

In this paper, a hybrid algorithm consisting of particle swarm optimization and pattern search algorithm is proposed to evaluate and optimize the design and operation of microgrids (MGs) in combined gas and power networks. Key performance indicators (KPIs) are modeled and proposed to evaluate and assess MGs. The paper begins by proposing a comprehensive study to define KPIs, which are used to evaluate the following MG parameters: economical efficiency, reliability, environmental conservation, and power quality. Multi‐objective evaluation functions are then developed by building a relationship matrix of MG and KPI components. The results are then displayed as optimized power generation percentages for each technology with values for four KPI categories: cost, quality, reliability and environmental friendliness. Two case studies are examined in this paper; both the province of Ontario and Toronto regional zone under all system parameters with varying percentage of generation via gas technology. Results indicated that the optimal scenario for both Ontario and Toronto was achieved at hybrid PSO–patern search percentage generation via gas technology with improved cost KPI and other KPIs remaining approximately constant. Copyright © 2016 John Wiley & Sons, Ltd.     

Effect of particle size on the flame retardancy of poly(butylene succinate)/Mg(OH)2 composites

Poly(butylene succinate)/magnesium hydroxide (PBS/Mg(OH)₂) composites were prepared by melt compounding to investigate the effect of particle size on the flame retardancy of PBS. Their flammability properties were investigated by limiting oxygen index, UL‐94, and cone calorimeter tests, which suggested that the medium‐sized Mg(OH)₂‐5 µm displayed the best flame retardancy. The residual char structure were analyzed and indicated that Mg(OH)₂‐5 µm could form a better protective layer than other sized particles, leading to the better flame retardancy to PBS. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.     

Fabrication of Si3N4 ceramics by post-reaction sintering using Si–Y2O3–Al2O3 nanocomposite particles prepared by mechanical treatment

Post-reaction sintering of a powder compact of Si and sintering aids is a useful technique for fabricating silicon nitride (Si₃N₄) ceramics at low costs. In order to inhibit the inhomogeneous and uncontrollable exothermic nitridation of Si in the powder compact, Si–Y₂O₃–Al₂O₃ nanocomposite particles are designed as an aid for post-reaction sintering. These Si–Y₂O₃–Al₂O₃ nanocomposite particles are prepared via mechanical treatment applying high shear stress. Scanning electron microscopy (SEM) observations show that Y₂O₃ and Al₂O₃ particles are homogenously dispersed, and fixed to the Si particles. A green compact prepared using the Si–Y₂O₃–Al₂O₃ nanocomposite particles results in lower electrical resistivity than that prepared using a powder mixed by wet ball-milling, which suggests that Si particles in the green compact prepared using the nanocomposite particles are isolated by Y₂O₃ and Al₂O₃ particles. The isolation of Si particles by the sintering aids successfully prevents the Si particles from melting and agglomerating during the nitridation process, resulting in a higher nitridation ratio and higher α-Si₃N₄ phase content due to the inhibition of rapid heat transfer caused by the exothermic reaction. The nitridation ratio also increases with the applied power during mechanical treatment. As a result of firing the homogeneously nitrided powder compacts at high temperatures, Si₃N₄ ceramics with homogeneous microstructure and improved density are successfully fabricated in this manner.     

大粒径磨料作用下高铝砖气固冲蚀磨损的"粒径效应"

以平均粒径分别为0.28 mm、0.40 mm、0.52 mm、0.80 mm的棕刚玉为磨料对I等高铝砖进行常温垂直气固冲蚀磨损试验,对磨料和靶材冲后表面进行扫描电镜显微结构分析,在宽粒径范围内研究磨料粒径对靶材耐磨性与冲蚀机制的影响.借助ANSYS/LS-NYNA软件建立多粒子冲蚀模型,分析不同磨料粒径下的冲蚀行为.结果表明:I等高铝砖出现"粒径效应",临界粒径0.40 mm;靶材最大等效应力随磨料粒径的增加而增加;平均粒径≥0.40 mm时磨料发生破碎,0.28 mm、0.40 mm、0.80 mm磨料冲蚀下靶材的主要冲蚀机制分别是基质和骨料微切削、基质和骨料断裂、缺陷处断裂.     

Precisely Controlled Microsphere Design via Visible‐Light Cross‐Linking of Functional Prepolymers

A new strategy for particle synthesis is enabled by utilizing modern synthetic, polymer, and photochemical techniques to facilitate the synthesis of highly narrow–disperse multifunctional microspheres from visible‐light induced crosslinking of prepolymers in both a single and dual polymer system. The approach requires no stabilizers, bases, or initiators, and proceeds at ambient temperature to yield microspheres with a tunable size range (0.25–5 µm) in less than 4 h, depending largely on solvent composition, but also polymer concentration (2–10 mg mL^?1), ratio, and irradiation intensity (3–20 W). Critically, the visible‐light induced dimerization reaction exploited herein enables simple functional particle syntheses via a single polymer system. Underpinned by an in‐depth kinetic analysis of the particle formation as well as a detailed small molecule study, the mechanism for particle formation is also elucidated. Importantly, inherent advantages of the system are exploited for surface functionalization of residual acrylate and hydroxyl groups (generating inherently fluorescent particles).     

Rolling Kanban: a new visual tool to schedule family batch manufacturing processes with kanban

Rolling Kanban is the name of a kanban management methodology for batch processes manufacturing environment. Proposed in the early 2000s by FESTO Consulting, for two decades we have lost knowledge both from operative and scientific point of view. Basically, Rolling Kanban means a visual planning methodology based on the production of product-families and variants where: (i) set-up times are reduced between the products of the same family, and (ii) relevant times (dozens of minutes if not even hours) must be considered for changeover between products of different families. In addition, the cyclic production sequence between different product families cannot be maintained. Considering that very few technical information and documents are available about this approach, the main objective of this paper is to retrieve and present, for the first time to the scientific community, the Rolling Kanban methodology. Besides, a real industrial implementation concerning a manufacturer of domestic fittings is discussed as a case study. More specifically, two novel versions of the original Rolling Kanban technique are fully presented to effectively overcome certain limits and criticalities found during its operative use, such as the difficulty to realise a pull production, considering increased set-up time for changeover between products of different families.     

Parallel Multipopulation Differential Evolutionary Particle Swarm Optimization for Voltage and Reactive Power Control

This paper presents parallel multipopulation differential evolutionary particle swarm optimization (DEEPSO) for voltage and reactive power control (VQC). The problem can be formulated as a mixed integer nonlinear optimization problem and various evolutionary computation techniques have been applied to the problem including PSO, differential evolution (DE), and DEEPSO. Since VQC is one of the online controls, speed‐up of computation is required. Moreover, there is still room for improvement in solution quality. This paper applies parallel multipopulation DEEPSO in order to speed up the calculation and improve solution quality. The proposed method is applied to IEEE 30, 57, and 118 bus systems. The results indicate that the proposed method can realize fast computation and minimize more active power losses than the conventional evolutionary computation techniques.