高瓦斯突出矿井底抽巷一巷多用技术研究与实践

胡底煤业为高瓦斯高突出高矿压的三高矿井。对高瓦斯高突出的治理，该矿采用“地面网络预抽、区域模块降量、底抽穿层保掘、高位顺层保采”的四级模式，施工底抽巷，实现“底抽穿层消突、区域模块降量、异巷探测增效、多巷通风治患、循环排矸减压”等“一巷多用”。该4种方法已在1303（上）、1305（上）工作面应用并推广，取得了良好的效果，大大提高了掘进效率且满足抽放、探放水、通风需要；回填矸石初步设计现已经完善，待下一步掘进将实现填矸作用。     

燃料电池空气电极的孔道结构调控

燃料电池是将化学能转化成电能的装置，其空气电极催化层的设计，既要包含丰富的、易于接近的反应活性位，也要具备高度连通的电子、质子以及反应物、产物传质通道，因此电极必须具有特定三维几何结构形貌和有序分布的各功能化孔道，确保催化活性位得以充分利用以及反应可以连续进行。针对催化剂孔道的几何结构调控，本文调研了最近报道的一系列研究工作，从模板法、高温相变法、模板/相变复合方法以及基于金属有机框架材料进行孔道设计等四种主要方法出发，综述了该领域的最新研究进展。     

换能器结构参数对聚合物超声塑化黏弹生热的影响

为研究超声换能器结构参数对聚合物超声塑化过程黏弹性生热的影响，首先确定超声黏弹性生热系统的组成，进行纵振超声换能器结构设计；然后分析超声黏弹性生热过程及超声黏弹性生热原理；最后采用单一变量法分析超声换能器的主要结构参数对其纵振频率及工具头前端质点最大振幅的影响，将其实际输出的纵振激励加载于熔融聚合物，研究其结构参数对聚合物超声黏弹性生热过程及达到聚合物玻璃化转变温度所用时间的影响。结果表明，随纵振激励作用时间的增加，聚合物温度非线性升高；放大比对聚合物温度变化影响最大，前盖板厚度和工具头长度次之，影响最小的是变幅杆长度。     

Parallel chaos optimization algorithm with migration and merging operation

Chaos optimization algorithm (COA) utilizes the chaotic maps to generate the pseudo-random sequences mapped as the decision variables for global optimization applications. A kind of parallel chaos optimization algorithm (PCOA) has been proposed in our former studies to improve COA. The salient feature of PCOA lies in its pseudo-parallel mechanism. However, all individuals in the PCOA search independently without utilizing the fitness and diversity information of the population. In view of the limitation of PCOA, a novel PCOA with migration and merging operation (denoted as MMO-PCOA) is proposed in this paper. Specifically, parallel individuals are randomly selected to be conducted migration and merging operation with the so far parallel solutions. Both migration and merging operation exchange information within population and produce new candidate individuals, which are different from those generated by stochastic chaotic sequences. Consequently, a good balance between exploration and exploitation can be achieved in the MMO-PCOA. The impacts of different one-dimensional maps and parallel numbers on the MMO-PCOA are also discussed. Benchmark functions and parameter identification problems are used to test the performance of the MMO-PCOA. Simulation results, compared with other optimization algorithms, show the superiority of the proposed MMO-PCOA algorithm.     

Weighted Superposition Attraction (WSA): A swarm intelligence algorithm for optimization problems – Part 2: Constrained optimization

This paper is the second one of the two papers entitled “Weighted Superposition Attraction (WSA) Algorithm”, which is about the performance evaluation of the WSA algorithm in solving the constrained global optimization problems. For this purpose, the well-known mechanical design optimization problems, design of a tension/compression coil spring, design of a pressure vessel, design of a welded beam and design of a speed reducer, are selected as test problems. Since all these problems were formulated as constrained global optimization problems, WSA algorithm requires a constraint handling method for tackling them. For this purpose we have selected 6 formerly developed constraint handling methods for adapting into WSA algorithm and analyze the effect of the used constraint handling method on the performance of the WSA algorithm. In other words, we have the aim of producing concluding remarks over the performance and robustness of the WSA algorithm through a set of computational study in solving the constrained global optimization problems. Computational study indicates the robustness and the effectiveness of the WSA in terms of obtained results, reached level of convergence and the capability of coping with the problems of premature convergence, trapping in a local optima and stagnation.     

Multiobjective PSO based adaption of neural network topology for pixel classification in satellite imagery

The proposed work involves the multiobjective PSO based adaption of optimal neural network topology for the classification of multispectral satellite images. It is per pixel supervised classification using spectral bands (original feature space). This paper also presents a thorough experimental analysis to investigate the behavior of neural network classifier for given problem. Based on 1050 number of experiments, we conclude that following two critical issues needs to be addressed: (1) selection of most discriminative spectral bands and (2) determination of optimal number of nodes in hidden layer. We propose new methodology based on multiobjective particle swarm optimization (MOPSO) technique to determine discriminative spectral bands and the number of hidden layer node simultaneously. The accuracy with neural network structure thus obtained is compared with that of traditional classifiers like MLC and Euclidean classifier. The performance of proposed classifier is evaluated quantitatively using Xie-Beni and β indexes. The result shows the superiority of the proposed method to the conventional one.     

An optimization model for evaluating the economic impact of availability and maintenance notions during the synthesis and design of a power plant

In this paper, we introduce an optimization strategy in order to comprehensively quantify the impact of availability and maintenance notions during the early stages of synthesis and design of a new natural gas combined cycle power plant. A detailed state-space approach is thoroughly discussed, where influence of maintenance funds on each component's repair rate is directly assessed.In this context, analysis of the reliability characteristics of the system is centered at two designer-adopted parameters, which largely influence the obtained results: the number of components which may fail independently at the same time, and the number of simultaneous failure/repair events.Then, optimal solutions are evaluated as the availability-related parameters and the amount of resources assigned for maintenance actions are varied across a wide range of feasible values, which enable obtaining more accurate and detailed estimations of the expected economic performance for the project when compared with traditional economic evaluation approaches.     

A Global Maximum Error Controller Based Nonlinearity Aware TPWL Method for Reducing Nonlinear Systems

Abstract

Model order reduction is a common practice to reduce large order systems so that their simulation and control become easy. Nonlinearity aware trajectory piecewise linear is a variation of trajectory piecewise linearization technique of order reduction that is used to reduce nonlinear systems. With this scheme, the reduced approximation of the system is generated by weighted sum of the linearized and reduced sub-models obtained at certain linearization points on the system trajectory. This scheme uses dynamically inspired weight assignment that makes the approximation nonlinearity aware. Just as weight assignment, the process of linearization points selection is also important for generating faithful approximations. This article uses a global maximum error controller based linearization points selection scheme according to which a state is chosen as a linearization point if the error between a current reduced model and the full order nonlinear system reaches a maximum value. A combination that not only selects linearization points based on an error controller but also assigns dynamic inspired weights is shown in this article. The proposed scheme generates approximations with higher accuracies. This is demonstrated by applying the proposed method to some benchmark nonlinear circuits including RC ladder network and inverter chain circuit and comparing the results with the conventional schemes.     

Crossing the Border: From Keto- to Imine Reduction in Short-Chain Dehydrogenases/Reductases

The family of NAD(P)H-dependent short-chain dehydrogenases/reductases (SDRs) comprises numerous biocatalysts capable of C=O or C=C reduction. The highly homologous noroxomaritidine reductase (NR) from Narcissus sp. aff. pseudonarcissus and Zt_SDR from Zephyranthes treatiae, however, are SDRs with an extended imine substrate scope. Comparison with a similar SDR from Asparagus officinalis (Ao_SDR) exhibiting keto-reducing activity, yet negligible imine-reducing capability, and mining the Short-Chain Dehydrogenase/Reductase Engineering Database indicated that NR and Zt_SDR possess a unique active-site composition among SDRs. Adapting the active site of Ao_SDR accordingly improved its imine-reducing capability. By applying the same strategy, an unrelated SDR from Methylobacterium sp. 77 (M77_SDR) with distinct keto-reducing activity was engineered into a promiscuous enzyme with imine-reducing activity, thereby confirming that the ability to reduce imines can be rationally introduced into members of the “classical” SDR enzyme family. Thus, members of the SDR family could be a promising starting point for protein approaches to generate new imine-reducing enzymes.     

CO压缩机优化运行总结

为确保原CO压缩机在净化系统升级改造项目实施后能安全稳定运行,采取了流程优化、工艺条件论证、设备缺陷排查、联锁指标重新确定等一系列措施。改造措施实施后,CO压缩机出口压力稳定在3.23 MPa左右,有效缓解了前后工序压力波动对压缩机稳定运行的影响,各级水冷却器的换热效果也得到了较大的改善。