Exergy analysis of the diesel pre-reforming solid oxide fuel cell system with anode off-gas recycling in the SchIBZ project. Part I: Modeling and validation

Solid oxide fuel cell (SOFC) systems with anode off-gas recirculation (AGR) and diesel pre-reforming are advantageous because they can operate with the current fuel infrastructure. In the SchIBZ-project, the prototype of such a SOFC system for maritime applications has already been commissioned. In this first paper, we model the system devices to conduct an exergy analysis of this real SOFC plant and validate them with experimental values from experiments in laboratory scale. The results of our simulation agree well with the experimental values. The calculations with the validated results may be closer to the real thermodynamic behavior of such system components than previous literature.     

Adaptive neuro-fuzzy algorithm applied to predict and control multi-phase flow rates through wellhead chokes

A Takagi-Sugeno adaptive neuro-fuzzy inference system (TSFIS) model is developed and applied to a dataset of wellhead flow-test data for the Resalat oil field located offshore southern Iran, the objective is to assist in the prediction and control of multi-phase flow rates of oil and gas through the wellhead chokes. For this purpose, 182 test data points (Appendix 1) related to the Resalat field are evaluated. In order to predict production flow rate (Q_L) expressed as stock-tank barrels per day (STB/D), this dataset includes four selected input variables: upstream pressure (Pwh); wellhead choke sizes (D64); gas to liquid ratio (GLR); and, base solids and water including some water-soluble oil emulsion (BS&W). The test data points evaluated include a wide range of oil flow rate conditions and values for the four input variables recorded. The TSFIS algorithm applied involves five data processing steps: a) pre-processing, b) fuzzification, c) rules base and adaptive neuro-fuzzy inference engine, d) defuzzification, and e) post-processing of the fuzzy model. The developed TSFIS model for the Resalat oil field database predicted oil flow rate to a high degree of accuracy (root mean square error = 247 STB/D, correlation coefficient = 0.9987), which improves substantially on the commonly used empirical algorithms used for such predictions. TSFIS can potentially be applied in wellhead choke fuzzy controllers to stabilize flow in specific wells based on real-time input data records.     

An engineering method for complex structural optimization involving both size and topology design variables

This work presents an engineering method for optimizing structures made of bars, beams, plates, or a combination of those components. Corresponding problems involve both continuous (size) and discrete (topology) variables. Using a branched multipoint approximate function, which involves such mixed variables, a series of sequential approximate problems are constructed to make the primal problem explicit. To solve the approximate problems, genetic algorithm (GA) is utilized to optimize discrete variables, and when calculating individual fitness values in GA, a second-level approximate problem only involving retained continuous variables is built to optimize continuous variables. The solution to the second-level approximate problem can be easily obtained with dual methods. Structural analyses are only needed before improving the branched approximate functions in the iteration cycles. The method aims at optimal design of discrete structures consisting of bars, beams, plates, or other components. Numerical examples are given to illustrate its effectiveness, including frame topology optimization, layout optimization of stiffeners modeled with beams or shells, concurrent layout optimization of beam and shell components, and an application in a microsatellite structure. Optimization results show that the number of structural analyses is dramatically decreased when compared with pure GA while even comparable to pure sizing optimization.     

无极绳绞车生产应用探究

以无极绳绞车生产应用为对象开展探究,结合具体工程实际,在分析无极绳绞车构成的基础上,对无极绳绞车和调度绞车应用性能做出比对分析,总结无极绳绞车的优点,进而对无极绳绞车在井下综采作业面的应用进行阐述,希望能够为无极绳绞车在其他矿井生产中的应用提供借鉴和参考。     

环锭纱络筒质量的检测与控制

为探讨环锭纱络筒过程中纱线质量的影响因素,通过设置对比实验的方法测试络筒纱线的毛羽数、条干不匀率、强伸性和卷绕点分布,得到同一纱筒上不同半径纱层的纱线质量数据,并进行研究分析。结果表明:中层筒子纱线的毛羽较少,对应的纱线比较光滑,CV值较低,条干均匀度高;最内层和最外层纱线的强力最大;最外层纱线重叠率最低。生产中可以采取改进络筒工艺参数、选择适宜的槽筒材质等措施,提高卷绕质量及络筒质量。     

Empirical examination of critical failure factors of continuous improvement deployments: stage-wise results and a contingency theory perspective

Organisations implement various Continuous Improvement (CI) practices such as Total Quality Management, Lean, Six Sigma and Lean Six Sigma (LSS) for improving their processes. Drawing from the success and failure stories of these structured CI practices, scholars enumerated Critical Success Factors and Critical Failure Factors (CFFs). This study empirically examines the occurrence of various CFFs across different stages of CI deployment. Further, from a contingency theoretic perspective, this study investigates their associations with contextual variables by collecting survey data from 213 business units from the USA, the UK, China, and India. Principal Component Analysis is used to group CFFs across five CI deployment stages leading to an empirically refined framework for CI. Crosstab analysis using the chi-square likelihood ratio presented associations of CFFs with contextual variables. Findings reveal significant differences in the occurrence of CFFs across countries. There is evidence that LSS is less prone to failures when compared with TQM, Lean and Six Sigma. The occurrence of CFFs has been relatively lower in smaller and medium-sized enterprises operating in the service sector. Findings also reveal that lessons learned from each CI deployment cycle, contribute to organisational learning, and thence, leading to success at the strategic CI level of maturity.     

Generalized F-discrepancy-based point selection strategy for dependent random variables in uncertainty quantification of nonlinear structures

In the performance evaluation of structures under disastrous actions, for example, earthquakes, it is important to take into account the randomness of structural parameters. Generally, these random parameters are treated either as independent or perfectly dependent, but practically they are partly dependent. This article aims at developing a point selection strategy for uncertainty quantification of nonlinear structures involving probabilistically dependent random parameters characterized by copula function. For this purpose, the point selection strategy for structures involving independent basic variables is first revisited. As an improvement, a generalized F-discrepancy diminishing oriented iterative screening algorithm is proposed. Then, combining with the conditional sampling method, a conditional point set rearrangement method and a conditional iterative screening-rearrangement method are proposed for probabilistically dependent variables. These new point selection strategies are readily incorporated into the probability density evolution method for uncertainty quantification of nonlinear structures involving dependent random parameters, which is characterized by copula function. The proposed methods are illustrated by two examples including a shear frame with hysteretic restoring forces and a reinforced concrete frame structure with the damage constitutive model of concrete, where the material parameters are probabilistically dependent. The results demonstrate the effectiveness of the proposed method. Problems to be studied are discussed.     

Sensor network design based on system-wide reliability criteria. Part I: Objectives

Reliability based criteria are quite popular for optimal sensor network design. We present a modified definition of system reliability for sensor network design for two applications: reliable estimation of variables in a steady state linear flow process, and reliable fault detection and diagnosis for any process. Unlike the weakest-link based definition of system reliability in the literature, the proposed definition considers the entire system and is consistent with the reliability concept used in classical reliability literature. For each application, dual approaches for defining system reliability are proposed, and their analogy with the reliability problem in the classical reliability literature is established. Using examples and stochastic simulations, the advantage of using the proposed system reliability in contrast to the existing definition is illustrated. Part II of this series of articles presents methods for efficient generation of the system reliability function and its use in optimization-based approaches for designing optimal sensor networks.     

Recursive least squares estimation methods for a class of nonlinear systems based on non-uniform sampling

Many dynamic processes in practice have nonlinear characteristics and must be described by using nonlinear models. It remains to be a challenging problem to build the models of such nonlinear systems and to estimate their parameters. This article studies the parameter estimation problem for a class of Hammerstein-Wiener nonlinear systems based on non-uniform sampling. By means of the auxiliary model identification idea, an auxiliary model-based recursive least squares algorithm is derived for the systems. In order to enhance the computational efficiency, an auxiliary model-based hierarchical least squares algorithm is proposed by utilizing the hierarchical identification principle. The simulation results confirm the effectiveness of the proposed algorithms.     

Benefits of variable structure techniques for parameter estimation in stochastic systems using least squares method and instrumental variables

In this paper, we study the benefits of the equivalent control method, implemented on parameter estimation algorithms like the least squares method and instrumental variables on continuous‐time stochastic systems. Here, we show how the equivalent‐control, adapted to the stochastic case, improves the performance of these estimation algorithms in continuous‐time systems under white noise perturbations. Some numerical examples show the effectiveness of these techniques. Copyright © 2014 John Wiley & Sons, Ltd.