Stochastic optimization for real-time operation of alumina blending process

In this paper, the stochastic optimization blending operation is applied to the alumina production in this paper. A new binomial distribution based stochastic scenario optimization used together with the sample selection approach is utilized to design the optimal set point for control, under which the probability of quality indices of the raw slurry being within the tolerance region is high enough in the presence of uncertainties caused by fluctuation of the raw material and disturbances. Through practical industrial experiments, it is observed that the proposed stochastic optimization method is effective and the computational cost is low.     

A semi-quantitative approach to risk analysis, as an alternative to QRAs

In quantitative risk analysis (QRA) risk is quantified using probabilities and expected values, for example expressed by PLL values, FAR values, IR values and F–N curves. The calculations are tedious and include a strong element of arbitrariness. The value added by the quantification can certainly be questioned. In this paper, we argue that such analyses often are better replaced by semi-quantitative analyses, highlighting assessments of hazards and barriers, risk influencing factors (RIFs) and safety improvement measures. The assessments will be based on supporting information produced by risk analysts, including hard data and analyses of failure causes and mechanisms, barrier performance, scenario development, etc. The approach acknowledges that risk cannot be adequately described and evaluated simply by reference to summarising probabilities and expected values. There is a need for seeing beyond the standard probabilistic risk results of a QRA. Key aspects to include are related to uncertainties in phenomena and processes, and manageability factors. Such aspects are often ignored in standard QRAs.     

永磁同步电机速度控制器的设计

针对永磁同步电机（PMSM）伺服系统中存在着模型参数不确定性的问题,将永磁同步电机的控制问题转换为H∞混合的灵敏问题,根据鼠。理论设计出控制器。仿真结果表明该控制方法使永磁同步电机控制系统具有很好的鲁棒性,能有效的抑制参数摄动对系统的影响。     

Comparison of different uncertainty techniques in urban stormwater quantity and quality modelling

Urban drainage models are important tools used by both practitioners and scientists in the field of stormwater management. These models are often conceptual and usually require calibration using local datasets. The quantification of the uncertainty associated with the models is a must, although it is rarely practiced. The International Working Group on Data and Models, which works under the IWA/IAHR Joint Committee on Urban Drainage, has been working on the development of a framework for defining and assessing uncertainties in the field of urban drainage modelling. A part of that work is the assessment and comparison of different techniques generally used in the uncertainty assessment of the parameters of water models. This paper compares a number of these techniques: the Generalized Likelihood Uncertainty Estimation (GLUE), the Shuffled Complex Evolution Metropolis algorithm (SCEM-UA), an approach based on a multi-objective auto-calibration (a multialgorithm, genetically adaptive multi-objective method, AMALGAM) and a Bayesian approach based on a simplified Markov Chain Monte Carlo method (implemented in the software MICA). To allow a meaningful comparison among the different uncertainty techniques, common criteria have been set for the likelihood formulation, defining the number of simulations, and the measure of uncertainty bounds. Moreover, all the uncertainty techniques were implemented for the same case study, in which the same stormwater quantity and quality model was used alongside the same dataset. The comparison results for a well-posed rainfall/runoff model showed that the four methods provide similar probability distributions of model parameters, and model prediction intervals. For ill-posed water quality model the differences between the results were much wider; and the paper provides the specific advantages and disadvantages of each method. In relation to computational efficiency (i.e. number of iterations required to generate the probability distribution of parameters), it was found that SCEM-UA and AMALGAM produce results quicker than GLUE in terms of required number of simulations. However, GLUE requires the lowest modelling skills and is easy to implement. All non-Bayesian methods have problems with the way they accept behavioural parameter sets, e.g. GLUE, SCEM-UA and AMALGAM have subjective acceptance thresholds, while MICA has usually problem with its hypothesis on normality of residuals. It is concluded that modellers should select the method which is most suitable for the system they are modelling (e.g. complexity of the model’s structure including the number of parameters), their skill/knowledge level, the available information, and the purpose of their study.     

电力系统概率小扰动稳定性研究进展

随着电力系统新能源渗透率的不断增大,考虑到新能源具有随机性强、波动性大等特征,会对高渗透率的新能源电力系统小扰动稳定性产生影响,从而制约电力系统的发展。为此,文章着重针对小扰动的来源、对电力系统的影响以及分析方法进行了总结归纳,首先简要介绍了小扰动的相关概念,后从风电等不确定新能源发电、电动汽车等新型负荷接入电网以及网络安全三方面阐述了各自对电力系统的影响,并对小扰动分析方法进行归类,分析了蒙特卡洛法(MCM)、随机响应面法(SRSM)和概率分配法(PCM)的相关原理以及研究现状。最后介绍了现阶段小扰动的抑制措施并提出了对未来电网发展方向的一些思考。     

Wind speed estimation uncertainties: effects of climatological and micrometeorological parameters

To achieve structures that are risk-consistent, structural reliability methods must be used that account for uncertainties with respect to the relevant parameters affecting the estimation of wind effects. In this paper, we obtain measures of uncertainties in the estimation of the wind speeds upwind of structures. These uncertainties are due to incomplete knowledge with respect to the relevant extreme climatological and micrometeorological parameters. Advances in wind engineering and improvements in computational capabilities now make it possible to improve upon earlier estimates available in the literature. The work presented in this paper is a phase of a broader NIST project aimed at developing user-friendly software for the estimation of probabilities of failure of low-rise structures subjected to wind loads.     

不确定非线性系统的无颤振动态变结构控制

本文研究有界非匹配不确定性SISO非线性系统的跟踪问题，为克服变结构控制的抖动现象，设计了动念变结构控制方案，得到本质上时间连续的变结构控制律，并且考虑了广系统在有界模型不确定性及外加干扰情况下控制的鲁捧性。最后给出的仿真实例证实了理论结果。     

Robust switched predictive control for multi-phase batch processes with uncertainties and unknown disturbances

A robust switched predictive control method is designed to deal with the multi-phase batch processes with the inevitable uncertainties, time-varying delay and unknown disturbances. Firstly, the output tracking errors are augmented into the state variables and then the improved robust switched predictive control law is established using the new augmented state variables. To this end, the novel switched system is built to represent the different phases of multi-phase batch processes. Secondly, making full use of the robust predictive control theory, Lyapunov function theory, switched system theory, linear matrix inequality (LMI) theory and mode-dependent average dwell time method, the sufficient conditions in terms of LMI constraints are derived to ensure that the switching system is asymptotically controllable and stable. During the process of derivation, based on the traditional performance indicators, the H-infinite performance index is introduced, so that the system in different phases can still operate stably under the uncertainty and external interference. The average dwell time and the control law gain of each phase are thus calculated by solving these LMI constrains. Ultimately, the simulation results on the injection molding process show that the proposed method can make the controlled system run stably.     

Computational stochastic structural analysis (COSSAN) – a software tool

The paper provides an overview of the current status of the COSSAN™ [Institute of Engineering Mechanics, Leopold-Franzens University, Innsbruck, Austria, EU. COSSAN. Computational stochastic structural analysis, User’s Manual, Part B, 2001; Part A, 1996] software developed at the Institute of Engineering Mechanics of the Leopold Franzens University, Innsbruck, Austria, EU. Two options provided by COSSAN™ are described: (1) A ‘Stand Alone Tool Box’ which is an event driven modular general purpose code and (2) the ‘Third Party Communication Tools’ which allow to employ deterministic Third Party (FE-) codes for stochastic analysis without the need to access and modify the third party source code. The Stand Alone Tool Box covers a fairly wide field of stochastic methods including various sampling techniques, random fields, fatigue analysis, reliability based optimization, random vibration, Monte Carlo simulation and FE-analysis. The Third Party Communication Tools are designed to extend existing deterministic FE-codes for considering uncertainties and consequently perform stochastic analyses using methods based on Monte Carlo procedures.     

A lumped model for thermal decomposition of urea. Uncertainties analysis and selective non-catalytic reduction of NO

A two-step mechanism for non-catalytic thermal decomposition of urea was investigated. It involves a first order reaction for the thermolysis of urea and a pseudo-first order reaction for hydrolysis of isocyanic acid. Rate constants of these reactions were tuned on experimental measurements of NH₃ and HNCO concentrations at 423-723 K and 1083-1383 K in isothermal flow reactors made of aluminum and ceramic, respectively. A regression analysis by taking the uncertainties in the kinetic parameters into account shown that the resultant pairs of Arrhenius expressions for thermo-hydrolyzing urea were different with a 90% confidence when obtained from the different sets of experimental data. Additional results of ammonia concentration from experiments of urea solution decomposition reported in the literature revealed the reliability of the Arrhenius expressions tuned on the reaction rates obtained in the highest range of temperature in the ceramic reactor. The consistency of these equations was also verified by a comparison between calculated and experimental rates of selective non-catalytic reduction of nitric oxide with urea as reducing agent. A statistical analysis based on the identification of possible tendencies in these residuals again evidences the weakness of the Arrhenius expressions based on the experiments performed at lower temperatures, which was attributed to a non-expected catalytic effect of aluminum reactor walls on reactions rates of urea thermal decomposition.