Sizing of multi-stage Op Amps by combining design equations with the gm/ID method

Analog integrated circuit design has as integral parts both analytical reasoning and numerical validation in the process from topology construction to sizing. Given a circuit topology, different circuit sizing results can be obtained from different processes of sizing inference. Sizing methods by simulation-based numerical searching have been a continuously studied subject. However, almost all approaches in this category require an overwhelming number of circuit simulations to arrive at an optimized sizing result. On the other hand, many published manual sizing methods by using the conventional device equations also require repeated SPICE simulations to correct the equation-based sizing results. This paper proposes a systematic gm/ID-based initial sizing method specifically customized for designing multiple-stage operational amplifiers (Op Amps). A main feature of the proposal is to use circuit-level design equations as constraints on the gm/ID table lookup method to substantially reduce the uncertainty in the sizing calculations. As a result, a significant amount of SPICE based correction work can be reduced to complete an initial sizing. The proposed sizing procedure includes a few regular sizing rules customized to the configuration of multi-stage Op Amps. We validate the proposed sizing method by application to several multi-stage Op Amp examples with a capacitive load or Miller compensation. Simulations have justified that the produced initial sizing results can achieve most of the prespecified design targets.     

Implementing lean practices in manufacturing SMEs: testing ‘critical success factors’ using Necessary Condition Analysis

Lean practices are known to increase operational performance. Previous research has identified critical success factors for implementing lean practices. This research aims to examine the extent to which success factors are critical for various degrees of lean practice implementation. Using multiple-respondent self-assessments from 33 Dutch manufacturing small and medium-sized enterprises (SMEs), we conducted a Necessary Condition Analysis. Our findings indicated that the criticality of success factors is progression dependent. In the initial stages of the lean journey, SMEs could improve their lean practices in a bottom-up manner through local factors such as a learning focus, improvement training and support congruence. When lean practices are more advanced, some company-wide factors must be present: top management support, a shared improvement vision and a supplier link. Our findings question the universality of success factors such as strategic involvement and indicate the need for a more dynamic model of lean implementation.     

楼宇型分布式能源系统设备容量和运行策略优化研究

设备容量优化和运行策略优化是分布式能源系统设计，运行的关键问题。为实现分布式能源系统的经济效益，能效水平和环境效益最大化，针对楼宇型分布式能源系统建立了相对普适化的物理模型和数学模型，以粒子群优化算法和线性规划相结合，采用两阶段优化方法计算系统的最优容量配置，并给出运行策略。以某写字楼的分布式能源系统为例，得到最优的系统设备容量和全年逐时运行策略，并采用遍历法验证计算结果的准确性。优化的分布式能源系统与传统供能系统相比，费用年值降低7.79%，年总能耗降低24.18%，污染物排放量减少了62.77 %。     

Recent activities in the field of Nuclear Operational Management and Nuclear Safety Engineering

Recent activities in the field of Nuclear Operational Management and Nuclear Safety Engineering, the studies related to risk analysis methodology, design, and operational management, physical phenomena, and emergency preparedness and nuclear security, have been progressed. Especially, ‘risk analysis methodology’ and ‘design and operational management’ are the main categories of the field, in which more than half of published articles on Journal of Nuclear Science and Technology are related to these categories.     

A framework for improving the efficiency of operational testing through Bayesian adaptive design

When developing a product, it is important to consider product performance from a user perspective. This type of evaluation can be done through operational testing—assessing the ability of representative users to satisfactorily accomplish tasks with the product in operationally representative environments. This process can be expensive and time-consuming, but is critical to understanding whether the product can adequately do the job for which it was designed. We show how an existing design of experiments (DOEs) process for operational testing can be leveraged to construct a Bayesian adaptive design. This design, nested within the larger design created by the DOE process, allows interim analyses to stop testing early for success or futility. Representative simulations are presented to demonstrate how these interim analyses can be used in an operational test setting, and reductions in necessary test events are shown. The application of Bayesian-adaptive design methods will allow future operational testing to be conducted in less time and at less expense, on average, without compromising the ability of the existing process to verify the product meets the user's needs.     

Distribution-free composite Shewhart-GWMA Mann-Whitney charts for monitoring the process location

The Mann-Whitney (MW) statistic is one of the most recommended two-sample statistical tests when the assumption of normality fails to hold due to its robustness and fascinating properties especially when small sample sizes are involved. In order to improve the sensitivity of the generally weighted moving average (GWMA) monitoring scheme toward the detection of large shifts, in this paper, a new distribution-free phase II composite Shewhart-GWMA (CSG) scheme is proposed using the MW U statistic. The performance of the proposed scheme is investigated using the average run-length (ARL) and average extra quadratic loss (AEQL) values through extensive simulations. The performance of this newly proposed monitoring scheme is found to be superior when compared to numerous memory-type MW schemes (some existing and others introduced in this paper) in many situations. An illustrative example is given to demonstrate the implementation of the CSG MW U scheme using real-life data.     

Structural and operational management of Turkish airports: a bootstrap data envelopment analysis of efficiency

This paper explores the structural and operational dimensions of the efficiencies of airports. The two-stage procedure is suggested to assess the efficiencies of airports in this study. In the first-stage, Classification and Regression Tree, which is one of the machine-learning approaches used to divide the airports into homogeneous and thus comparable sub-groups. In the second stage, the bootstrap data envelopment analysis approach obtains more precise structural and operational efficiency scores. To illustrate the proposed framework use, we applied it to a real case associated with Turkish airports. The results demonstrate that this framework presents a more comprehensive assessment of airport performance rather than conventional data envelopment analysis models. Moreover, it provides to show the deficiencies of the structural and operational management of airports. The findings can help anywhere airport authorities as well as Turkish airport authorities.     

An integrated dimensionality reduction and surrogate optimization approach for plant-wide chemical process operation

With liquefied natural gas becoming increasingly prevalent as a flexible source of energy, the design and optimization of industrial refrigeration cycles becomes even more important. In this article, we propose an integrated surrogate modeling and optimization framework to model and optimize the complex CryoMan Cascade refrigeration cycle. Dimensionality reduction techniques are used to reduce the large number of process decision variables which are subsequently supplied to an array of Gaussian processes, modeling both the process objective as well as feasibility constraints. Through iterative resampling of the rigorous model, this data-driven surrogate is continually refined and subsequently optimized. This approach was not only able to improve on the results of directly optimizing the process flow sheet but also located the set of optimal operating conditions in only 2 h as opposed to the original 3 weeks, facilitating its use in the operational optimization and enhanced process design of large-scale industrial chemical systems.     

A machine learning‐based operational control framework for reducing energy consumption of an amine‐based gas sweetening process

This study proposes a data‐driven operational control framework using machine learning‐based predictive modeling with the aim of decreasing the energy consumption of a natural gas sweetening process. This multi‐stage framework is composed of the following steps: (a) a clustering algorithm based on Density‐Based Spatial Clustering of Applications with Noise methodology is implemented to characterize the sampling space of all possible states of the operation and to determine the operational modes of the gas sweetening unit, (b) the lowest steam consumption of each operational mode is selected as a reference for operational control of the gas sweetening process, and (c) a number of high‐accuracy regression models are developed using the Gradient Boosting Machines algorithm for predicting the controlled parameters and output variables. This framework presents an operational control strategy that provides actionable insights about the energy performance of the current operations of the unit and also suggests the potential of energy saving for gas treating plant operators. The ultimate goal is to leverage this data‐driven strategy in order to identify the achievable energy conservation opportunity in such plants. The dataset for this research study consists of 29 817 records that were sampled over the course of 3 years from a gas train in the South Pars Gas Complex. Furthermore, our offline analysis demonstrates that there is a potential of 8% energy saving, equivalent to 5 760 000 Nm³ of natural gas consumption reduction, which can be achieved by mapping the steam consumption states of the unit to the best energy performances predicted by the proposed framework.     

涡流空气分级机熵产与分级性能分析

利用热力学第二定律中的熵产理论对涡流空气分级机各不可逆因素引起的熵产进行分析，通过粉料分级试验对其分级性能进行验证，获得了黏性熵产、湍流熵产和壁面熵产分布特点及操作参数对熵产和分级精度的影响规律。熵产分析结果表明，涡流空气分级机内湍流熵产和壁面熵产占总熵产的比例高达56.41%和43.11%，湍流熵产主要产生于转笼叶片间和转笼内部，进风口和细粉出口壁面剪切引起较大壁面熵产；此外，转笼转速和进口风速变化分别仅对转笼区域和切向进风口区域内气流运动熵产影响较大，进口风速-转笼转速处于8.6m/s、 800r/min和18m/s、1200r/min操作工况附近时，涡流空气分级机内总熵产/总能变化率较小，分级流场稳定性较高，对粗、细颗粒分离有利，该工况下分级机的粉料分级试验效果较好，说明熵产理论可用于涡流分级机内流动分析及其操作参数的优化匹配。