基于α-FeOOH催化H2O2蒸气的低温烟气脱硝实验

针铁矿是分布广泛、储量丰富的铁氧化物，其主要成分为α-羟基氧化铁（α-FeOOH）。为了探究针铁矿在催化H₂O₂烟气脱硝反应中的性能，本文通过沉淀-水解法制备了α-FeOOH，并在自行搭建的实验台上开展了α-FeOOH催化H₂O₂的低温烟气脱硝实验研究，深入分析了H₂O₂流量、H₂O₂浓度、汽化温度、反应温度和共存气体浓度等工况参数对脱硝性能的影响。采用离子色谱（IC）分析了单独脱硝反应和同时脱硫脱硝反应后的含氧酸成分，结合各项表征分析技术考察了催化剂反应前后的理化特性和稳定性。实验结果显示，随着汽化温度和反应温度的升高，NO的脱除效率先增加后降低；增加H₂O₂浓度对脱硝效率有明显的促进作用。当汽化温度为140℃、反应温度为160℃时，以2.5mL/h注入10mol/L的H₂O₂脱硝效率达到80%。当SO₂浓度为1000μL/L时，脱硝效率提高至86.4%。离子色谱分析结果显示，单独脱硝反应和同时脱硫脱硝反应后含氧酸产物为HNO₃和H₂SO₄。反应前后催化剂的表征结果显示，α-FeOOH在脱硝反应后依然具有良好的稳定性，显示出针铁矿在低温烟气脱硝工艺中的潜在应用前景。     

Adaptive backstepping repetitive learning control design for nonlinear discrete‐time systems with periodic uncertainties

This paper addresses a tracking problem for uncertain nonlinear discrete‐time systems in which the uncertainties, including parametric uncertainty and external disturbance, are periodic with known periodicity. Repetitive learning control (RLC) is an effective tool to deal with periodic unknown components. By using the backstepping procedures, an adaptive RLC law with periodic parameter estimation is designed. The overparameterization problem is overcome by postponing the parameter estimation to the last backstepping step, which could not be easily solved in robust adaptive control. It is shown that the proposed adaptive RLC law without overparameterization can guarantee the perfect tracking and boundedness of the states of the whole closed‐loop systems in presence of periodic uncertainties. In addition, the effectiveness of the developed controller is demonstrated by an implementation example on a single‐link flexible‐joint robot. Copyright © 2014 John Wiley & Sons, Ltd.     

用于发光温度传感的金属有机框架材料

金属有机框架(Metal organic frameworks,MOFs)由于其显著的结构多样性和可调的发光性能,为制备不同种类的发光传感器提供了良好契机。近年来,利用发光MOFs探测温度传感技术受到了人们的广泛关注。结合对发光测温的描述后,总结了发光型MOF温度计的最新研究进展,重点介绍了双发射型MOF在温度传感领域中的广泛应用。     

Hybrid make-to-stock and make-to-order systems: a taxonomic review

Production planning and control (PPC) systems that employ aspects from both make-to-order (MTO) and make-to-stock (MTS) production control are known as hybrid MTS/MTO systems. While both MTO and MTS separately have been studied extensively, their combined use has received less attention. However, the literature on this topic is growing and this paper shows that the review performed in this paper is an important addition to the field. We categorise relevant literature according to a novel taxonomy and show that hybrid MTS/MTO production control can be used in different contexts. In addition, an overview of the modelling techniques and methods used in these papers is provided. Based on the reviewed literature, relevant research questions and directions for future research are identified. Finally, it is shown that hybrid MTS/MTO production control is prevalent in practice by discussing research with industrial applications. The paper contains an overview of research on hybrid MTS/MTO production control to be used as reference for researchers active in the field, and provides managerial insights and directions for future research on this topic.     

Anti‐Sway Crane Control Considering Wind Disturbance and Container Mass

A method for estimating the sway angle using an observer has already been proposed. The state observer estimates the sway angle accurately and must use the detected sway angle value. However, the estimated sway angle has an error owing to rope length error, friction force, and wind. Moreover, the container mass cannot be determined, and therefore the observer parameter is not suitable. We already proposed robust antisway control for overcoming rope length error without adding a new sensor. Further, we designed a friction disturbance observer to cancel out the influence of the friction force. In this paper, we first propose a container mass estimation method when a crane system performs rolling up control. The observer parameter can be selected using the estimated mass value. Second, in crane parallel shift control, we propose a robust antisway control even when there is a wind disturbance. We design a wind disturbance observer and propose a wind disturbance estimator to separate the friction observer output from the wind disturbance observer output. We confirm through experiments that the proposed method can reduce vibration.     

Global stable tracking control of underactuated ships with input saturation

In this paper, tracking control of underactuated ship in the presence of input saturation is addressed. By dividing the tracking error dynamic system into a cascade of two subsystems, the torques in surge and yaw axes are designed separately using the backstepping technique. More specifically, we design the yaw axis torque in such a way that its corresponding subsystem is finite time stable, which makes it to be de-coupled from the second subsystem after a finite time. This enables us to design the torque in the surge axis independently. It is shown that the closed-loop system is stable and the mean-square tracking errors can be made arbitrarily small by choosing design parameters. Simulation results also verify the effectiveness of the proposed scheme.     

电信运营商信息安全管控架构及集中策略研究

本文对运营商信息安全集中管控架构演进和信息安全管控策略面临的问题进行了深入分析，提出了信息安全集中管控架构的演进方向建议及对信息安全监控策略工作优化的若干意见。     

新型低GWP高温热泵工质HFO-1234ze(Z)的研究进展

高温热泵可以有效回收工业余热，达到节能减排和保护环境的目的。目前，有关高温热泵技术的研究热点在于寻找一种全球变暖潜能值(GWP)低、使用性能良好的工质，以替代现有CFC-114、HFC-245fa工质。对新型环境友好型工质HFO-1234ze(Z)进行了综述，其GWP＜1，临界温度高于423 K，是一种潜在的高温热泵替代工质。总结了近年来国内外学者对HFO-1234ze(Z)的合成技术、热力学性质、输运性质、传热性能等方面的研究，并分析了HFO-1234ze(Z)在高温热泵系统中应用的可行性，认为HFO-1234ze(Z)在高温热泵中具有较好的工作性能和发展前景。     

SBBR反应器中耐冷微生物的驯化与识别

以模拟生活污水为处理对象，三种不同填料为生物膜载体，运行 A、B、C三个SBBR，通过逐步降温（25、20、15、10、6和5℃）培养驯化处理低温污水的生物膜。运行251周期，三个反应器COD出水浓度均低于50 mg/L；B反应器对氨氮去除效果优于A和C。特别在低温5℃时，A、B和C三个反应器氨氮平均出水浓度分别为14.1、3.79和14.1 mg/L。高通量测序结果显示，5℃时，三个反应器中均以降解有机物的微生物为主；只有B反应器中驯化富集出了嗜冷硝化菌（Candidatus Nitrotoga）；而其他有硝化作用的菌属在B反应器中的丰度也高于A和C反应器；不利于脱氮的类固氮菌（Elstera）在A和C反应器中丰度较高。此结果可从微生物角度解释三个反应器处理效果的差异性，表明B反应器处理低温污水有显著优势，其所用填料可结合低温菌进一步开发利用。     

An overview on fault diagnosis and nature-inspired optimal control of industrial process applications

Fault detection, isolation and optimal control have long been applied to industry. These techniques have proven various successful theoretical results and industrial applications. Fault diagnosis is considered as the merge of fault detection (that indicates if there is a fault) and fault isolation (that determines where the fault is), and it has important effects on the operation of complex dynamical systems specific to modern industry applications such as industrial electronics, business management systems, energy, and public sectors. Since the resources are always limited in real-world industrial applications, the solutions to optimally use them under various constraints are of high actuality. In this context, the optimal tuning of linear and nonlinear controllers is a systematic way to meet the performance specifications expressed as optimization problems that target the minimization of integral- or sum-type objective functions, where the tuning parameters of the controllers are the vector variables of the objective functions. The nature-inspired optimization algorithms give efficient solutions to such optimization problems. This paper presents an overview on recent developments in machine learning, data mining and evolving soft computing techniques for fault diagnosis and on nature-inspired optimal control. The generic theory is discussed along with illustrative industrial process applications that include a real liquid level control application, wind turbines and a nonlinear servo system. New research challenges with strong industrial impact are highlighted.