Feedforward control of the transverse strip profile in hot-dip galvanizing lines

Hot-dip galvanizing is a standard technology to produce coated steel strips. The primary objective of the galvanizing process is to establish a homogeneous zinc layer with a defined thickness. One condition to achieve this objective is a uniform transverse distance between the strip and the gas wiping dies, which blow off excessive liquid zinc. Therefore, a flat strip profile at the gas wiping dies is required. However, strips processed in such plants often exhibit residual curvatures which entail unknown flatness defects of the strip. Such flatness defects cause non-uniform air gaps and hence an inhomogeneous zinc coating thickness. Modern hot-dip galvanizing lines often use electromagnets to control the transverse strip profile near the gas wiping dies. Typically, the control algorithms ensure a flat strip profile at the electromagnets because the sensors for the transverse strip displacement are also located at this position and it is unfeasible to mount displacement sensors directly at the gas wiping dies. This brings along that in general a flatness defect remains at the gas wiping dies, which in turn entails a suboptimal coating.In this paper, a model-based method for a feedforward control of the strip profile at the position of the gas wiping dies is developed. This method is based on a plate model of the axially moving strip that takes into account the flatness defects in the strip. First, an estimator of the flatness defects is developed and validated for various test strips and settings of the plant. Using the validated mathematical model, a simulation study is performed to compare the state-of-the-art control approach (flat strip profile at the electromagnets) with the optimization-based feedforward controller (flat strip profile at the gas wiping dies) proposed in this paper. Moreover, the influence of the distance between the gas wiping dies and the electromagnets is investigated in detail.     

Optimal design of nonlinear model predictive controller based on new modified multitracker optimization algorithm

The controller design for the robotic manipulator faces different challenges such as the system's nonlinearities and the uncertainties of the parameters. Furthermore, the tracking of different linear and nonlinear trajectories represents a vital role by the manipulator. This paper suggests an optimal design for the nonlinear model predictive control (NLMPC) based on a new improved intelligent technique and it is named modified multitracker optimization algorithm (MMTOA). The proposed modification of the MTOA is carried out based on opposition-based learning (OBL) and quasi OBL approaches. This modification improves the exploration behavior of the MTOA to prevent it from becoming trapped in a local optimum. The proposed method is applied on the robotic manipulator to track different linear and nonlinear trajectories. The NLMPC parameters are tuned by the MMTOA rather than the trial and error method of the designer. The proposed NLMPC based on MMTOA is compared with the original MTOA, genetic algorithm, and cuckoo search algorithm in literature. The superiority and effectiveness of the proposed controller are confirmed to track different linear and nonlinear trajectories. Furthermore, the robustness of the proposed method is emphasized against the uncertainties of the parameters.     

A hybrid homogeneously weighted moving average control chart for process monitoring

Several modifications and enhancements to control charts in increasing the performance of small and moderate process shifts have been introduced in the quality control charting techniques. In this paper, a new hybrid control chart for monitoring process location is proposed by combining two homogeneously weighted moving average (HWMA) control charts. The hybrid homogeneously weighted moving average (HHWMA) statistic is derived using two smoothing constants λ₁ and λ₂ . The average run length (ARL) and the standard deviation of the run length (SDRL) values of the HHWMA control chart are obtained and compared with some existing control charts for monitoring small and moderate shifts in the process location. The results of study show that the HHWMA control chart outperforms the existing control charts in many situations. The application of the HHWMA chart is demonstrated using a simulated data.     

干扰条件下的“北斗”信号解扩重构DOA估计

在干扰条件下，卫星导航抗干扰波束形成算法往往需要卫星信号波达方向（Direction-of-Arrival，DOA）的先验信息。但当存在低信噪比信号或主动干扰源时，常规的DOA估计算法性能急剧下降甚至失效。针对此问题，提出了一种被干扰信号压制的低信噪比“北斗”信号的DOA估计算法。该算法首先通过对接收信号进行子空间投影抑制干扰信号，然后对抑制干扰后的信号进行解扩重构处理，最后通过多重信号分类算法完成对“北斗”信号的DOA估计。仿真结果表明，在干扰信号干信比80 dB条件下，“北斗”信号DOA估计误差在5°以内，为下一步进行波束形成计算提供了高精度的入射角信息。     

芽孢杆菌Bacillus sp.dwc-2对模拟地下水中U(Ⅵ)的还原行为研究

在厌氧条件下研究了西南地区一种典型土壤微生物芽孢杆菌Bacillus sp.dwc-2对模拟地下水中U(Ⅵ)的还原行为，重点考察了时间、无机阴离子、腐殖酸（HA）及富里酸（FA）对还原的影响，并利用TEM、EDS、SAED和XPS对还原后的样品进行了表征。结果表明：在pH=7.0、c_NaHCO3=5 mmol/L和T=303 K条件下，Bacillus sp.dwc-2对U(Ⅵ)的还原率随时间的增加而增加，24 h内最大还原率为12.2%，此后则随时间的增加逐渐降低；HA和FA对U(Ⅵ)的微生物还原行为有一定影响，其中HA和FA浓度为25 mg/L时，U(Ⅵ)的还原在24 h最明显，其还原率分别为14.2%和16.2%，但随着HA和FA浓度的继续增加，因在U(Ⅵ)离子与HA、FA形成的配合物表面形成致密的腐殖层，抑制了电子的转移，阻止了U(Ⅵ)的还原。此外，研究表明HCO₃^-也会抑制U(Ⅵ)的还原。TEM-SAED和XPS分析证实了还原过程中U(Ⅳ)的存在。上述结果可为真实环境中微生物还原U(Ⅵ)提供基础数据和参考。     

基于残差决策的自动飞行控制系统远程实时故障诊断

针对自动飞行控制系统结构复杂、关联部件众多,发生故障时诊断时间长,从而影响飞机运行效率的问题,提出一种基于飞机通信寻址报告系统(ACARS)的远程实时故障诊断方案。首先,分析自动飞行控制系统的故障特点,设计搭建检测滤波器;然后,利用ACARS数据链实时发送的自动飞行控制系统的关键信息进行相关部件的残差计算,并根据残差决策算法进行故障诊断及定位;最后,针对不同故障部件残差间的差异大、决策门限无法统一的缺点,提出基于二次差值的残差决策改进算法,减缓了检测对象的整体变化趋势,降低了随机噪声和干扰的影响,避免了将瞬态故障诊断为系统故障的情况。实验仿真结果表明,基于二次差值的改进残差决策算法避免了多决策门限的复杂性,在采样时间为0.1 s的情况下,故障检测所需时间大约为2 s,故障检测时间大幅降低,有效故障检测率大于90%。     

Transmission system restoration with co-optimization of repairs,load pickups,and generation dispatch

This paper studies the restoration of a transmission system after a significant disruption such as a natural disaster. It considers the co-optimization of repairs, load pickups, and generation dispatch to produce a sequencing of the repairs that minimizes the size of the blackout over time. The core of this process is a Restoration Ordering Problem (ROP), a non-convex mixed-integer nonlinear program that is outside the capabilities of existing solver technologies. To address this computational barrier, the paper examines two approximations of the power flow equations: The DC model and the recently proposed LPAC model. Systematic, large-scale testing indicates that the DC model is not sufficiently accurate for solving the ROP. In contrast, the LPAC power flow model, which captures line losses, reactive power, and voltage magnitudes, is sufficiently accurate to obtain restoration plans that can be converted into AC-feasible power flows. An experimental study also suggests that the LPAC model provides a robust and appealing tradeoff between accuracy and computational performance for solving the ROP.     

Mathematical modeling of growth of Salmonella spp. and spoilage microorganisms in raw oysters

The main objective of this study was to develop the primary and secondary models to describe the growth kinetics of Salmonella as well as background microorganisms in raw, shucked oysters. Samples, inoculated with a cocktail of two Salmonella serotypes, S. Typhimurium (CICC22956) and S. Enteritidis (CICC21482), were incubated at 4, 8, 12, 16, 20, 25, 30, 33, 37, 40, and 43 °C. Growth of Salmonella was observed at all temperatures, except at 4 °C. The background microorganisms grew at all temperatures. All growth curves clearly exhibited lag, exponential and stationary phases, and were analyzed using the Huang growth model. Three secondary models (Ratkowsky square-root, Huang square-root, and Cardinal parameter models) were compared for evaluating the effect of temperature on bacterial growth rates. Data analysis was performed using IPMP 2013, a free predictive microbiology software tool developed by the USDA ARS.The Cardinal parameters model underestimated the specific rates of the microorganisms at low temperatures. The Huang square-root model was more suitable than the Ratkowsky square-root model for describing the effect of temperature on growth of Salmonella, while the Ratkowsky square-root model, on the other hand, was more suitable for background microorganisms. For both Salmonella and background microorganisms, the logarithms of the lag phase were expressed as linear functions of the logarithms of specific growth rates. The results of this study can be used by the food retailers and regulatory agencies to estimate the microbial shelf-life of raw, shucked oysters.     

Evaluation of hydrodynamic behavior of the perforated gas distributor of industrial gas phase polymerization reactor using CFD-PBM coupled model

A 2D computational fluid dynamics (Eulerian–Eulerian) multiphase flow model coupled with a population balance model (CFD-PBM) was implemented to investigate the fluidization structure in terms of entrance region in an industrial-scale gas phase fluidized bed reactor. The simulation results were compared with the industrial data, and good agreement was observed. Two cases including perforated distributor and complete sparger were applied to examine the flow structure through the bed. The parametric sensitivity analysis of time step, number of node, drag coefficient, and specularity coefficient was carried out. It was found that the results were more sensitive to the drag model. The results showed that the entrance configuration has significant effect on the flow structure. While the dead zones are created in both corners of the distributors, the perforated distributor generates more startup bubbles, heterogeneous flow field, and better gas–solid interaction above the entrance region due to jet formation.