Spurenstoffabtrennung mit keramischen Nanofiltrationsmembranen als Rotationsscheibenfilter

Reducing micropollutant pollution of water bodies is an important objective of water management and an integral part of environmental policy. Ceramic nanofiltration membranes were developed as multichannel membranes of increased membrane area and rotating disk filters. The membranes developed show retention of over 80 % for PEG 400. The membranes are currently being tested for the separation of micropollutants from wastewater contaminated with pharmaceuticals. With the help of a downstream oxidative process, the trace substances remaining in the permeate are degraded.     

Reusable Polyacrylonitrile-Sulfur Extractor of Heavy Metal Ions from Wastewater

Mercury, lead, and cadmium are among the most toxic and carcinogenic heavy metal ions (HMIs), posing serious threats to the sustainability of aquatic ecosystems and public health. There is an urgent need to remove these ions from water by a cheap but green process. Traditional methods have insufficient removal efficiency and reusability. Structurally robust, large surface-area adsorbents functionalized with high-selectivity affinity to HMIs are attractive filter materials. Here, an adsorbent prepared by vulcanization of polyacrylonitrile (PAN), a nitrogen-rich polymer, is reported, giving rise to PAN-S nanoparticles with cyclic π-conjugated backbone and electronic conductivity. PAN-S can be coated on ultra-robust melamine (ML) foam by simple dipping and drying. In agreement with hard/soft acid/base theory, N- and S-containing soft Lewis bases have strong binding to Hg²⁺, Pb²⁺, Cu²⁺, and Cd²⁺, with extraordinary capture efficiency and performance stability. Furthermore, the used filters, when collected and electrochemically biased in a recycling bath, can release the HMIs into the bath and electrodeposit on the counter-electrode as metallic Hg⁰, Pb⁰, Cu⁰, and Cd⁰, and the PAN-S@ML filter can then be reused at least 6 times as new. The electronically conductive PAN-S@ML filter can be fabricated cheaply and holds promise for scale-up applications.     

Distributed Kalman filter for linear system with complex multi-channel stochastic uncertain parameter and decoupled local filters

This article considers a distributed Kalman filtering problem for linear system contaminated by complex multi-channel random uncertain parameter in which a number of nodes cooperative without central coordination to estimate a common state based on local measurements and data received from neighbors. We propose an approach to eliminate this error propagation. The proposed local filters are guaranteed to be stable under some mild conditions on certain global structural data, and their fusion yields the centralized Kalman estimate. Then, we extend this method to smoothing and deconvolution algorithm. Finally, simulation experiments demonstrate the validity of the proposed approach.     

Adaptive nonlinear control of three-phase shunt active power filters with magnetic saturation

The problem of controlling three-phase shunt active power filters (SAPF) is addressed in presence of nonlinear loads. Previous works generally design control for SAPF based on standard models that assume the involved magnetic coil to be linear. In reality, the magnetic characteristics of these components are nonlinear (especially in the presence of large magnetic flux density in the ferromagnetic core). In this paper, a new oriented control model for SAPF-load system, taking into account for the nonlinearity of coil characteristics, is developed. The control objective is twofold: (i) compensating for the current harmonics and the reactive power absorbed by the nonlinear load; (ii) regulating the inverter DC capacitor voltage. To this end, based on the new model, a nonlinear controller is developed, using the backstepping technical design. It is therefore able to ensure good performances over a wide range of variation of the load current. Moreover, the controller is made adaptive for compensating the uncertainty on the switching loss power. The performances of the proposed adaptive controller are formally analyzed using tools from the Lyapunov stability and the averaging theory. The supremacy of the proposed controller with respect to standard control solutions is illustrated through simulation.     

Nonlinear adaptive filtering using kernel‐based algorithms with dictionary adaptation

Nonlinear adaptive filtering has been extensively studied in the literature, using, for example, Volterra filters or neural networks. Recently, kernel methods have been offering an interesting alternative because they provide a simple extension of linear algorithms to the nonlinear case. The main drawback of online system identification with kernel methods is that the filter complexity increases with time, a limitation resulting from the representer theorem, which states that all past input vectors are required. To overcome this drawback, a particular subset of these input vectors (called dictionary) must be selected to ensure complexity control and good performance. Up to now, all authors considered that, after being introduced into the dictionary, elements stay unchanged even if, because of nonstationarity, they become useless to predict the system output. The objective of this paper is to present an adaptation scheme of dictionary elements, which are considered here as adjustable model parameters, by deriving a gradient‐based method under collinearity constraints. The main interest is to ensure a better tracking performance. To evaluate our approach, dictionary adaptation is introduced into three well‐known kernel‐based adaptive algorithms: kernel recursive least squares, kernel normalized least mean squares, and kernel affine projection. The performance is evaluated on nonlinear adaptive filtering of simulated and real data sets. As confirmed by experiments, our dictionary adaptation scheme allows either complexity reduction or a decrease of the instantaneous quadratic error, or both simultaneously. Copyright © 2015 John Wiley & Sons, Ltd.     

Low-power class-AB 4th-order low-pass filter based on current conveyors with dynamic mismatch compensation of biasing errors

A 4^th-order Butterworth class-AB current-mode low-pass filter is proposed, based on second-generation Current Conveyors (CCII). Class-AB operation allows high-power efficiency and driving large loads with small quiescent currents. The CCII topology uses the class-AB output buffer with error amplifiers: this topology is known to be sensitive to mismatch errors, which cause offsets in the error amplifiers, affecting the biasing current of the stage. This problem is solved via a control loop, which compensates the effect of mismatches. The technique is shown to be effective in Monte Carlo simulations with process variations and mismatches. Simulations have been carried out in 40 nm CMOS technology. The proposed filter achieves good power efficiency, thanks to the class-AB architecture, and good dynamic range, thanks to the closed-loop output buffer. A cut-off frequency of 6 MHz, with 184 μW of total quiescent power consumption, is achieved, with a THD of -55 dB and a SNR of 49 dB.     

Long-term performance analysis of chemical filters in clean rooms based on a prediction model

Chemical filters are the most important devices for removing gas-phase pollutants in clean rooms. However, the testing concentration of chemical filters is too high for reflecting their performance in a real clean room environment. This study tested the adsorption performance of chemical filters in the two most commonly used shapes at different concentrations. Then, the Langmuir equation and Wheeler-Jonas kinetic equation were combined to establish an adsorption performance prediction model of chemical filters under actual conditions. The predicted values of the model were in good agreement with the experimental results, which indicated the high accuracy of the prediction model. The model does not need to test the microscopic parameters of the adsorbent and can maintain high accuracy at low concentrations. A fast method for calculating the service life of chemical filters was also presented. Based on this model, the total cost of using a chemical filter with a high carbon content in microelectronic clean rooms could be decreased by 45% due to decreasing the number of filter replacements over 3 months. So a chemical filter with a high carbon content should be preferred over a filter with low resistance in microelectronic clean rooms.     

陶瓷过滤器反向脉冲清洗控制方法的比较

在高温高压条件下清除煤（烟）气中的微细尘粒,有效的方法是使用刚性陶瓷过滤器。而刚性陶瓷过滤器的在线清洗,唯一可行的方法是反向脉冲喷射冷气体。目前使用时间和压降控制法控制反向脉冲喷射冷气体。本从能量消耗角度出发,并从数学上加以严格论证,得到了时间控制法所消耗的推动功最少这一结论,因此建议使用时间控制法控制在线清洗。