实现拜尔法叶滤过程的自动化控制

简要介绍了当今世界氧化铝生产自动控制过程的特点及中铝山西分公司拜尔法叶滤过程自动控制的现状,系统阐述了利用集散控制系统(DCS)对叶滤工序实施自动化改造的途径和手段,并对其实施效果进行了总结和评价。

优化拜耳法沉降流程降低碱耗提高氧化铝回收率

拜耳法沉降工序任务分离出铝酸钠溶液及洗涤赤泥中的氧化钠,尽可能的最大回收赤泥附着碱,降低碱耗,同时在洗涤过程中减少氧化铝水解造成氧化铝损失。本论文主要从三个方面进行论述:①从叶滤滤饼浆液回收氧化铝及碱,减少进入洗涤系统的氧化铝及碱,提高洗涤效率;②氢氧化铝洗液进稀释槽,降低进洗涤系统的碱,利用溶出浆液的高温溶化氢氧化铝洗液中氢氧化铝细颗粒,达到回收洗液中细颗粒氢氧化铝的目的 ;③减少对一次洗液浓度的影响因素,从而稳定热水加入量,提高洗涤效率。

Theoretical analysis of adaptive harmonic window and its application in frequency extraction of vibration signal

The goal of this paper is to find an excellent adaptive window function for extracting the weak vibration signal and high frequency vibration signal under strong noise.The relationship between windowing transform andfiltering is analyzed first in the paper.The advantage of adjustable time-frequency window of wavelet transform is introduced.Secondly the relationship between harmonic wavelet and multiple analytic band-pass filter is analyzed.The coherence of the multiple analytic band-pass filter and harmonic wavelet base function is discussed,and the characteristic that multiple analytic band-pass filter included in the harmonic wavelet transform is founded.Thirdly,by extending the harmonic wavelet transform,the concept of the adaptive harmonic window and its theoretical equation without decomposition are put forward in this paper.Then comparing with the Hanning window,the good performance of restraining side-lobe leakage possessed by adaptive harmonic window is shown,and the adaptive characteristics of window width changing and analytical center moving of the adaptive harmonic window are presented.Finally,the proposed adaptive harmonic window is applied to weak signal extraction and high frequency orbit extraction of high speed rotor under strong noise,and the satisfactory results are achieved.The application results show that the adaptive harmonic window function can be successfully applied to the actual engineering signal processing.

Hybrid ToA and IMU indoor localization system by various algorithms

In this paper, we integrate inertial navigation system (INS) with wireless sensor network (WSN) to enhance the accuracy of indoor localization. Inertial measurement unit (IMU), the core of the INS, measures the accelerated and angular rotated speed of moving objects. Meanwhile, the ranges from the object to beacons, which are sensor nodes with known coordinates, are collected by time of arrival (ToA) approach. These messages are simultaneously collected and transmitted to the terminal. At the terminal, we set up the state transition models and observation models. According to them, several recursive Bayesian algorithms are applied to producing position estimations. As shown in the experiments, all of three algorithms do not require constant moving speed and perform better than standalone ToA system or standalone IMU system. And within them, two algorithms can be applied for the tracking on any path which is not restricted by the requirement that the trajectory between the positions at two consecutive time steps is a straight line.

A novel sparse filtering approach based on time-frequency feature extraction and softmax regression for intelligent fault diagnosis under different speeds

Modern agricultural mechanization has put forward higher requirements for the intelligent defect diagnosis.However,the fault features are usually learned and classified under all speeds without considering the effects of speed fluctuation.To overcome this deficiency,a novel intelligent defect detection framework based on time-frequency transformation is presented in this work.In the framework,the samples under one speed are employed for training sparse filtering model,and the remaining samples under different speeds are adopted for testing the effectiveness.Our proposed approach contains two stages:1)the time-frequency domain signals are acquired from the mechanical raw vibration data by the short time Fourier transform algorithm,and then the defect features are extracted from time-frequency domain signals by sparse filtering algorithm;2)different defect types are classified by the softmax regression using the defect features.The proposed approach can be employed to mine available fault characteristics adaptively and is an effective intelligent method for fault detection of agricultural equipment.The fault detection performances confirm that our approach not only owns strong ability for fault classification under different speeds,but also obtains higher identification accuracy than the other methods.

THE FILTRATION OF DILUTE MICROPARTICLE SUSPENSION WITH ROTARY VANE FILTER PRESS

The experimental results of processing the solutions with trace suspended micro particles by a dynamic rotary vane filter press at production site are presented in this paper. Furthermore the effects of the conditions in the productive operation and the method of processing are summarized.

2- DFourier Transform Method for Optical Measurement of 3 - DSurface

A method of 2-D Fourier transform used in 3-D surface profilometry is proposed,analyzed and compared with 1-D Fourier transform method in theory and practical measuring result.It was proved that the 2-D Fourier transform method has more advantages over 1-D Fourier transform method in biggest crook-rate limits,accuracy and sensitivity of measuring.Study on measuring object surface details with large crook-rate changing accurately used new higher-power index low-pass filter of spatial frequency domain.A new method of automatic produced reference grating image and error-correcting is proposed.One undeform row of deform grating image is used to extend a complete reference grating image,and some error-correcting method is used to process the result to get accurate surface shape and the deflection of reference surface normal line deviated from the axle of camera.By this new method,one deform rectangle grating image is only used to get the 3-D shape accurately.

ixed Cadzow filtering method in fractional Fourier domain

Conventional frequency domain method used in random noise attenuation singular value decomposition （SVD） filtering processing causes bending event damage. To mitigate this problem, we present a mixed Cadzow filtering method based on fractional Fourier transform to suppress random noise in 3D seismic data. First, the seismic data is transformed to the time-frequency plane via the fractional Fourier transform. Second, based on the Eigenimage filtering method and Cadzow filtering method, the mixed high-dimensional Hankel matrix is built; then, SVD is performed. Finally, random noise is eliminated effectively by reducing the rank of the matrix. The theoretical model and real applications of the mixed filtering method in a region of Sichuan show that our method can not only suppress noise effectively but also preserve the frequency and phase of effective signals quite well and significantly improve the signal-to-noise ratio of 3D post-stack seismic data.

Translation-invariant wavelet denoising of full-tensor gravity-gradiometer data

Denoising of full-tensor gravity-gradiometer data involves detailed information from field sources, especially the data mixed with high-frequency random noise. We present a denoising method based on the translation-invariant wavelet with mixed thresholding and adaptive threshold to remove the random noise and retain the data details. The novel mixed thresholding approach is devised to filter the random noise based on the energy distribution of the wavelet coefficients corresponding to the signal and random noise. The translation- invariant wavelet suppresses pseudo-Gibbs phenomena, and the mixed thresholding better separates the wavelet coefficients than traditional thresholding. Adaptive Bayesian threshold is used to process the wavelet coefficients according to the specific characteristics of the wavelet coefficients at each decomposition scale. A two-dimensional discrete wavelet transform is used to denoise gridded data for better computational efficiency. The results of denoising model and real data suggest that compared with Gaussian regional filter, the proposed method suppresses the white Gaussian noise and preserves the high-frequency information in gravity-gradiometer data. Satisfactory denoising is achieved with the translation-invariant wavelet.

Assimilating the LAI Data to the VEGAS Model Using the Local Ensemble Transform Kalman Filter: An Observing System Simulation Experiment

Information on the spatial and temporal patterns of surface carbon flux is crucial to understanding of source/sink mechanisms and projection of future atmospheric CO2 concentrations and climate. This study presents the construction and implementation of a terrestrial carbon cycle data assimilation system based on a dynamic vegetation and terrestrial carbon model Vegetation-Global-Atmosphere-Soil(VEGAS) with an advanced assimilation algorithm, the local ensemble transform Kalman filter(LETKF, hereafter LETKF-VEGAS). An observing system simulation experiment(OSSE) framework was designed to evaluate the reliability of this system, and numerical experiments conducted by the OSSE using leaf area index(LAI) observations suggest that the LETKF-VEGAS can improve the estimations of leaf carbon pool and LAI significantly, with reduced root mean square errors and increased correlation coefficients with true values, as compared to a control run without assimilation. Furthermore, the LETKF-VEGAS has the potential to provide more accurate estimations of the net primary productivity(NPP) and carbon flux to atmosphere(CFta).

Influence of Na^+,K^+,Mg^(2+),Ca^(2+),and Fe^(3+) on filterability and settleability of drilling sludge

Solid–liquid separation is a vital step in drilling sludge disposal, and the filterability and settleability of drilling sludge are the main evaluating indicators for the separation process. The influence of Na^+,K^+,Mg^(2+),Ca^(2+),and Fe^(3+) on drilling sludge filterability and settleability was investigated in our research. The water content,filtration rate, supernatant volume and supernatant turbidity were measured to evaluate the filterability and settleability of drilling sludge. Meanwhile, the zeta potential, specific surface area of sludge flocs, particle size distribution and Fourier-transformed infrared spectra were employed to clarify the influencing mechanism.The experimental results showed that the filterability and settleability of drilling sludge were related to concentration and types of cations. Mg^(2+),Ca^(2+),and Fe^(3+) performed better than Na^+, K^+, and the cations with smaller hydrated radius got superior solid–liquid separation behavior at same valence. Finally, the spectra indicated that no chemical adsorption occurred between inorganic cations and drilling sludge flocs. The variation of surface charge and flocs growth after adding different inorganic cations were the reasons for the changes of the filterability and settleability.

Research on hybrid power filter of 6 kV power grid in coal mine

Studied the harmonic control of the 6 kV power grid in a coal mine substation.Taking harmonic suppression and reactive power compensation into account, and complyingwith the economic and efficient technical line of the smart grid, a new hybrid activefilter was proposed and applied to the power grid in the coal mine with the advantagessuch as large capacity, low cost and low loss.In order to improve detection speed and reducethe succeeding errors to improve the filtering performance of the active power filter,the DFT (Discrete Fourier Transform) sliding window algorithm based on coordinatetransformation and improved hysteresis control method was proposed.The Matlab simulationresults show that the hybrid active filter is satisfactory, can improve the grid powerfactor and can meet the requirements of improving the power quality in the coal mine.

Approximate Gaussian conjugacy： parametric recursive filtering under nonlinearity, multimodality, uncertainty, and constraint, and beyond

Since the landmark work of R. E. Kalman in the 1960s, considerable efforts have been devoted to time series state space models for a large variety of dynamic estimation problems. In particular, parametric filters that seek analytical estimates based on a closed-form Markov-Bayes recursion, e.g., recursion from a Gaussian or Gaussian mixture （GM） prior to a Gaussian/GM posterior （termed ＇Gaussian conjugacy＇ in this paper）, form the backbone for a general time series filter design. Due to challenges arising from nonlinearity, multimodality （including target maneuver）, intractable uncertainties （such as unknown inputs and/or non-Gaussian noises） and constraints （including circular quantities）, etc., new theories, algorithms, and technologies have been developed continuously to maintain such a conjugacy, or to approximate it as close as possible. They had contributed in large part to the prospective developments of time series parametric filters in the last six decades. In this paper, we review the state of the art in distinctive categories and highlight some insights that may otherwise be easily overlooked. In particular, specific attention is paid to nonlinear systems with an informative observation, multimodal systems including Gaussian mixture posterior and maneuvers, and intractable unknown inputs and constraints, to fill some gaps in existing reviews and surveys. In addition, we provide some new thoughts on alternatives to the first-order Markov transition model and on filter evaluation with regard to computing complexity.

Calculation of the VKI turbine blade with LES and DES

The prediction of the laminar to turbulent transition is essential in the calculation of turbine blades, compressor blades or airfoils of airplanes since a non negligible part of the flow field is laminar or transitional. In this paper we compare the prediction capability of the Detached Eddy Simulation （DES） with the Large Eddy Simulation （LES） using the high-pass filtered （HPF） Smagorinsky model （Stolz et al.[1]） when applied to the calculation of transitional flows on turbine blades. Detailed measurements from Canepa et al.[2] of the well known VKI-turbine blade served to compare our results with the experiments. The calculations have been made on a fraction of the blade （10%） using non-reflective boundary conditions of Freund at the inlet and outlet plane extended to internal flows by Magagnato et al.[3] in combination with the Synthetic Eddy Method （SEM） proposed by Jarrin et al.[4]. The SEM has also been extended by Pritz et al.[5] for compressible flows. It has been repeatedly shown that hybrid approaches can satisfactorily predict flows of engineering relevance. In this work we wanted to investigate if they can also be used successfully in this difficult test case.

MULTISTAGE FILTERS FOR IDENTIFICATION OF EUKARYOTIC PROTEIN CODING REGIONS

A class of multistage filters, namely, real narrowband bandpass filter （RNBPF） has been previously used for identification of protein coding regions. This filter passes the frequency component at 2π/3 along with its conjugate. This conjugate frequency compo- nent may degrade the identification accuracy. To improve the identification accuracy, two types of multistage filters are proposed in this paper. A complex narrowband bandpass filter （CNBPF） is proposed for suppressing the conjugate frequency component which, in turn, reduces the background noise present in the deoxyribonucleic acid （DNA） spec- trum and improves identification accuracy. By cascading RNBPF with moving average filter （RNBPFMA）, another type of multistage filter is proposed. As moving average filter smooth out the rapid variations in the DNA spectrum, RNBPFMA improves the identification accuracy. The computational complexity of RNBPFMA is less than that of CNBPF. The RNBPF and proposed multistage filters are compared with previously reported short-time discrete Fourier transform （ST-DFT） method in terms of compu- tational complexity. It is found that multistage filters reduce the computational load to a greater extent compared to ST-DFT method. The identification accuracy of the proposed CNBPF and RNBPFMA methods is compared with existing anti-notch filter and RNBPF methods. The results show that proposed methods outperform existing methods in terms of identification accuracy for benchmark data sets.