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The raffinate in the extraction process contains plenty of acid and metallic ions, the dispose of which causes the resource loss and environmental pollution. The present study proposed an innovative way to solve this problem by using raffinate as the leaching agent in the copper leaching process. The effects of significant leaching parameters were investigated and optimized using the central composite design (CCD) of response surface methodology (RSM). A maximum leaching efficiency of 96.74 % can be obtained under the 53.22 Kg/t of initial acidity, 2.54 h of leaching time and 5.95 of liquid/solid ratio. In addition, the comparative features of samples before and after leaching process were analyzed by using the X-ray diffraction (XRD), sieving test, scanning electron microscope (SEM) and adsorption-desorption isotherms. We found that the majority of copper components have been removed from copper ores, causing the reduction of size distributions. Specifically, the content of

The ions activation of quartz is a thorny problem that deteriorates the flotation of metal ore. Polymer inhibitors have exhibited excellent depression performance in the selective flotation of metal-ion activated minerals. In this study, the inhibitory role of sodium alginate (NaAl) in the flotation of Ca2+-activated quartz was thoroughly explored by zeta potential, dynamic light scattering (DLS), Fourier transform infrared (FTIR) analysis, X-ray photoelectron spectroscopy (XPS) analysis and adsorption test. The flotation results suggested that NaAl could inhibit the flotation of Ca2+-activated quartz in sodium oleate (NaOl) system to different degrees, depending on the addition sequence of NaOl and NaAl. More significant depression of Ca2+-activated quartz could be achieved when NaAl was added prior to NaOl. It was found that prominent mutual interaction existed among Ca2+, NaOl and NaAl in the bulk solution, probably forming calcium alginate–calcium oleate crosslink product, which was not affected by the addition sequence of these two reagents. However, the interfacial behaviors of these two reagents on Ca2+-activated quartz surfaces differed under different reagent conditions. The addition of NaAl after NaOl could not only partially desorb the pre-adsorbed NaOl from mineral surfaces, but also decrease the mineral surface hydrophobicity by polymer coating, which therefore deteriorated the flotation of Ca2+-activated quartz. In contrast, when NaAl was added prior to NaOl, the pre-adsorption of NaAl made the further adsorption of NaOl rather difficult, which was likely attributed to the reticulated structure of NaAl and the associated steric hindrance effect. As a result, the flotation of Ca2+-activated quartz was tightly depressed.

The prediction method of the residual service life of power MOSFET is studied in this paper. By analyzing the data collected under the existing thermal overload accelerated aging experiment, after processing the data, the failure threshold was set by using the prediction algorithm based on data drive and model to predict the residual life of power MOSFET. The traditional SVR algorithm requires a lot of parameter selection and only a few parameter convergence. The prediction algorithm model is based on Extended Kalman Filter, the extended Kalman filter is relative to the advantage of support vector machine (SVM) regression is used to predict the variance is small, and can be found in a wide range of required to predict the sample interval and the predicted results are more accurate, the test results verify the feasibility of this method.

Iron ions activation of quartz is a tricky issue for efficiently sorting of target minerals in ferrous minerals flotation. The selection of an effective depressant is one of the most significant approaches to addressing this problem. In this study, the effects of calcium lignosulphonate (CLS), a widely studied novel depressant, on the flotation of Fe3+-activated quartz were investigated. The potential depression role of CLS in separating hematite from Fe3+-activated quartz in a sodium oleate (NaOl) flotation system was further explored. The flotation results indicated that the separation of hematite from quartz could be achieved by the selective depression of quartz in the presence of CLS. The results of zeta potential and spectroscopy analysis showed that CLS could absorb onto the surfaces of both hematite and Fe3+-activated quartz. The presence of CLS hindered the further adsorption of NaOl on quartz, whereas the NaOl adsorption onto hematite surfaces was limitedly affected. Compared with hematite, CLS might be more prone to react with the Fe sites on the surface of Fe3+-activated quartz, resulting in a relatively high final adsorption density on quartz surfaces, which was confirmed by X-ray photoelectron spectroscopy analysis and adsorption tests.

This paper studies the finite/fixed-time projective inner/outer synchronization of coupled memristor-based neural networks. An improved stability theory is proved to link finite-time and fixed-time synchronization only under the same one controller, which is more flexible and practical. Some sufficient conditions are acquired by means of Lyapunov methods, inequality techniques and designing appropriate controller to ensure finite/fixed-time projective inner/outer synchronization. The setting time of finite-time synchronization is discussed in detail and more accurate results are obtained. Some examples are presented to examine the feasibility and effectiveness of corresponding results.

Based on deep learning, a multi-classification algorithm network is designed for the large amount of data generated in spacecraft test. In the algorithm, the initial offsets and weights of a multi-layer neural network are initialized using an auto-encoder method. The initialized parameters are monitored by the gradient descent method to make the dimension data more separable. Many shortcomings of traditional algorithms can be effectively overcome using this algorithm. For example, the storage space can be reduced and the calculation time can be saved when the data is large or complex. Expert knowledge of the spacecraft health management platform can be provided through the study of measured data. Experimental data shows that the depth learning algorithm which is based on SAE has higher accuracy in spacecraft multi-class signal testing.

On the background of Lunar unmanned sampling and Mars exploration, the working modes are getting bigger in numbers and classes, and the functions needed to support are also becoming more. Such as the docking mechanisms in the process of Rendezvous and Docking, antenna or camera pointing parts used in Lunar or Mars observation, thermal control caused by space temperature alternation, SSPA or TWTA operations for the communication between earth and lunar. It has been proved that the operating characteristics for electromagnetic and mechanical moving parts show different kinds of figures in data value sets. Base on this, using multi-classsification and distance computation, the result of quantitative distance calculation of different data sets got in different life period for the same working mode, can be used as the proof to demonstrate the health status for a specific mode.

The circumlunar free return and reentry flight vehicle of the 3rd phase of China lunar exploration program is the first mission of jump reentry out of the earth in China, which has the attribute of high-integration and new-flight process. This paper presents a three-step method, which is a full-scale means and ways of full-mission flight validation. Firstly, the full-flight process is divided into some mission profiles according to their characters. Secondly, the case scenarios are identified against the profiles. Lastly, the test cases collection is achieved. This method has been put into practice and has good effect.

The spacecraft electrical signal characteristic data exist a large amount of data, high dimension features, computational complexity degree and low rate of identification problems. This paper proposes the feature extraction method based on wavelet de-noising and the classification method based on random forest (RF) algorithm. Considering the time complexity, the method of wavelet de-noising is used to compress the data and reduce the dimension and then applied to classification. The random forest algorithm has superior performance in dealing with the large amount of data. The experimental results show that compared with other algorithms, the proposed method shows excellent performance in accuracy, computational efficiency, stability in dealing with spacecraft electrical signal data.

With the increasing demand of space information in our country, domestic aircraft is highly emphasized on the autonomy of mission planning, such as unmanned sampling of deep space exploration (space robot) and unmanned autonomous inspection technology, which has a high degree of intelligent autonomous control. Therefore, it is urgent to carry out the health diagnosis of the dynamic process of the spacecraft to provide the correct quantitative support with intelligent means.

In spacecraft electrical signal characteristic data, there exists a large amount of data with high-dimensional features, a high computational complexity degree, and a low rate of identification problems, which causes great difficulty in fault diagnosis of spacecraft electronic load systems. This paper proposes a feature extraction method that is based on deep belief networks (DBN) and a classification method that is based on the random forest (RF) algorithm; The proposed algorithm mainly employs a multi-layer neural network to reduce the dimension of the original data, and then, classification is applied. Firstly, we use the method of wavelet denoising, which was used to pre-process the data. Secondly, the deep belief network is used to reduce the feature dimension and improve the rate of classification for the electrical characteristics data. Finally, we used the random forest algorithm to classify the data and comparing it with other algorithms. The experimental results show that compared with other algorithms, the proposed method shows excellent performance in terms of accuracy, computational efficiency, and stability in addressing spacecraft electrical signal data.

This paper proposes a novel multi-label classification method for resolving the spacecraft electrical characteristics problems which involve many unlabeled test data processing, high-dimensional features, long computing time and identification of slow rate. Firstly, both the fuzzy c-means (FCM) offline clustering and the principal component feature extraction algorithms are applied for the feature selection process. Secondly, the approximate weighted proximal support vector machine (WPSVM) online classification algorithms is used to reduce the feature dimension and further improve the rate of recognition for electrical characteristics spacecraft. Finally, the data capture contribution method by using thresholds is proposed to guarantee the validity and consistency of the data selection. The experimental results indicate that the method proposed can obtain better data features of the spacecraft electrical characteristics, improve the accuracy of identification and shorten the computing time effectively.

As most electronic system structure is complex and uncertain, this paper presents a new efficiency method for spacecraft electrical characteristics identification. PCA (Principal Component Analysis) feature extraction, offline FCM (Fuzzy C-means) clustering and online SVM (Support Vector Machine) classifier is introduced into the registration model. At first step of the algorithm, get an expert training set by FCM clustering method, then using PCA feature extraction to get principal component. After get expert training set and using PCA feature extraction for SVM classifier make this method fast and effective. These are the foundation of online spacecraft electrical characteristics identification. A series of spacecraft electrical characteristics data demonstrate that the proposed method is more accuracy than the traditional way.