湿法炼锌赤铁矿法沉铁行为

湿法炼锌工艺过程中赤铁矿法沉铁技术是一种沉淀渣量小、分离效率高的锌、铁分离方法。针对湿法炼锌工艺过程中赤铁矿法沉铁技术开展了硫酸亚铁的氧化水解沉淀行为的研究，考察了时间、酸度、温度、硫酸盐浓度、晶种返回量等主要因素对赤铁矿法沉铁效果的影响、沉淀产物的析出特性和物相表征。     

Artificial neural networks as alternative tool for minimizing error predictions in manufacturing ultradeformable nanoliposome formulations

This work was aimed at determining the feasibility of artificial neural networks (ANN) by implementing backpropagation algorithms with default settings to generate better predictive models than multiple linear regression (MLR) analysis. The study was hypothesized on timolol-loaded liposomes. As tutorial data for ANN, causal factors were used, which were fed into the computer program. The number of training cycles has been identified in order to optimize the performance of the ANN. The optimization was performed by minimizing the error between the predicted and real response values in the training step. The results showed that training was stopped at 10?000 training cycles with 80% of the pattern values, because at this point the ANN generalizes better. Minimum validation error was achieved at 12 hidden neurons in a single layer. MLR has great prediction ability, with errors between predicted and real values lower than 1% in some of the parameters evaluated. Thus, the performance of this model was compared to that of the MLR using a factorial design. Optimal formulations were identified by minimizing the distance among measured and theoretical parameters, by estimating the prediction errors. Results indicate that the ANN shows much better predictive ability than the MLR model. These findings demonstrate the increased efficiency of the combination of ANN and design of experiments, compared to the conventional MLR modeling techniques.     

Purification of brines by chemical precipitation and ion‐exchange processes for obtaining battery‐grade lithium compounds

Lithium salts are very important in the production of lithium batteries since they are used as precursors for the fabrication of cathode materials that require very low level of impurities (battery grade). Usually, the lithium extraction process from brine first yields lithium carbonate, which is then used as raw material for the production of other lithium compounds. However, it implies an increase in investment costs, considering more equipment and process stages. To remove the impurities and produce battery‐grade lithium compounds directly from brines, a laboratory‐scale process was developed using the methods of ion exchange and chemical precipitation. Thus, impurity‐free brine ready to be used in an industrial membrane electrolysis process is obtained. Different sequences and operating conditions were investigated for the purification of lithium‐concentrated brines, removing the main impurities of the natural brines: calcium, magnesium, and sulfate. For the characterization of solutions, crystals, and ion‐exchange resins, atomic absorption spectrophotometry, scanning electron microscopy, and X‐ray scattering spectroscopy were used. The results indicate that during the chemical precipitation process, lithium‐concentrated brine reacted with some additives (precipitating agents) at different stages in the batch reactors. Subsequently, the pulp obtained was sedimented and filtered, eliminating or reducing the impurities of the lithium brine. Thus, the most efficient precipitation sequence was evaluated as a function of the removal percentage of the species. The removal efficiencies obtained for Ca⁺², Mg⁺², and SO₄^?2 were of 98.93%, 99.93%, and 97.14%, respectively. Thereafter, the use of the ion‐exchange resins reduced the concentration of Ca⁺² and Mg⁺² to the values below 1 ppm. The combined use of both processes provided promising results that could be applied in the industry.     

Passivity and synchronisation of complex dynamical networks with multiple derivative couplings

Two network models with multiple derivative couplings and different dimensions of output and input vectors are investigated in this paper. The problem of passivity for the proposed network models is analysed by utilising some inequality techniques and Lyapunov functional method, and several synchronisation conditions for complex dynamical networks with multiple derivative couplings (CDNMDC) are given. Moreover, by employing adaptive state feedback control strategy, some sufficient conditions for guaranteeing passivity and synchronisation of CDNMDC are obtained. In the end, we give two examples to verify the effectiveness of the results.     

On monitoring the process mean of autocorrelated observations with measurement errors using the w-of-w runs-rules scheme

Runs-rules have been widely used since the 1950s in industrial and nonindustrial process monitoring applications to improve the performance of basic and other traditional monitoring schemes. However, none of the studies on runs-rules have accounted for a process with a combined effect of measurement errors and autocorrelation. Hence, in this paper, the use of the w-of-w runs-rules to improve the performance of the Shewhart $\overline{X}$ scheme using an additive model with a constant variance and a first-order autoregressive model is proposed. To reduce the combined negative effect of measurement errors and autocorrelation, we implement a sampling strategy based on rational subgroups in which (a) multiple measurements per item are taken (instead of a standard single measurement) and (b) non-neighboring observations are gathered. Moreover, the latter sampling strategy is incorporated into the values of probability elements of a Markov chain matrix which is used to derive some closed-form expressions for the zero- and steady-state run-length distribution. The main finding of this study is that, with respect to some overall performance measures, the proposed scheme outperforms the existing Shewhart $\overline{X}$ scheme by a significant margin. A real-life example is used to illustrate the practical implementation of the proposed scheme.     

Gelation properties of goose liver protein recovered by isoelectric solubilisation/precipitation process

Isoelectric solubilisation/precipitation (ISP) process was applied to goose liver (GL) for protein extraction. The gelation properties of proteins extracted by acid processes (ACP, pH 2.0, 2.5 and 3.0) and alkaline processes (ALP, pH 11.0, 11.5 and 12.0) were estimated, where the unextracted ground GL was set as the control. Nearly 58.39~79.00% of GL proteins were recovered by ISP treatments. High molecular weight (100~250 kDa) proteins were found to be partially hydrolysed by ACP, while few changes in proteins occurred during ALP. As evidenced by rheological and textural measurements, ALP proteins formed gels with high elasticity and superior texture, whereas ACP proteins had inferior gelation properties. Moreover, ALP proteins were able to form a highly interconnected and homogeneous three‐dimensional microstructure. Predominantly, gels produced by 11.0 had optimal texture and the lowest cooking loss (P < 0.05). These results suggested that the ISP process (ALP) is a potential method to improve the economic value of GL.     

Bootstrap procedures for detecting multiple persistence shifts in heteroskedastic time series

This article proposes new bootstrap procedures for detecting multiple persistence shifts in a time series driven by non-stationary volatility. The assumed volatility process can accommodate discrete breaks, smooth transition variation as well as trending volatility. We develop wild bootstrap sup-Wald tests of the null hypothesis that the process is either stationary [I(0)] or has a unit root [I(1)] throughout the sample. We also propose a sequential procedure to estimate the number of persistence breaks based on ordering the regime-specific bootstrap p-values. The asymptotic validity of the advocated procedures is established both under the null of stability and a variety of persistence change alternatives. A comparison with existing tests that assume homoskedasticity illustrates the finite sample improvements offered by our methods. An application to OECD inflation rates highlights the empirical relevance of the proposed approach and weakens the case for persistence change relative to existing procedures.     

碳化钒析出对X80管线钢焊接热影响区韧性的影响

通过Gleeble-3500热模拟机对钒(V)含量分别为0.06%和0.02%的X80管线钢进行了峰值温度为1350℃和750℃的焊接热模拟试验,利用硬度计和光学显微镜(OM)分别分析了焊接热模拟试样硬度和试样中马氏体-奥氏体岛状组织(M-A)的数量、尺寸和形貌。利用透射电镜(TEM)对母材、粗晶区和部分相变区中粒子析出进行了表征。结果表明,与0.02%含V量X80管线钢相比,0.06%含V量的X80管线钢由于V含量较高,在受到多道次焊接热循环影响时,部分相变区有较大量碳化钒(VC)粒子析出,明显阻碍位错运动,使其强度明显提高的同时脆性增大、塑性变形难以进行,导致其焊接热影响区韧性下降。