Transformation behavior of ferrous sulfate during hematite precipitation for iron removal

The transformation behavior of ferrous sulfate was examined during hematite precipitation for iron removal in hydrometallurgical zinc.Specifically,the effects of the method used for oxygen supply(pre-crystallization or pre-oxidation of ferrous sulfate)and temperature(170–190℃)on the redissolution and oxidation–hydrolysis of ferrous sulfate were studied.The precipitation characteristics and phase characterization of the hematite product were investigated.The results showed that the solubility of ferrous sulfate was considerably lower at elevated temperatures.The dissolution behavior of ferrous sulfate crystals was influenced by both the concentrations of free acid and zinc sulfate and the oxydrolysis of ferrous ions.Rapid oxydrolysis of ferrous ions may serve as the dissolution driving force.Hematite precipitation proceeded via the following sequential steps:crystallization,redissolution,oxidation,and precipitation of ferrous sulfate.The dissolution of ferrous sulfate was slow,which helped to maintain a low supersaturation environment,thereby affording the production of high-grade hematite.

Modeling of goethite iron precipitation process based on time-delay fuzzy gray cognitive network

The goethite iron precipitation process consists of several continuous reactors and involves a series of complex chemical reactions,such as oxidation reaction,hydrolysis reaction and neutralization reaction.It is hard to accurately establish a mathematical model of the process featured by strong nonlinearity,uncertainty and time-delay.A modeling method based on time-delay fuzzy gray cognitive network(T-FGCN)for the goethite iron precipitation process was proposed in this paper.On the basis of the process mechanism,experts’practical experience and historical data,the T-FGCN model of the goethite iron precipitation system was established and the weights were studied by using the nonlinear hebbian learning(NHL)algorithm with terminal constraints.By analyzing the system in uncertain environment of varying degrees,in the environment of high uncertainty,the T-FGCN can accurately simulate industrial systems with large time-delay and uncertainty and the simulated system can converge to steady state with zero gray scale or a small one.

铁成分对硫化锌精矿的半导体性质及化学反应性的影响

硫化锌精矿(以下简称精矿)除含有主要元素锌与硫外,还含有其它元素,尤其是含有相当多的铁。在湿法处理精矿时,其铁成分的行为取决于铁的矿物存在形式。精矿中的铁可以铁闪锌矿[(Zn,Fe)S)、黄铁矿、黄铜矿的形式存在。铁闪锌矿的铁含量可在很大的范围内变化,其物理化学性质也随铁含量而变化,因此对精矿的湿法直接处理产生很大的影响。本文研究出一种可靠的、确定精矿矿物组成的化学分析方法,着重于测定铁在精矿各矿物中的分布,及铁闪锌矿铁含量的变化规律。在此基础上,实验研究了精矿中的铁(尤其是铁闪锌矿中的铁)对精矿的半导体性质的影响规律,应用半导体电化学原理讨论了精矿铁含量对精矿的直接浸出(常压、硫酸介质)的影响机理。

Asynchronous Fuzzy Cognitive Networks Modeling and Control for Goethite Iron Precipitation Process

Goethite iron precipitation process is a key step in direct leaching process of zinc,whose aim is to remove ferrous ions from zinc sulphate solution.The process consists of several cascade reactors,and each of them contains complex chemical reactions featured by strong nonlinearity and large time delay.Therefore,it is hard to build up an accurate mathematical model to describe the dynamic changes in the process.In this paper,by studying the mechanism of these reactions and combining historical data and expert experience,the modeling method called asynchronous fuzzy cognitive networks(AFCN)is proposed to solve the various time delay problem.Moreover,the corresponding AFCN model for goethite iron precipitation process is established.To control the process according to fuzzy rules,the nonlinear Hebbian learning algorithm(NHL)terminal constraints is firstly adopted for weights learning.Then the model parameters of equilibrium intervals corresponding to different operating conditions can be calculated.Finally,the matrix meeting the expected value and the weight value of steady states is stored into fuzzy rules as prior knowledge.The simulation shows that the AFCN model for goethite iron precipitation process could precisely describe the dynamic changes in the system,and verifies the superiority of control method based on fuzzy rules.

Collaborative R＆D between multicrystalline silicon ingots and battery efficiency improvement--effect of shadow area in multicrystalline silicon ingots on cell efficiency

We characterized strip-like shadows in cast multicrystalline silicon(mc-Si) ingots. Blocks and wafers were analyzed using scanning infrared microscopy, photoluminescence spectroscopy, laser scanning confocal microscopy, field-emission scanning electron microscopy, X-ray energy-dispersive spectrometry, and microwave photoconductivity decay technique. The effect on solar cell performance is discussed. The results show that the non-microcrystalline shadow region in Si ingots consists of precipitates of Fe, O, and C. The size of these Fe–O–C precipitates found at the shadow region is25 μm. Fe–O–C impurities can slightly reduce the minority carrier lifetime of the wafers while severely decrease in shunt resistance, leading to the increase in reverse current of the solar cells and degradation in cell efficiency.