Effect of Sulfate Ion Concentration on the Oxidation Kinetics for the Ceric Sulfate-Ethylene Glycol System

The paper introduces the experiments of sulfate attack on concrete under stress state. The concrete (MSFAC) was made with machine-made sand and fly ash. Before immersed in sodium solution with sulfate-ion concentration of 50000mg/L, the concrete beams with 100mm×100mm ×400mm dimension were in flexural tensile stress at levels of 20%, 40% and 60% of flexural tensile strength by the special designed stress devices. The sulfate-ion content in different erosion depths of concrete was measured at attack age of 30, 90, 180, 270, 360, 540 and 720 days by the chemical titration method. The results show that the apparent damage such as rough surface and cement past peeling off became more serious with the increasing tensile stress level and the prolongation of attack age, while much more obvious crystallization of gypsum was in surface cracks, the sulfate-ion content in the first layer near surface did not reflect the real status of concrete affected by tensile stress levels. The sulfate-ion contents of inside layers increased basically with the attack age at stress levels lower than 60% of tensile strength of concrete. In conditions of the experiment, the sulfate-ion content reduced when the attack age was longer than 540 days at the stress level of 60%.

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An Optical Fiber Sensor Based on Fluorescence Lifetime for the Determination of Sulfate Ions

Sensors ◽

10.3390/s21030954 ◽

2021 ◽

Vol 21 (3) ◽

pp. 954

Author(s):

Liyun Ding ◽

Panfeng Gong ◽

Bing Xu ◽

Qingjun Ding

Keyword(s):

Optical Fiber ◽

Fluorescence Lifetime ◽

Optical Fiber Sensor ◽

Phase Delay ◽

Ion Concentration ◽

Fiber Sensor ◽

Precipitation Method ◽

Cellulose Acetate Membrane ◽

Sulfate Ion ◽

Sulfate Ions

A new optical fiber sensor based on the fluorescence lifetime was prepared for specific detection of sulfate ion concentration, where 1,1′-(anthracene-9,10-diylbis(methylene))bis(3-(dodecylcarbamoyl)pyridin-1-ium) acted as the sulfate fluorescent probe. The probe was immobilized in a porous cellulose acetate membrane to form the sensitive membrane by the immersion precipitation method, and polyethylene glycol 400 acted as a porogen. The sensing principle was proven, as a sulfate ion could form a complex with the probe through a hydrogen bond, which led to structural changes and fluorescence for the probe. The signals of the fluorescence lifetime data were collected by the lock-in amplifier and converted into the phase delay to realize the detection of sulfate ions. Based on the phase-modulated fluorometry, the relationship between the phase delay of the probe and the sulfate ion concentration was described in the range from 2 to 10 mM. The specificity and response time of this optical fiber sensor were also researched.

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A BP Neural Network Based on GA for Optimizing Energy Consumption of Copper Electrowinning

Mathematical Problems in Engineering ◽

10.1155/2020/1026128 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10

Author(s):

Jing Wu ◽

Yanming Cheng ◽

Cheng Liu ◽

Ilkyoo Lee ◽

Wenlin Huang

Keyword(s):

Neural Network ◽

Energy Consumption ◽

Current Efficiency ◽

Bp Neural Network ◽

Minimum Energy ◽

Copper Ion ◽

Ion Concentration ◽

Sulfate Ion ◽

Minimum Energy Consumption ◽

Copper Electrowinning

In this paper, achieving minimum energy consumption in the copper electrowinning process is taken as the research objective. In the traditional production process, sulfate ion concentration, copper ion concentration, and current density are carried out according to the empirical value, which cannot ensure the energy consumption reaching the optimal level. Therefore, this paper proposes a BP neural network model to optimize energy consumption according to the relationship between current density, sulfate ion concentration, copper ion concentration, electrolytic tank voltage, and current efficiency, and the established BP neural network model is trained by using real data from the enterprise. The simulation results show that there is a definite error between the predicted electrolytic tank voltage and current efficiency and corresponding to predict electrolytic tank voltage and current efficiency measured at the production site. The BP neural network improved by GA is proposed to further improve the prediction accuracy of the BP neural network. Simulation results indicate that the prediction error of electrolytic tank voltage and current efficiency is greatly reduced that meets the accuracy requirements, and then the minimum energy consumption can be calculated. On the premise of guaranteeing the quality of copper electrowinning, the current density, sulfate ion concentration, and copper ion concentration corresponding to the minimum energy consumption accurately predicted by this method can be respectively adjusted in real time, which realizes the optimization of energy consumption in the process of copper electrowinning under the background of low carbon and environmental protection.

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