Optimization of Cooling Process of Iron Ore Pellets Based on Mathematical Model and Data Mining

Cooling process of iron ore pellets in a circular cooler has great impacts on the pellet quality and systematic energy exploitation. However, multi-variables and non-visualization of this gray system is unfavorable to efficient production. Thus, the cooling process of iron ore pellets was optimized using mathematical model and data mining techniques. A mathematical model was established and validated by steady-state production data, and the results show that the calculated values coincide very well with the measured values. Based on the proposed model, effects of important process parameters on gas-pellet temperature profiles within the circular cooler were analyzed to better understand the entire cooling process. Two data mining techniques—Association Rules Induction and Clustering were also applied on the steady-state production data to obtain expertise operating rules and optimized targets. Finally, an optimized control strategy for the circular cooler was proposed and an operation guidance system was developed. The system could realize the visualization of thermal process at steady state and provide operation guidance to optimize the circular cooler.

Intelligent Control of Grate-kiln-cooler Process of Iron Ore Pellets Using a Combination of Expert System Approach and Takagi-Sugeno Fuzzy Model

Grate-kiln-cooler has become a major process of producing iron ore pellets in China. Due to the diversity of the raw materials used and the multi-device multi-variable characteristics,this process still encounters with control problem. An attempt was proposed to deal with this issue. The three-device-integrated feature of the process was firstly analyzed to obtain control strategy,and then an intelligent control system using a combination of expert system approach and Takagi-Sugeno（ T-S） fuzzy model was developed. Expert system approach was used to diagnose and remedy the abnormal conditions,while T-S fuzzy model was used to stabilize the thermal state. In the construction of T-S fuzzy rules,antecedents were identified by fuzzy c-mean clustering algorithm incorporated with subtractive clustering algorithm,and consequent parameters were identified by recursive least square algorithm. The control system was applied in a Chinese pelletizing plant and the application results demonstrated its effectiveness of stabilizing the thermal states within three devices.

Modeling iron release from cast iron pipes in an urban water distribution system caused by source water switch

Significant iron release from cast iron pipes in water distribution systems(WDSs),which usually occurs during the source water switch period,is a great concern of water utilities because of the potential occurrence of"red water"and customer complaints.This study developed a new method which combined in-situ water stagnation experiments with mathematical models and numerical simulations to predict the iron release caused by source water switch.In-situ water stagnation experiments were conducted to determine the total iron accumulation in nine cast iron pipes in-service in Beijing when switching the local water to tre ated Danjiangkou Reservior water.Results showe d that the difference in the concentration increment of total iron in 24 hr(ΔCITI,24),i.e.short-term iron release,caused by source water switch was mainly dependent on the difference in the key quality parameters(pH,hardness,nitrate,Larson Ratio and dissolved oxygen(DO))between the two source waters.The iron release rate(RFe)after switch,i.e.long-term iron release,was closely related to the pipe properties as well as the DO and total residual chlorine(TRC)concentrations.Mathematical models ofΔCITI,24 and RFe were developed to quantitatively reveal the relationship between iron release and the key quality parameters.The RFe model could successfully combine with EPANET-MSX,a numerical simulator of water quality for WDSs to extend the iron release modeling from pipe level to network level.The new method is applicable to predicting iron release during source water switch,thus facilitating water utilities to take preventive actions to avoid"red water".

Temperature Field Simulation Model for Rotary Kiln of Iron Ore Oxidized Pellet

Based on conduction,convection and radiation heat transfer among the flue gas,kiln wall,and the pellet bed material,and on the basis of the coal combustion and analysis of reaction heat of pellet induration in the rotary kiln,the temperature field model of rotary kiln was established.Using visual studio net,matlab and open source computer vision library as development tools,combining with the ActiveX data objects database technology,the temperature field simulation system for rotary kiln of iron ore oxidized pellet production was developed.Temperature distribution of pellet and flue gas in rotary kiln was dynamically displayed.

Model of Iron Ore Sintering Based on Melt and Mineral Formation

A model of iron ore sintering was built with consideration of fuel combustion, catalysis of sinter mixture as well as formation of melt and mineral, which was verified via sintering pot tests and showed a good fit to the experi- mental results. The effect of bed depth on temperature was reflected by the residence time in high-temperature zone, rather than the top value of the temperature, which was weakened by melt formation as well as hematite decomposi- tion. Moreover, the effect of bed depth, fuel content and distribution on sintering process was different, which was reflected by temperature profiles and the rule of calcium ferrite formation. The formation of melt as well as magnetite was a process which was decided by kinetic factors, while the formation of calcium ferrite was related to fuel blend- ing conditions, which is determined by thermodynamics when the fuel ratio inside sinter granules is low or fuel con- tent is high, otherwise, it is determined by kinetics.

Kinetics of nitrobenzene degradation coupled to indigenous microorganism dissimilatory iron reduction stimulated by emulsified vegetable oil

Widespread contamination by nitrobenzene（NB） in sediments and groundwater requires better understanding of the biogeochemical removal process of the pollutant. NB degradation, coupled with dissimilatory iron reduction, is one of the most efficient pollutant removal methods. However, research on NB degradation coupled to indigenous microorganism dissimilatory iron reduction stimulated by electron donors is still experimental. A model for remediation in an actual polluted site does not currently exist.Therefore, in this study, the dynamics was derived from the Michaelis–Menten model（when the mass ratio of emulsified vegetable oil and NB reached the critical value 91：1）. The effect of SO4^（2-）, NO3^-, Ca^（2＋）/Mg^（2＋）, and the grain size of aquifer media on the dynamics were studied, and the NB degradation dynamic model was then modified based on the most significant factors. Utilizing the model, the remediation time could be calculated in a contaminated site.