Effect of Ore Pulp Density and pH Value on Silica in Iron Ore

Abstract

Froth Flotation is viewed as the most broadly utilised strategy for mineral beneficiation. In metal beneficiation, buoyancy is a procedure in which essential minerals are isolated from useless material or other significant minerals by inciting them to accumulate in and on the outside of a foam layer. Sulfide and non-sulfide minerals just as local metals are recouped by foam buoyancy.

This procedure depends on the capacity of specific synthetic substances to change the surface properties of the mineral(s). Different synthetic substances are utilised to produce the foam, and still, others are utilised to change the pH. Certain synthetics are even equipped for discouraging the buoyancy of minerals that are either to be recuperated sometime in the not too distant future or not to be recouped. In this experiment, we mainly focus on the silica content as it becomes a slug during steel production, and we want to eliminate or reduce its content in the iron ore.

Since froth flotation is a good example of an engineering “system”, the various important parameters are highly inter-related. It is therefore important to take all of the factors into account in froth flotation operations. Changes in the settings of one factor (such as feed rate) will automatically cause or demand changes in other parts of the system (such as flotation rate, particle size recovery, air flow, pulp density, etc.) As a result, it is challenging to study the effects of any single factor in isolation, and compensation effects within the system can keep process changes from producing the expected effects (Klimpel, 1995). This makes it difficult to develop predictive models for froth flotation, although work is being done to develop simple models that can predict the performance of the circuit from easily-measurable parameters such as solids recovery and tailings solid content (Rao et al., 1995).

In the experiment, the ore pulp pH and density are manipulated by three levels, and the effect on the silica concentrate that is obtained in the slurry is studied and analysed in the design of the experiment.

Problem Statement

Iron ore flotation is an efficient method to remove impurities from iron ore. Some amount of silicon dioxide is found in the ore even after separation from the magnetic and hydraulic mechanism. The most widely used method of flotation is the reverse cationic flotation route. The low-grade iron ores contain high grades of silica and alumina. They are detrimental for the production of steel as they form high viscous slag in blast furnaces resulting in increased manufacturing necessities. Hence it needs to be removed before the ore is utilised for other purposes.

The main goal of this experiment is to know if the ore pulp density and ore pulp pH value can affect the amount of silica that is present in the iron ore so that it could help the engineers involved in the flotation process. The final ore pulp quality is dependent upon the concentration of silica as the higher the silica concentration is, the more impure the final ore is. It also helps in eliminating errors and taking advanced actions to purify the ore before it is sent to manufacturing units.

Froth flotation is an exceptionally flexible technique for physically isolating particles dependent on contrasts in the capacity of air rises to specifically hold fast to explicit mineral surfaces in a mineral/water slurry. The particles with appended air bubbles are then conveyed to the surface and expelled, while the particles that remain totally wetted remain in the fluid stage. Froth flotation can be adjusted to an expansive scope of mineral detachments, as it is conceivable to utilise compound medicines to specifically change mineral surfaces with the goal that they have the vital properties for the partition. It is as of now being used for some assorted applications, with a couple of models being: isolating sulfide minerals from silica gangue (and from other sulfide minerals); isolating potassium chloride (sylvite) from sodium chloride (halite); isolating coal from fiery remains shaping minerals; expelling silicate minerals from iron metals; isolating phosphate minerals from silicates; and even non-mineral applications, for example, de-inking reused newsprint. It is especially valuable for handling fine-grained metals that are not agreeable to customary gravity fixation.

An engineer is carrying out an experiment to test the silica concentrate in an iron ore so that it is easy to eliminate the silica content before the batch is sent for steel processing. An engineer can understand the content and reduce it as per the market needs. The flotation system contains many interrelated components and a significant variation in one aspect of the component can result in affecting other areas as well. From the operation components, ore pulp density is varied at 171,172 and 174 kg/m^3 respectively and repeated two times for the same level at the same time on different days. However, from the chemical component section pH is measured by three levels that are 100,101 and 102. The response for this experiment is noted down and is given in the table below.

The engineer would like to know if the Iron ore pulp density and pH affect the silica concentrate that is found in the response. This would help the engineer characterise and optimise his experiment.

Experimental Plan

During the process of froth flotation four things happen:

• In order to achieve hydrophobic surface particles, reagent conditioning takes place
• Collection and upward transport by bubbles
• Froth is formed on the flotation cell
• There is a separation of the mineral part from the rest

The flotation process has three stages [1]

1. Roughing
2. Cleaning
3. Scavenging 

There is a huge mixing tank in which bubbles are formed and the bubbles travel down the column whereas the slurry is on the top. This machine works on the principle of a high shear rate that creates turbulent conditions. Conducive conditions should be created so that while mixing the components they attach to the bubble and come out as slurry on top of the column for retrieving the minerals.

After the iron ore is fed to the froth flotation system and the iron ore pulp is obtained. This pulp is analysed with the sample size of the pulp being 171kg/m^3 density and the chemical component of the ore being manipulated to 100 PH. The silica concentrate that is obtained from the slurry is measured and noted down. With the same factors being applied a second reading is taken and noted down. Similarly, the density and PH is varied, and the readings are noted down for densities 172,173kg/m^3 and 101,102pH respectively. It is done randomly with different factors taken into consideration and repeated twice for accuracy.

Experimental Analysis

Data Visualisation

    Histogram for ore pulp pH (Left)                                    Histogram for ore pulp density (Right)

Histograms were made for both factors, ore pulp density and ore pulp pH. The histograms for pH and density do not appear to be normally distributed and are approximately skewed right distribution. However, there is insufficient evidence to make any conclusion.

From the dotplots for both factors, it can be observed that most of the data are on the left-hand side between 1.26 and 1.44. The dotplots still cannot provide enough information for us to make a conclusion.

There are no outliers in both pH and density boxplots

-          The figure for pH boxplot shows that medians are quite different.

-          The median for pH 102 is higher than the other two levels.

-          In the density boxplot, the medians are very similar.

                           Line Plot of Mean (Silica)

Line plot of mean indicates that there is an interaction between the two factors. Moreover, low pH and density can produce a lower mean of the concentration of silica.

Factorial Design

1. Building a statistical model for the experiment

y_ijk= μ+τ_i+β_j+ (τβ)_ij+ε_ijk

2. State the hypothesis

H_o: τ₁= τ₂= τ₃

H_a: at least one of the τ_i does not equate to zero

H_o:β₁=β₂=β₃

H_a: at least one of the β_j is not equal to zero

3. State the underlying assumptions

-          The observations of silica concentration are normally distributed

-          The silica concentration observations are identical-independent-distributed

4. State the test statistics and show calculations

-          Using Two-Factor Factorial Design

The ANOVA Table

Using the ANOVA table, we can see that p-value for ore pulp pH and density are more than α = 0.05. As these two factors are both outside the rejection region, we can conclude that different levels of pH and density do not affect the concentration of silica significantly. Ore pulp pH is slightly more important than ore pulp density. Furthermore, the high p-value for interaction indicates that the interaction between pH and density is not important.

The main effect plot for ore pulp pH and density is shown below:

The high concentration of silica suggests that there is a greater percentage of impurity. As it can be observed from the main effect plot, setting pH at 100 and density at 172 can generate a lower concentration of silica (less impure)

Main Effects Plot

The plot below demonstrates the interaction between ore pulp pH and density for the concentration of silica

From the interaction plot, we can see that using pH 100 can produce the lowest amount of impurity compared to other levels. At pH 100, ore pulp with 174 in density creates the lowest percentage of silica at the end process.

Interaction Plot

5. Model adequacy check for two-factor factorial design

Four-in-One Plot

Assessing the adequacy

-          In the normal probability plot, the dots lie around the line, meaning that the error is normally distributed.

-          The histogram illustrates that the error has zero mean

-          The error variance is not constant as the dots are not spread out evenly in scatter plot.

-          The error variance is independent.

To ensure that the error has zero mean.

-          As the descriptive statistics are shown below, the error has zero mean

Scatterplot of residuals vs pH (left)                                      Scatter plot of residual vs Density(Right)

      Scatterplot of residuals vs pH and density

-          The scatterplot of residual vs pH illustrates that the vertical dot distribution of each level is fairly even although the distribution is considerably different between each level.

-          In the scatterplot of residual and density, it is glaring to see that the dots spread out rather equally in vertical lines even though there are some variations at 171 and 172. 

-          Only the group of pH 101 and 174 have constant error variance. The other combinations do not generate constant residual variances.

-          It is fairly to say that the ANOVA result is valid according to the scatterplots above.

Discussion and Conclusion

The main purpose of the experiment is to reduce the concentration of silica so the final ore pulp quality can be improved.

Based on the analysis we have conducted, it can be concluded that both pH and density do not have a significant influence on the concentration of silica. However, ore pulp pH is more critical compared to ore pulp density as the p-value of pH is lower than that of density.

While it is clear that using pH 100 with 172 density can produce a lower concentration of silica by only looking at the main effect plot, the conclusion would differ when we take the interaction plot into account. As can be observed from the interaction plot, the best pH to be implemented remains at 100. However, the density has shifted from 172 to 174. The outcome is due to the result of averaging on the main effect.

In conclusion, although the ore pH and ore density do not have substantial influences on the silica concentration, the ore pulp with pH 100 and density at 174 should be put into effect in order to bring out purer final ore pulp in the end.

The advantage of the experiment is that ore pulp pH and density are the primary treatment of the flotation. These two factors are the main culprit of impurity of final ore products. By studying what levels of pH and density should be used in the flotation process can greatly reduce the waste of the resources.

On the other hand, the downside of the experiment is that the dataset is not ideal. As many factors are affecting the process, only the two main factors were being analysed. In this case, there is insufficient evidence to say that pH and density have an impact on silica concentration. More factors should be studied to make a stronger conclusion.

As to the assessment of the experiment, there are a few things that can be improved. First of all, the oxidation rate of ore can affect the flotation process. Different batches of ore have different oxidation rates. In this experiment, different batches of ore were put into the process before flotation and were not separated. This means that the batch may possibly be a variable and is not analysed. Moreover, the degree of oxidation also has an impact on the pH of the slurry. All of the above causes can lead to an incorrect conclusion for the experiment.

Secondly, although the main factors of the experiment are pH value and density, other properties during the flotation process such as ore pulp flow, amina flow, starch flow can also be an influence. Regarding the ore pulp density, when the ore pulp is thin, the recovery rate would be low, resulting in the better quality of the pulp

Lastly, the flotation machine that is used, machine production capacity and flotation time, operators are all the possible factors that should be considered. Ideally, the above factors are held constant, but it could be difficult to control all the factors in practice. As flotation is a very complex process, the experiment should include more variables and use more levels in order to get a better result.

References

• Chen, Xumeng & Peng, Yongjun. (2018). Managing clay minerals in froth flotation—A critical review. Mineral Processing and Extractive Metallurgy Review. 39. 1-19. 10.1080/08827508.2018.1433175.
• Chevron Phillips Chemical Company, C. (2019). Introduction to Mineral Processing. [online] Cpchem.com.
• EduardoMagalhãesOliveira (2017) Quality Prediction in a Mining Process. Available from: https://www.kaggle.com/edumagalhaes/quality-prediction-in-a-mining-process/metadata [Accessed from 16 ‎April ‎2019]
• Klimpel, R. R. (1995), “The Influence of Frother Structure on Industrial Coal Flotation”, High Efficiency Coal Preparation (Kawatra, ed.), Society for Mining, Metallurgy, and Exploration, Littleton, CO, pp. 141-151
• Mineral Processing & Metallurgy. (2019). Froth Flotation Process.
• Martins, P.F.F., Morais, C.A., Lameiras, F.S. and Albuquerque, R.O. (2017) Silica and Iron Recovery from a Residue of Iron Ore Flotation. Journal of Minerals and Materials Characterization and Engineering, 5, 153-160
• Rao, T. C., Govindarajan, B., and Barnwal, J. P. (1995), “A Simple Model for Industrial Coal Flotation Operation”, High-Efficiency Coal Preparation (Kawatra, ed.), Society for Mining, Metallurgy, and Exploration, Littleton, CO, pp. 177-185
• Shandong Xinhai Mining Technology & Equipment Inc. (2019). The flotation process is not effective? Have you considered these 8 factors? Available from: https://kknews.cc/news/ey8gvbn.html [Accessed from 16 ‎April ‎2019]

Contribution Statement

Regarding the abstract and problem statement sections, my main task is to find useful datasets, validate the datasets we found and figure out how it is relevant to the experiment. The task includes doing a simple data analysis to see if the dataset is ideal for the experiment. Also, I would need to go through some relevant papers to understand the background and the purpose of the experiment and put together the resources that are related to the problem statement.

In the part of the experimental plan, my group and I worked together to gather and make the plan for the experiment and make sure that the data we used is valid. Because we needed at least two replications, we selected the data at the same time on two different days from the dataset.

For the experimental analysis, I first put the data into Minitab to create different plots so that we could do the data visualisation. Next, I needed to evaluate what kind of test should be used to analyse the data. This was then followed by the test statistics where I tested out the data and the interaction between the two factors. Finally, I also conducted the model adequacy check on Minitab to know whether or not the test is valid.

For discussion and conclusion part, I made appropriate conclusions based on the test result and ensure the result is supported by the analysis we did. My group along with myself had a meetup to discuss what can be improved in this experiment. After this, I combined the discussion and conclusion together.

All in all, the report was divided by a few parts. After every part was done, I combined all the documents together and changed them into the same format so the report can be presented in a consistent manner.

My contribution to this assignment was verifying the data and searching the relevance of the data to the subject we are studying. Many trials and error experimental plans were made so that we could choose the ideal one.

Another task in hand was to also verify the experimental plan of the whole data set and to correlate it with the concepts that have been taught to us. In addition, it was my responsibility to do research on the experiment that was done, in order to testify whether the problem statement is of high value.

I needed to know about the topic that is being dealt with and cite relevant sources so that it can be further evidence to the fact that it is of high importance for engineers. Moreover, I needed to take the vast data set that was obtained as a result of the experiment and freeze it into a more relevant table that could be comprehended easily.

This data set was then included in the problem statement as a part of the assignment. I also sat with my group mate and analysed the data visualisation and the data analysis section so that we could agree on the conclusion and discussions.