EFFECT OF ROCK IMPURITIES AS DETERMINED FROM PRODUCTION DATA FINAL REPORT. Hassan El-Shall and Regis Stana Principal Investigators.

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3 EFFECT OF ROCK IMPURITIES AS DETERMINED FROM PRODUCTION DATA FINAL REPORT Hassan El-Shall and Regis Stana Principal Investigators with Mohammed Qavi and Lekkala Navajeevan Department of Materials Science and Engineering Particle Engineering Research Center University of Florida Gainesville, Florida Prepared for Florida Institute for Phosphate Research 1855 West Main Street Bartow, Florida USA Project Manager: G. Michael Lloyd, Jr. FIPR Project Number January 2004

4 DISCLAIMER The contents of this report are reproduced herein as received from the contractor. The report may have been edited as to format in conformance with the FIPR Style Manual. The opinions, findings and conclusions expressed herein are not necessarily those of the Florida Institute of Phosphate Research, nor does mention of company names or products constitute endorsement by the Florida Institute of Phosphate Research.

5 PERSPECTIVE Since phosphate rock is not a pure material but contains varying amounts of impurities, it has long been recognized that certain quantities of impurities can and do cause problems in the manufacture of phosphoric acid by the wet process. Maximum desirable levels of the most common impurities are generally well known and specifications for rock quality have been spelled out. However, there is a second variable that contributes to the problem of processing phosphate rock, i.e., the plant operating conditions versus varying impurity levels in the rock fed to the phosphoric acid plant. This study is an attempt to tie all these variables together and determine how to best operate a wet phosphoric acid plant in the light of the varying impurity levels encountered in the phosphate rock being processed. iii

6 ABSTRACT Over the years, FIPR has spent several hundred thousand dollars on laboratory and pilot plant studies to determine the effect of rock impurities on the phosphoric acid attack tank performance. In addition, many phosphate companies have struggled with justifying projects that will remove these impurities from the rock or improve operating control systems. This study used operating plant data to determine the effect of rock quality as well as operating variables on the performance of the plants. v

7 ACKNOWLEDGMENTS The authors wish to express their appreciation to the Florida phosphate companies for providing the operating data used in this study. vi

8 TABLE OF CONTENTS PERSPECTIVE... iii ABSTRACT... iv ACKNOWLEDGMENTS... vi EXECUTIVE SUMMARY... 1 INTRODUCTION... 3 METHODS AND TECHNIQUES... 5 Approach... 5 Overall Findings Analysis of Individual Reactors Reactor A Regressions Reactor B Regressions Reactor C Regressions Reactor D Regressions Reactor E Regressions Reactor F Regressions Reactor G Regressions Reactor H Regressions Reactor J Regressions Reactor K Regressions Reactor L Regressions Summary of Regression Coefficients Effect of Rock BPL Effect of Rock CaO Effect of Rock Fe 2 O Effect of Rock Al 2 O Effect of Rock MgO Effect of Rock % Solids Effect of Rock % Fine Solids Effect of Rock % Coarse Solids Effect of Reactor P 2 O 5 /Filtered Specific Gravity Effect of Reactor P 2 O 5 /Filtered Specific Gravity Standard Deviation Effect of Reactor Rate Effect of Reactor Rate Standard Deviation vii

9 TABLE OF CONTENTS (CONT.) Effect of Reactor Sulfate Effect of Reactor Sulfate Standard Deviation Summary of Interactions Effect of Rock BPL Effect of Rock CaO Effect of Rock Aluminum Effect of Rock Magnesium Effect of Rock Iron Effect of Rate Specific Recommendations for Improvements by Reactor Reactor A Reactor B Reactor C Reactor D Reactor E Reactor F, Filter Reactor F, Filter Reactor G, Filter Reactor G, Filter Reactor H Reactor J Reactor K Reactor L, Filter Reactor L, Filter CONCLUSIONS RECOMMENDATIONS REFERENCES viii

10 LIST OF FIGURES Figure Page 1. Plot of Rolling Average Data Set with 1 Significant Outlier in Data Plot of Rolling Average Data Set with Significant Outlier Removed Example of Non-Linear Relationship Between Total P 2 O 5 Loss and Reactor Sulfate Example of Non-Linear Relationship Between Total P 2 O 5 Loss and Rock MgO Total P 2 O 5 Loss vs. Sulfate Standard Deviation for Reactor A Total P 2 O 5 Loss vs. Sulfate Standard Deviation for Reactor B Total P 2 O 5 Loss vs. Sulfate Standard Deviation for Reactor D Total P 2 O 5 Loss vs. Sulfate Standard Deviation for Reactor E Total P 2 O 5 Loss vs. Sulfate Standard Deviation for Reactor F, First Filter Total P 2 O 5 Loss vs. Sulfate Standard Deviation for Reactor F, Second Filter Total P 2 O 5 Loss vs. Sulfate Standard Deviation for Reactor G, First Filter Total P 2 O 5 Loss vs. Sulfate Standard Deviation for Reactor G, Second Filter Total P 2 O 5 Loss vs. Sulfate Standard Deviation for Reactor J Total P 2 O 5 Loss vs. Sulfate Standard Deviation for Reactor K Total P 2 O 5 Loss vs. Sulfate Standard Deviation for Reactor L, First Filter Total P 2 O 5 Loss vs. Sulfate Standard Deviation for Reactor L, Second Filter Average Sulfate Shift Standard Deviation vs. Average Rock P 2 O 5 Differences Between Analyses Average Sulfate Shift Standard Deviation vs. Average Rock MgO Differences Between Analyses Average Sulfate Shift Standard Deviation vs. Average Rock Al 2 O 3 Differences Between Analyses Average Sulfate Shift Standard Deviation vs. Average Rock Fe 2 O 3 Differences Between Analyses Total P 2 O 5 Losses vs. Reactor Sulfate for Reactor A Total P 2 O 5 Losses vs. Reactor Sulfate for Reactor J ix

11 LIST OF TABLES Table Page 1. Data Received for Each Reactor Additional Data Received for Each Reactor Sulfate Standard Deviation and Rock Variability File Format for Excel Files on CD Regression Results for Reactor A: Total P 2 O 5 Losses Regression Results for Reactor A: Citrate Acid Soluble P 2 O 5 Losses Regression Results for Reactor A: Citrate Acid Insoluble P 2 O 5 Losses Regression Results for Reactor A: Water Soluble P 2 O 5 Losses Regression Results for Reactor A: Rock Rate Regression Results for Reactor B: Total P 2 O 5 Losses Regression Results for Reactor B: Water Insoluble P 2 O 5 Losses Regression Results for Reactor B: Water Soluble P 2 O 5 Losses Regression Results for Reactor B: Rock Rate Regression Results for Reactor C: Total P 2 O 5 Losses Regression Results for Reactor D: Total P 2 O 5 Losses Regression Results for Reactor D: Citrate Soluble P 2 O 5 Losses Regression Results for Reactor D: Citrate Insoluble P 2 O 5 Losses Regression Results for Reactor D: Water Soluble P 2 O 5 Losses Regression Results for Reactor D: Sulfuric Acid Regression Results for Reactor E: Total P 2 O 5 Losses Regression Results for Reactor E: Water Insoluble P 2 O 5 Losses Regression Results for Reactor E: Water Soluble P 2 O 5 Losses Regression Results for Reactor E: Rock Rate Regression Results for Reactor F (Filter 1): Total P 2 O 5 Losses Regression Results for Reactor F (Filter 1): Citrate Soluble P 2 O 5 Losses Regression Results for Reactor F (Filter 1): Citrate Insoluble P 2 O 5 Losses Regression Results for Reactor F (Filter 1): Water Soluble P 2 O 5 Losses xi

12 LIST OF TABLES (CONT.) Table Page 28. Regression Results for Reactor F: Rock Rate Regression Results for Reactor F (Filter 2): Total P 2 O 5 Losses Regression Results for Reactor F (Filter 2): Citrate Soluble P 2 O 5 Losses Regression Results for Reactor F (Filter 2): Citrate Insoluble P 2 O 5 Losses Regression Results for Reactor F (Filter 2): Water Soluble P 2 O 5 Losses Regression Results for Reactor G (Filter 1): Total P 2 O 5 Losses Regression Results for Reactor G (Filter 1): Citrate Soluble P 2 O 5 Losses Regression Results for Reactor G (Filter 1): Citrate Insoluble P 2 O 5 Losses Regression Results for Reactor G (Filter 1): Water Soluble P 2 O 5 Losses Regression Results for Reactor G: Rock Rate Regression Results for Reactor G (Filter 2): Total P 2 O 5 Losses Regression Results for Reactor G (Filter 2): Citrate Soluble P 2 O 5 Losses Regression Results for Reactor G (Filter 2): Citrate Insoluble P 2 O 5 Losses Regression Results for Reactor G (Filter 2): Water Soluble P 2 O 5 Losses Regression Results for Reactor H: Total P 2 O 5 Losses Regression Results for Reactor J: Total P 2 O 5 Losses Regression Results for Reactor J: Water Insoluble P 2 O 5 Losses Regression Results for Reactor J: Water Soluble P 2 O 5 Losses Regression Results for Reactor J: Rock Rate Regression Results for Reactor K: Total P 2 O 5 Losses Regression Results for Reactor K: Citrate Soluble P 2 O 5 Losses Regression Results for Reactor K: Citrate Insoluble P 2 O 5 Losses Regression Results for Reactor K: Water Soluble P 2 O 5 Losses Regression Results for Reactor K: Rock Rate Regression Results for Reactor L (Filter 1): Total P 2 O 5 Losses Regression Results for Reactor L (Filter 1): Citrate Soluble P 2 O 5 Losses Regression Results for Reactor L (Filter 1): Citrate Insoluble P 2 O 5 Losses Regression Results for Reactor L (Filter 1): Water Soluble P 2 O 5 Losses xii

13 LIST OF TABLES (CONT.) Table Page 56 Regression Results for Reactor L: Rock Rate Regression Results for Reactor L (Filter 2): Total P 2 O 5 Losses Regression Results for Reactor L (Filter 2): Citrate Soluble P 2 O 5 Losses Regression Results for Reactor L (Filter 2): Citrate Insoluble P 2 O 5 Losses Regression Results for Reactor L (Filter 2): Water Soluble P 2 O 5 Losses Summary Table of Regression Coefficients for Rock BPL Summary Table of Regression Coefficients for Rock CaO Summary Table of Regression Coefficients for Rock Fe 2 O Summary Table of Regression Coefficients for Rock Al 2 O Summary Table of Regression Coefficients for Rock MgO Summary Table of Regression Coefficients for Rock % Solids Summary Table of Regression Coefficients for Rock % Fine Solids Summary Table of Regression Coefficients for Rock % Coarse Solids Summary Table of Regression Coefficients for Reactor P 2 O 5 /Filtered Specific Gravity Summary Table of Regression Coefficients for Reactor P 2 O 5 /Filtered Specific Gravity Standard Deviation Summary Table of Regression Coefficients for Reactor Rate Summary Table of Regression Coefficients for Reactor Rate Standard Deviation Summary Table of Regression Coefficients for Reactor Sulfate Summary Table of Regression Coefficients for Reactor Sulfate Standard Deviation Statistically Significant Interactions with Rock BPL Statistically Significant Interactions with Rock CaO Statistically Significant Interactions with Rock Aluminum Statistically Significant Interactions with Rock Magnesium Statistically Significant Interactions with Rock Iron Statistically Significant Interactions with Operating Rate xiii

14 EXECUTIVE SUMMARY Over the years, FIPR has funded many experimental projects to determine the effect of rock impurities on the phosphoric acid attack tank performance. Many phosphate companies have struggled to justify projects that would remove these impurities from the rock or improve operating control systems, without having an accurate estimate of the value for anticipated improvements. Thanks to state-of-the-art data analysis techniques, it is now possible to analyze production data in such a way as to determine: 1. The effect of each rock impurity and rock grind. 2. The value of improved attack tank sulfate control. 3. The value of improved attack tank P 2 O 5 control. 4. The effect on recovery of increased production rate. 5. Optimum operating conditions for shifts in rock quality. This report gives the analysis for each company. University of Florida student researchers at the Particle Engineering Research Center performed the work. To protect proprietary information, the reactor associated with each data set was assigned a code letter and the students only knew the data set by the assigned letter. All data were normalized, so that no absolute values of rate, recovery, etc., are used in the reports. All results are reported as relative (i.e., for Reactor A, an increase of 50% in MgO caused a 22% increase in overall losses or a 25% decrease in production rate). The benefits to the industry of this study are twofold: 1. A range of parameter values that can be used both in-house and by others to evaluate or justify projects. 2. Each company is to be given the data associated with their assigned letter (and only their letter) so they can see how they fit with other companies. Five Florida phosphate companies provided data on 11 reactors and 14 filters. Eight of the reactor data sets were for 18 months or more and were rich with information. Three of the reactor data sets were for six months to one year and had too much missing data to give significant information. Analysis of the data showed the following: 1. Each reactor is unique, with no two reactors having the same set of significant variables. 2. Operating conditions are more significant than rock quality in determining the overall P 2 O 5 recovery from the rock or the operating rate. 1

15 3. Significant improvements in both rate and recovery could be achieved for several reactors by changing the operating set points for sulfate or P 2 O 5 strength. 4. The most important rock qualities affecting the overall P 2 O 5 recovery are the rock aluminum and rock magnesium content. However, the effect of MgO was much smaller than previously reported, probably due to the wide use of filter aids or flocs by the industry. 5. The most important rock quality affecting the operating rate is the rock BPL. Rock magnesium content was not found to have a significant effect. 6. Overall, the sulfate standard deviation during roughly the previous shift was the most significant variable affecting plant performance. 2

16 INTRODUCTION It has long been recognized that MgO concentrations in phosphate rock above 0.5% have an adverse effect on the production of phosphoric acid and subsequent fertilizer products. In fact, FIPR has invested considerable effort in trying to define and manage the problem (El-Shall 1994; Hanna and Anazia 1990; Jacobs Engineering 1995; Laird and Hanson 1997; Smith and others 1997). While the effect on the ability to produce on-grade DAP is calculable from various models, the effect on phosphoric acid production can only be determined from test work. In 1995, a FIPR study was completed by Jacobs Engineering Group, Inc. and A. N. Baumann on this subject. The effect of rock MgO on the P 2 O 5 recovery and filtration rate was determined in a series of pilot plant studies. While rock MgO was the major variable in the test series, rock iron, aluminum, and BPL also varied. In addition, while efforts were made to control the reactor P 2 O 5 strength and sulfate, they also varied from their targets during the operating time periods. As a result, the magnitude of the effect of MgO could best be estimated ± 50%. 3

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18 METHODS AND TECHNIQUES Over the last several years, rock mined in Central Florida has seen an increase in MgO content. While efforts have been made to avoid mining very high MgO rock, it continues to be mined and in spite of industry efforts has been sent to the phosphoric acid plants. As such, at least some data exist that better define the effects of increased MgO on the phosphoric acid plants. In addition, the normal variation in rock grind, Al 2 O 3, Fe 2 O 3 and BPL provide data to determine the effects of these rock variables. Finally, the normal variations in plant operating conditions of rate, sulfate, and P 2 O 5 strength provide a good estimate of the effect of these variables on plant performance. APPROACH All Florida phosphate companies were requested to provide at least one year (preferably two or three) of operating data for one or more operating phosphoric acid reactors. The data included rock quality information (BPL, CaO, Fe 2 O 3, Al 2 O 3, MgO, grind, % solids), rock feed rate to attack, sulfuric acid feed (all) to attack, sulfate analyses, P 2 O 5 analyses, and data on gypsum cake losses. While at some time all six phosphate companies agreed to provide the needed data, due to cutbacks and the time required to gather the needed data, only five actually provided data. Of these, three companies provided at least 18 months of data on eight reactors (11 filters) and two companies provided less than one year of data on three reactors (three filters). While most of the data was received electronically, some was received on paper and was entered into the spreadsheets. When the data was received, all mention of the company/reactor train was deleted and the data set was assigned a code letter. The letters A through L (letter I not assigned) were randomly assigned to the data sets. In all data analysis and reports, the data sets are referred to by the assigned letter. Tables 1 and 2 summarize the data received for each reactor. 5

19 Table 1. Data Received for Each Reactor. 6 A B C D E Reactor Rock Variable Provided Frequency Operating Variable Provided Frequency Coarse rock, fine rock, rock % Rate and sulfate hourly, Rock rate, reactor sulfate, & solids, rock BPL, CaO, Fe 2 O 3, Every 24 hrs #1 filtrate P #1 filtrate P Al 2 O 3 & MgO 2 O 2 O 5 every 5 12 hrs Coarse rock, rock % solids, rock Coarse rock every 8 hrs, Rock rate and sulfuric rate, All hourly BPL, Fe 2 O 3, Al 2 O 3, &MgO others, every 24 hrs reactor sulfate and P 2 O 5 Coarse rock, fine rock, rock Rock size and BPL every No rate data, #1 filtrate P 2 O 5 Every 8 hrs F (2 filters) G (2 filters) H J K L (2 filters) BPL, CaO, Fe 2 O 3, Al 2 O 3 & MgO Rock % solids, rock BPL, CaO, Fe 2 O 3, Al 2 O 3 & MgO Coarse rock, rock % solids, rock BPL, Fe 2 O 3, Al 2 O 3, &MgO Coarse rock, fine rock, rock % solids, rock BPL, CaO, Fe 2 O 3, Al 2 O 3 & MgO Rock % solids, rock BPL, Fe 2 O 3, Al 2 O 3 & MgO Coarse rock, fine rock, rock BPL, CaO, Fe 2 O 3, Al 2 O 3 & MgO Coarse rock, rock % solids, rock BPL, Fe 2 O 3, Al 2 O 3, &MgO Coarse rock, fine rock, rock % solids, rock BPL, CaO, Fe 2 O 3, Al 2 O 3 & MgO Rock % solids, rock BPL, Fe 2 O 3, Al 2 O 3 & MgO 8 hrs, others, every 24 hrs Every 12 hrs, but many missing Coarse rock every 8 hrs, others, every 24 hrs Every 24 hrs only Rock rate, sulfuric rate, reactor sulfate and P 2 O 5 Rock rate and sulfuric rate, reactor sulfate and P 2 O 5 Rock rate, reactor sulfate, & #1 filtrate P 2 O 5 Rates hourly, sulfate and P 2 O 5 4/day All hourly Every 4 hrs Filtered acid specific gravity Hourly Rock size and BPL every 8 hrs, others, every 24 hrs Coarse rock every 8 hrs, others, every 24 hrs Every 24 hrs No rate data, #1 filtrate P 2 O 5 only Rock rate and sulfuric rate, reactor sulfate and P 2 O 5 Rock rate, reactor sulfate, & #1 filtrate P 2 O 5 Rate and sulfate hourly, #1 filtrate P 2 O 5 every 12 hrs Every 8 hrs All hourly Every 4 hrs Filtered acid specific gravity Hourly Rate and sulfate hourly, #1 filtrate P 2 O 5 every 12 hrs

20 Table 2. Additional Data Received for Each Reactor. 7 Reactor Gypsum P 2 O 5 Loss Data Provided Frequency Time Period A Total loss, water soluble loss, citrate soluble loss & citrate insoluble loss Every 12 hrs 24 months B Total loss, water soluble loss, & non water soluble loss Every 8 hrs 24 months C Total loss only Every 8 hrs 6 months D Total loss, water soluble loss, citrate soluble loss & citrate insoluble loss Every 24 hrs 12 months E Total loss, water soluble loss, & non water soluble loss Every 8 hrs 24 months F (2 filters) Total loss, water soluble loss, citrate soluble loss & citrate insoluble loss Every 12 hrs 24 months G (2 filters) Total loss, water soluble loss, citrate soluble loss & citrate insoluble loss Every 12 hrs 18 months H Total loss only Every 8 hrs 6 months J Total loss, water soluble loss, & non water soluble loss Every 8 hrs 24 months K Total loss, water soluble loss, citrate soluble loss & citrate insoluble loss Every 12 hrs 24 months L (2 filters) Total loss, water soluble loss, citrate soluble loss & citrate insoluble loss Every 12 hrs 18 months

21 Microsoft Excel was used for all data analyses, plots, and statistical evaluations. Although this program has some limitations (the most severe of which is that only 16 independent variables can be used in a regression), it was used because it is available and understood by all in the industry. All data sets were initially filtered to remove major errors. In general, the bogus data were either unrealistic large values or very small values (generally computer collected data on flows of meters that did not go to zero when flow was stopped). While most of the erroneous data were simply eliminated, some were corrected by inspection (value of 50% MgO in the rock was changed to 0.50% since these values were used prior and after and proved consistent with the BPL reported). The filtering algorithms are included in the Excel spreadsheets provided to each company along with the identified data changes. Dividing each variable by the average value of that variable then normalized the data. This step ensured that no company proprietary information could be seen by the investigators. The normalized data is used in this report. The only exception to normalizing to a value of 1.0 was the P 2 O 5 losses other than the total losses. For all other P 2 O 5 losses, the loss was divided by the average of the total losses. As a result, all the various types of losses measured (citrate insoluble, citrate soluble, water soluble) added up to an average value of one. Averages and standard deviations were calculated for the operating data for the same time period as the phosphate loss data (8, 12, or 24 hours). They were also calculated for the same time interval, but at times ending one to as many as 12 hours prior. The next step was to look for outliers in the data set. It was necessary to remove all data where there were mechanical reasons for poor performance as well as data that were not self-consistent (i.e., analytical results that were impossible). In most cases the data set was removed, but in some cases the data set was repaired (e.g., rock with a 6.5 BPL changed to 65 BPL, assuming consistent I, A, and MgO values). In general, total P 2 O 5 losses in excess of three times the average were eliminated from the data set. From past experience, these errors are from mechanical problems. The normal approach for determining the effect of many variables on a single measure of performance (such as the gypsum cake P 2 O 5 losses) is to use multi-linear regression. However, since at least some of the independent variables could have nonlinear relationships, (such as sulfate) this would not work per se. Initially, non-linearity was checked by doing a plot of the dependent variable vs. each independent variable using a rolling average of the data. Typically a 10% rolling average was used (i.e., if there are 1,000 data sets, then the data was ordered on the independent variable and a 100 (10% of 1,000) point rolling average of the data was plotted). While this technique is not foolproof, it does point out where non-linearities might exist. Where nonlinearities were seen, then the square of the independent variable was included in the data set for regressions. 8

22 An additional benefit to making the rolling average plots was that it often pointed out significant outliers that may have passed the previous screenings. If the plot took a large step change and it could be smoothed by eliminating one data set, it was considered an outlier and removed. Examples of this are shown in Figures 1 and 2. Total P2O5 Loss vs Reactor P2O5 St Dev Previous 8 hrs Total P2O5 Loss Bad Value Reactor P2O5 St Dev Previous 8 hrs Figure 1. Plot of Rolling Average Data Set with 1 Significant Outlier in Data. Figure 2. Plot of Rolling Average Data Set with Significant Outlier Removed. 9

23 Figures 3 and 4 are examples of variables that show significant non-linear relationships. Total P2O5 Loss vs Sulfate 1.3 Total P2O5 Loss Sulfate previous 8 hrs Figure 3. Example of Non-Linear Relationship Between Total P 2 O 5 Loss and Reactor Sulfate. Total P2O5 Loss vs Rock MgO 2 Shifts Prior 1.1 Total P2O5 Loss Rock MgO 2 Shifts Prior Figure 4. Example of Non-Linear Relationship Between Total P 2 O 5 Loss and Rock MgO. 10

24 The next step was to put the data into data sets that had the correct time delays. That is, the gypsum cake P 2 O 5 loss for the first shift sample might best relate to the rock analyses from the previous shift and the average sulfate value in the attack tank over a period of time ending a few hours earlier. The correct time delay was determined by optimum regression analysis for the time period deemed probable from the tank sizes and sample spots for each reactor system. For all the data, the data average period was the same as the time period for the gypsum cake sample analyses (8, 12 or 24 hours, depending on the reactor). A full regression of the data set was then run, using a backward step method. All independent variables, the squared and cubed terms, where appropriate, were included, and then the least significant ones were removed in a stepwise fashion, until only the significant variables remained (at a 95% confidence level). The final equation indicates all the variables that are significant and in some cases, the optimum values for non linear variables such as sulfate. For this study, two dependent variables were used. They were those related to P 2 O 5 losses (as many as three types of losses given in the data) and the operation rate. A second method of data analysis, called grouping, was also used to determine any possible interactions. While this method can be more powerful than regression analysis (when properly applied), it is limited by the data set and was not possible for all data sets. In grouping, the data sets were first sorted by one rock variable. Then a group of data (typically 5% of the total data or 100 sets) was removed from each end of the set for further examination (highest and lowest values of the selected independent variable). These were sorted on one of the dependent variables (phosphate losses or rate) and divided into two sets (high loss set and low loss set). The averages and standard deviations for each variable were calculated and compared. The Students T value was calculated for each variable to determine if there were significant differences in the data subsets. To be able to make statistically significant statements about possible operating changes that could mitigate any negative effects of a rock parameter, there could not be any significant difference in any of the rock parameters. While most of the time this was true (at a 95% confidence level), at times some of the rows of data had to be interchanged to make this true. Finally, the data was inspected for any operating variables that were significantly different between the high and low independent variable sets that could have caused the differences in the dependent variable. From this analysis, it was possible to make such conclusions as At high rock MgO, lower losses are associated with low reactor P 2 O 5 strength (this is used as an example only; it is not necessarily true). OVERALL FINDINGS While most of the findings were specific to each reactor or company, one was essentially the same for all reactors. This was the effect of sulfate standard deviation on the overall P 2 O 5 loss. Figures 5-16 show the strong correlation between the sulfate standard deviation and the overall P 2 O 5 loss during the time period. In general, lower losses are associated with lower sulfate standard deviations. The only reactors not shown in the Figures are Reactors C and H, since no sulfate values were provided. Also, the 11

25 data for Reactor D is somewhat questionable. As shown in Table 2, the sulfates for Reactor D were only recorded up to 4 times per day and the losses were reported on a daily basis. From Figures 5-16, it can be seen that the sulfate standard deviation ranges from a low of less than 0.2 (Figure 6, Reactor B) to 1 (Figures 9 and 10, Reactor F). If the sulfate controls could be improved to that achieved by Reactor B (0.07 average standard deviation), total P 2 O 5 losses would be reduced by 5-25%. High sulfate standard deviations are caused both by variable rock quality and by poor sulfate control. This study can only address the probable impact of variable rock quality. To do this accurately would require hourly rock analyses coinciding with the sulfate analyses. Since none of the phosphate companies routinely runs this type of analyses, the best estimate of rock variability is the change in rock analysis between each sample. 12

26 Total P2O5 Loss vs Sulfate St Dev Previous 12 hrs Total P2O5 Loss Sulfate St Dev Previous 12 hrs Figure 5. Total P 2 O 5 Loss vs. Sulfate Standard Deviation for Reactor A. Total P2O5 Loss vs Sulfate St Dev Previous 8 hrs Total P2O5 Loss Sulfate St Dev Previous 8 hrs Figure 6. Total P 2 O 5 Loss vs. Sulfate Standard Deviation for Reactor B. 13

27 Total P2O5 Loss vs Sulfate St Dev Previous 24 hrs Total P2O5 Loss Sulfate Standard Deviation Previous 24 hrs Figure 7. Total P 2 O 5 Loss vs. Sulfate Standard Deviation for Reactor D. Total P2O5 Loss vs Sulfate St Dev Previous 8 hrs Total P2O5 Loss Sulfate St Dev Previous 8 hrs Figure 8. Total P 2 O 5 Loss vs. Sulfate Standard Deviation for Reactor E. 14

28 Total P2O5 Loss vs Sulfate St Dev Previous 12 hrs Total P2O5 Loss Sulfate St Dev Previous 12 hrs Figure 9. Total P 2 O 5 Loss vs. Sulfate Standard Deviation for Reactor F First Filter. Total P2O5 Loss vs Sulfate St Dev Previous 12 hrs Total P2O5 Loss Sulfate Standard Deviation Previous 12 hrs Figure 10. Total P 2 O 5 Loss vs. Sulfate Standard Deviation for Reactor F: Second Filter. 15

29 Total P2O5 Loss vs Sulfate St Dev Previous 12 hrs Total P2O5 Loss Sulfate St Dev Previous 12 hrs Figure 11. Total P 2 O 5 Loss vs. Sulfate Standard Deviation for Reactor G: First Filter. Total P2O5 Loss vs Sulfate St Dev Previous 12 hrs Total P2O5 Loss Sulfate St Dev Previous 12 hrs Figure 12. Total P 2 O 5 Loss vs. Sulfate Standard Deviation for Reactor G: Second Filter. 16

30 Total P2O5 Loss vs Sulfate St Dev Previous 8 hrs Total P2O5 Loss Sulfate St Dev Previous 8 hrs Figure 13. Total P 2 O 5 Loss vs. Sulfate Standard Deviation for Reactor J. Total P2O5 Loss vs Sulfate St Dev Previous 12 hrs Total P2O5 Loss Sulfate Standard Deviation Previous 12 hrs Figure 14. Total P 2 O 5 Loss vs. Sulfate Standard Deviation for Reactor K. 17

31 Total P2O5 Loss vs Sulfate St Dev Previous 12 hrs Total P2O5 Loss Sulfate St Dev Previous 12 hrs Figure 15. Total P 2 O 5 Loss vs. Sulfate Standard Deviation for Reactor L: First Filter. Total P2O5 Loss vs Sulfate St Dev Previous 12 hrs Total P2O5 Loss Sulfate St Dev Previous 12 hrs Figure 16. Total P 2 O 5 Loss vs. Sulfate Standard Deviation for Reactor L: Second Filter. 18

32 Table 3 shows the average sulfate standard deviation for each shift and the average absolute change in rock analyses by reactor (not all are on the same time interval as the shifts; see Table 2). Again, Reactor C is missing, and the results for Reactors D and H are on a different basis from the rest. Table 3. Sulfate Standard Deviation and Rock Variability. Reactor Average Sulfate Standard Deviation by Shift Average Change in Rock Phosphate Between Shifts Average Change in Rock Iron Between Shifts Average Change in Rock Aluminum Between Shifts Average Change in Rock Magnesium Between Shifts A B D E F G J K L Figures show plots and regressions for each of these rock variables. Average Sulfate Shift Standard Deviation y = x R 2 = Average Rock P2O5 Shift to Shift Differences Figure 17. Average Sulfate Shift Standard Deviation vs. Average Rock P 2 O 5 Differences Between Analyses. 19

33 Average Sulfate Shift Standard Deviation y = x R 2 = Average Rock MgO Shift to Shift Differences Figure 18. Average Sulfate Shift Standard Deviation vs. Average Rock MgO Differences Between Analyses. Average Sulfate Shift Standard Deviation y = x R 2 = Average Rock Al2O3 Shift to Shift Differences Figure 19. Average Sulfate Shift Standard Deviation vs. Average Rock Al 2 O 3 Differences between Analyses. 20

34 Average Sulfate Shift Standard Deviation y = x R 2 = Average Rock Fe2O3 Shift to Shift Differences Figure 20. Average Sulfate Shift Standard Deviation vs. Average Rock Fe 2 O 3 Differences Between Analyses. From Figures it can be seen that the most significant relationship between the sulfate standard deviation and the various measures of rock variability is the one involving the changes in the rock magnesium. This is most likely because the magnesium is present as dolomite (mixture of calcium and magnesium carbonate) and does consume sulfate. Reactors B, E, G and J appear to have the best control, in that they are below the line for all the plots. Reactor F clearly shows the poorest control as it is well above the regression line in all the plots. ANALYSIS OF INDIVIDUAL REACTORS Since there are over 500 hundred graphs and 1,000 regressions involved in this study, it is not practical to show them all in this report. However, they are available on a data CD kept on file at the FIPR Library. This information is available upon request. The file format for each reactor is the same and is given in Table 4. Table 4. File Format for Excel Files on CD. Worksheet Name Contents Normalized Data All data by time normalized to 1.0 Data Set Up Includes calculation shift averages, standard deviations, and numerous time lags Data for Regressions All data as a single row for each work shift. Compacted Data Previous worksheet data with all cases removed that had missing data Graphs Rolling average graphs of each of the dependent variables with each independent variables Graphs, filter 2 Same as previous sheet if required for second filter Regressions Final regressions for each type of loss and the rate Interactions Interactions for each type of losses and the rate 21

35 Reactor A Regressions For Reactor A, the final regression for total P 2 O 5 losses with only the significant variables is given in Table 5. Table 5. Regression Results for Reactor A: Total P 2 O 5 Losses. Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 1078 ANOVA df SS MS F Significance F Regression E-38 Residual Total Coefficients Standard Error t Stat P-value Lower 95% Intercept Filter P2O5 6 Hr Delay rock rate last 12 hrs Rock Al2O hrs Previous Rock % Solids^ Rock % Solids 6-18 hrs Previous sulfate delay 5 hr sulfate^ rock rate St Dev delay 11 hr Rock MgO 6-18 hrs Previous Rock (-200) 6-18 hrs Previous Rock CaO 6-18 hrs Previous Rock P2O5^ Rock P2O hrs Previous The regression table gives the coefficient for each variable with a positive coefficient meaning that an increase in that variable will increase the loss. The table also gives the t Stat and P-value. The higher the absolute value of the t Stat, the more significant is the variable. The regression result shows that the total P 2 O 5 losses are most affected by the filter P 2 O 5 strength (and presumably the reactor P 2 O 5 ), which shows that higher strength will decrease the losses. It also shows that higher rates are associated with higher losses. Of the rock variables, the aluminum is the most significant (higher aluminum, lower losses). The rock total solids show a non-linear relationship, with the lowest losses occurring at about 5% lower than the average solids during the data period. The effect of sulfate shows that the maximum losses occur at a value of 1.23, and that either going higher or lower (preferred) will significantly reduce the losses. Figure 21 shows this graphically. Clearly, reducing the target sulfate by 20% will reduce the total P 2 O 5 losses by 15%. 22

36 Total P2O5 Loss vs Sulfate Total P2O5 Loss Sulfate previous 12 hrs Figure 21. Total P 2 O 5 Losses vs. Reactor Sulfate for Reactor A. Rock rate changes and increased rock calcium appear to increase losses. Increased rock magnesium and increased fines (-200) appear to decrease losses. Finally, the rock BPL shows that maximum losses occur at a BPL at 3% lower than the average of all the data (higher rock BPL reduces losses). Table 6 shows the regression for the citrate soluble P 2 O 5 losses. Table 6. Regression Results for Reactor A: Citrate Acid Soluble P 2 O 5 Losses. Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 1078 ANOVA df SS MS F Significance F Regression E-41 Residual Total Coefficients Standard Error t Stat P-value Lower 95% Intercept Rock CaO 6-18 hrs Previous Rock Fe2O hrs Previous Rock MgO 6-18 hrs Previous Rock % Solids 6-18 hrs Previous Rock (+035) 6-18 hrs Previous rock rate delay 1 hr rock rate St Dev delay 6 hr sulfate last 12 hrs CaO^ sulfate^

37 The regression shows that the citric acid soluble P 2 O 5 losses are adversely affected by increases in the rock magnesium and the rock rate. The losses are decreased by increases in the rock iron, rock % solids, rock % +35, and the rock rate standard deviation. The effect of the rock calcium shows that the maximum losses occur at a value of Higher sulfate decreases these losses. Table 7 shows the regression for the citrate insoluble P 2 O 5 losses. Table 7. Regression Results for Reactor A: Citrate Acid Insoluble P 2 O 5 Losses. Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 1078 ANOVA df SS MS F Significance F Regression E-33 Residual Total Coefficients Standard Error t Stat P-value Lower 95% Intercept Rock CaO 6-18 hrs Previous Rock Al2O hrs Previous Rock % Solids 6-18 hrs Previous Rock (+035) 6-18 hrs Previous Rock (-200) 6-18 hrs Previous Filter P2O5 1 Hr Delay rock rate last 12 hrs rock rate St Dev delay 8 hr rock P2O5^ %rock solids^ The regression shows that high rock calcium, increased rock rate, and increased rock rate standard deviation are associated with increased citric acid insoluble P 2 O 5 losses. Increased rock aluminum, rock +35, rock -200, rock P 2 O 5 and filter P 2 O 5 all appear to decrease the citric acid insoluble P 2 O 5 losses. The losses show a minimum at rock total solids of Table 8 shows the regression for the water soluble losses. 24

38 Table 8. Regression Results for Reactor A: Water Soluble P 2 O 5 Losses. Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 1078 ANOVA df SS MS F Significance F Regression E-27 Residual Total Coefficients Standard Error t Stat P-value Lower 95% Intercept Rock CaO hrs Previous Rock Fe2O hrs Previous Rock Al2O hrs Previous Rock MgO hrs Previous Rock % Solids hrs Previous Filter P2O5 8 Hr Delay rock rate delay 6 hr rock rate St Dev delay 11 hr sulfate last 12 hrs % rock solids^ The regression shows that high rock calcium, iron, rock rate, rock rate standard deviation, and high sulfate all appear to increase the water soluble P 2 O 5 losses. Increased rock aluminum, magnesium, and filter P 2 O 5 all seem to decrease the water soluble P 2 O 5 losses. The losses show a minimum at rock total solids of Table 9 shows the regression for the rock rate. 25

39 Table 9. Regression Results for Reactor A: Rock Rate. Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 1078 ANOVA df SS MS F Significance F Regression E-37 Residual Total Coefficients Standard Error t Stat P-value Lower 95% Intercept Filter P2O5 11 Hr Delay sulfate delay 9 hr Rock Fe2O3 Current Shift rock rate St Dev delay 11 hr %solids^ Rock % Solids Current Shift Rock (+035) Current Shift Rock Al2O3 Current Shift Al2O3^ The regression shows that high filter P 2 O 5, high rock rate standard deviation, and high rock +35 seem to increase the rock rate. Increased reactor sulfate, rock iron, and aluminum all tend to decrease the rock rate. The rate shows a maximum at rock total solids of and a minimum at rock aluminum of 1.2. Reactor B Regressions For Reactor B, the final regression for total P 2 O 5 losses, with only the significant variables, is given in Table

40 Table 10. Regression Results for Reactor B: Total P 2 O 5 Losses. Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 1854 ANOVA df SS MS F Significance F Regression E-30 Residual Total Coefficients Standard Error t Stat P-value Lower 95% Intercept Rock MgO 1 shift prior rate stdev 1 hr delay rate St D^ Rock P2O5 1 shift prior rock rate 6 hr delay Rate^ Rock Al2O3 1 shift prior sulfate stdev 8 hr delay Reactor P2O5^ reactor P2O5 6 hr delay Sulfate^ sulfate 5 hr delay TS 1 shift prior The regression result shows that increased rock rate and increased rock aluminum will tend to increase the total P 2 O 5 losses. Increased rock MgO, total solids and P 2 O 5, and sulfate standard deviation are associated with decreased total P 2 O 5 losses. The total losses show a maximum at a rock rate standard deviation, a rock rate of 1.14, and a minimum at a reactor P 2 O 5 strength of The effect of sulfate shows that the minimum losses occur at a value of 1.03, and that either going higher or lower will significantly increase the losses. Table 11 shows the regression for the water insoluble P 2 O 5 losses. 27

41 Table 11. Regression Results for Reactor B: Water Insoluble P 2 O 5 Losses. Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 1854 ANOVA df SS MS F Significance F Regression E-15 Residual Total Coefficients Standard Error t Stat P-value Lower 95% Intercept Rock P2O5 1 shift prior Rock Fe2O3 1 shift prior rock rate 9 hr delay rate stdev 9 hr delay sulfate 7 hr delay reactor P2O5 stdev 8 hr delay Rate ^ rate stdev ^ The regression shows that increasing reactor sulfate tends to increase the water insoluble losses. Decreasing the rock P 2 O 5 and iron or the reactor P 2 O 5 standard deviation seems to decrease the water insoluble losses. The water insoluble losses show a maximum at a rock rate of 1.21 and a rock rate standard deviation of Table 12 shows the regression for the water soluble P 2 O 5 losses. 28

42 Table 12. Regression Results for Reactor B: Water Soluble P 2 O 5 Losses. Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 1854 ANOVA df SS MS F Significance F Regression E-22 Residual Total Coefficients Standard Error t Stat P-value Lower 95% Intercept Rock P2O5 1 shift prior Rock Al2O3 1 shift prior Rock MgO 1 shift prior rock rate 2 hr delay rate stdev 5 hr delay sulfate stdev 6 hr delay reactor P2O5 7 hr delay reactor P2O5 stdev previous 8 hrs Rate^ reactorp2o5 ^ ratestdev ^ The regression shows that increasing the rock aluminum and the reactor P 2 O 5 standard deviation is associated with increased water insoluble losses. Decreasing the rock P 2 O 5 and magnesium or the reactor sulfate standard deviation seems to decrease the water insoluble losses. The water insoluble losses show a maximum at a rock rate of 0.98 and a rock rate standard deviation of The losses show a minimum at a reactor P 2 O 5 strength of Table 13 shows the regression for the rock rate. 29

43 Table 13. Regression Results for Reactor B: Rock Rate. Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 1854 ANOVA df SS MS F Significance F Regression E-71 Residual Total CoefficientsStandard Error t Stat P-value Lower 95% Intercept rate stdev previous 8 hrs sulfate stdev plus 1 hr reactor P2O5 plus 1 hr MgO ^ reactor P2O5 ^ sulfate 9 hr delay Fe2O3 ^ sulfate ^ Rock Fe2O3 2 shifts prior TS 2 shifts prior The regression shows that the rock rate increases with increasing rock iron content. The rock rate decreases with increasing rock iron, magnesium and total solids, rock rate standard deviation and sulfate standard deviation. The rock rate shows a minimum at sulfate of The rate shows a maximum at a reactor P 2 O 5 strength of Reactor C Regressions For Reactor C, the final regression for total P 2 O 5 losses, with only the significant variables, is given in Table