Analysis of Sludge Formed in R.M.C Plant

Analysis of Sludge Formed in R.M.C PlantArjita Biswas and P.R AdaviDepartment of Civil Engineering, Maharashtra Institute of Technology, S.No.124, Paud Road, Kothrud, Pune 411038arjita@live.com----------------------------------------------------------------------------------------------------------------------------------------------.Abstract-- Ready-mixed concrete is used in the civilengineering and construction business, for example:bridge, free way, dam and buildings. In RMC plant,concrete is produced at plant and it is delivered to site.Initial setting time of concrete is 30 minutes. To prolongthe setting time, plasticizers are used in RMC.Pumpability and Flowability are the major issues in RMC;hence slump is the major criteria with the strength. Atransit mixer takes approximately 45min to 180 minutes todeliver concrete and return to the site depending on thedistance of site from the plant and other conditions. Someconcrete particles/ slurry stick to the blades of transitmixer, walls and floor of the transit mixer which isnormally termed as sludge in technical terms.Key words: Ready Mix Concrete, SludgeI. INTRODUCTIONREADYMIX concrete plant operations are large consumers ofwater. Sludge water is the waste wash water from concretemixing plants and agitator trucks. Approximately 200 litres ofwater are used to produce one cubic metre of concrete from acentral batch plant. The figure from a truck mix plant ishigher, at 300 litres. In addition approximately 500 - 1500litres of water are used to wash down the plant and yard at theend of each production day, plus 100 litres to wash out eachmixer - the central mixer and every truck used that day.With the growing demand for ready mixed concrete, thedisposal of sludge water is becoming an increasingenvironmental concern. Each working day approximately700–1300 litres of wash water are required for a singleconcrete Truck.Due to the large amount of suspended matter and highalkalinity untreated sludge water cannot be legally dischargedinto urban sewers. In general, the procedure for sludge waterdisposal utilizes two series-connected sedimentation basins.The first basin receives leftover concrete and wash water fromthe concrete plant and trucks.The overflow sludge water containing suspended fine particlesis transferred to the second basin. After a settling period, thewater from both basins is flushed to the municipal drains.Leftover concrete and sediment from the first basin andmuddy sludge from the second basin are placed in a landfill.The problem of Sludge is severe with:1. The older transit mixers2. More Detention time at site3. Failure of transit Mixer4. Climatic problem5. Severe traffic jam6. Any other unforeseen situation.As the time increases setting are at a faster rate, slump reducesdrastically and flowability reduces at a faster rate. It becomesmore difficult to remove the settled/ sticked sludge formed inthe transit mixer.Approximately 120-200 liters of water is used for cleaning ofeach transit mixture to remove the sludge from the blades oftransit mixtures, wall and floor of the transit mixer.II. BASIC OBJECTIVEBasic objective of the entire operation is To minimize the sludge formed during transportationof Ready Mix Concrete from Plant to site and Back. To minimize the water usage used during removal ofsludge formed in transit mixer and for cleaning of transitmixer. To find out alternative solution for reutilization ofsludge formed during transportation of Ready Mix Concreteand also from the fines coming out of Sediment tanks.Scope of Project: Analysis of data of daily production ofready mix concrete, sludge formed during the transportationof ready mix concrete form plant to site and back to the site,quantity of water used to wash the material.Production of Concrete and Sludge Formation in the month ofAugust: Daily production of concrete was recorded for thePlant for one month. The Concrete produced was of differentgrade but for simplicity we have considered the totalproduction per day ignoring the grade of concrete. Date wiseproduction of sludge was also recorded for three months.60

FrequencySludge in Cu.mSludge in Cu.mAKGEC INTERNATIONAL JOURNAL OF TECHNOLOGY, Vol. 2, No. 2TABLE 1 Production of Concrete and Sludge Formation in the month of AugustDateProduction inCu.minAugSludgein Cu.min AugProductionin Cu.m insepSludgein Cu.min sepProduction inCu.m inOctSludgeinCu.m inOct01 95 1.6 202 2.47 100 1.6102 112 1.61 48 1.39 300 2.9503 78 1.55 210 2.49 360 3.1704 230 2.31 260 2.95 310 2.6605 118 1.64 320 3.45 80 1.5606 230 2.78 270 3 220 2.2507 215 6 215 2.55 200 2.1908 90 1.58 190 2.25 240 2.3409 110 4.5 110 1.63 210 2.2110 175 1.71 310 3.21 120 1.6311 58 1.44 170 2.1 250 2.412 64 1.46 180 2.06 70 1.5313 220 2.17 190 2.31 180 1.7314 180 1.73 120 1.69 190 2.1415 125 1.65 150 1.85 220 2.2716 230 2.79 210 2.53 270 2.3917 117 1.63 215 2.74 300 2.8918 110 1.62 110 1.59 350 3.1519 220 2.24 135 1.73 160 1.6220 68 1.48 155 1.91 320 321 72 1.51 200 2.4 300 2.9322 52 1.43 215 2.64 270 2.3823 50 1.42 170 2.15 220 2.2824 71 1.5 112 1.65 210 2.2225 60 1.47 146 1.82 270 2.4226 120 4.55 185 2.21 150 1.6727 90 1.58 315 3.27 290 2.4528 80 1.54 300 3.33 225 2.329 75 1.55 180 2.09 180 1.8830 80 1.55 190 2.35 240 2.37Total6805 68.59 5783 81 6565 68.59Individual Value Plot of Sludge in Cu.mBoxplot of Sludge in Cu.m654321Figure 1 Boxplot of sludge in m 3 for August.123456Sludge in Cu.mFigure 3 Individual value plot of Sludge in m 3 for August.2520Histogram (with Normal Curve) of Sludge in Cu.mMean 2.038StDev 1.076N 3165Scatterplot of Sludge in Cu.m vs Production in Cu.m1541032500 1 2 3 4 5 6Sludge in Cu.mFigure 2 Histogram of Sludge in m 3 for August.150100150200250Production in Cu.mFigure 4 Scatter plot of Sludge in m 3 vs Production in m 3 for Aug.61

FrequencyResidualPercentResidualAKGEC INTERNATIONAL JOURNAL OF TECHNOLOGY, Vol. 2, No. 2Statistical Analysis of SludgeTABLE 2 BASIC DESCRIPTIVE STATISTICS OF SLUDGE FOR THE MONTH OF AUGUSTVariabl Mea SE St. Variance Coeff Var Min Max Range Median Q1 Q3e n Mean DevSludge 2.03 0.193 1.076 1.159 52.81 1.42 6.0 4.58 1.6 1.51 2.17m3 8Regression Analysis: Sludge in m 3 versus Production in m 3 for Month of August.The Regression Equation is : Sludge in m 3 = 1.01 + 0.00866 Production in m 3TABLE 3: REGRESSION ANALYSISPredictor Coef SE Coef T PConstant 1.0075 0.3842 2.62 0.014Production in m 3 0.008661 0.002887 3.00 0.005S = 0.956470 R-Sq = 23.7% R-Sq (adj) = 21.1%TABLE 4: ANALYSIS OF VARIANCESource DF SS MS F PRegression 1 8.2340 8.2340 9.00 0.005Residual Error 29 26.5302 0.9148Total 30 34.7642TABLE 5: UNUSUAL OBSERVATIONSObs Production Sludge in Fit SE Fit Residual St Residin Cu.m Cu.m7 215 6.000 2.870 0.326 3.130 3.48R9 110 4.500 1.960 0.174 2.540 2.70R26 120 4.500 2.047 0.172 2.503 2.66RR denotes an observation with a large standardized residual. Pearson correlation of Production in m 3 and Sludge in m 3 = 0.487.999050101-3.0161284Normal Probability PlotResidual Plots for Sludge in Cu.m3210-1-1.5 0.0 1.5 3.01.5ResidualHistogram3210Versus Fits2.02.5Fitted ValueVersus Order3.0is also shown by the p-value for the estimated coefficient ofSludge in Cu.m, which is 0.005. However the R 2 valueshows that Sludge in Cu.m explains 23.% of the variance inProduction in Cu.m, indicating that the model does not fits thedata extremely well.There are three outliners observation 7, 9and 26whereResidual is more than the standard Residual indicating somespecial cause of variation.Thus from this result we understand that though there iscorrelation between the sludge produced and the Production ofRMC other factors other then Production have to beconsidered for formation of sludge.0-101Residual23-1246810 12 14 16 18 20 22Observation OrderFigure 5 Residual plots for sludge in m 3 for the month of Aug.III. INTERPRETATION OF RESULTSThe p-value in the Analysis of Variance table (0.005) is lessthen 0.05 hence we reject the null hypothesis, indicating thatthe relationship between Sludge in Cu.m and Production inCu.m is statistically very significant at an α-level of .05. This24262830Analysis of Variance: Analysis Of Variance (ANOVA) helpsdetermine whether the variations are due to variabilitybetween or within methods. The within-method variations arevariations due to individual variation within treatment groups,whereas the between-method variations are due to differencesbetween the methods. In other words, it helps assess thesources of variation that can be linked to the independentvariables and determine how those variables interact andaffect the predicted variable.62

DataFrequencyPercentResidualDataSLUDGE ANALYSIS IN AN R.M.C PLANTThe ANOVA is based on the following assumptions: The treatment data must be normally distributed. The variance must be the same for all treatments. All samples are randomly selected. All the samples are independent.Analysis of variance tests the null hypothesis that all thepopulation means are equal at a significance level α:The null hypothesis will beH 0 : μ 1 (Sludge in Cu.m in Aug ) = μ 2 (Sludge in Cu.m inSept) = μ 3 (Sludge in Cu.m in Oct)The Alternate hypothesis will beH a : μ 1 (Sludge in Cu.m in Aug ) ≠ μ 2 (Sludge in Cu.m inSept) ≠ μ 3 (Sludge in Cu.m in Oct).TABLE 6 ONE-WAY ANOVA: SLUDGE IN m 3 FORTHE MONTHS OF AUGUST, SEPTEMBER, OCTOBERUSING MINITABSource DF SS MS F PFactor 2 1.312 0.656 1.13 0.328Error 87 50.582 0.581Total 89 51.894S = 0.7625 R-Sq = 2.53% R-Sq(adj) = 0.29%Level N Mean StDevSludge in Cu.m in Aug 30 2.0530 1.0918Sludge in Cu.m in sep 30 2.3270 0.5634Sludge inCu.m in Oct 30 2.2863 0.4846Individual 95% CIs For Mean Based onPooled StDevLevel ---------+---------+---------+---------+Sludge in m 3 in Aug (----------*----------)Sludge in m 3 in sep (----------*----------)Sludge in m 3 in Oct (----------*-----------)---------+---------+---------+---------+2.00 2.25 2.50 2.75Pooled St Dev = 0.7625Individual Value Plot of Sludge in Cu.m in August, Sludge in Cu.m in september, Sludge in Cu.m in october654321Sludge in Cu.m in August Sludge in Cu.m in september Sludge in Cu.m in octoberFi gure 7 Individual Value plot of sludge in m 3Residual Plots for Sludge in Cu.m in August, Sludge in Cu.m in september, Sludge in Cu.m in octoberNormal Probability Plot99.99990501010.1-2024ResidualHistogram40Versus Fits4.53.01.50.02.12.22.3Fitted Value30Boxplot of Sludge in Cu.m in August, Sludge in Cu.m in september, Sludge in Cu.m in october65420100-101 2Residual34Figure 8 Residual Plot of sludge for the months of August,September and October.321Sludge in Cu.m in August Sludge in Cu.m in september Sludge in Cu.m in octoberFigure 6 Box plot of Sludge for the monhs of Aug, Sept, OctInterpretation of Results: From Minitab we find F stat= 1.33,from F-table the critical value of F for α = 0.05 with thedegrees of freedom n 1 =2 and n 2 =87 is 3.1. Because 3.1 isgreater than 1.33, we cannot reject the null hypothesis. Weconclude that there is not a statistically significant differencebetween the means of formation of sludge in Cu.m in themonth of August , September and October i.e there is novariation in the formation of sludge with respect to time..63

SLUDGE ANALYSIS IN AN R.M.C PLANTand October i.e there is no variation in the formation ofsludge with respect to time.The cause and effect diagram helped us to identify thepossible causes for formation of sludge and by studying eachof the above process in depth we would be able to effectimprovements in each of the processes and how theimprovement in processes could reduce formation of sludgeVI. REFERENCES1. Seung Heon Han, Myung Jin Chae, keon Soon, Ho Dong Ryu. SixSigma Based Approach to improve performance in ConstructionOperations, Journal of Management in Engineering, Vol-24, No 1,Jan 2008, pp 21- 31.2. Low Sui Pheng, Mok Sze Hui. Implementing and Applying SixSigma in Construction, Journal of Construction Engineering andManagement, Vol.130, No 4, Aug 2004 , pp 482-489.3. Frederick Buggie, Implementing and Applying Six Sigma inConstruction, Journal of Construction Engineering andManagement, Vol.130, No 4, Aug 2004 , pp 482-489.4. Forrest . W. Breyfogle Implementing Six Sigma, IIndEdition,John Wiley and Sons Publication.5. Peter Pande, Robert Neuman, Roland R Cavanagh. The Six SigmaWay, Tata McGraw-hill Publishing Co. Ltd.6. Ahire, S., Landeros, R., Golhar, D. Total quality management: aliterature review and an agenda for future research, Production andOperations Management, pp. 277-307.7. Sousa, R., Voss. Quality management Re-visited: a ReflectiveReview and Agenda for Future Research, Journal of OperationsManagement, vol. 20, pp 91-109.8. Linderman, K., Schroeder, R., Zaheer, S., Choo. Six Sigma: Agoal theoretic perspective. Journal of Operations Management, Vol.21, No. 2, Mar., pp. 193-203.9. Zain, Z., Dale, B., Kehoe. Total quality management: anexamination of the writings from a UK perspective, The TQMMagazine, vol. 13, num. 2, Feb., pp. 129-137.10. Dean, J., Bowen. Managing theory and total quality: improvingresearch and practice through theory development, Academy ofManagement Review, Vol. 19, No. 3, pp. 392.418.11. Harry. Six Sigma: A Breakthrough Strategy for Profitability,Quality Progress, vol. 31, num. 5, May, pg. 60-6265