Lowery, David P.

Western Dry Kiln Clubs

Western Dry Kiln Clubs. Meeting (23rd : 1972)

Undetermined

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PREDICTING MOISTURE CONTENT IN PONDEROSA PINE BOARDSDavid P. LoweryIntermountain Forest and Range Experiment Station,USDA Forest Service, Missoula, MontanaIntroductionThe ability to predict or measure the percent moisture content,both before and during the drying process, has been a research goalfor many years. Commercial moisture meters are not reliable at thehigher moisture content levels and hence, cannot be used for this pur-pose. If the moisture content of green boards could be determined thenthey could be sorted into groups of similar wetness which should im-prove drying efficiency. Also, the drying efficiency would probably beimproved if the wood moisture content during drying were known.Earlier research aimed at estimating the moisture content during dry-ing has related the average percent moisture content to the wood's sur-face temperature and the elapsed drying time.'The following study evaluates additional wood characteristics andattempts to relate them to the average percent moisture content.Among the additional factors examined were: green wood hardness;surface temperature; percent sapwood and heartwood; green volumeand weight; and elapsed drying time.ProcedureClear lengths of green ponderosa pine boards were obtained fromvarious Missoula sawmills. The material was primarily flat sawn,cut from the outermost log surfaces, and, as a result, contained vary-ing amounts of sapwood. Test speciments, 1 by 6 by 20 inches, werecut from these boards and stored under water until used.Four hardness tests were made on each face of the 6-inch boardand the results of the eight tests were averaged. The tests werespaced over the board surfaces to assure a fair sampling. Except forminor differences in specimen size, shape, and roughness, the hard-ness test was the same as that described by ASTM. 2 In general, thistest requires the embedding of a 0.444-inch diameter ball to a depthone-half its diameter, as determined by the tightening of a collaragainst the specimen. The hardness tests were made in a Tinius Olsontesting machine.When all hardness tests were complete, the specimens were re-placed under water. Prior to drying, the green volume of the speci-men was determined by the water immersion method. The specimenwas then weighed and the percent of sapwood and heartwood estimatedand the data recorded. After coating the specimen ends with an as-phalt paint, to prevent end drying, the specimen was placed in a metalframe which was attached to the specimen ends. The frame was sus-pended in an oven from a balance located on top of the oven (Figure 1).The oven was a forced air circulation type that had been modifiedby having its doors replaced by a plywood cover. A small door in theKotok, E. S. D. P. Lowery, and C. E. Jensen. Surface.temperature as an indicator of wood moisture content during drying.Forest Prod. J. 19(9):80-82. 1969.2American Society for Testing Materials ASTM standards onwood, wood preservatives, and related materials. Amer. Soc. forTesting Mat. Philadelphia, Pa. 1959.1cover permitted access to the oven interior and a small viewing portcut in the door allowed visual access. When not being used, the view-ing port was plugged with a rubber stopper. Prior to use, the ovenwas preheated to the desired temperature, 110 G (230 F).During the drying process an infrared radiometer, aimed throughthe open viewing port, measured the surface temperature of the speci-men. Specimen weight and surface temperature were recorded at 10-minute intervals throughout the drying period. When the specimen'ssurface temperature and the oven temperature were approximatelyequal (the temperatures would be identical only at zero percent mois-ture content) the specimen was removed from the oven, taken from themetal frame and placed in a second oven maintained at 104 C (219 F)and dried to a constant weight.The ovendry weight of the specimen was used to calculate theinitial and intermediate moisture content percentages and the specificgravity (ovendry weight '7 green volume).All study data were subjected to multiple regression analyses.Although based on a limited number of specimens, these analyses iden-tify some of the stronger variables that should be considered in thedevelopment of prediction models for commercial application. Pre-diction models for commercial use should be based on an adequatenumber of observations obtained from specimen boards representativeof those to which the predictors are to be applied.Results and Discussion In order to evaluate the importance of the different independentvariables in the prediction of wood moisture content an array of alterna-tive models was developed.For several reasons, green wood hardness was used as an inde-pendent variable. It has been well documented that wood hardnessvalues are affected by moisture contents that are less than the fibersaturation point (about 30% moisture content). If a similar relation-ship could also be established between hardness and higher moisturecontents, then a method of estimating green wood moisture contentmight be developed. Of course, the use of such a method in commer-cial production would be dependent on the development of a continuouslumber hardness tester; however, available evidence indicates thatsuch a development is quite possible.The heartwood of practically all softwood species has a loweraverage percent moisture content than the sapwood. This fact has beenknown for a long time and the heartwood-sapwood sorting of green lum-ber for drying has been predicated upon this knowledge. However, itis extremely difficult to distinguish between the heartwood and sapwoodof ponderosa pine with any degree of certainty. Therefore, this vari-able was omitted from the analysis.Available measuring equipment can be used to rapidly and accur-ately determine the two variables, green weight and green volume. Itwas assumed that for speciments having the same volume, any majorweight differences could be attributed to differences in the amount ofwater present. This is not strictly true since differences in specificgravity would also affect the weight; however, the effect of specificgravity differences was assessed as relatively minor in the studysamples.A procedure somewhat similar to the above has been proposed by60. 12	 36.0.58	 24. 9.81	 17. 5M. Salamon 3 for segregating packages of softwood lumber for drying.In this procedure, the package of lumber is weighed and the volumecomputed from actual measurements. Using the average specific grav-ity of the species and the green volume, an estimate of the ovendryweight is calculated, and this ovendry estimate is used in conjunctionwith the green weight to calculate an estimate of the package's moisturecontent. This procedure permits sorting the green lumber packages byapproximate moisture content and, by so doing, a reduced drying timeis claimed.The results of the regression analyses are shown in Table 1. Thistable shows that use of the green weight, green volume, and hardnessvariables gives accurate predictions of the initial moisture content ofponderosa pine boards. These same variables plus elapsed drying timescan be used to predict the moisture content during drying.Table 1. The prediction of ponderosa pine percent moisture contentbased on regression analysis of different study variables. Model	 Independent variables included in 	 1	 Standard2RNo.	 the models	 deviationfromregressionPredicting initial % moisture content (N = 14)1	 Green volume2	 Green weight3	 Green volume, green weight4	 Green volume, greenhardnessPredicting % moisture content during drying (N = 196) 5	 Surface temperature X hardness 2	.76	 20. 76	 Green volume X green weight Xhardness X elapsed time 2	.93	 11. 11The "coefficient of determination" (R 2 ) indicates the proportion of thevariance in Y (percent moisture content) that is associated with theregression.2These are interaction models as opposed to the others which areadditive.The prediction equation developed has the following form:MC =		 IMCIMC -	 - 15 	 x T110 + 0.92727 x 10 4 x LMC 2.8where: IMC = 227.7 - 0.08152 GV + 0.1301 GW - 0.4027 HDwhere:MC = estimated percent moisture contentIMC = estimated initial percent moisture contentCV = green volume (cubic centimeters)GW = green weight (grams)HD = hardnessT = elapsed drying time (minutes)3Salamon, M. Forklift weighing speeds kiln drying. Can. ForestInd. 90(10):57, 59. 1970.weight,Limits of use: green volume, 1,800 to 2,100 cubic centimeters;green weight, 1,300 to 2,000 grams; hardness, 300 to 460.Models based on this equation are shown in Figure 2. Thesemodels, based on green weights of 1,300 and 2,000 grams, hardnessindices of 300 and 460, green volumes of 1,800, 1,950, and 2,100, showthat all of the variables had a great practical influence on the time re-quired to dry to 15 percent moisture content. Also, as would be ex-pected, the models show that for a constant weight the smaller thegreen volume the higher the initial moisture content. Further, in ac-cordance with expectation, the higher the initial moisture content, thelonger the time required to obtain a desired moisture content level. Acomparison of models A and B shows that when the green volume isheld constant, the green weight difference is due primarily to the differ-ence in percent initial moisture content. Finally, the models show thatwhen green weight and green volume are held constant, the percentmoisture content of the wood is reflected in the hardness index.Conclusions Regression analyses were used to evaluate the relationships be-tween percent moisture content and the following: green wood hardness;percent heartwood; green weight and volume; and elapsed drying time.The analyses showed that for ponderosa pine, green weight, greenvolume, and hardness can be used to predict initial moisture contentwith great precision (R 2 = 0. 98). This estimated initial moisture con-tent in conjunction with elapsed drying time can then be used to predictthe percent moisture content during drying (R 2 = 0. 93).Although the analyses were based on a limited number of speci-mens, the strongest predictive variables have been isolated and theseshould be considered for inclusion in models developed for commercialapplication. Individuals mills anticipating the use of this method shoulddevelop their own equations to fit local conditions.8L1800950 GREENVOLUME(tunic2100	 centimeters)BALANCEFigure 1. A specimen suspended inside a drying ovenfrom a balance located on the oven top.(A)	 GREEN WEIGHT: 1300 g.HARDNESS INDEX:_300(B) GREEN WEIGHT: 2000 g.HARDNESS INDEX:_30025000100	 200	 300	 400ELAPSED DRYING. TIME (Minutes)	 `950 vGoRi EuEmNE2100	 cenTimube.te° rs)Figure 2. Models showing relationships between greenvolumes, percent moisture content, andelapsed drying times for green weight of1, 300 grams (A) and 2, 000 grams (B) andfor hardness indices of 300 and 460.

Predicting moisture content in ponderosa pine boards

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Predicting moisture content in ponderosa pine boards

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