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Application of traceable sample area measurement in the calibration of online dry grammage measurement used in board production
Measurement 46 (2013) 1585–1591
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Measurement journal homepage: www.elsevier.com/locate/measurement
Application of traceable sample area measurement in the calibration of online dry grammage measurement used in board production Juha Kangasrääsiö ⇑ JMK Instruments Oy, Ankkuritie 1, FI-70460 Kuopio, Finland1
a r t i c l e
i n f o
Article history: Received 24 August 2012 Received in revised form 17 December 2012 Accepted 24 December 2012 Available online 5 January 2013 Keywords: Online measurement Grammage calibration Moisture calibration Automatic process control Traceable area measurement
a b s t r a c t A method is described for the simultaneous calibration of online grammage and moisture measurement by applying machine reel sampling and traceable sample area measurement. The area measurement is based on a flatbed scanner and image analysis. It is demonstrated that a relative uncertainty of 0.5% can be achieved in dry grammage calibration at the 95% confidence level. The determination of grammage and moisture were performed by applying the EN ISO 536 and the EN ISO 287 standards, respectively. The described technique can also be applied with the calibrations performed for the paper industry. Ó 2013 Elsevier Ltd. All rights reserved.
1. Introduction In board production, the automatic multivariable control of product dry grammage is based on online grammage (basis weight, total mass per unit product area) and moisture content measurements [1]. Because the dry raw material consumption per unit product area has a major economic impact on the profitability of the board production, it is vital to be able to control this consumption as accurately as possible with these measurements. Grammage and moisture are measured online with a scanning measurement frame (Fig. 1). The grammage measurement is based on the attenuation of beta radiation in the product web, and the moisture measurement is based either on the relative attenuation of different infrared (IR) wavelengths into the web, or on sensing the dielectric constant of the web, which correlates with the amount of water in the web, with a microwave resonance method
⇑ Tel.: +358 17 2828 547; fax: +358 17 2828 590. 1
E-mail address: juha.kangasraasio@precical.fi JMK Instruments Oy is a FINAS Accredited Calibration Laboratory.
0263-2241/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.measurement.2012.12.017
[2]. The grammage measurement is independent measurement, whereas the moisture measurement usually requires support from simultaneous grammage measurement. The calibration of the grammage and moisture measurements at the mill is commonly separated into two parts [2,3]. At first, the basic errors of the measurement system are minimised by performing a basic calibration/ adjustment with a set of reference standards. This basic calibration/adjustment creates the basis for the grammage and moisture measurements. It has previously been shown that the basic measurement errors of different grammage and moisture measurement systems can be significantly reduced by applying traceable methods during the basic calibration procedure [4,5]. During the second part of the calibration, the fine-tuning parameters (grade parameters) of each product grade or grade group are optimised. The grade calibration of the grammage measurement is usually performed either by comparing the average machine reel grammage, measured by the online grammage sensor, with the respective average calculated from the net weights, lengths and widths of the rolls cut from the machine reel (the average
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Fig. 1. A scanning measurement frame and a machine reel. Courtesy of UPM-Kymmene Oyj.
reel grammage method) [6], or by taking laboratory samples from a machine reel and comparing the online results with the respective laboratory results. The sampling method and the means for determining a sample grammage have been standardised [7,8]. The grade calibration of the moisture measurement is commonly performed by taking samples from a machine reel and by determining the moisture content of the samples using a standardised method [7,9]. The grade calibration of the grammage measurement with the average reel grammage method performs well if the moisture content of the web does not change between the scanner frame and the roll scale located in the mill’s packaging area. However, the moisture content of the web usually does not remain constant after the scanner frame because the moisture content of the board material tends toward its environmental equilibrium value (approximately 5% moisture) as the product material is exposed to atmosphere, and thus, the reference of the grammage calibration changes respectively. Because the moisture samples are usually taken from the machine reel, the grammage and the moisture references are based on a differently conditioned web, and therefore, the dry grammage calculated from these references is more or less erroneous. Moreover, in this case the grammage and the moisture references also vary in a different way as the environmental conditions vary between different seasons. Therefore, a fundamental problem concerning this calibration practice is the fact that the calibration of the grammage and moisture measurement is based on differently varying references, and thus, the dry grammage calculated from the readings of the online sensors, and utilised by the automatic control system, varies between different seasons. This problem can be decreased if the grade calibration of both the grammage and the moisture measurement is based on machine reel sampling. When the grade calibration of the grammage measurement is based on unconditioned, ‘as taken’ samples from a machine reel, the area of the samples must be determined. The samples are usually cut manually with a model plate and a knife. Another approach is to cut the sample smaller with a cutting device before weighing it. However, accurate area determination requires that the shape of a sample
is quite regular because the area determination is usually performed manually with a ruler. It is not necessary to know the area of a sample when determining its moisture content, and therefore, the moisture content and grammage of the web are traditionally determined using separate sample sets. Both the grammage and moisture samples must be cut within a short amount of time after the machine reel has been produced and sealed hermetically, in a careful manner, to avoid any moisture content changes. It can sometimes be quite a challenging job to obtain two representative sample sets from a machine reel because the moisture content of the board may change quite fast. On the other hand, the separate sample sets may be somewhat different also because the properties of the board material vary locally a great deal due to process variations. In any case, the determination of the grammage and moisture content of a web from separate sample sets increases the uncertainty of the dry grammage calibration. The aim of this paper is to describe how to streamline the machine reel sampling-based grade calibration of online grammage and moisture measurements using a method in which the grammage and moisture content of board material are determined from the same sample set by applying the respective ISO standards [7–9]. With the described method, the uncertainty of the dry grammage calibration can also be decreased. The method is based on the application of traceable sample area measurement using a flatbed scanner and image analysis. With this technique, the area of a sample can be determined accurately and the requirements for the size and shape of a sample can be relaxed. The described method, together with the application of traceable measurement techniques and uncertainty estimations, improves the state-of-the art calibration practice. The calibrations were performed according to the EN ISO/IEC 17025 standard [10], which is the requirement for calibration laboratories, and the reported uncertainties were estimated according to GUM (Guide to the Expression of Uncertainty in Measurement) guidelines [11]. 2. Traceable sample area measurement 2.1. Flatbed scanner calibration The scanner used in this study was an Epson Expression 1680. It was used in the 8 byte greyscale mode with a resolution of 600 dpi. A geometric and radiometric calibration was performed for the scanner as described in the Ref. [12]. Correction tables were created during the calibration for the local differences in the effective pixel size and for the local differences in the greyscale response of the scanner. 2.2. Edge detection and area measurement A sample was lightly pressed against the scanner window during the area measurement. It was scanned against a black background in order to increase the contrast between the sample material and the background.
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Fig. 2. An enlargement of the edge structure of a board sample as captured by the scanner. The small picture shows an example of the grey value histogram of a captured image, where the x-axis is the grey value and the y-axis is the amount effective pixels (in logarithmic scale). The lower (left) and higher threshold levels are also shown.
The edge of the sample is usually rough and can even be tilted. Therefore, the edge is not detected as a sharp transition from black to white; rather, the grey values of the pixels vary more or less smoothly across the edge area. To estimate the uncertainty of the edge detection, two different grey value threshold levels were used to evaluate the darkest and lightest possible grey value by which the edge could still be identified via grey value thresholding (Figs. 2 and 3). With these two levels, the minimum and maximum area of a test piece was estimated for the uncertainty budget. Before the thresholding, the greyscale values of the scanned image were corrected by the measurement software based on the correction table created during the scanner calibration.
The measured area should include all the possible pin holes and transparent thin areas of the sample, and therefore, just the outer edge of the sample was identified. The measurement software calculated the sample area by summing up all the effective pixels lying inside the outer edge of the sample, and performed the pixel size corrections based on the correction table created during scanner calibration. A custom-made and traceably calibrated reference standard (a 100 mm diameter circle etched on a 140 140 3 mm glass plate, with a line width of 100 lm) was scanned several times both before and after the sample scanning. The calculated sample area was scaled by the ratio of the calibrated area of the reference standard and the average scanned area of the reference to decrease the uncertainty caused by the possible drift of the scanner. 2.3. Uncertainty estimation The estimation of measurement uncertainty, according to GUM [11], includes a mathematical measurement model together with a description of each uncertainty component. The measurement model is the expression used when calculating a sample area together with error sources named as corrections. The measurement model for a sample area A was
A¼
AR þ 2AR að296:15 TÞ þ dAT þ dAd ARs þ dAi þ dAres þ dArep Þ
Fig. 3. An example of edge locations identified at two different threshold levels.
ðAs þ dAh ð1Þ
where AR is the calibrated area of the reference, a is the thermal expansion coefficient of the reference, T is the calibration temperature of the reference, dAT is the correction for the temperature drift of the reference at the scanner, dAd is the correction for the drift of the reference since
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Fig. 4. Examples of the greyscale curves of different paper and board grades (a total of 86 grades), where the x-axis is the grey value and the y-axis is the amount of effective pixels (in logarithmic scale).
the previous calibration, ARs is the scanned area of the reference, As is the scanned area of a sample, dAh is the correction for sample thickness, dAi is the correction for illumination, dAres is the correction for scanner resolution and dArep is the correction for repeatability. It was noticed during this study that the observed projected area of the reference decreases by 0.35% per mm when the reference is lifted above the scanner window (a perspective distortion). Therefore, because a sample is a three-dimensional object, its observed projection is distorted, as is the measured area of a sample which was calculated as an average from the estimated maximum and minimum area of a sample. In order to correct this distortion, the measured average area was scaled 0.175% upwards for each millimetre of sample thickness. An example of the uncertainty budget is presented in the reference article [12]. A typical relative uncertainty achieved during the area measurements for this study was 0.2% at the 95% confidence level. 2.4. Application range To study the applicability of the edge detection method, several different kinds of paper and board grades, from lightweight newsprint paper (approximately 35 g/m2) to heavyweight boards (approximately 500 g/m2), were scanned. As can be noticed from the results (Fig. 4), all of these grades had a clear ‘valley’ between the black background and the sample material, where the threshold levels can be set. For the light-coloured materials, the threshold levels can be set quite far apart from each other, for example, the lower level may be set at a grey value of 100 and the higher level may be set at a value of 200. For the dark-coloured materials, the threshold levels have to be set more closely together. The edge detection technique described above can be applied with all of the scanned materials. 3. Grade calibration with machine reel sampling To calibrate the scanning online grammage and moisture measurements, samples were taken from the machine reels. The samples were analysed in a laboratory to define
their grammage, moisture content and dry grammage. These results were then compared with the respective online sensor readings. 3.1. Basic calibration of grammage and moisture measurements To provide the best possible starting point for the grade calibration, a traceable basic calibration was performed for the online grammage measurement before the machine reel sampling and the observed errors were corrected by performing an adjustment, as described in the reference article [4]. A respective basic calibration/adjustment was also performed for the online moisture measurement. In case the moisture sensor was an IR-based sensor, the basic calibration was performed with calibrated product samples, which were sealed between two glass plates, as described in the reference article [5]. In some of the board production lines, there are also microwave moisture sensors in use. For these sensors the basic calibration was based on calibrated product samples, which were sealed in plastic bags. 3.2. Machine reel sampling When the production process was stable, the last grammage and moisture CD-profiles (cross-machine direction) before the reel turn-up were recorded to document the online sensor readings at the end of a machine reel. The samples were taken from the machine reel immediately after a reel turn-up. First, the cutting positions of the samples were marked on the machine reel so that the samples could be cut from ten different, evenly spaced positions in a cross-machine direction. The size of each sample was approximately 250 mm in the machine direction (MD) and approximately 180 mm in the cross-machine direction. The samples were manually cut from the reel, one or two layers from the exterior of the reel were discarded and the rest of the sheets (10–25 sheets) were quickly sealed in pre-weighed and numbered barrier plastic bags. The samples were transported to our laboratory after the sampling. The sampling procedure was repeated to obtain samples from three different machine reels with the same
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product grade. The online measurement system was zeroed (standardised) between each sampling. For each machine reel, the product grade, the reel number, the time of the reel turn-up, the internal temperatures of the sensors and the CD-positions of the samples were documented. 3.3. Laboratory analysis of the samples The bagged samples were weighed in a laboratory typically within eight hours after the samples had been collected. The wet masses of the samples were obtained by subtracting the pre-weighed masses of the plastic bags. Then, the samples were taken out of the sealed plastic bags one by one and the area of every sheet was measured with the calibrated flatbed scanner. The grammage of a sample was calculated by dividing the wet mass of the sample by the total area of the sample. The uncertainty of the grammage was determined for each sample. Typically, the relative uncertainty of the grammage determination was below 0.2% from the result at the 95% confidence level. Thus, for example, the uncertainty of a 500 g/m2 sample was below 1 g/m2. After the area measurement, the sample sheets were spread out and dried in an oven (forced air circulation) at a temperature of 105 ± 2 °C. The sample sheets were dried until a constant dry mass was achieved. The dried samples were set in pre-weighed plastic bags, cooled and weighed. The moisture percentage of the sample was calculated by dividing the water mass (dry mass subtracted from the wet mass) of the sample by the wet mass of the sample and by multiplying the result by 100. The uncertainty of the moisture percentage determination was evaluated for
each sample. Typically, the uncertainty of the moisture content determination was below 0.2% moisture at the 95% confidence level. Therefore, for instance, the true moisture content of a 5.0% moisture sample was greater than 4.8% moisture and lower than 5.2% moisture. Finally, the dry grammage was also calculated by dividing the dry mass by the total area of the sample. The uncertainty of the dry grammage determination was evaluated for each sample. Typically, the relative uncertainty of the dry grammage determination was below 0.2% from the result at the 95% confidence level. All of the weighing was done with a traceably calibrated and maintained laboratory balance, with measurement capability of 0.00016 m + 0.00025 g at the 95% confidence level. The variable m in the previous expression represents the mass. 3.4. The estimation of best achievable uncertainties The best achievable uncertainties of the online measurement calibrations were estimated by taking samples from several different board production lines and from different grades and by comparing the online measurement results with the respective sampling results. The online grammage and moisture measurement systems usually have linear parameters (grade parameters) by which the measurement systems can be adjusted to give the correct measurement results Ri for each product grade (or grade group) i:
Ri ¼ ai Rs þ bi
ð2Þ
where Rs is the basic grammage or moisture reading of the measurement system, and ai and bi are the adjustment parameters for the product grade (or grade group) i.
Table 1 A comparison of the online grammage, moisture and dry grammage measurement results with the respective machine reel sampling results from different board production lines. Machine
A A A A A A B B B B B B C C C C C C Average Expanded uncertainty (k = 2) Relative expanded uncertainty (%)
Grade
1 1 1 2 2 2 3 3 3 4 4 4 5 5 5 6 6 6
Grammage
Moisture
Dry grammage
Online (g/m2)
Sampling (g/m2)
Error (g/m2)
Online (%)
Sampling (%)
Error (%)
Online (g/m2)
Sampling (g/m2)
Error (g/m2)
447.7 441.7 436.9 449.7 449.8 436.1 192.5 193.5 192.4 279.7 279.2 279.3 244.2 246.9 245.8 277.7 278.1 281.0
447.1 440.7 438.4 449.4 450.4 435.8 193.2 193.9 191.4 280.0 279.1 279.0 244.1 246.6 246.3 278.3 277.6 280.8
0.6 1.0 1.5 0.3 0.6 0.3 0.7 0.4 1.0 0.3 0.1 0.3 0.1 0.3 0.5 0.6 0.5 0.2
5.2 4.9 4.6 6.5 6.5 5.0 8.1 8.2 7.9 8.8 8.7 8.8 8.2 8.1 8.0 7.8 7.6 7.6
5.2 4.9 4.7 6.5 6.4 5.1 8.2 8.2 7.8 8.8 8.7 8.7 8.1 8.1 8.0 7.7 7.5 7.7
0.0 0.0 0.1 0.0 0.1 0.1 0.1 0.0 0.1 0.0 0.0 0.1 0.1 0.0 0.0 0.1 0.1 0.1
424.4 420.1 416.8 420.5 420.6 414.3 176.9 177.6 177.2 255.1 254.9 254.7 224.2 226.9 226.1 256.0 257.0 259.6
423.9 419.1 417.8 420.2 421.6 413.6 177.4 178.0 176.5 255.4 254.8 254.7 224.3 226.6 226.6 256.9 256.8 259.2
0.5 1.0 1.0 0.3 1.0 0.7 0.5 0.4 0.7 0.3 0.1 0.0 0.1 0.3 0.5 0.9 0.2 0.4
314.0
314.0
0.0 1.3
7.3
7.2
0.0 0.2
292.4
292.4
0.0 1.2
0.4
0.4
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For the estimation of the best achievable uncertainty of grade calibration, the best calibration data sets from different production lines were collected and analysed statistically (Table 1). Each laboratory result in the table is the average value of a sample profile. The online results, which are the average values of the online measurement profiles, were adjusted by optimising the grade parameters for each grade separately by the least squares method. The grammage adjustment was performed by optimising just the slope-parameter (ai), whereas the moisture adjustment was performed by optimising the offset-parameter (bi). The laboratory results were then subtracted from the optimised online results to determine the residual errors. Finally, the uncertainty of the comparison was estimated by calculating the standard deviation of the residual errors and by multiplying the result by a coverage factor (k = 2) to obtain the expanded uncertainty, which, for a normal distribution, corresponds to an approximately 95% confidence level. By combining the uncertainty of the laboratory determination and the comparison of the online and sample results, and by rounding the result to the nearest decimal, the relative uncertainty of the grammage calibration can be estimated as being 0.4% at the 95% confidence level. Similarly, the uncertainty of the moisture calibration can be estimated as being 0.3% moisture at the 95% confidence level. Finally, by combining the uncertainties of the grammage and the moisture calibrations, the relative uncertainty of the dry grammage calibration can be estimated as being 0.5% at the 95% confidence level.
4. Discussion There are several image thresholding techniques [13] by which the ‘optimal’ threshold level setting for the edge detection can be automatically performed individually for each sample. The sample area could be detected using this ‘optimal’ threshold level instead of by calculating the average value from the estimated minimum and maximum areas. However, performing the area measurement with two threshold levels brings along a straightforward way to estimate the measurement uncertainty. If the edge of the sample is not straight-cut, part of the edge structure may be masked out from the scanner by the sample itself. In this case, the measured area and the uncertainty estimate can be erroneous. The uncertainty of the area measurement may then be decreased by scanning both sides of the sample and by calculating the average area. The EN ISO 536 standard [8] accepts a 1% relative uncertainty for area measurements. The relative uncertainty of the sample area measurement with the traceable flatbed scanner based method was typically 0.2% at the 95% confidence level, and thus, the new sample area measurement method described in this paper meets the requirements of the standard. The moisture content of the sample was determined using the oven-drying method by applying the EN ISO 287 standard [9]. It has been reported previously that some moisture might still remain in the material after drying be-
cause the oven is ventilated with laboratory air, which is not completely dry (standardised testing conditions are +23 ± 1 °C and 50 ± 2%rh [14]). The remaining moisture content varies with different product grades, but it is usually less than 0.2% moisture, even though it can, in some cases, be more than 1% moisture [15–17]. The uncertainty caused by this error was not estimated for this study, and therefore, the reported uncertainty of moisture determination does not account for this error. The comparison of the online measurements results with the sampling results is usually the dominating uncertainty source in the calibration. This is caused by the fact that the online and the laboratory measurements cannot be performed from exactly the same web area, and therefore, the local variation in web properties causes errors in the comparison. The measurement head of a scanner scans across the product web once every 30–60 s. The product web typically travels several 100 m during a scan. Thus, the measurement head measures a diagonal trajectory of the web area that is several hundred metres long and a few metres wide. For this study, the samples were taken from ten different, equally spaced CD-positions. Each sample includes material from a maximum of 150 m in the MD-direction. This error can be reduced by performing the sampling during a stable production process. The error can possibly also be decreased by scanning just part of the web width with the scanner and by taking the samples from that specific part of the machine reel. However, by taking the samples from different CD-positions also the correctness of the online measurement profiles can be checked. When the sampling is repeated for several product grades, the residual errors may increase because of the grade sensitivity of the online measurements. In these cases, the residual errors can be decreased by applying separate grade parameter sets for the different grade groups. The moisture content of the web may change significantly between the scanner and the machine reel, as reported in the reference article [18], and therefore, performing the sampling in different seasons will increase the observed residual errors. Also the basic errors of the grammage and moisture measurements may drift, and thus, they will increase the uncertainty of the online measurements unless the basic errors are regularly minimised, as described in the reference articles [4,5]. It has previously been reported that the relative uncertainty of comparing an online measurement result to the respective reference result in the average reel grammage method is approximately 0.4% (2r) [6]. In this study, it was demonstrated (Table 1) that the same level of uncertainty can also be achieved using the machine reel sampling method. The previously reported uncertainty of comparing the moisture results with full-width, end-ofreel samples is approximately 0.2% moisture (2r) [6]. The same order of uncertainty was also achieved in this study (Table 1). However, by calibrating grammage and moisture measurements simultaneously with the described machine reel sampling method, rather than by performing the calibration with the average reel weight and separate moisture sampling method, the problem of differently varying grammage and moisture references in different seasons
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can be avoided, and thus, the uncertainty of the dry grammage measurement can be decreased. According to the manufacturers’ specifications, the relative accuracy of online grammage measurement is 0.25% or less [19], and the accuracy of online moisture measurement is 0.15% moisture or less [20,21]. Nevertheless, the online measurement on a production line cannot perform any better than the calibration methods applied in the mill allow for, and therefore, there is a need for reliable calibration methods. Even though the described application concerns calibrations performed on board machines, the technique can also be applied with the calibrations performed on paper machines.
5. Conclusions The traceable sample area measurement method described in this paper, which is based on a flatbed scanner and image analysis, meets the requirements of the EN ISO 536 standard [8]. By applying this new area measurement method, both the grammage and the moisture content of board material can be determined from the same sample set taken from a machine reel. It was demonstrated that these samples can be used for the simultaneous grade calibration of the online grammage and moisture measurements, resulting in a relative uncertainty of 0.5% for the dry grammage calibration at the 95% confidence level. The grade calibration method, together with the application of traceable measurement techniques and uncertainty estimations, improves the state-of-the art calibration practice. This new technique can also be applied with calibrations performed for the paper industry.
References [1] R. Ritala, Managing paper operation, in: K. Leiviskä (Ed.), Process and Maintenance Management, Papermaking Science and Technology, Book 14, second ed., Paper Engineers’ Association/Paperi ja Puu Oy Helsinki, 2009, pp. 190–234.
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[2] K. Fiegel et al., Measurement Systems and Product Variability, Paper Machine Quality Control Systems (QCSs), vol. 1, Tappi Press, Norcross, 2010, pp. 1–34, 39–44, and 53–89. [3] E.T. Dennebaum, Verifying the accuracy of scanning sensors, Tappi J. 70 (6) (1987) 65–68. [4] J. Kangasrääsiö, Improving integrity of on-line grammage measurement with basic calibration, ISA Trans. 49 (2010) 257–263. [5] J. Kangasrääsiö, Improving the integrity of IR based online moisture measurement used in the paper and board industry, Measurement 44 (2011) 1937–1944. [6] D.B. Brewster, Calibration of on-machine caliber gauges for reel building, Pulp. Pap. Can. 84 (8) (1983) 50–54. [7] CEN European Committee for Standardisation, European standard EN ISO 186 Paper and board, Sampling to determine average quality (ISO 186:2002), Brussels, 2002, p. 9. [8] CEN European Committee for Standardisation, European standard EN ISO 536 Paper and board, Determination of grammage (ISO 536:1995), Brussels, 1996, p. 7. [9] CEN European Committee for Standardisation, European standard EN ISO 287 Paper and board – Determination of moisture content of a lot – Oven-drying method (ISO 287:2009), Brussels, 2009, p. 17. [10] CEN and CENELEC, European standard EN ISO/IEC 17025 General requirements for the competence of testing and calibration laboratories (ISO/IEC 17025:2005), Brussels, 2005, p. 61. [11] ISO, Guide to the Expression of Uncertainty in Measurement, Geneva, 1993, p. 110. [12] J. Kangasrääsiö, B. Hemming, Calibration of a flatbed scanner for traceable paper area measurement, Meas. Sci. Technol. 20 (107003) (2009) p. 4. [13] M. Sezgin, B. Sankur, Survey over image thresholding techniques and quantitative performance evaluation, J. Electron. Imaging 13 (1) (2004) 146–165. [14] CEN European Committee for Standardisation, European standard EN 20187 Paper, Board and Pulps Standard Atmosphere for Conditioning and Testing and Procedure for Monitoring the Atmosphere and Conditioning of Samples (ISO 187:1990), Brussels, 1993, p. 10. [15] G. Broughton, A.K. Hobbs, Moisture determination in paper by oven drying, Tappi 35 (5) (1952) 217–219. [16] R.R. Rounsley, The determination of moisture content, Tappi 46 (9) (1963) 517–520. [17] D. Knox, Moisture determination by machine sampling at reel, Tappi 49 (3) (1966) 72A–77A. [18] J.J. Howard, L.M. Chase, Infrared moisture measurement of paper and board, Valves and Infl. On Control, IMC/BPBIF Symp. Maidstone, 1978, pp. 81–92. [19] I. Kinghorn, Smart sensor technology – the next generation, Paper Technol. (1994) 39–41. [20] Anonymous, IQWebMoisture and IQBoardMoisture, Product Brochure, Metso Automation Inc., 2008. [21] Anonymous, Voith LSC Sensor - Model 5120 & 5123 Same spot infrared moisture sensor, Product Brochure, Voith Paper Automation GmbH & Co. KG, 2009.
Contents lists available at SciVerse ScienceDirect
Measurement journal homepage: www.elsevier.com/locate/measurement
Application of traceable sample area measurement in the calibration of online dry grammage measurement used in board production Juha Kangasrääsiö ⇑ JMK Instruments Oy, Ankkuritie 1, FI-70460 Kuopio, Finland1
a r t i c l e
i n f o
Article history: Received 24 August 2012 Received in revised form 17 December 2012 Accepted 24 December 2012 Available online 5 January 2013 Keywords: Online measurement Grammage calibration Moisture calibration Automatic process control Traceable area measurement
a b s t r a c t A method is described for the simultaneous calibration of online grammage and moisture measurement by applying machine reel sampling and traceable sample area measurement. The area measurement is based on a flatbed scanner and image analysis. It is demonstrated that a relative uncertainty of 0.5% can be achieved in dry grammage calibration at the 95% confidence level. The determination of grammage and moisture were performed by applying the EN ISO 536 and the EN ISO 287 standards, respectively. The described technique can also be applied with the calibrations performed for the paper industry. Ó 2013 Elsevier Ltd. All rights reserved.
1. Introduction In board production, the automatic multivariable control of product dry grammage is based on online grammage (basis weight, total mass per unit product area) and moisture content measurements [1]. Because the dry raw material consumption per unit product area has a major economic impact on the profitability of the board production, it is vital to be able to control this consumption as accurately as possible with these measurements. Grammage and moisture are measured online with a scanning measurement frame (Fig. 1). The grammage measurement is based on the attenuation of beta radiation in the product web, and the moisture measurement is based either on the relative attenuation of different infrared (IR) wavelengths into the web, or on sensing the dielectric constant of the web, which correlates with the amount of water in the web, with a microwave resonance method
⇑ Tel.: +358 17 2828 547; fax: +358 17 2828 590. 1
E-mail address: juha.kangasraasio@precical.fi JMK Instruments Oy is a FINAS Accredited Calibration Laboratory.
0263-2241/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.measurement.2012.12.017
[2]. The grammage measurement is independent measurement, whereas the moisture measurement usually requires support from simultaneous grammage measurement. The calibration of the grammage and moisture measurements at the mill is commonly separated into two parts [2,3]. At first, the basic errors of the measurement system are minimised by performing a basic calibration/ adjustment with a set of reference standards. This basic calibration/adjustment creates the basis for the grammage and moisture measurements. It has previously been shown that the basic measurement errors of different grammage and moisture measurement systems can be significantly reduced by applying traceable methods during the basic calibration procedure [4,5]. During the second part of the calibration, the fine-tuning parameters (grade parameters) of each product grade or grade group are optimised. The grade calibration of the grammage measurement is usually performed either by comparing the average machine reel grammage, measured by the online grammage sensor, with the respective average calculated from the net weights, lengths and widths of the rolls cut from the machine reel (the average
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Fig. 1. A scanning measurement frame and a machine reel. Courtesy of UPM-Kymmene Oyj.
reel grammage method) [6], or by taking laboratory samples from a machine reel and comparing the online results with the respective laboratory results. The sampling method and the means for determining a sample grammage have been standardised [7,8]. The grade calibration of the moisture measurement is commonly performed by taking samples from a machine reel and by determining the moisture content of the samples using a standardised method [7,9]. The grade calibration of the grammage measurement with the average reel grammage method performs well if the moisture content of the web does not change between the scanner frame and the roll scale located in the mill’s packaging area. However, the moisture content of the web usually does not remain constant after the scanner frame because the moisture content of the board material tends toward its environmental equilibrium value (approximately 5% moisture) as the product material is exposed to atmosphere, and thus, the reference of the grammage calibration changes respectively. Because the moisture samples are usually taken from the machine reel, the grammage and the moisture references are based on a differently conditioned web, and therefore, the dry grammage calculated from these references is more or less erroneous. Moreover, in this case the grammage and the moisture references also vary in a different way as the environmental conditions vary between different seasons. Therefore, a fundamental problem concerning this calibration practice is the fact that the calibration of the grammage and moisture measurement is based on differently varying references, and thus, the dry grammage calculated from the readings of the online sensors, and utilised by the automatic control system, varies between different seasons. This problem can be decreased if the grade calibration of both the grammage and the moisture measurement is based on machine reel sampling. When the grade calibration of the grammage measurement is based on unconditioned, ‘as taken’ samples from a machine reel, the area of the samples must be determined. The samples are usually cut manually with a model plate and a knife. Another approach is to cut the sample smaller with a cutting device before weighing it. However, accurate area determination requires that the shape of a sample
is quite regular because the area determination is usually performed manually with a ruler. It is not necessary to know the area of a sample when determining its moisture content, and therefore, the moisture content and grammage of the web are traditionally determined using separate sample sets. Both the grammage and moisture samples must be cut within a short amount of time after the machine reel has been produced and sealed hermetically, in a careful manner, to avoid any moisture content changes. It can sometimes be quite a challenging job to obtain two representative sample sets from a machine reel because the moisture content of the board may change quite fast. On the other hand, the separate sample sets may be somewhat different also because the properties of the board material vary locally a great deal due to process variations. In any case, the determination of the grammage and moisture content of a web from separate sample sets increases the uncertainty of the dry grammage calibration. The aim of this paper is to describe how to streamline the machine reel sampling-based grade calibration of online grammage and moisture measurements using a method in which the grammage and moisture content of board material are determined from the same sample set by applying the respective ISO standards [7–9]. With the described method, the uncertainty of the dry grammage calibration can also be decreased. The method is based on the application of traceable sample area measurement using a flatbed scanner and image analysis. With this technique, the area of a sample can be determined accurately and the requirements for the size and shape of a sample can be relaxed. The described method, together with the application of traceable measurement techniques and uncertainty estimations, improves the state-of-the art calibration practice. The calibrations were performed according to the EN ISO/IEC 17025 standard [10], which is the requirement for calibration laboratories, and the reported uncertainties were estimated according to GUM (Guide to the Expression of Uncertainty in Measurement) guidelines [11]. 2. Traceable sample area measurement 2.1. Flatbed scanner calibration The scanner used in this study was an Epson Expression 1680. It was used in the 8 byte greyscale mode with a resolution of 600 dpi. A geometric and radiometric calibration was performed for the scanner as described in the Ref. [12]. Correction tables were created during the calibration for the local differences in the effective pixel size and for the local differences in the greyscale response of the scanner. 2.2. Edge detection and area measurement A sample was lightly pressed against the scanner window during the area measurement. It was scanned against a black background in order to increase the contrast between the sample material and the background.
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Fig. 2. An enlargement of the edge structure of a board sample as captured by the scanner. The small picture shows an example of the grey value histogram of a captured image, where the x-axis is the grey value and the y-axis is the amount effective pixels (in logarithmic scale). The lower (left) and higher threshold levels are also shown.
The edge of the sample is usually rough and can even be tilted. Therefore, the edge is not detected as a sharp transition from black to white; rather, the grey values of the pixels vary more or less smoothly across the edge area. To estimate the uncertainty of the edge detection, two different grey value threshold levels were used to evaluate the darkest and lightest possible grey value by which the edge could still be identified via grey value thresholding (Figs. 2 and 3). With these two levels, the minimum and maximum area of a test piece was estimated for the uncertainty budget. Before the thresholding, the greyscale values of the scanned image were corrected by the measurement software based on the correction table created during the scanner calibration.
The measured area should include all the possible pin holes and transparent thin areas of the sample, and therefore, just the outer edge of the sample was identified. The measurement software calculated the sample area by summing up all the effective pixels lying inside the outer edge of the sample, and performed the pixel size corrections based on the correction table created during scanner calibration. A custom-made and traceably calibrated reference standard (a 100 mm diameter circle etched on a 140 140 3 mm glass plate, with a line width of 100 lm) was scanned several times both before and after the sample scanning. The calculated sample area was scaled by the ratio of the calibrated area of the reference standard and the average scanned area of the reference to decrease the uncertainty caused by the possible drift of the scanner. 2.3. Uncertainty estimation The estimation of measurement uncertainty, according to GUM [11], includes a mathematical measurement model together with a description of each uncertainty component. The measurement model is the expression used when calculating a sample area together with error sources named as corrections. The measurement model for a sample area A was
A¼
AR þ 2AR að296:15 TÞ þ dAT þ dAd ARs þ dAi þ dAres þ dArep Þ
Fig. 3. An example of edge locations identified at two different threshold levels.
ðAs þ dAh ð1Þ
where AR is the calibrated area of the reference, a is the thermal expansion coefficient of the reference, T is the calibration temperature of the reference, dAT is the correction for the temperature drift of the reference at the scanner, dAd is the correction for the drift of the reference since
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Fig. 4. Examples of the greyscale curves of different paper and board grades (a total of 86 grades), where the x-axis is the grey value and the y-axis is the amount of effective pixels (in logarithmic scale).
the previous calibration, ARs is the scanned area of the reference, As is the scanned area of a sample, dAh is the correction for sample thickness, dAi is the correction for illumination, dAres is the correction for scanner resolution and dArep is the correction for repeatability. It was noticed during this study that the observed projected area of the reference decreases by 0.35% per mm when the reference is lifted above the scanner window (a perspective distortion). Therefore, because a sample is a three-dimensional object, its observed projection is distorted, as is the measured area of a sample which was calculated as an average from the estimated maximum and minimum area of a sample. In order to correct this distortion, the measured average area was scaled 0.175% upwards for each millimetre of sample thickness. An example of the uncertainty budget is presented in the reference article [12]. A typical relative uncertainty achieved during the area measurements for this study was 0.2% at the 95% confidence level. 2.4. Application range To study the applicability of the edge detection method, several different kinds of paper and board grades, from lightweight newsprint paper (approximately 35 g/m2) to heavyweight boards (approximately 500 g/m2), were scanned. As can be noticed from the results (Fig. 4), all of these grades had a clear ‘valley’ between the black background and the sample material, where the threshold levels can be set. For the light-coloured materials, the threshold levels can be set quite far apart from each other, for example, the lower level may be set at a grey value of 100 and the higher level may be set at a value of 200. For the dark-coloured materials, the threshold levels have to be set more closely together. The edge detection technique described above can be applied with all of the scanned materials. 3. Grade calibration with machine reel sampling To calibrate the scanning online grammage and moisture measurements, samples were taken from the machine reels. The samples were analysed in a laboratory to define
their grammage, moisture content and dry grammage. These results were then compared with the respective online sensor readings. 3.1. Basic calibration of grammage and moisture measurements To provide the best possible starting point for the grade calibration, a traceable basic calibration was performed for the online grammage measurement before the machine reel sampling and the observed errors were corrected by performing an adjustment, as described in the reference article [4]. A respective basic calibration/adjustment was also performed for the online moisture measurement. In case the moisture sensor was an IR-based sensor, the basic calibration was performed with calibrated product samples, which were sealed between two glass plates, as described in the reference article [5]. In some of the board production lines, there are also microwave moisture sensors in use. For these sensors the basic calibration was based on calibrated product samples, which were sealed in plastic bags. 3.2. Machine reel sampling When the production process was stable, the last grammage and moisture CD-profiles (cross-machine direction) before the reel turn-up were recorded to document the online sensor readings at the end of a machine reel. The samples were taken from the machine reel immediately after a reel turn-up. First, the cutting positions of the samples were marked on the machine reel so that the samples could be cut from ten different, evenly spaced positions in a cross-machine direction. The size of each sample was approximately 250 mm in the machine direction (MD) and approximately 180 mm in the cross-machine direction. The samples were manually cut from the reel, one or two layers from the exterior of the reel were discarded and the rest of the sheets (10–25 sheets) were quickly sealed in pre-weighed and numbered barrier plastic bags. The samples were transported to our laboratory after the sampling. The sampling procedure was repeated to obtain samples from three different machine reels with the same
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product grade. The online measurement system was zeroed (standardised) between each sampling. For each machine reel, the product grade, the reel number, the time of the reel turn-up, the internal temperatures of the sensors and the CD-positions of the samples were documented. 3.3. Laboratory analysis of the samples The bagged samples were weighed in a laboratory typically within eight hours after the samples had been collected. The wet masses of the samples were obtained by subtracting the pre-weighed masses of the plastic bags. Then, the samples were taken out of the sealed plastic bags one by one and the area of every sheet was measured with the calibrated flatbed scanner. The grammage of a sample was calculated by dividing the wet mass of the sample by the total area of the sample. The uncertainty of the grammage was determined for each sample. Typically, the relative uncertainty of the grammage determination was below 0.2% from the result at the 95% confidence level. Thus, for example, the uncertainty of a 500 g/m2 sample was below 1 g/m2. After the area measurement, the sample sheets were spread out and dried in an oven (forced air circulation) at a temperature of 105 ± 2 °C. The sample sheets were dried until a constant dry mass was achieved. The dried samples were set in pre-weighed plastic bags, cooled and weighed. The moisture percentage of the sample was calculated by dividing the water mass (dry mass subtracted from the wet mass) of the sample by the wet mass of the sample and by multiplying the result by 100. The uncertainty of the moisture percentage determination was evaluated for
each sample. Typically, the uncertainty of the moisture content determination was below 0.2% moisture at the 95% confidence level. Therefore, for instance, the true moisture content of a 5.0% moisture sample was greater than 4.8% moisture and lower than 5.2% moisture. Finally, the dry grammage was also calculated by dividing the dry mass by the total area of the sample. The uncertainty of the dry grammage determination was evaluated for each sample. Typically, the relative uncertainty of the dry grammage determination was below 0.2% from the result at the 95% confidence level. All of the weighing was done with a traceably calibrated and maintained laboratory balance, with measurement capability of 0.00016 m + 0.00025 g at the 95% confidence level. The variable m in the previous expression represents the mass. 3.4. The estimation of best achievable uncertainties The best achievable uncertainties of the online measurement calibrations were estimated by taking samples from several different board production lines and from different grades and by comparing the online measurement results with the respective sampling results. The online grammage and moisture measurement systems usually have linear parameters (grade parameters) by which the measurement systems can be adjusted to give the correct measurement results Ri for each product grade (or grade group) i:
Ri ¼ ai Rs þ bi
ð2Þ
where Rs is the basic grammage or moisture reading of the measurement system, and ai and bi are the adjustment parameters for the product grade (or grade group) i.
Table 1 A comparison of the online grammage, moisture and dry grammage measurement results with the respective machine reel sampling results from different board production lines. Machine
A A A A A A B B B B B B C C C C C C Average Expanded uncertainty (k = 2) Relative expanded uncertainty (%)
Grade
1 1 1 2 2 2 3 3 3 4 4 4 5 5 5 6 6 6
Grammage
Moisture
Dry grammage
Online (g/m2)
Sampling (g/m2)
Error (g/m2)
Online (%)
Sampling (%)
Error (%)
Online (g/m2)
Sampling (g/m2)
Error (g/m2)
447.7 441.7 436.9 449.7 449.8 436.1 192.5 193.5 192.4 279.7 279.2 279.3 244.2 246.9 245.8 277.7 278.1 281.0
447.1 440.7 438.4 449.4 450.4 435.8 193.2 193.9 191.4 280.0 279.1 279.0 244.1 246.6 246.3 278.3 277.6 280.8
0.6 1.0 1.5 0.3 0.6 0.3 0.7 0.4 1.0 0.3 0.1 0.3 0.1 0.3 0.5 0.6 0.5 0.2
5.2 4.9 4.6 6.5 6.5 5.0 8.1 8.2 7.9 8.8 8.7 8.8 8.2 8.1 8.0 7.8 7.6 7.6
5.2 4.9 4.7 6.5 6.4 5.1 8.2 8.2 7.8 8.8 8.7 8.7 8.1 8.1 8.0 7.7 7.5 7.7
0.0 0.0 0.1 0.0 0.1 0.1 0.1 0.0 0.1 0.0 0.0 0.1 0.1 0.0 0.0 0.1 0.1 0.1
424.4 420.1 416.8 420.5 420.6 414.3 176.9 177.6 177.2 255.1 254.9 254.7 224.2 226.9 226.1 256.0 257.0 259.6
423.9 419.1 417.8 420.2 421.6 413.6 177.4 178.0 176.5 255.4 254.8 254.7 224.3 226.6 226.6 256.9 256.8 259.2
0.5 1.0 1.0 0.3 1.0 0.7 0.5 0.4 0.7 0.3 0.1 0.0 0.1 0.3 0.5 0.9 0.2 0.4
314.0
314.0
0.0 1.3
7.3
7.2
0.0 0.2
292.4
292.4
0.0 1.2
0.4
0.4
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For the estimation of the best achievable uncertainty of grade calibration, the best calibration data sets from different production lines were collected and analysed statistically (Table 1). Each laboratory result in the table is the average value of a sample profile. The online results, which are the average values of the online measurement profiles, were adjusted by optimising the grade parameters for each grade separately by the least squares method. The grammage adjustment was performed by optimising just the slope-parameter (ai), whereas the moisture adjustment was performed by optimising the offset-parameter (bi). The laboratory results were then subtracted from the optimised online results to determine the residual errors. Finally, the uncertainty of the comparison was estimated by calculating the standard deviation of the residual errors and by multiplying the result by a coverage factor (k = 2) to obtain the expanded uncertainty, which, for a normal distribution, corresponds to an approximately 95% confidence level. By combining the uncertainty of the laboratory determination and the comparison of the online and sample results, and by rounding the result to the nearest decimal, the relative uncertainty of the grammage calibration can be estimated as being 0.4% at the 95% confidence level. Similarly, the uncertainty of the moisture calibration can be estimated as being 0.3% moisture at the 95% confidence level. Finally, by combining the uncertainties of the grammage and the moisture calibrations, the relative uncertainty of the dry grammage calibration can be estimated as being 0.5% at the 95% confidence level.
4. Discussion There are several image thresholding techniques [13] by which the ‘optimal’ threshold level setting for the edge detection can be automatically performed individually for each sample. The sample area could be detected using this ‘optimal’ threshold level instead of by calculating the average value from the estimated minimum and maximum areas. However, performing the area measurement with two threshold levels brings along a straightforward way to estimate the measurement uncertainty. If the edge of the sample is not straight-cut, part of the edge structure may be masked out from the scanner by the sample itself. In this case, the measured area and the uncertainty estimate can be erroneous. The uncertainty of the area measurement may then be decreased by scanning both sides of the sample and by calculating the average area. The EN ISO 536 standard [8] accepts a 1% relative uncertainty for area measurements. The relative uncertainty of the sample area measurement with the traceable flatbed scanner based method was typically 0.2% at the 95% confidence level, and thus, the new sample area measurement method described in this paper meets the requirements of the standard. The moisture content of the sample was determined using the oven-drying method by applying the EN ISO 287 standard [9]. It has been reported previously that some moisture might still remain in the material after drying be-
cause the oven is ventilated with laboratory air, which is not completely dry (standardised testing conditions are +23 ± 1 °C and 50 ± 2%rh [14]). The remaining moisture content varies with different product grades, but it is usually less than 0.2% moisture, even though it can, in some cases, be more than 1% moisture [15–17]. The uncertainty caused by this error was not estimated for this study, and therefore, the reported uncertainty of moisture determination does not account for this error. The comparison of the online measurements results with the sampling results is usually the dominating uncertainty source in the calibration. This is caused by the fact that the online and the laboratory measurements cannot be performed from exactly the same web area, and therefore, the local variation in web properties causes errors in the comparison. The measurement head of a scanner scans across the product web once every 30–60 s. The product web typically travels several 100 m during a scan. Thus, the measurement head measures a diagonal trajectory of the web area that is several hundred metres long and a few metres wide. For this study, the samples were taken from ten different, equally spaced CD-positions. Each sample includes material from a maximum of 150 m in the MD-direction. This error can be reduced by performing the sampling during a stable production process. The error can possibly also be decreased by scanning just part of the web width with the scanner and by taking the samples from that specific part of the machine reel. However, by taking the samples from different CD-positions also the correctness of the online measurement profiles can be checked. When the sampling is repeated for several product grades, the residual errors may increase because of the grade sensitivity of the online measurements. In these cases, the residual errors can be decreased by applying separate grade parameter sets for the different grade groups. The moisture content of the web may change significantly between the scanner and the machine reel, as reported in the reference article [18], and therefore, performing the sampling in different seasons will increase the observed residual errors. Also the basic errors of the grammage and moisture measurements may drift, and thus, they will increase the uncertainty of the online measurements unless the basic errors are regularly minimised, as described in the reference articles [4,5]. It has previously been reported that the relative uncertainty of comparing an online measurement result to the respective reference result in the average reel grammage method is approximately 0.4% (2r) [6]. In this study, it was demonstrated (Table 1) that the same level of uncertainty can also be achieved using the machine reel sampling method. The previously reported uncertainty of comparing the moisture results with full-width, end-ofreel samples is approximately 0.2% moisture (2r) [6]. The same order of uncertainty was also achieved in this study (Table 1). However, by calibrating grammage and moisture measurements simultaneously with the described machine reel sampling method, rather than by performing the calibration with the average reel weight and separate moisture sampling method, the problem of differently varying grammage and moisture references in different seasons
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can be avoided, and thus, the uncertainty of the dry grammage measurement can be decreased. According to the manufacturers’ specifications, the relative accuracy of online grammage measurement is 0.25% or less [19], and the accuracy of online moisture measurement is 0.15% moisture or less [20,21]. Nevertheless, the online measurement on a production line cannot perform any better than the calibration methods applied in the mill allow for, and therefore, there is a need for reliable calibration methods. Even though the described application concerns calibrations performed on board machines, the technique can also be applied with the calibrations performed on paper machines.
5. Conclusions The traceable sample area measurement method described in this paper, which is based on a flatbed scanner and image analysis, meets the requirements of the EN ISO 536 standard [8]. By applying this new area measurement method, both the grammage and the moisture content of board material can be determined from the same sample set taken from a machine reel. It was demonstrated that these samples can be used for the simultaneous grade calibration of the online grammage and moisture measurements, resulting in a relative uncertainty of 0.5% for the dry grammage calibration at the 95% confidence level. The grade calibration method, together with the application of traceable measurement techniques and uncertainty estimations, improves the state-of-the art calibration practice. This new technique can also be applied with calibrations performed for the paper industry.
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