- Email: [email protected]
A First Approach to the Use of CO2 Emissions as a Maintenance Indicator in Industrial Plants
Available online at www.sciencedirect.com
ScienceDirect Procedia Engineering 63 (2013) 678 – 686
The Manufacturing Engineering Society International Conference, MESIC 2013
A first approach to the use of CO2 emissions as a maintenance indicator in industrial plants Luis Miguel Calvo, Rosario Domingo* Department of Manufacturing Engineering, Universidad Nacional de Educación a Distancia (UNED); C/ Juan del Rosal, 12 Madrid 28040, Spain
Abstract Maintenance operations are particular important in continuous systems and even more so when they apply to the part considered the system’s bottleneck. Although complex support systems that help in decision-making decision therefore tend to be used, in industrial plants use of a simple indicator can facilitate management and help improve results. Concern for the environment and environmental legislation is meanwhile prompting a need to reduce CO2 emissions. Hence the proposal for the introduction of the CO2 emissions/Production ratio indicator. This paper will therefore both evaluate whether it is suitable as an indicator of system efficiency, with consideration for availability, stoppage time, average stoppage time and the duration of micro-stops, and also consider the ratio between monthly emissions and emissions on days with stops. © © 2013 2013 The The Authors. Authors. Published Published by by Elsevier Elsevier Ltd. Ltd. Fabricacion Selection and peer-review under responsibility of Universidad de Zaragoza, Dpto Ing Diseño y Fabricacion. Keywords: Industrial process; Maintenance; CO2 emissions; Indicator
1. Introduction One of the aspects that matters most in industrial manufacturing is control of the production process. Although this involves the use of numerous systems that provide endless data, not all this data can be used to measure the efficiency of the manufacturing process.
* Corresponding author. Tel.: +34 91-398-6455. E-mail address: [email protected]
1877-7058 © 2013 The Authors. Published by Elsevier Ltd. Selection and peer-review under responsibility of Universidad de Zaragoza, Dpto Ing Diseño y Fabricacion doi:10.1016/j.proeng.2013.08.226
Luis Miguel Calvo and Rosario Domingo / Procedia Engineering 63 (2013) 678 – 686
679
Maintenance work is particularly important in this control, particularly in continuous processes, and even more so when these are focused in the system bottleneck (Lin and Ni (2009)); hence, in paper manufacturing, the drying process can be identified as a bottleneck. Complex support systems therefore tend to be used to assist decisionmaking (Lin and Ni (2009)), although in the industrial plant, use of a single indicator can facilitate management and help to improve results. There is also a general awareness of greenhouse gas emissions and of their impact on the atmosphere and on the environment. Two emissions rights allocation schemes have therefore been devised since 2005 (Royal Decree 5/2004; Royal Decree 1866/2004; Royal Decree 60/2005; Royal Decree 777/2006; Royal Decree 1370/2006) intended to raise industry’s awareness of the importance of controlling emissions generated in its processes and to force it to reduce the volume of these emissions. This third scheme (Law 13/2010), which begins in 2013 and will be valid until 2020, represents a further step and this time involves the obligation drastically to reduce or even to eliminate them through the imposition of a carbon tax. It has turned CO2 emissions into a significant cost in production processes with a high thermal energy consumption and this will definitely have considerable impact on the competitiveness of factories and may lead to the closure of more than one plant. Since the initial allocation scheme of 2005, which arose from the Kyoto Protocol, CO2 emissions have often been seen since as a result of the production process. Emissions only represent an administrative cost and are perceived more as a tax than as a possible tool with which to evaluate production process operation. 1.1 Background The concept of sustainability has been associated with efficiency in production (Aguado et al., 2013), but not with maintenance. A review of information published on paper manufacturing and its drying process has notably yielded publications focused on determining the influence of the drying process on paper characteristics (Karlsson, 2000; Hostetler et al., 2005), new systems for improving the efficiency of the process (Laurijssen et al., 2010; Martin et al., 2004), the influence of drying elements in the drying process (Barber, 2011) on design and sizing (Bauer et al., 1998) of this section. Numerous studies have been found on the energy aspect (Sivill et al., 2005; Zvolinschi et al., 2006) and analysis thereof for maximizing energy recovery (Laurijssen et al., 2010; Sivill and Ahtila, 2009), on reducing energy consumption (Austing, 2010) through control of the production process and the energy efficiency/productivity ratio (Virtanen et al., 2010). In the numerous references on TPM for improving production (Chand and Shirvani, 2000), no reference has been found to the use of environmental indicators or to CO2 emissions as an indicator to evaluate production efficiency and capacity. The CO2 emissions generated are affected significantly by the condition of the system’s thermal storage facility. Placing a limit on CO2 emissions represents a real limit to production. The importance of monitoring and controlling them is therefore clear. In most industrial production plants it is thought that CO2 emissions can only be reduced by replacing equipment with more modern and/or efficient units and by encouraging energy saving policies. Equipment, however, eventually deteriorates or fails to function according to the parameters for which it was designed and therefore installation efficiency may stray from nominal efficiency. 1.2 Objectives This study aims is intended to demonstrate that the CO2 Emissions/Production indicator can be employed as a valid indicator for measuring the status of maintenance and fine-tuning of the machinery involved in the process. This indicator can be used as a tool to help determine the causes of decreases in production arising from inefficiencies and to establish the operating conditions with which to increase production capacity in the system bottleneck.
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Luis Miguel Calvo and Rosario Domingo / Procedia Engineering 63 (2013) 678 – 686
2. Methodology The methodology used is the case study. A paper manufacturing plant has been used as a reference and data on its bottleneck, the drying section, which is the section responsible for 100% of this plant’s CO2 emissions, have been taken. The data used in the study, collected at a suitable frequency and measured in accordance with applicable Spanish law (Royal Decree 1315/2005, Law 1/2005, Decision 2007/589/EC), are those relating to production, emissions, availability –measured according to Nakajima (1988), number of stops and a large number of variables associated with the drying section. These variables are analyzed statistically to determine the relationship among them and the indicator under consideration. The number of dryers, the evaporation capacity of the drying system and the need to produce paper with a controlled dryness condition the speed of the process, which determines the difference between the theoretical production capacity and the real production yielded. The indicator to be introduced, CO2 emissions (ton)/ paper production (ton), is obtained as a ratio of the CO2 emissions established for each period and the tons of paper produced at the plant (sub-facility) in the same period. The proposed indicator (CO2 emissions (ton)/paper production (ton)) fulfils the following premises: 1. Its generation is unambiguously defined. 2. It can be reproduced and reviewed in the same facility over time. 3. It can be reproduced at different facilities. 4. It can be compared at different facilities and a product benchmark can be established. Calculation of CO2 emissions is based on the methodology outlined in the “Calculation of emissions and emission factors” section of the GHG Inventory Report (Ministry of the Environment, 2011) for the Implementation of the Emissions Trading Directive, reflected in equation (1). (1) CO2 emissions (ton) Data of activity Emission factor Oxidation factor Calculation of sub-facility output in order to establish the indicator in the periods of reference is based on ECOFIS methodological guidelines for the European Commission (2011, 2013). The study is based on data from 30 months in the period including 2009, 2010 and the first half of 2011, as this is considered a representative period of production in which there were neither changes in machine conditions nor alterations that changed their production capacity. Machine availability is defined as the percentage of time that the machine is useful for production. The time it is out of service or unavailable includes all downtime for corrective or preventive maintenance, from the moment it is out of service until it becomes operational or available for production again. Equation (2) shows the total time T, where Tt is the envisaged planned production time for the machine less the downtime or waiting time for causes not attributable to the production machine (tei), ts the set-up time and tp the production time. (2) T T f t ei t s t p t ei Tfs, meanwhile, is the time in which the machine is out of service and consists of time lost for maintenance and/or breakdown that affects plant availability and arises from the following: tac for breakdown or corrective maintenance of known origin, tad for breakdown or corrective maintenance of unknown origin, tam for micro-stops, tmp for preventive maintenance, and tpp for production requirements, as expressed in equation (3). T fs
tac
tad
tam t mp t pp
(3)
Lastly, availability is defined by the equation (4). Availability
Tt
T fs Tt
(4)
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Luis Miguel Calvo and Rosario Domingo / Procedia Engineering 63 (2013) 678 – 686
The machine adapts product quality continuously by varying working conditions, set-up times ts are considered losses of units produced and calculated with production reprocessed upon start-up of production after breakdown, stoppage or change of product. Waiting time (tei) is not taken into account because production planning is not evaluated. Operating and stop times have been collected from daily production reports. All downtime (tac, tad, tam, tmp, tpp) has been brought together as one time Tfs (see equation (3)), as the particular contribution of each to the indicator under consideration is outside the scope of the study. Transitional start-up periods after a long stop for reasons not attributable to production, Christmas stops, patrons saints’ days, strikes and downtime due to absence of production planning on account of lack of orders have also been removed in this section. Because of high efficiency of the machine, downtime hours are quantified per month rather than per day, although availability is calculated on a daily basis using hours as a unit. 3. The indicator “CO2 Emissions/Production” versus Availability, Downtime, Average Stop Time and Length of Short Stops
CO2 emissions (ton) / Production (ton)
To streamline analysis and interpretation of data, the indicator “CO2 Emissions/Production” is compared to each aspect by which it may be affected such as availability, downtime, average stop time, length of short stops, and the relation between the above indicator with consideration for CO2 emissions per monthly unit of production and CO2 emissions per unit of production on days with stoppage. The variation of the indicator with regard to overall machine availability is considered first, regardless of productivity and quality. This information is initially analyzed by monthly averages to yield the results shown in Fig. 1.
0.260 0,26 0.255 0,255 0.250 0,25 0.245 0,245 0.240 0,24 0.235 0,235 0.230 0,23 0.225 0,225 0.220 0,22 0.215 0,215 0.95 0,95
0.96 0,96
0.97 0,97
0.98 0,98
0.99 0,99
Availability Fig. 1. Availability – CO2 emissions (ton)/Paper production (ton).
Fig. 1 shows the relation between machine availability and CO2 emissions. It indicates that when there is higher monthly availability, the monthly average of the indicator is lower than in periods with low availability. Upon initial observation, machine downtime appears to be a source of CO2 emissions. Fig. 2, Downtime - CO2 Emissions/Production, analyses variation of the indicator according to the number of hours of downtime. The more downtime hours there are, the higher the value of the indicator, which corroborates the information in Fig. 1. Hours of downtime may, however, be the result of a single long-term stop or of several short stops, an aspect that, like the origin of the stops, is not detailed in Fig. 2.
Luis Miguel Calvo and Rosario Domingo / Procedia Engineering 63 (2013) 678 – 686
CO2 emissions (ton) / Production (ton)
682
0,26 0.260 0.255 0,255 0.250 0,25 0.245 0,245 0.240 0,24 0.235 0,235 0.230 0,23 0.225 0,225 0.220 0,22 0.215 0,215 0
5
10
15
20
25
30
Downtime (hour) Fig. 2. Downtime – CO2 emissions (ton)/Paper production (ton).
CO2Emisiones emissions (ton) CO2 (ton) / / Producción (ton) Production (ton)
To analyze the information on the variation of the indicator (as a monthly average), the indicator “CO2 emissions (ton)/Paper production (ton)” is analyzed first, with consideration for the global data for the month (production, availability and emissions) and the calculation is repeated for the same month with consideration only for the days of the month in which the availability was less than 1. This indicator is calculated again. 0.260 0,295 0.255 0,285 0.250 0,275 0.245 0,265 0.240 0,255 0.235 0,245 0.230
0,235 0.225 0.220 0,225 0.215 0,215 1
3
5
7
9
11
13
15
17
19
21
23
25
27
29
Mes
Month Average values medios Datos mensuales
Average values of days withcon downtime Datos mensuales de días paradas
Fig. 3. Comparison of monthly values and average values of days with downtime of CO2 emissions (ton)/Paper production (ton).
Fig. 3 shows that in all cases, the mean of the indicator “CO2 Emissions/Paper Production” on days with an availability of less than one unit is higher than the general monthly average for this indicator. This confirms a priori that machine availability is related to the value of this indicator. Cyclical behaviour is also observed in emissions, which means in the future it will be necessary to consider external variables that influence the manufacturing of the paper, since months with higher emissions (months 1, 2, 3, 12, 14, 23, 24, 25 and 26) generally correspond to periods of lower temperature and higher humidity outside.
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Luis Miguel Calvo and Rosario Domingo / Procedia Engineering 63 (2013) 678 – 686
Emisiones CO2 (ton) / / CO2 emissions (ton) Producción (ton) Production (ton)
0.260 0,26 0.255 0,255 0.250 0,25 0.245 0,245 0.240 0,24 0.235 0,235 0.230 0,23 0.225 0,225 0,22 0.220 0,215 0.215 0
1
2
3
4
5
6
Average stop (hours) Duración mediaduration de parada (horas) Fig. 4. Average stop duration – CO2 emissions (ton)/Production (ton).
CO2 emissions (ton)/ / Emisiones CO2 (ton) Production Producción (ton) (ton)
Fig. 4, “Average stop duration - CO2 Emissions/Paper Production”, analyses variation of the indicator with monthly hours of average stop duration. In this case a weak relation can be observed between the two concepts and thus it cannot be considered a conclusive value with which to predict the variation of the indicator. A slight but unsteady tendency for the indicator to increase is observed, as the duration of the stop grows longer. The process analyzed is continuous and displays great inertia to change. Variations in the main indicators are very slow and barely distinguishable in the production of a coil that lasts for approximately one hour. The thermal system has a long heating time and for this very reason is not affected by occasional variations such as micro-stops. This is evidenced by the study in Fig. 5, which features analysis of the variation of the indicator with microstops of less than one hour, based on daily production reports from 2009 to 2011. Note how the inertia of the system makes the indicator insensitive to micro-stops and/or short stops of less than an hour. 0.260 0,260 0.255 0,255 0.250 0,250 0.245 0,245 0.240 0,240 0.235 0,235 0.230 0,230 0.225 0,225 0.220 0,220 0.215 0,215
0
0,2 0.2
0,4 0.4
0,60.6
0,8 0.8
11
Durationdeof shortcortas stops(horas) Duración paradas Fig. 5. Duration of short stops – CO2 emissions (ton)/Paper production (ton).
1.21,2
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Luis Miguel Calvo and Rosario Domingo / Procedia Engineering 63 (2013) 678 – 686
4. An Initial Adjustment of the Indicator “CO2 Emissions/Production” with regard to the Variables Considered Quantification of data for availability, downtime and mean duration versus monthly CO2 Emissions/Production (Ind_100) and CO2 Emissions/Production on days with stoppage (Ind_<100) is shown in Table 1 and in Fig. 6. Table 1 shows the model with the greatest adjustment, after calibration of twelve regression models for each variable. Table 1. Adjustment models. Model Ind_100-Availability
Ind _ 100
0.54 0.75
Ind_100-Downtime
Ind _ 100
0.21 0.001· Downtime
Ind_100-Mean duration Ind_<100-Availability Ind_<100-Downtime
11.65 7.3· Availability
Ind _ 100
Ind _ 100
4.06 0.17 / Mean duration
0,24 0.24
0.24 0,24
0.5251
27.57
0.2485
6.17
0.1586
2.51
0.23 0,23
0.23 0,23
0.22 0,22
0.22 0,22 0.21 0,21
12
16 20 24 28 Downtime Tiempo_parada
0,3 0.30 0,28 0.28 0.26 0,26 0.24 0,24
0.22
0.22 0,22 0,22 0.95 0,96 0.96 0,97 0.97 0,98 0.98 0.99 8 12 16 20 24 28 0,95 0,99 Availability Tiempo_parada Disponibilidad Downtime
Fig. 6. Indicator adjustment.
0
1
2
3
4
5
6
Mean duration Duracion Media 0,3 0.30
Ind_No_100
0.26 0,26
9.61
0,24 0.24
Ind_No_100
Ind_No_100
0,28 0.28
0.3100
Ind_100
Ind_100
0,25 0.25
0,3 0.30
27.57
1
0,25 0.25
0.21 0.21 0,21 0,21 0.95 0.96 0.97 0.98 0.99 0.95 0.96 0.97 0.98 0.99 0,95 0,96 0,97 0,98 0,99 8 Availability Disponibilidad
Ind_<100
0.39 3.29 / Downtime
0,25 0.25
0.22 0,22
0.5251
1
0,26 0.26
0.23 0,23
36.7
1
0,26 0.26
0,24 0.24
0.6058
1
4.2 0.26 / Mean duration
Ind _ 100
R2 (%)
Availability
0,26 0.26
Ind_100
Ind_100
Ind_<100-Mean duration
Ind _ 100
Correlation
0.28 0,28 0.26 0,26 0.24 0,24 0.22
0,22
0
1
2
3
4
5
Mean duration Duracion Media
6
Luis Miguel Calvo and Rosario Domingo / Procedia Engineering 63 (2013) 678 – 686
685
5. Conclusions A new indicator, CO2 Emissions/Production has been introduced in order to observe its relation with the variables of machine availability arising from maintenance. The result obtained indicates initially that this ratio can be useful in identifying variables with which to improve plant efficiency and sustainability, although this will require analysis of daily data. This study also shows that there may be other variables that may influence the indicator such as the quality of product manufactured and the weight of paper. Each quality manufactured has a different formulation both of raw materials and of added products, which may yield specific characteristics with regard to the ease of removing the water it contains and therefore a different initial CO2 emissions (ton)/paper production (ton) indicator level. Analysis of the process and study of the data therefore continues in order to determine which other process variables have an influence, which of these are the most significant, and which should be taken into account in future work. References Aguado S., Alvarez R., Domingo R., 2013. Model of efficient and sustainable improvements in a lean production system through processes of environmental innovation. Journal of Cleaner Production 47, 141-148. Austing P. Reducing energy consumption in papermaking using advanced process control. University of Cambridge (2010). Barber E. Xerium Dryer Fabric Cleaning, www.xerium.com [consulted in 2011]. Bauer W., Zeyringer E., Ullrich H., 1998. Important drying parameters for the layout of a new production line. 1998 Coating Conference Proceedings. Bicudo L.C., 2008. Avalacioes na área de secagem e sua influencia na qualidade do papel. Revista técnica Opapel 4, 48-56. Chand G., Shirvani B., 2000. Implementing of TPM in cellular manufacture. Journal of Materials Processing Technology 103, 149-154. Decision 2007/589/EC, establishing guidelines for the monitoring and reporting of greenhouse gas emissions pursuant to Directive 2003/87/EC of the European Parliament and of the Council. OJEU L 229, of 18 July 2007. European Commission, 2011. Climate Action, methodological guides ECOFIS. http://ec.europa.eu/clima/policies/ets/benchmarking/documentation_en.htm European Commission, 2013. “Benchmarks for free allocation”, European Decision, reference Documents and guidelines for calculating allocation of emission rights in different processes. Ministry of the Environment, 2011. GHG Inventory report May 2011, Appendix 8, Reference of the Inventory for the Application of the Emissions Trading Directive, Spanish Ministry of the Environment. Hostetler R.E., Pelletier D., Cloutie C., 2005. Effect of drying conditions on the development of binder strength in double coated SBS paperboard. Proc. of TAPPI Coating Conference. Karlsson M., 2000. Papermaking part 2, Drying. Papermaking Science and Technology series, Vol. 9. Finnish Paper Engineers Association, Tappi Press. Laurijssen J., de Gream F.J., Worrel E., Faaij A., 2010. Optimizing the energy efficiency of conventional multi-cylinder dryers in the paper industry. Energy 35, 3738-3750. Law 1/2005, regulating the greenhouse gas emissions trading scheme. BOE (Spanish Official State Bulletin) 59 of 9 March 2005. Law 13/2010, amending Law 1/2005, which regulates the emission rights trading regime. BOE (Spanish Official State Bulletin) 163 of 5 July 2010. Lin L., J. Ni J., 2012. Decision support systems for effective maintenance operations. CIRP Annals - Manufacturing Technology 61, 411-414. Lin L., Ni J., 2009. Short-term decision support system for maintenance task prioritization. International Journal of Production Economics 121, 195-202. Martin A., Drotz M., Talja R., Kiajaluoto S., Puumalainen T., 2004. Energy analysis of impulse technology; research-scale experimental papermaking trials and simulations of industrial aplications. Applied Thermal Engineering 24, 2411-2425. Nakajima S., 1988. An Introduction to TPM. Productivity Press, Portland, OR. Royal Decree 1315/2005, establishing the bases of the greenhouse gas emissions monitoring and verification systems in facilities. BOE (Spanish Official State Bulletin) 136 of 4 November 2005. Royal Decree 1370/2006, approving the Spanish National Plan for the allocation of greenhouse gas emission allowances, 2008-2012. BOE (Spanish Official State Bulletin) 282 of 24 November 2006. Royal Decree 1866/2004, approving the Spanish National Plan for the allocation of emission allowances for the 2005- 2007 period. (Council of State in BOE 217 of 08/09/04). BOE (Spanish Official State Bulletin) 216 of 6 September 2004. Royal Decree 5/2004, regulating the greenhouse gas emission allowance trading scheme. BOE (Spanish Official State Bulletin) 208 of 27 August 2004.
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Royal Decree 60/2005, amending Royal Decree 1866/2004, approving the Spanish National Plan for the allocation of emission allowances for the 2005- 2007 period, 2005-2007. BOE (Spanish Official State Bulletin) 19 of 21 January 2005. Royal Decree 777/2006, amending Royal Decree 1866/2004, approving the Spanish National Plan for the allocation of emission allowances for the 2005- 2007 period. BOE (Spanish Official State Bulletin) 150 of 23 June 2006. Sivill L., Ahtila P., Taimisto M., 2005. Thermodynamic simulation of dryer section heat recovery in paper machines. Applied Thermal Engineering 25, 1273-1292. Sivill L., Ahtila P., 2009. Energy efficiency improvement of dryer section heat recovery systems in paper machines – a case of study. Applied Thermal Engineering 29, 3663-3668. Triantafillopoulos N., 2010. Nano enhances – Paper coating performance. Revista TAPPI-PIMA Paper 360 November-December, 30-34. Virtanen E., Haverin T., Mynttinen S., 2010. Eficiencia energética, operatividad y limpieza: Claves para la productividad en la sequería. Revista técnica El Papel October-November, 30-35. Zvolinschi A., Johannessen E., Kjelstrup S., 2006. The second-law optimal operation of a paper drying machine. Chemical Engineering Science 61, 3653-3662.
ScienceDirect Procedia Engineering 63 (2013) 678 – 686
The Manufacturing Engineering Society International Conference, MESIC 2013
A first approach to the use of CO2 emissions as a maintenance indicator in industrial plants Luis Miguel Calvo, Rosario Domingo* Department of Manufacturing Engineering, Universidad Nacional de Educación a Distancia (UNED); C/ Juan del Rosal, 12 Madrid 28040, Spain
Abstract Maintenance operations are particular important in continuous systems and even more so when they apply to the part considered the system’s bottleneck. Although complex support systems that help in decision-making decision therefore tend to be used, in industrial plants use of a simple indicator can facilitate management and help improve results. Concern for the environment and environmental legislation is meanwhile prompting a need to reduce CO2 emissions. Hence the proposal for the introduction of the CO2 emissions/Production ratio indicator. This paper will therefore both evaluate whether it is suitable as an indicator of system efficiency, with consideration for availability, stoppage time, average stoppage time and the duration of micro-stops, and also consider the ratio between monthly emissions and emissions on days with stops. © © 2013 2013 The The Authors. Authors. Published Published by by Elsevier Elsevier Ltd. Ltd. Fabricacion Selection and peer-review under responsibility of Universidad de Zaragoza, Dpto Ing Diseño y Fabricacion. Keywords: Industrial process; Maintenance; CO2 emissions; Indicator
1. Introduction One of the aspects that matters most in industrial manufacturing is control of the production process. Although this involves the use of numerous systems that provide endless data, not all this data can be used to measure the efficiency of the manufacturing process.
* Corresponding author. Tel.: +34 91-398-6455. E-mail address: [email protected]
1877-7058 © 2013 The Authors. Published by Elsevier Ltd. Selection and peer-review under responsibility of Universidad de Zaragoza, Dpto Ing Diseño y Fabricacion doi:10.1016/j.proeng.2013.08.226
Luis Miguel Calvo and Rosario Domingo / Procedia Engineering 63 (2013) 678 – 686
679
Maintenance work is particularly important in this control, particularly in continuous processes, and even more so when these are focused in the system bottleneck (Lin and Ni (2009)); hence, in paper manufacturing, the drying process can be identified as a bottleneck. Complex support systems therefore tend to be used to assist decisionmaking (Lin and Ni (2009)), although in the industrial plant, use of a single indicator can facilitate management and help to improve results. There is also a general awareness of greenhouse gas emissions and of their impact on the atmosphere and on the environment. Two emissions rights allocation schemes have therefore been devised since 2005 (Royal Decree 5/2004; Royal Decree 1866/2004; Royal Decree 60/2005; Royal Decree 777/2006; Royal Decree 1370/2006) intended to raise industry’s awareness of the importance of controlling emissions generated in its processes and to force it to reduce the volume of these emissions. This third scheme (Law 13/2010), which begins in 2013 and will be valid until 2020, represents a further step and this time involves the obligation drastically to reduce or even to eliminate them through the imposition of a carbon tax. It has turned CO2 emissions into a significant cost in production processes with a high thermal energy consumption and this will definitely have considerable impact on the competitiveness of factories and may lead to the closure of more than one plant. Since the initial allocation scheme of 2005, which arose from the Kyoto Protocol, CO2 emissions have often been seen since as a result of the production process. Emissions only represent an administrative cost and are perceived more as a tax than as a possible tool with which to evaluate production process operation. 1.1 Background The concept of sustainability has been associated with efficiency in production (Aguado et al., 2013), but not with maintenance. A review of information published on paper manufacturing and its drying process has notably yielded publications focused on determining the influence of the drying process on paper characteristics (Karlsson, 2000; Hostetler et al., 2005), new systems for improving the efficiency of the process (Laurijssen et al., 2010; Martin et al., 2004), the influence of drying elements in the drying process (Barber, 2011) on design and sizing (Bauer et al., 1998) of this section. Numerous studies have been found on the energy aspect (Sivill et al., 2005; Zvolinschi et al., 2006) and analysis thereof for maximizing energy recovery (Laurijssen et al., 2010; Sivill and Ahtila, 2009), on reducing energy consumption (Austing, 2010) through control of the production process and the energy efficiency/productivity ratio (Virtanen et al., 2010). In the numerous references on TPM for improving production (Chand and Shirvani, 2000), no reference has been found to the use of environmental indicators or to CO2 emissions as an indicator to evaluate production efficiency and capacity. The CO2 emissions generated are affected significantly by the condition of the system’s thermal storage facility. Placing a limit on CO2 emissions represents a real limit to production. The importance of monitoring and controlling them is therefore clear. In most industrial production plants it is thought that CO2 emissions can only be reduced by replacing equipment with more modern and/or efficient units and by encouraging energy saving policies. Equipment, however, eventually deteriorates or fails to function according to the parameters for which it was designed and therefore installation efficiency may stray from nominal efficiency. 1.2 Objectives This study aims is intended to demonstrate that the CO2 Emissions/Production indicator can be employed as a valid indicator for measuring the status of maintenance and fine-tuning of the machinery involved in the process. This indicator can be used as a tool to help determine the causes of decreases in production arising from inefficiencies and to establish the operating conditions with which to increase production capacity in the system bottleneck.
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Luis Miguel Calvo and Rosario Domingo / Procedia Engineering 63 (2013) 678 – 686
2. Methodology The methodology used is the case study. A paper manufacturing plant has been used as a reference and data on its bottleneck, the drying section, which is the section responsible for 100% of this plant’s CO2 emissions, have been taken. The data used in the study, collected at a suitable frequency and measured in accordance with applicable Spanish law (Royal Decree 1315/2005, Law 1/2005, Decision 2007/589/EC), are those relating to production, emissions, availability –measured according to Nakajima (1988), number of stops and a large number of variables associated with the drying section. These variables are analyzed statistically to determine the relationship among them and the indicator under consideration. The number of dryers, the evaporation capacity of the drying system and the need to produce paper with a controlled dryness condition the speed of the process, which determines the difference between the theoretical production capacity and the real production yielded. The indicator to be introduced, CO2 emissions (ton)/ paper production (ton), is obtained as a ratio of the CO2 emissions established for each period and the tons of paper produced at the plant (sub-facility) in the same period. The proposed indicator (CO2 emissions (ton)/paper production (ton)) fulfils the following premises: 1. Its generation is unambiguously defined. 2. It can be reproduced and reviewed in the same facility over time. 3. It can be reproduced at different facilities. 4. It can be compared at different facilities and a product benchmark can be established. Calculation of CO2 emissions is based on the methodology outlined in the “Calculation of emissions and emission factors” section of the GHG Inventory Report (Ministry of the Environment, 2011) for the Implementation of the Emissions Trading Directive, reflected in equation (1). (1) CO2 emissions (ton) Data of activity Emission factor Oxidation factor Calculation of sub-facility output in order to establish the indicator in the periods of reference is based on ECOFIS methodological guidelines for the European Commission (2011, 2013). The study is based on data from 30 months in the period including 2009, 2010 and the first half of 2011, as this is considered a representative period of production in which there were neither changes in machine conditions nor alterations that changed their production capacity. Machine availability is defined as the percentage of time that the machine is useful for production. The time it is out of service or unavailable includes all downtime for corrective or preventive maintenance, from the moment it is out of service until it becomes operational or available for production again. Equation (2) shows the total time T, where Tt is the envisaged planned production time for the machine less the downtime or waiting time for causes not attributable to the production machine (tei), ts the set-up time and tp the production time. (2) T T f t ei t s t p t ei Tfs, meanwhile, is the time in which the machine is out of service and consists of time lost for maintenance and/or breakdown that affects plant availability and arises from the following: tac for breakdown or corrective maintenance of known origin, tad for breakdown or corrective maintenance of unknown origin, tam for micro-stops, tmp for preventive maintenance, and tpp for production requirements, as expressed in equation (3). T fs
tac
tad
tam t mp t pp
(3)
Lastly, availability is defined by the equation (4). Availability
Tt
T fs Tt
(4)
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The machine adapts product quality continuously by varying working conditions, set-up times ts are considered losses of units produced and calculated with production reprocessed upon start-up of production after breakdown, stoppage or change of product. Waiting time (tei) is not taken into account because production planning is not evaluated. Operating and stop times have been collected from daily production reports. All downtime (tac, tad, tam, tmp, tpp) has been brought together as one time Tfs (see equation (3)), as the particular contribution of each to the indicator under consideration is outside the scope of the study. Transitional start-up periods after a long stop for reasons not attributable to production, Christmas stops, patrons saints’ days, strikes and downtime due to absence of production planning on account of lack of orders have also been removed in this section. Because of high efficiency of the machine, downtime hours are quantified per month rather than per day, although availability is calculated on a daily basis using hours as a unit. 3. The indicator “CO2 Emissions/Production” versus Availability, Downtime, Average Stop Time and Length of Short Stops
CO2 emissions (ton) / Production (ton)
To streamline analysis and interpretation of data, the indicator “CO2 Emissions/Production” is compared to each aspect by which it may be affected such as availability, downtime, average stop time, length of short stops, and the relation between the above indicator with consideration for CO2 emissions per monthly unit of production and CO2 emissions per unit of production on days with stoppage. The variation of the indicator with regard to overall machine availability is considered first, regardless of productivity and quality. This information is initially analyzed by monthly averages to yield the results shown in Fig. 1.
0.260 0,26 0.255 0,255 0.250 0,25 0.245 0,245 0.240 0,24 0.235 0,235 0.230 0,23 0.225 0,225 0.220 0,22 0.215 0,215 0.95 0,95
0.96 0,96
0.97 0,97
0.98 0,98
0.99 0,99
Availability Fig. 1. Availability – CO2 emissions (ton)/Paper production (ton).
Fig. 1 shows the relation between machine availability and CO2 emissions. It indicates that when there is higher monthly availability, the monthly average of the indicator is lower than in periods with low availability. Upon initial observation, machine downtime appears to be a source of CO2 emissions. Fig. 2, Downtime - CO2 Emissions/Production, analyses variation of the indicator according to the number of hours of downtime. The more downtime hours there are, the higher the value of the indicator, which corroborates the information in Fig. 1. Hours of downtime may, however, be the result of a single long-term stop or of several short stops, an aspect that, like the origin of the stops, is not detailed in Fig. 2.
Luis Miguel Calvo and Rosario Domingo / Procedia Engineering 63 (2013) 678 – 686
CO2 emissions (ton) / Production (ton)
682
0,26 0.260 0.255 0,255 0.250 0,25 0.245 0,245 0.240 0,24 0.235 0,235 0.230 0,23 0.225 0,225 0.220 0,22 0.215 0,215 0
5
10
15
20
25
30
Downtime (hour) Fig. 2. Downtime – CO2 emissions (ton)/Paper production (ton).
CO2Emisiones emissions (ton) CO2 (ton) / / Producción (ton) Production (ton)
To analyze the information on the variation of the indicator (as a monthly average), the indicator “CO2 emissions (ton)/Paper production (ton)” is analyzed first, with consideration for the global data for the month (production, availability and emissions) and the calculation is repeated for the same month with consideration only for the days of the month in which the availability was less than 1. This indicator is calculated again. 0.260 0,295 0.255 0,285 0.250 0,275 0.245 0,265 0.240 0,255 0.235 0,245 0.230
0,235 0.225 0.220 0,225 0.215 0,215 1
3
5
7
9
11
13
15
17
19
21
23
25
27
29
Mes
Month Average values medios Datos mensuales
Average values of days withcon downtime Datos mensuales de días paradas
Fig. 3. Comparison of monthly values and average values of days with downtime of CO2 emissions (ton)/Paper production (ton).
Fig. 3 shows that in all cases, the mean of the indicator “CO2 Emissions/Paper Production” on days with an availability of less than one unit is higher than the general monthly average for this indicator. This confirms a priori that machine availability is related to the value of this indicator. Cyclical behaviour is also observed in emissions, which means in the future it will be necessary to consider external variables that influence the manufacturing of the paper, since months with higher emissions (months 1, 2, 3, 12, 14, 23, 24, 25 and 26) generally correspond to periods of lower temperature and higher humidity outside.
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Emisiones CO2 (ton) / / CO2 emissions (ton) Producción (ton) Production (ton)
0.260 0,26 0.255 0,255 0.250 0,25 0.245 0,245 0.240 0,24 0.235 0,235 0.230 0,23 0.225 0,225 0,22 0.220 0,215 0.215 0
1
2
3
4
5
6
Average stop (hours) Duración mediaduration de parada (horas) Fig. 4. Average stop duration – CO2 emissions (ton)/Production (ton).
CO2 emissions (ton)/ / Emisiones CO2 (ton) Production Producción (ton) (ton)
Fig. 4, “Average stop duration - CO2 Emissions/Paper Production”, analyses variation of the indicator with monthly hours of average stop duration. In this case a weak relation can be observed between the two concepts and thus it cannot be considered a conclusive value with which to predict the variation of the indicator. A slight but unsteady tendency for the indicator to increase is observed, as the duration of the stop grows longer. The process analyzed is continuous and displays great inertia to change. Variations in the main indicators are very slow and barely distinguishable in the production of a coil that lasts for approximately one hour. The thermal system has a long heating time and for this very reason is not affected by occasional variations such as micro-stops. This is evidenced by the study in Fig. 5, which features analysis of the variation of the indicator with microstops of less than one hour, based on daily production reports from 2009 to 2011. Note how the inertia of the system makes the indicator insensitive to micro-stops and/or short stops of less than an hour. 0.260 0,260 0.255 0,255 0.250 0,250 0.245 0,245 0.240 0,240 0.235 0,235 0.230 0,230 0.225 0,225 0.220 0,220 0.215 0,215
0
0,2 0.2
0,4 0.4
0,60.6
0,8 0.8
11
Durationdeof shortcortas stops(horas) Duración paradas Fig. 5. Duration of short stops – CO2 emissions (ton)/Paper production (ton).
1.21,2
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4. An Initial Adjustment of the Indicator “CO2 Emissions/Production” with regard to the Variables Considered Quantification of data for availability, downtime and mean duration versus monthly CO2 Emissions/Production (Ind_100) and CO2 Emissions/Production on days with stoppage (Ind_<100) is shown in Table 1 and in Fig. 6. Table 1 shows the model with the greatest adjustment, after calibration of twelve regression models for each variable. Table 1. Adjustment models. Model Ind_100-Availability
Ind _ 100
0.54 0.75
Ind_100-Downtime
Ind _ 100
0.21 0.001· Downtime
Ind_100-Mean duration Ind_<100-Availability Ind_<100-Downtime
11.65 7.3· Availability
Ind _ 100
Ind _ 100
4.06 0.17 / Mean duration
0,24 0.24
0.24 0,24
0.5251
27.57
0.2485
6.17
0.1586
2.51
0.23 0,23
0.23 0,23
0.22 0,22
0.22 0,22 0.21 0,21
12
16 20 24 28 Downtime Tiempo_parada
0,3 0.30 0,28 0.28 0.26 0,26 0.24 0,24
0.22
0.22 0,22 0,22 0.95 0,96 0.96 0,97 0.97 0,98 0.98 0.99 8 12 16 20 24 28 0,95 0,99 Availability Tiempo_parada Disponibilidad Downtime
Fig. 6. Indicator adjustment.
0
1
2
3
4
5
6
Mean duration Duracion Media 0,3 0.30
Ind_No_100
0.26 0,26
9.61
0,24 0.24
Ind_No_100
Ind_No_100
0,28 0.28
0.3100
Ind_100
Ind_100
0,25 0.25
0,3 0.30
27.57
1
0,25 0.25
0.21 0.21 0,21 0,21 0.95 0.96 0.97 0.98 0.99 0.95 0.96 0.97 0.98 0.99 0,95 0,96 0,97 0,98 0,99 8 Availability Disponibilidad
Ind_<100
0.39 3.29 / Downtime
0,25 0.25
0.22 0,22
0.5251
1
0,26 0.26
0.23 0,23
36.7
1
0,26 0.26
0,24 0.24
0.6058
1
4.2 0.26 / Mean duration
Ind _ 100
R2 (%)
Availability
0,26 0.26
Ind_100
Ind_100
Ind_<100-Mean duration
Ind _ 100
Correlation
0.28 0,28 0.26 0,26 0.24 0,24 0.22
0,22
0
1
2
3
4
5
Mean duration Duracion Media
6
Luis Miguel Calvo and Rosario Domingo / Procedia Engineering 63 (2013) 678 – 686
685
5. Conclusions A new indicator, CO2 Emissions/Production has been introduced in order to observe its relation with the variables of machine availability arising from maintenance. The result obtained indicates initially that this ratio can be useful in identifying variables with which to improve plant efficiency and sustainability, although this will require analysis of daily data. This study also shows that there may be other variables that may influence the indicator such as the quality of product manufactured and the weight of paper. Each quality manufactured has a different formulation both of raw materials and of added products, which may yield specific characteristics with regard to the ease of removing the water it contains and therefore a different initial CO2 emissions (ton)/paper production (ton) indicator level. Analysis of the process and study of the data therefore continues in order to determine which other process variables have an influence, which of these are the most significant, and which should be taken into account in future work. References Aguado S., Alvarez R., Domingo R., 2013. Model of efficient and sustainable improvements in a lean production system through processes of environmental innovation. Journal of Cleaner Production 47, 141-148. Austing P. Reducing energy consumption in papermaking using advanced process control. University of Cambridge (2010). Barber E. Xerium Dryer Fabric Cleaning, www.xerium.com [consulted in 2011]. Bauer W., Zeyringer E., Ullrich H., 1998. Important drying parameters for the layout of a new production line. 1998 Coating Conference Proceedings. Bicudo L.C., 2008. Avalacioes na área de secagem e sua influencia na qualidade do papel. Revista técnica Opapel 4, 48-56. Chand G., Shirvani B., 2000. Implementing of TPM in cellular manufacture. Journal of Materials Processing Technology 103, 149-154. Decision 2007/589/EC, establishing guidelines for the monitoring and reporting of greenhouse gas emissions pursuant to Directive 2003/87/EC of the European Parliament and of the Council. OJEU L 229, of 18 July 2007. European Commission, 2011. Climate Action, methodological guides ECOFIS. http://ec.europa.eu/clima/policies/ets/benchmarking/documentation_en.htm European Commission, 2013. “Benchmarks for free allocation”, European Decision, reference Documents and guidelines for calculating allocation of emission rights in different processes. Ministry of the Environment, 2011. GHG Inventory report May 2011, Appendix 8, Reference of the Inventory for the Application of the Emissions Trading Directive, Spanish Ministry of the Environment. Hostetler R.E., Pelletier D., Cloutie C., 2005. Effect of drying conditions on the development of binder strength in double coated SBS paperboard. Proc. of TAPPI Coating Conference. Karlsson M., 2000. Papermaking part 2, Drying. Papermaking Science and Technology series, Vol. 9. Finnish Paper Engineers Association, Tappi Press. Laurijssen J., de Gream F.J., Worrel E., Faaij A., 2010. Optimizing the energy efficiency of conventional multi-cylinder dryers in the paper industry. Energy 35, 3738-3750. Law 1/2005, regulating the greenhouse gas emissions trading scheme. BOE (Spanish Official State Bulletin) 59 of 9 March 2005. Law 13/2010, amending Law 1/2005, which regulates the emission rights trading regime. BOE (Spanish Official State Bulletin) 163 of 5 July 2010. Lin L., J. Ni J., 2012. Decision support systems for effective maintenance operations. CIRP Annals - Manufacturing Technology 61, 411-414. Lin L., Ni J., 2009. Short-term decision support system for maintenance task prioritization. International Journal of Production Economics 121, 195-202. Martin A., Drotz M., Talja R., Kiajaluoto S., Puumalainen T., 2004. Energy analysis of impulse technology; research-scale experimental papermaking trials and simulations of industrial aplications. Applied Thermal Engineering 24, 2411-2425. Nakajima S., 1988. An Introduction to TPM. Productivity Press, Portland, OR. Royal Decree 1315/2005, establishing the bases of the greenhouse gas emissions monitoring and verification systems in facilities. BOE (Spanish Official State Bulletin) 136 of 4 November 2005. Royal Decree 1370/2006, approving the Spanish National Plan for the allocation of greenhouse gas emission allowances, 2008-2012. BOE (Spanish Official State Bulletin) 282 of 24 November 2006. Royal Decree 1866/2004, approving the Spanish National Plan for the allocation of emission allowances for the 2005- 2007 period. (Council of State in BOE 217 of 08/09/04). BOE (Spanish Official State Bulletin) 216 of 6 September 2004. Royal Decree 5/2004, regulating the greenhouse gas emission allowance trading scheme. BOE (Spanish Official State Bulletin) 208 of 27 August 2004.
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Royal Decree 60/2005, amending Royal Decree 1866/2004, approving the Spanish National Plan for the allocation of emission allowances for the 2005- 2007 period, 2005-2007. BOE (Spanish Official State Bulletin) 19 of 21 January 2005. Royal Decree 777/2006, amending Royal Decree 1866/2004, approving the Spanish National Plan for the allocation of emission allowances for the 2005- 2007 period. BOE (Spanish Official State Bulletin) 150 of 23 June 2006. Sivill L., Ahtila P., Taimisto M., 2005. Thermodynamic simulation of dryer section heat recovery in paper machines. Applied Thermal Engineering 25, 1273-1292. Sivill L., Ahtila P., 2009. Energy efficiency improvement of dryer section heat recovery systems in paper machines – a case of study. Applied Thermal Engineering 29, 3663-3668. Triantafillopoulos N., 2010. Nano enhances – Paper coating performance. Revista TAPPI-PIMA Paper 360 November-December, 30-34. Virtanen E., Haverin T., Mynttinen S., 2010. Eficiencia energética, operatividad y limpieza: Claves para la productividad en la sequería. Revista técnica El Papel October-November, 30-35. Zvolinschi A., Johannessen E., Kjelstrup S., 2006. The second-law optimal operation of a paper drying machine. Chemical Engineering Science 61, 3653-3662.