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Modelling the electrical energy profile of a batch manufacturing pharmaceutical facility

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Abstract

Sustainable manufacturing practices are a dominating consideration for legacy factories. Major attention is being applied to improving current practices to more sustainable ones. This research provides a case study of a batch manufacturing pharmaceutical facility and compares a number of approaches to modelling the electrical energy consumption in the plant. An accurate model of the electrical energy in the facility will allow more sustainable approaches to be developed. This can be achieved by improving current processes to reduce the electrical load. Historical electrical energy data were modelled using traditional time series methods. Historical manufacturing data and the electrical energy data were used to develop machine learning models using a feedforward neural network and a random forest. All of the approaches were then compared. The major challenge posed in model development and validation was acquiring data suitable for machine learning. The manufacturing data were stored in hand-written records. These records needed to be digitised and then go through a number of transformative steps before the data were suitable for modelling. The random forest model successfully modelled the energy profile of the facility. The model can be used to predict and better manage the plant electrical energy load.

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References

  1. Ahmad, M.W., Mourshed, M., Rezgui, Y.: Trees vs neurons: comparison between random forest and ANN for high-resolution prediction of building energy consumption. Energy Build. 147, 77–89 (2017). https://doi.org/10.1016/j.enbuild.2017.04.038

    Article  Google Scholar 

  2. Allaire, J., Chollet, F.: keras: R Interface to ’Keras’ (2018). https://CRAN.R-project.org/package=keras. R package version 2.2.4

  3. Allaire, J., Tang, Y.: tensorflow: R Interface to ’TensorFlow’ (2018). https://CRAN.R-project.org/package=tensorflow. R package version 1.10

  4. Blum, A., Langley, P.: Selection of relevant features and examples in machine learning. Artif. Intell. 97(1–2), 245–271 (1997). https://doi.org/10.1016/S0004-3702(97)00063-5

    Article  MathSciNet  MATH  Google Scholar 

  5. Box, G., Jenkins, G.: Time Series Analysis: Forecasting and Control, 1st edn. Holden-Day, Eagle Farm (1970)

    MATH  Google Scholar 

  6. Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123–140 (1996). https://doi.org/10.1007/BF00058655

    Article  MATH  Google Scholar 

  7. Breiman, L.: Random Forests. Mach. Learn. 45(1), 5–32 (2001). https://doi.org/10.1023/A:1010933404324

    Article  MATH  Google Scholar 

  8. Breiman, L., Friedman, J., Stone, C., Olshen, R.: Classification and Regression Trees. Chapman and Hall, Baco Raton (1984)

    MATH  Google Scholar 

  9. Chae, Y.T., Horesh, R., Hwang, Y., Lee, Y.M.: Artificial neural network model for forecasting sub-hourly electricity usage in commercial buildings. Energy Build. 111, 184–194 (2016). https://doi.org/10.1016/j.enbuild.2015.11.045

    Article  Google Scholar 

  10. Corporation, M., Weston, S.: doParallel: Foreach Parallel Adaptor for the ‘parallel’ Package (2019). https://CRAN.R-project.org/package=doParallel. R package version 1.0.15

  11. De Stefani, J., Le Borgne, Y.A., Caelen, O., Hattab, D., Bontempi, G.: Batch and incremental dynamic factor machine learning for multivariate and multi-step-ahead forecasting. Int. J. Data Sci. Anal. 7(4), 311–329 (2019). https://doi.org/10.1007/s41060-018-0150-x

    Article  Google Scholar 

  12. Deb, C., Zhang, F., Yang, J., Lee, S.E., Shah, K.W.: A review on time series forecasting techniques for building energy consumption. Renew. Sustain. Energy Rev. 74, 902–924 (2017). https://doi.org/10.1016/j.rser.2017.02.085

    Article  Google Scholar 

  13. Efron, B.: Bootstrap methods: another look at the Jackknife. Ann. Stat. 7(1), 1–26 (1979). https://doi.org/10.1214/aos/1176344552

    Article  MathSciNet  MATH  Google Scholar 

  14. Fan, C., Wang, J., Gang, W., Li, S.: Assessment of deep recurrent neural network-based strategies for short-term building energy predictions. Appl. Energy 236, 700–710 (2019). https://doi.org/10.1016/j.apenergy.2018.12.004

    Article  Google Scholar 

  15. Flath, C.M., Stein, N.: Towards a data science toolbox for industrial analytics applications. Comput. Ind. 94, 16–25 (2018). https://doi.org/10.1016/j.compind.2017.09.003

    Article  Google Scholar 

  16. Gahm, C., Denz, F., Dirr, M., Tuma, A.: Energy-efficient scheduling in manufacturing companies: a review and research framework. Eur. J. Oper. Res. 248(3), 744–757 (2016). https://doi.org/10.1016/j.ejor.2015.07.017

    Article  MathSciNet  MATH  Google Scholar 

  17. Grolemund, G., Wickham, H.: Dates and times made easy with lubridate. J. Stat. Softw. 40(3), 1–25 (2011)

    Article  Google Scholar 

  18. Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3(3), 1157–1182 (2003). https://doi.org/10.1016/j.aca.2011.07.027

    Article  MATH  Google Scholar 

  19. Hahnloser, R.H.R., Sarpeshkar, R., Mahowald, M.A., Douglas, R.J., Seung, H.S.: Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit. Nature 405(6789), 947–951 (2000). https://doi.org/10.1038/35016072

    Article  Google Scholar 

  20. Harjunkoski, I., Maravelias, C.T., Bongers, P., Castro, P.M., Engell, S., Grossmann, I.E., Hooker, J., Méndez, C., Sand, G., Wassick, J.: Scope for industrial applications of production scheduling models and solution methods. Comput. Chem. Eng. 62, 161–193 (2014). https://doi.org/10.1016/j.compchemeng.2013.12.001

    Article  Google Scholar 

  21. Heaton, J.: Introduction to Neural Networks with Java, 2nd edn. Heaton Research, New York (2008)

    Google Scholar 

  22. Heaton, J.: Artificial Intelligence for Humans, Volume 3: Deep Learning and Neural Networks. Heaton Research, New York (2015)

    Google Scholar 

  23. Hyndman, R.J., Athanasopoulos, G.: Forecasting: Principles and Practice, 2nd edn. OTexts, New York (2018)

    Google Scholar 

  24. Hyndman, R.J., Khandakar, Y.: Automatic time series forecasting: the forecast package for R. J. Stat. Softw. 26(3), 1–22 (2008)

    Google Scholar 

  25. Jiménez-González, C., Constable, D.J.C., Ponder, C.S.: Evaluating the ‘Greenness’ of chemical processes and products in the pharmaceutical industry: a green metrics primer. Chem. Soc. Rev. 41(4), 1485–1498 (2012). https://doi.org/10.1039/C1CS15215G

    Article  Google Scholar 

  26. Kuhn, M., Johnson, K.: Applied Predictive Modeling. Springer, New York (2013)

    Book  Google Scholar 

  27. Kuhn, M., Wing, J., Weston, S., Williams, A., Keefer, C., Engelhardt, A., Cooper, T., Mayer, Z., Kenkel, B., the R Core Team, Benesty, M., Lescarbeau, R., Ziem, A., Scrucca, L., Tang, Y., Candan, C., Hunt, T.: caret: Classification and Regression Training (2018). https://CRAN.R-project.org/package=caret. R package version 6.0-81

  28. Lee, C.K.M., Zhang, S.Z., Ng, K.K.H.: Development of an industrial Internet of things suite for smart factory towards re-industrialization. Adv. Manuf. 5(4), 335–343 (2017). https://doi.org/10.1007/s40436-017-0197-2

    Article  Google Scholar 

  29. Liaw, A., Wiener, M.: Classification and regression by randomforest. R News 2(3), 18–22 (2002)

    Google Scholar 

  30. Marsh, J.L., Eyers, D.R.: Increasing production efficiency through electronic batch record systems : a case study. In: Sustainable Design and Manufacturing 2016, pp. 261–269. Springer, Berlin (2016)

  31. Mat Daut, M.A., Hassan, M.Y., Abdullah, H., Rahman, H.A., Abdullah, M.P., Hussin, F.: Building electrical energy consumption forecasting analysis using conventional and artificial intelligence methods: a review. Renew. Sustain. Energy Rev. 70, 1108–1118 (2017). https://doi.org/10.1016/j.rser.2016.12.015

    Article  Google Scholar 

  32. Microsoft, Weston, S.: foreach: Provides Foreach Looping Construct for R (2017). https://CRAN.R-project.org/package=foreach. R package version 1.4.4

  33. Molina-Solana, M., Ros, M., Ruiz, M.D., Gómez-Romero, J., Martin-Bautista, M.: Data science for building energy management: a review. Renew. Sustain. Energy Rev. 70, 598–609 (2017). https://doi.org/10.1016/j.rser.2016.11.132

    Article  Google Scholar 

  34. Mulrennan, K., Awad, M., Donovan, J., Macpherson, R., Tormey, D.: Identifying highly variable and energy intensive batch manufacturing processes using statistical methodologies. In: Proceedings of the 17th International Conference on Manufacturing Research, ICMR 2019 (2019)

  35. Mulrennan, K., Donovan, J., Tormey, D., Macpherson, R.: A data science approach to modelling a manufacturing facility’s electrical energy profile from plant production data. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 387–391. IEEE (2018). https://doi.org/10.1109/DSAA.2018.00050

  36. Nair, V., Hinton, G.E.: Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th International Conference on International Conference on Machine Learning, ICML’10, pp. 807–814. Omnipress, USA (2010). http://dl.acm.org/citation.cfm?id=3104322.3104425

  37. Nichiforov, C., Stamatescu, I., Fagarasan, I., Stamatescu, G.: Energy consumption forecasting using ARIMA and neural network models. In: 2017 5th International Symposium on Electrical and Electronics Engineering (ISEEE), pp. 1–4. IEEE (2017). https://doi.org/10.1109/ISEEE.2017.8170657

  38. O’Donovan, P., Leahy, K., Bruton, K., O’Sullivan, D.T.J.: An industrial big data pipeline for data-driven analytics maintenance applications in large-scale smart manufacturing facilities. J. Big Data 2(1), 25 (2015). https://doi.org/10.1186/s40537-015-0034-z

    Article  Google Scholar 

  39. Pearson, C.H.: Dates and times in excel (2018). http://www.cpearson.com/excel/datetime.htm

  40. Peng, T., Xu, X.: Energy-efficient machining systems: a critical review. Int. J. Adv. Manuf. Technol. 72(9–12), 1389–1406 (2014). https://doi.org/10.1007/s00170-014-5756-0

    Article  Google Scholar 

  41. R Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing (2018). http://www.R-project.org/. R version 3.5.0

  42. RStudio Team: RStudio: Integrated Development Environment for R. RStudio, Inc., Boston, MA (2018). http://www.rstudio.com/. RStudio version 1.1.447

  43. Rumelhart, D., Hinton, G., Williams, R.: Learning representations by back-propagating errors. Nature 323(6088), 533–536 (1986). https://doi.org/10.1038/323533a0

    Article  MATH  Google Scholar 

  44. Shrouf, F., Gong, B., Ordieres-Meré, J.: Multi-level awareness of energy used in production processes. J. Clean. Prod. 142, 2570–2585 (2017). https://doi.org/10.1016/j.jclepro.2016.11.019

    Article  Google Scholar 

  45. Shrouf, F., Ordieres, J., Miragliotta, G.: Smart factories in Industry 4.0: A review of the concept and of energy management approached in production based on the Internet of Things paradigm. In: 2014 IEEE International Conference on Industrial Engineering and Engineering Management, vol. 2015-January, pp. 697–701. IEEE (2014). https://doi.org/10.1109/IEEM.2014.7058728

  46. Shumway, R.H., Stoffer, D.S.: Time Series Analysis and Its Applications. Springer Texts in Statistics. Springer, Berlin (2017)

    Book  Google Scholar 

  47. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929–1958 (2014)

    MathSciNet  MATH  Google Scholar 

  48. Stolpe, M., Blom, H., Morik, K.: Sustainable industrial processes by embedded real-time quality prediction. In: Sustainability, vol. 645. Springer, Berlin (2016). https://doi.org/10.1007/978-3-319-31858-5_10

  49. Vaghefi, A., Jafari, M., Bisse, E., Lu, Y., Brouwer, J.: Modeling and forecasting of cooling and electricity load demand. Appl. Energy 136, 186–196 (2014). https://doi.org/10.1016/j.apenergy.2014.09.004

    Article  Google Scholar 

  50. Walsh, S.: A summary of climate averages for Ireland 1981-2010. Technical Report, Met Éireann (2012). http://www.met.ie/climate-ireland/SummaryClimAvgs.pdf

  51. Wang, Y., Gan, D., Sun, M., Zhang, N., Lu, Z., Kang, C.: Probabilistic individual load forecasting using pinball loss guided LSTM. Appl. Energy 235, 10–20 (2019). https://doi.org/10.1016/j.apenergy.2018.10.078

    Article  Google Scholar 

  52. Wang, Z., Srinivasan, R.S.: A review of artificial intelligence based building energy use prediction: contrasting the capabilities of single and ensemble prediction models. Renew. Sustain. Energy Rev. 75, 796–808 (2017). https://doi.org/10.1016/j.rser.2016.10.079

    Article  Google Scholar 

  53. Wang, Z., Wang, Y., Zeng, R., Srinivasan, R.S., Ahrentzen, S.: Random forest based hourly building energy prediction. Energy Build. (2018). https://doi.org/10.1016/j.enbuild.2018.04.008

    Article  Google Scholar 

  54. Wickham, H.: Reshaping data with the reshape package. J. Stat. Softw. 21(12), 1–20 (2007)

    Article  Google Scholar 

  55. Wickham, H.: ggplot2, 2 edn. Use R! Springer International Publishing (2016). https://doi.org/10.1007/978-3-319-24277-4

  56. Wickham, H.: tidyverse: Easily Install and Load the ’Tidyverse’ (2017). https://CRAN.R-project.org/package=tidyverse. R package version 1.2.1

  57. Wickham, H., Bryan, J.: readxl: Read Excel Files (2018). https://CRAN.R-project.org/package=readxl. R package version 1.1.0

  58. Wickham, H., Francois, R., Henry, L., Muller, K.: dplyr: A Grammar of Data Manipulation (2018). https://CRAN.R-project.org/package=dplyr. R package version 0.7.8

  59. Zhang, Z., Tang, R., Peng, T., Tao, L., Jia, S.: A method for minimizing the energy consumption of machining system: integration of process planning and scheduling. J. Clean. Prod. 137, 1647–1662 (2016). https://doi.org/10.1016/j.jclepro.2016.03.101

    Article  Google Scholar 

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Acknowledgements

The North West Centre for Advanced Manufacturing (NW CAM) project is supported by the European Union’s INTERREG VA Programme, managed by the Special EU Programmes Body (SEUPB). The views and opinions in this document do not necessarily reflect those of the European Commission or the Special EU Programmes Body (SEUPB).

If you would like further information about NW CAM please contact the lead partner, Catalyst Inc, for details.

The authors wish to thank Eugene McHenry (GlaxoSmithKline, Sligo, Ireland), Ruaidhri McDaid (GlaxoSmithKline, Sligo, Ireland) and GlaxoSmithKline for supporting the project.

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  1. PEM Research Centre, Institute of Technology Sligo, Sligo, Ireland

    Konrad Mulrennan, Mohamed Awad, John Donovan & David Tormey

  2. GlaxoSmithKline, Sligo, Ireland

    Russell Macpherson

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  1. Konrad Mulrennan
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This paper is an extension with significant additions of the DSAA’2018 Application Track paper titled ‘A data science approach to modelling a manufacturing facility’s electrical energy profile from plant production data’ [35].

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Mulrennan, K., Awad, M., Donovan, J. et al. Modelling the electrical energy profile of a batch manufacturing pharmaceutical facility. Int J Data Sci Anal 10, 285–300 (2020). https://doi.org/10.1007/s41060-020-00217-1

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