Startseite Alternative method for determining basis weight in papermaking by using an interactive soft sensor based on an artificial neural network model
Artikel
Lizenziert
Nicht lizenziert Erfordert eine Authentifizierung

Alternative method for determining basis weight in papermaking by using an interactive soft sensor based on an artificial neural network model

  • José L. Rodríguez-Álvarez EMAIL logo , Rogelio López-Herrera , Iván E. Villalón-Turrubiates , Jorge L. García-Alcaraz , José R. Díaz-Reza , Jesús L. Arce-Valdez , Osbaldo Aragón-Banderas und Arturo Soto-Cabral
Veröffentlicht/Copyright: 8. Juli 2022
Veröffentlichen auch Sie bei De Gruyter Brill

Abstract

Currently, there are two procedures to determine the basis weight in papermaking processes: the measurements made by the quality control laboratory or the measurements made by the quality control system. This research presents an alternative to estimating basis weight-based artificial neural network (ANN) modeling. The NN architecture was constructed by trial and error, obtaining the best results using two hidden layers with 48 and 12 neurons, respectively, in addition to the input and output layers. Mean absolute error and mean absolute percentage error was used for the loss and metric functions, respectively. Python was used in the training, validation, and testing process. The results indicate that the model can reasonably determine the basis weight given the independent variables analyzed here. The R 2 reached by the model was 94 %, and MAE was 12.40 grams/m2. Using the same dataset, the fine tree regression model showed an R 2 of 99 % and an MAE of 3.35 grams/m2. Additionally, a dataset not included in the building process was used to validate the method’s performance. The results showed that ANN-based modeling has a higher predictive capability than the regression tree model. Therefore, this model was embedded in a graphic user interface that was developed in Python.

Award Identifier / Grant number: 487109

Funding statement: This work was supported in part by the National Council of Science and Technology [Consejo Nacional de Ciencia y Tecnología (CONACYT)] under Grant 487109.

  1. Conflict of interest: The authors declare no conflicts of interest.

References

ABB (2021) Weight Virtual Measurement for paper, packaging and tissue. Retrieved from https://new.abb.com/pulp-paper/abb-in-pulp-and-paper/papermaking.Suche in Google Scholar

Adamopoulos, S., Anthony, K., Rapti, E., Birbilis, D. (2016). Predicting the properties of corrugated base papers using multiple linear regression and artificial neural networks. Drewno 59:61–72. doi:10.12841/wood.1644-3985.144.13.Suche in Google Scholar

Amiri, M., Davande, H., Sadeghian, A., Chartier, S. (2010) Feedback associative memory based on a new hybrid model of generalized regression and self-feedback neural networks. Neural Netw. 23(7):892–904. doi:10.1016/j.neunet.2010.05.005.Suche in Google Scholar PubMed

Antanasijević, D., Pocajt, V., Ristić, M., Perić-Grujić, A. (2015) Modeling of energy consumption and related GHG (greenhouse gas) intensity and emissions in Europe using general regression neural networks. Energy 84:816–824. doi:10.1016/j.energy.2015.03.060.Suche in Google Scholar

Baruník, J., Křehlík, T. (2016) Combining high frequency data with non-linear models for forecasting energy market volatility. Expert Syst. Appl. 55:222–242.10.1016/j.eswa.2016.02.008Suche in Google Scholar

Bengio, Y. (2012) Practical recommendations for gradient-based training of deep architectures. In: Neural networks: Tricks of the trade. Springer. pp. 437–478.10.1007/978-3-642-35289-8_26Suche in Google Scholar

Camargo, M.E., Santos, G.M., Russo, S.L. (2010) Applied control charts for analysis of quality control. Paper presented at the 40th International Conference on Computers & Industrial Engineering.10.1109/ICCIE.2010.5668227Suche in Google Scholar

Canário, J.P., Mello, R., Curilem, M., Huenupan, F., Rios, R. (2020) In-depth comparison of deep artificial neural network architectures on seismic events classification. J. Volcanol. Geotherm. Res. 401. doi:10.1016/j.jvolgeores.2020.106881.Suche in Google Scholar

Chang, P., Li, Z. (2021) Over-complete deep recurrent neural network based on wastewater treatment process soft sensor application. Appl. Soft Comput. 105. doi:10.1016/j.asoc.2021.107227.Suche in Google Scholar

Chang, S., Aw, C. (1996) A neural fuzzy control chart for detecting and classifying process mean shifts. Int. J. Prod. Res. 34(8):2265–2278.10.1080/00207549608905024Suche in Google Scholar

Cheng, C.-B. (2005) Fuzzy process control: construction of control charts with fuzzy numbers. Fuzzy Sets Syst. 154(2):287–303.10.1016/j.fss.2005.03.002Suche in Google Scholar

Costela, F.M., Castro-Torres, J.J. (2020) Risk prediction model using eye movements during simulated driving with logistic regressions and neural networks. Transp. Res., Part F Traffic Psychol. Behav. 74:511–521. doi:10.1016/j.trf.2020.09.003.Suche in Google Scholar

Dayhoff, J.E. Neural network architectures: an introduction. Van Nostrand Reinhold Co, 1990.Suche in Google Scholar

De Assis, A.J., Maciel Filho, R. (2000) Soft sensors development for on-line bioreactor state estimation. Comput. Chem. Eng. 24(2-7):1099–1103.10.1016/S0098-1354(00)00489-0Suche in Google Scholar

Dudek-Burlikowska, M. (2005) Quality estimation of process with usage control charts type XR and quality capability of process Cp, Cpk. J. Mater. Process. Technol. 162:736–743.10.1016/j.jmatprotec.2005.02.210Suche in Google Scholar

Elman, J.L. (1990) Finding structure in time. Cogn. Sci. 14(2):179–211.10.4324/9781315784779-11Suche in Google Scholar

Fausett, L.V. Fundamentals of neural networks: architectures, algorithms, and applications. Pearson Education India, 2006.Suche in Google Scholar

Fortuna, L., Graziani, S., Rizzo, A., Xibilia, M.G. Soft sensors for monitoring and control of industrial processes. vol. 22, Springer, 2007.Suche in Google Scholar

Fu, X., Wang, L. (2003) Data dimensionality reduction with application to simplifying RBF network structure and improving classification performance. IEEE Trans. Syst. Man Cybern., Part B, Cybern. 33(3):399–409.10.1109/TSMCB.2003.810911Suche in Google Scholar PubMed

Gadeo-Martos, M.A., Fernandez-Prieto, J.A., Velasco, J.R. (2011) An architecture for performance optimization in a collaborative knowledge-based approach for wireless sensor networks. Sensors 11(10):9136–9159.10.3390/s111009136Suche in Google Scholar PubMed PubMed Central

Géron, A. Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems. O’Reilly Media, 2019.Suche in Google Scholar

Gülbay, M., Kahraman, C. (2006) Development of fuzzy process control charts and fuzzy unnatural pattern analyses. Comput. Stat. Data Anal. 51(1):434–451.10.1016/j.csda.2006.04.031Suche in Google Scholar

Gülbay, M., Kahraman, C. (2007) An alternative approach to fuzzy control charts: Direct fuzzy approach. Inf. Sci. 177(6):1463–1480.10.1016/j.ins.2006.08.013Suche in Google Scholar

Hashemi Fath, A., Madanifar, F., Abbasi, M. (2020) Implementation of multilayer perceptron (MLP) and radial basis function (RBF) neural networks to predict solution gas-oil ratio of crude oil systems. Petroleum 6(1):80–91. doi:10.1016/j.petlm.2018.12.002.Suche in Google Scholar

Haykin, S. A comprehensive foundation. Neural Networks, vol. 2. p. 41, 2004.Suche in Google Scholar

Heinisch, J., Lockner, Y., Hopmann, C. (2021) Comparison of design of experiment methods for modeling injection molding experiments using artificial neural networks. J. Manuf. Process. 61:357–368. doi:10.1016/j.jmapro.2020.11.011.Suche in Google Scholar

Hinton, G., Srivastava, N., Swersky, K. (2012) Neural networks for machine learning lecture 6a overview of mini-batch gradient descent. Retrieved from https://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf.Suche in Google Scholar

HONEYWELL (2021) Quality Control System 4.0. Retrieved from https://www.honeywellprocess.com/en-US/online_campaigns/QCS4.0/Pages/index.html.Suche in Google Scholar

Jaderberg, M., Dalibard, V., Osindero, S., Czarnecki, W.M., Donahue, J., Razavi, A., Vinyals, O., Green, T., Dunning, I., Simonyan, K. Fernando, C., Kavukcuoglu, K. (2017) Population based training of neural networks. arXiv preprint arXiv:1711.09846.Suche in Google Scholar

Kamyar, R., Lauri Pla, D., Husain, A., Cogoni, G., Wang, Z. (2021) Soft sensor for real-time estimation of tablet potency in continuous direct compression manufacturing operation. Int. J. Pharm. 602. doi:10.1016/j.ijpharm.2021.120624.Suche in Google Scholar PubMed

Karamichailidou, D., Kaloutsa, V., Alexandridis, A. (2021) Wind turbine power curve modeling using radial basis function neural networks and tabu search. Renew. Energy 163:2137–2152. doi:10.1016/j.renene.2020.10.020.Suche in Google Scholar

Karayiannis, N., Venetsanopoulos, A.N. Artificial neural networks: learning algorithms, performance evaluation, and applications. vol. 209, Springer Science & Business Media, 1992.Suche in Google Scholar

Kaya, İ., Kahraman, C. (2011) Process capability analyses based on fuzzy measurements and fuzzy control charts. Expert Syst. Appl. 38(4):3172–3184.10.1016/j.eswa.2010.09.004Suche in Google Scholar

Kilulya, K.F., Mamba, B.B., Ngila, C., Bush, T., Msagati, T.A.M. (2015) Evaluation of the influence of lipophilic extractive residues on dissolving pulp quality parameters by partial least squares method of chemometrics. Nord. Pulp Pap. Res. J. 30(3):402–410. doi:10.3183/npprj-2015-30-03-p402-410.Suche in Google Scholar

Kim, D., Kwon, J., Jeon, B., Park, Y.-L. (2020) Adaptive calibration of soft sensors using optimal transportation transfer learning for mass production and long-term usage. Adv. Intell. Syst. 2(6).10.1002/aisy.201900178Suche in Google Scholar

Kim, J., Abdella, G.M., Kim, S., Al-Khalifa, K.N., Hamouda, A.M. (2019) Control charts for variability monitoring in high-dimensional processes. Comput. Ind. Eng. 130:309–316.10.1016/j.cie.2019.02.012Suche in Google Scholar

Konate, A.A., Pan, H., Khan, N., Yang, J.H. (2015) Generalized regression and feed-forward back propagation neural networks in modeling porosity from geophysical well logs. J. Pet. Explor. Prod. Technol. 5(2):157–166.10.1007/s13202-014-0137-7Suche in Google Scholar

Lan, T., Tong, C., Yu, H., Shi, X., Luo, L. (2020) Nonlinear process monitoring based on decentralized generalized regression neural networks. Expert Syst. Appl. 150. doi:10.1016/j.eswa.2020.113273.Suche in Google Scholar

Mahmoud Ali, M., Omran, A.N.M., Abd-El-Hakeem Mohamed, M. (2021) Prediction the correlations between hardness and tensile properties of aluminum-silicon alloys produced by various modifiers and grain refineries using regression analysis and an artificial neural network model. Int. J. Eng. Sci. Technol. 24(1):105–111. doi:10.1016/j.jestch.2020.12.010.Suche in Google Scholar

Marklund, A., Hauksson, J.B., Edlund, U., Sjöström, M. (1998) Prediction of strength parameters for softwood kraft pulps: Multivariate data analysis based on physical and morphological parameters. Nord. Pulp Pap. Res. J. 13:211–219.10.3183/npprj-1998-13-03-p211-219Suche in Google Scholar

Merbold, H., Maas, D.J.H.C., v. Mechelen, J.L.M. (2016) Multiparameter sensing of paper sheets using terahertz time-domain spectroscopy: Caliper, fiber orientation, moisture, and the role of spatial inhomogeneity. Paper presented at the 2016 IEEE SENSORS (30 Oct.–3 Nov. 2016).10.1109/ICSENS.2016.7808683Suche in Google Scholar

Mezgár, I., Egresits, C., Monostori, L. (1997) Design and real-time reconfiguration of robust manufacturing systems by using design of experiments and artificial neural networks. Comput. Ind. 33(1):61–70. doi:10.1016/S0166-3615(97)00011-0.Suche in Google Scholar

Mohammadi, F., Pourzamani, H., Karimi, H., Mohammadi, M., Mohammadi, M., Ardalan, N., Khoshravesh, R., Pooresmaeil, H., Shahabi, S., Sabahi, M., Sadat miryonesi, F., Najafi, M., Yavari, Z., Mohammadi, F., Teiri, H., Jannati, M. (2021) Artificial neural network and logistic regression modelling to characterize COVID-19 infected patients in local areas of Iran. Biomed. J. 44(3):304–316. doi:10.1016/j.bj.2021.02.006.Suche in Google Scholar PubMed PubMed Central

Moody, J., Darken, C. (1989) Fast learning in networks of locally-tuned processing units. Neural Comput. 1(2):281–294.10.1162/neco.1989.1.2.281Suche in Google Scholar

Morala, P., Cifuentes, J.A., Lillo, R.E., Ucar, I. (2021) Towards a mathematical framework to inform neural network modelling via polynomial regression. Neural Netw. 142:57–72. doi:10.1016/j.neunet.2021.04.036.Suche in Google Scholar PubMed

Moreira, M.O., Balestrassi, P.P., Paiva, A.P., Ribeiro, P.F., Bonatto, B.D. (2021) Design of experiments using artificial neural network ensemble for photovoltaic generation forecasting. Renew. Sustain. Energy Rev. 135. doi:10.1016/j.rser.2020.110450.Suche in Google Scholar

Nabney, I.T. (1999) Efficient training of RBF networks for classification. Int. J. Neural Syst. 210–215.10.1049/cp:19991110Suche in Google Scholar

Napoli, G., Xibilia, M.G. (2011) Soft Sensor design for a Topping process in the case of small datasets. Comput. Chem. Eng. 35(11):2447–2456.10.1016/j.compchemeng.2010.12.009Suche in Google Scholar

Nie, X., Liang, J., Cao, J. (2019) Multistability analysis of competitive neural networks with Gaussian-wavelet-type activation functions and unbounded time-varying delays. Appl. Math. Comput. 356:449–468. doi:10.1016/j.amc.2019.03.026.Suche in Google Scholar

Niño-Adan, I., Landa-Torres, I., Manjarres, D., Portillo, E. (2021) Soft-sensor design for vacuum distillation bottom product penetration classification. Appl. Soft Comput. 102. doi:10.1016/j.asoc.2020.107072.Suche in Google Scholar

Paggi, H., Soriano, J., Rampérez, V., Gutiérrez, R., Lara, J.A. (2022) A distributed soft sensors model for managing vague and uncertain multimedia communications using information fusion techniques. Alex. Eng. J. 61(7):5517–5528. doi:10.1016/j.aej.2021.10.060.Suche in Google Scholar

Poechmuelloer, W., Halgamuge, S., Glesner, M., Schweikert, P., Pfeffermann, A. (1994). RBF and CBF neural network learning procedures. Paper presented at the Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN’94).10.1109/ICNN.1994.374197Suche in Google Scholar

Raunio, J.-P., Ritala, R. (2018) Active scanner control on paper machines. J. Process Control 72:74–90.10.1016/j.jprocont.2018.09.012Suche in Google Scholar

Rodriguez-Alvarez, J., Lopez-Herrera, R., Villalon-Turrubiates, I., Grijalva-Avila, G., Garcia-Alcaraz, J. (2021) Modeling and parameter optimization of the papermaking processes by using regression tree model and full factorial design. Tappi J. 20(2):123–137. doi:10.32964/TJ20.2.123.Suche in Google Scholar

Rodríguez-Álvarez, J.L., López-Herrera, R., Villalon-Turrubiates, I.E., Molina-Arredondo, R.D., Alcaraz, J.L.G., Hernández-Olvera, Ó.D. (2021) Analysis and control of the paper moisture content variability by using fuzzy and traditional individual control charts. Chemom. Intell. Lab. Syst. 208:1–12.10.1016/j.chemolab.2020.104211Suche in Google Scholar

Rooki, R. (2016) Application of general regression neural network (GRNN) for indirect measuring pressure loss of Herschel–Bulkley drilling fluids in oil drilling. Measurement 85:184–191. doi:10.1016/j.measurement.2016.02.037.Suche in Google Scholar

Rosli, N., Ibrahim, R., Ismail, I., Hassan, S.M., Chung, T.D. (2016) Neural network architecture selection for efficient prediction model of gas metering system. Paper presented at the 2016 2nd IEEE International Symposium on Robotics and Manufacturing Automation (ROMA) (25–27 Sept. 2016).10.1109/ROMA.2016.7847805Suche in Google Scholar

Saha, T.K., Pal, S., Sarkar, R. (2021) Prediction of wetland area and depth using linear regression model and artificial neural network-basedcellular automata. Ecol. Inform. 62. doi:10.1016/j.ecoinf.2021.101272.Suche in Google Scholar

Shams, S.R., Jahani, A., Kalantary, S., Moeinaddini, M., Khorasani, N. (2021) The evaluation on artificial neural networks (ANN) and multiple linear regressions (MLR) models for predicting SO2 concentration. Urban Clim. 37. doi:10.1016/j.uclim.2021.100837.Suche in Google Scholar

Shamsuzzaman, M., Alsyouf, I., Ali, A. (2015) Optimization design of X̄ &EWMA control chart for minimizing mean number of defective units per out-of-control case. Paper presented at the 2015 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM).10.1109/IEEM.2015.7385675Suche in Google Scholar

Sharma, N., Zakaullah, M., Tiwari, H., Kumar, D. (2015) Runoff and sediment yield modeling using ANN and support vector machines: a case study from Nepal watershed. Model. Earth Syst. Environ. 1(3):1–8.10.1007/s40808-015-0027-0Suche in Google Scholar

Shu, M.-H., Wu, H.-C. (2011) Fuzzy X and R control charts: Fuzzy dominance approach. Comput. Ind. Eng. 61(3):676–685.10.1016/j.cie.2011.05.001Suche in Google Scholar

Specht, D.F. (1991) A general regression neural network. IEEE Trans. Neural Netw. 2(6):568–576.10.1109/72.97934Suche in Google Scholar PubMed

Sun, M., Ma, Z., Li, Y. (2015) Maneuvering target tracking using IMM Kalman filter aided by Elman neural network. Paper presented at the 2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics.10.1109/IHMSC.2015.241Suche in Google Scholar

Sundaram, N.M., Sivanandam, S., Subha, R. (2016) Elman neural network mortality predictor for prediction of mortality due to pollution. Int. J. Appl. Eng. Res. 11(3):1835–1840.Suche in Google Scholar

Tarasov, D., Milder, O., Tyagunov, A. (2018) An Effect of the Paper Microelement Composition on Components of the Color Difference dE94 in Paper Whiteness Assesment. Paper presented at the 2018 2nd European Conference on Electrical Engineering and Computer Science (EECS).10.1109/EECS.2018.00050Suche in Google Scholar

Teuscher, C. Turing’s connectionism: an investigation of neural network architectures. Springer Science & Business Media, 2012.Suche in Google Scholar

Vinoth, B., Uma, G., Umapathy, M. (2022) Recurrent Neural Network based Soft Sensor for flow estimation in Liquid Rocket Engine Injector calibration. Flow Meas. Instrum. 83. doi:10.1016/j.flowmeasinst.2021.102105.Suche in Google Scholar

Wong, P.K., Gao, X.H., Wong, K.I., Vong, C.M. (2018) Efficient point-by-point engine calibration using machine learning and sequential design of experiment strategies. J. Franklin Inst. 355(4):1517–1538. doi:10.1016/j.jfranklin.2017.02.006.Suche in Google Scholar

Zadeh, L.A. (1965) Fuzzy sets. Inf. Control 8(3):338–353.10.21236/AD0608981Suche in Google Scholar

Zaman, B., Lee, M.H., Riaz, M. (2020) An improved process monitoring by mixed multivariate memory control charts: An application in wind turbine field. Comput. Ind. Eng. 106343.10.1016/j.cie.2020.106343Suche in Google Scholar

Zeng, L., Ge, Z. (2021) Bayesian network for dynamic variable structure learning and transfer modeling of probabilistic soft sensor. J. Process Control 100:20–29. doi:10.1016/j.jprocont.2021.02.004.Suche in Google Scholar

Zhao, X., Xuan, D., Zhao, K., Li, Z. (2020) Elman neural network using ant colony optimization algorithm for estimating of state of charge of lithium-ion battery. J. Energy Storage 32. doi:10.1016/j.est.2020.101789.Suche in Google Scholar

Zhiyuan, C., Jinsheng, S. (2015) Optimal design of AEWMA control chart with new sampling strategy. Paper presented at the 27th Chinese Control and Decision Conference (2015 CCDC).10.1109/CCDC.2015.7161659Suche in Google Scholar

Received: 2022-02-28
Accepted: 2022-06-24
Published Online: 2022-07-08
Published in Print: 2022-09-27

© 2022 Walter de Gruyter GmbH, Berlin/Boston

Heruntergeladen am 16.9.2025 von https://www.degruyterbrill.com/document/doi/10.1515/npprj-2022-0021/html
Button zum nach oben scrollen