Alternative method for determining basis weight in papermaking by using an interactive soft sensor based on an artificial neural network model
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José L. Rodríguez-Álvarez
, Rogelio López-Herrera
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
Funding source: Consejo Nacional de Ciencia y Tecnología
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.
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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
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Artikel in diesem Heft
- Frontmatter
- Chemical pulping
- Among-family variations of direct measurement values for chemical and pulp properties in 4-year-old Eucalyptus camaldulensis half-sib families in Thailand
- Crosslinking of surface-sizing starch with cyclodextrin units enhances the performance of paper as essential oil carrier
- Modeling a continuous digester extraction screen zone with an approximated flow model
- Mechanical pulping
- Advanced energy-saving optimization strategy in thermo-mechanical pulping by machine learning approach
- Paper technology
- Alternative method for determining basis weight in papermaking by using an interactive soft sensor based on an artificial neural network model
- Fabrication of bio-based composite fillers based on the combination of crystallization and gelation
- Paper chemistry
- Kinetics of cellulose degradation in bamboo paper
- Influence of DNA as additive for market pulp on tissue paper
- Recycling
- The recyclability and printability of electrophotographic printed paper
- Nanotechnology
- Production of cellulose nanofibers and sugars using high dry matter feedstock
- Chemical technology/modifications
- Comparison of fibers obtained from industrial corncob residue by two delignification methods and their application in papermaking
- Miscellaneous
- Effects on hand-sheet paper properties of pH in deinking process
- Fibers pre-treatments with sodium silicate affect the properties of suspensions, films, and quality index of cellulose micro/nanofibrils
Artikel in diesem Heft
- Frontmatter
- Chemical pulping
- Among-family variations of direct measurement values for chemical and pulp properties in 4-year-old Eucalyptus camaldulensis half-sib families in Thailand
- Crosslinking of surface-sizing starch with cyclodextrin units enhances the performance of paper as essential oil carrier
- Modeling a continuous digester extraction screen zone with an approximated flow model
- Mechanical pulping
- Advanced energy-saving optimization strategy in thermo-mechanical pulping by machine learning approach
- Paper technology
- Alternative method for determining basis weight in papermaking by using an interactive soft sensor based on an artificial neural network model
- Fabrication of bio-based composite fillers based on the combination of crystallization and gelation
- Paper chemistry
- Kinetics of cellulose degradation in bamboo paper
- Influence of DNA as additive for market pulp on tissue paper
- Recycling
- The recyclability and printability of electrophotographic printed paper
- Nanotechnology
- Production of cellulose nanofibers and sugars using high dry matter feedstock
- Chemical technology/modifications
- Comparison of fibers obtained from industrial corncob residue by two delignification methods and their application in papermaking
- Miscellaneous
- Effects on hand-sheet paper properties of pH in deinking process
- Fibers pre-treatments with sodium silicate affect the properties of suspensions, films, and quality index of cellulose micro/nanofibrils