{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T23:47:21Z","timestamp":1779925641318,"version":"3.53.1"},"reference-count":55,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2020,7,4]],"date-time":"2020-07-04T00:00:00Z","timestamp":1593820800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100010198","name":"Ministerio de Econom\u00eda, Industria y Competitividad, Gobierno de Espa\u00f1a","doi-asserted-by":"publisher","award":["TEC2017-84321-C4-4-R"],"award-info":[{"award-number":["TEC2017-84321-C4-4-R"]}],"id":[{"id":"10.13039\/501100010198","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010198","name":"Ministerio de Econom\u00eda, Industria y Competitividad, Gobierno de Espa\u00f1a","doi-asserted-by":"publisher","award":["DPI2016-77271-R"],"award-info":[{"award-number":["DPI2016-77271-R"]}],"id":[{"id":"10.13039\/501100010198","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Secretaria d\u2019Universitats i Recerca del Departament d\u2019Empresa i Coneixement de la Generalitat de Catalunya i del Fons Social Europeu","award":["2020 FI_B2 00038"],"award-info":[{"award-number":["2020 FI_B2 00038"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The evolution of industry towards the Industry 4.0 paradigm has become a reality where different data-driven methods are adopted to support industrial processes. One of them corresponds to Artificial Neural Networks (ANNs), which are able to model highly complex and non-linear processes. This motivates their adoption as part of new data-driven based control strategies. The ANN-based Internal Model Controller (ANN-based IMC) is an example which takes advantage of the ANNs characteristics by modelling the direct and inverse relationships of the process under control with them. This approach has been implemented in Wastewater Treatment Plants (WWTP), where results show a significant improvement on control performance metrics with respect to (w.r.t.) the WWTP default control strategy. However, this structure is very sensible to non-desired effects in the measurements\u2014when a real scenario showing noise-corrupted data is considered, the control performance drops. To solve this, a new ANN-based IMC approach is designed with a two-fold objective, improve the control performance and denoise the noise-corrupted measurements to reduce the performance degradation. Results show that the proposed structure improves the control metrics, (the Integrated Absolute Error (IAE) and the Integrated Squared Error (ISE)), around a 21.25% and a 54.64%, respectively.<\/jats:p>","DOI":"10.3390\/s20133743","type":"journal-article","created":{"date-parts":[[2020,7,6]],"date-time":"2020-07-06T09:49:11Z","timestamp":1594028951000},"page":"3743","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["Denoising Autoencoders and LSTM-Based Artificial Neural Networks Data Processing for Its Application to Internal Model Control in Industrial Environments\u2014The Wastewater Treatment Plant Control Case"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3931-9257","authenticated-orcid":false,"given":"Ivan","family":"Pisa","sequence":"first","affiliation":[{"name":"Wireless Information Networking (WIN) group, Escola d\u2019Enginyeria, Universitat Aut\u00f2noma de Barcelona, 08193 Bellaterra, Spain"},{"name":"Advanced Systems for Automation and Control (ASAC) group, Escola d\u2019Enginyeria, Universitat Aut\u00f2noma de Barcelona, 08193 Bellaterra, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2249-8594","authenticated-orcid":false,"given":"Antoni","family":"Morell","sequence":"additional","affiliation":[{"name":"Wireless Information Networking (WIN) group, Escola d\u2019Enginyeria, Universitat Aut\u00f2noma de Barcelona, 08193 Bellaterra, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3574-4697","authenticated-orcid":false,"given":"Jose Lopez","family":"Vicario","sequence":"additional","affiliation":[{"name":"Wireless Information Networking (WIN) group, Escola d\u2019Enginyeria, Universitat Aut\u00f2noma de Barcelona, 08193 Bellaterra, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8035-5199","authenticated-orcid":false,"given":"Ramon","family":"Vilanova","sequence":"additional","affiliation":[{"name":"Advanced Systems for Automation and Control (ASAC) group, Escola d\u2019Enginyeria, Universitat Aut\u00f2noma de Barcelona, 08193 Bellaterra, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1109\/MIE.2017.2649104","article-title":"The future of industrial communication: Automation networks in the era of the internet of things and industry 4.0","volume":"11","author":"Wollschlaeger","year":"2017","journal-title":"IEEE Ind. Electron. Mag."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Sarvari, P.A., Ustundag, A., Cevikcan, E., Kaya, I., and Cebi, S. (2018). Technology Roadmap for Industry 4.0. Industry 4.0: Managing The Digital Transformation, Springer.","DOI":"10.1007\/978-3-319-57870-5"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3067","DOI":"10.1016\/j.ijhydene.2010.10.077","article-title":"Diagnosis of polymer electrolyte fuel cells failure modes (flooding & drying out) by neural networks modeling","volume":"36","author":"Steiner","year":"2011","journal-title":"Int. J. Hydrogen Energy"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.conengprac.2017.09.018","article-title":"Automated weighted outlier detection technique for multivariate data","volume":"70","author":"Thennadil","year":"2018","journal-title":"Control Eng. Pract."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1016\/j.conengprac.2004.04.013","article-title":"Soft sensors for product quality monitoring in debutanizer distillation columns","volume":"13","author":"Fortuna","year":"2005","journal-title":"Control Eng. Pract."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"438","DOI":"10.1016\/j.isatra.2012.12.009","article-title":"Development of soft sensor for neural network based control of distillation column","volume":"52","author":"Rani","year":"2013","journal-title":"ISA Trans."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2079","DOI":"10.1007\/s11269-018-1919-3","article-title":"Manage sewer in-line storage control using hydraulic model and recurrent neural network","volume":"32","author":"Zhang","year":"2018","journal-title":"Water Resour. Manag."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Pisa, I., Sant\u00edn, I., Vicario, J.L., Morell, A., and Vilanova, R. (2019). ANN-based soft sensor to predict effluent violations in wastewater treatment plants. Sensors, 19.","DOI":"10.3390\/s19061280"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"159773","DOI":"10.1109\/ACCESS.2019.2950852","article-title":"LSTM based Wastewater Treatment Plants operation strategies for effluent quality improvement","volume":"7","author":"Pisa","year":"2019","journal-title":"IEEE Access"},{"key":"ref_10","unstructured":"Alex, J., Benedetti, L., Copp, J., Gernaey, K.V., Jeppsson, U., Nopens, I., Pons, M.N., Rieger, L., Rosen, C., and Steyer, J.P. (2008). Benchmark Simulation Model No. 1 (BSM1), Department of Industrial Electrical Engineering and Automation, Lund University. Technical Report."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2849","DOI":"10.1016\/j.compchemeng.2008.01.009","article-title":"Application of model predictive control to the BSM1 benchmark of wastewater treatment process","volume":"32","author":"Shen","year":"2008","journal-title":"Comput. Chem. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Vilanova, R., Santin, I., Pedret, C., and Barbu, M. (2018, January 10\u201312). Event-based control for dissolved oxygen and nitrogen in wastewater treatment plants. Proceedings of the 2018 22nd International Conference on System Theory, Control and Computing (ICSTCC), Sinaia, Romania.","DOI":"10.1109\/ICSTCC.2018.8540657"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.eswa.2016.06.028","article-title":"Soft-sensing estimation of plant effluent concentrations in a biological wastewater treatment plant using an optimal neural network","volume":"63","author":"Baratti","year":"2016","journal-title":"Expert Syst. Appl."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1016\/j.measurement.2018.01.001","article-title":"Integrated soft sensor with wavelet neural network and adaptive weighted fusion for water quality estimation in wastewater treatment process","volume":"124","author":"Cong","year":"2018","journal-title":"Measurement"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1031","DOI":"10.1016\/j.cej.2016.07.018","article-title":"Predictive control of an activated sludge process for long term operation","volume":"304","author":"Foscoliano","year":"2016","journal-title":"Chem. Eng. J."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.conengprac.2016.01.005","article-title":"Advanced decision control system for effluent violations removal in wastewater treatment plants","volume":"49","author":"Pedret","year":"2016","journal-title":"Control Eng. Pract."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.knosys.2017.12.019","article-title":"Tackling the start-up of a reinforcement learning agent for the control of wastewater treatment plants","volume":"144","author":"Gaudioso","year":"2018","journal-title":"Knowl. Based Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1016\/j.neucom.2017.08.059","article-title":"Adaptive fuzzy neural network control of wastewater treatment process with multiobjective operation","volume":"275","author":"Qiao","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.isatra.2019.03.021","article-title":"Robust internal model control of servo motor based on sliding mode control approach","volume":"93","author":"Li","year":"2019","journal-title":"ISA Trans."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Pisa, I., Morell, A., Vicario, J.L., and Vilanova, R. (2019, January 10\u201313). ANN-based Internal Model Control strategy applied in the WWTP industry. Proceedings of the 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Zaragoza, Spain.","DOI":"10.1109\/ETFA.2019.8868241"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.ymssp.2017.12.032","article-title":"Decoupling control of vehicle chassis system based on neural network inverse system","volume":"106","author":"Wang","year":"2018","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"994","DOI":"10.1007\/s12555-017-0362-1","article-title":"IMC based Controller Design for Automatic Generation Control of Multi Area Power System via Simplified Decoupling","volume":"16","author":"Kasireddy","year":"2018","journal-title":"Int. J. Control Autom. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhou, K., Li, M., Li, Y., Xie, M., and Huang, Y. (2019). An Improved Denoising Method for Partial Discharge Signals Contaminated by White Noise Based on Adaptive Short-Time Singular Value Decomposition. Energies, 12.","DOI":"10.3390\/en12183465"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Liu, P., Zheng, P., and Chen, Z. (2019). Deep learning with stacked denoising auto-encoder for short-term electric load forecasting. Energies, 12.","DOI":"10.3390\/en12122445"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4334","DOI":"10.1109\/TGRS.2018.2815281","article-title":"Multispectral satellite image denoising via adaptive cuckoo search-based Wiener filter","volume":"56","author":"Suresh","year":"2018","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_26","unstructured":"Copp, J.B. (2002). The Cost Simulation Benchmark: Description and Simulator Manual (Cost Action 624 and Action 682), Office for Official Publications of the European Union."},{"key":"ref_27","unstructured":"Henze, M., Grady, L., Gujer, W., Marais, G.V.R., and Matsuo, T. (1987). Activated Sludge Model No 1, IAWPRC. IAWPRC Scientific and technical Reports, No.1."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Henze, M., Gujer, W., Mino, T., and van Loosdrecht, M.C. (2000). Activated Sludge Models ASM1, ASM2, ASM2d and ASM3, IWA Publishing.","DOI":"10.2166\/wst.1999.0036"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Grieves, M., and Vickers, J. (2017). Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems. Transdisciplinary Perspectives on Complex Systems, Springer.","DOI":"10.1007\/978-3-319-38756-7_4"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Boschert, S., and Rosen, R. (2016). Digital twin\u2014the simulation aspect. Mechatronic Futures, Springer.","DOI":"10.1007\/978-3-319-32156-1_5"},{"key":"ref_31","unstructured":"Halling-S\u00f8rensen, B., and Jorgensen, S.E. (1993). The Removal of Nitrogen Compounds from Wastewater, Elsevier."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Vilanova, R., and Visioli, A. (2012). PID Control in the Third Millennium\u2014Lessons Learned and News Approaches, Springer.","DOI":"10.1007\/978-1-4471-2425-2"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Gernaey, K.V., Jeppsson, U., Vanrolleghem, P.A., Copp, J.B., and International Water Association (2014). Task Group on Benchmarking of Control Strategies forWastewater Treatment Plants. Benchmarking of Control Strategies for Wastewater Treatment Plants, IWA Publishing.","DOI":"10.2166\/9781780401171"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.envsoft.2014.05.008","article-title":"Analysing, completing, and generating influent data for WWTP modelling: A critical review","volume":"60","author":"Martin","year":"2014","journal-title":"Environ. Model. Softw."},{"key":"ref_35","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Da Silva, I.N., Spatti, D.H., Flauzino, R.A., Liboni, L.H.B., and dos Reis Alves, S.F. (2017). Artificial Neural Networks, Springer International Publishing.","DOI":"10.1007\/978-3-319-43162-8"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2222","DOI":"10.1109\/TNNLS.2016.2582924","article-title":"LSTM: A search space odyssey","volume":"28","author":"Greff","year":"2016","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_38","first-page":"1557","article-title":"Data-driven based IMC control","volume":"8","author":"Rojas","year":"2012","journal-title":"Int. J. Innov. Comput. Inf. Control"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Pisa, I., Morell, A., Vicario, J.L., and Vilanova, R. (2020, January 11\u201317). LSTM-based IMC approach applied in Wastewater Treatment Plants: Performance and Stability Analysis. Proceedings of the 21st IFAC World Congress, Berlin, Germany. IFAC-PapersOnline.","DOI":"10.1016\/j.ifacol.2020.12.782"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Boquet, G., Vicario, J.L., Morell, A., and Serrano, J. (2019, January 12\u201317). Missing Data in Traffic Estimation: A Variational Autoencoder Imputation Method. Proceedings of the ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK.","DOI":"10.1109\/ICASSP.2019.8683011"},{"key":"ref_41","unstructured":"Jones, E., Oliphant, T., Peterson, P., Virtanen, P., Gommers, R., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., and Weckesser, W. (2020, March 12). SciPy: Open Source Scientific Tools for Python. Available online: http:\/\/www.scipy.org\/."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/S0925-2312(01)00702-0","article-title":"Time series forecasting using a hybrid ARIMA and neural network model","volume":"50","author":"Zhang","year":"2003","journal-title":"Neurocomputing"},{"key":"ref_43","unstructured":"Oliphant, T.E. (2006). A Guide to NumPy, Trelgol Publishing."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"McKinney, W. (2010, January 28\u201330). Data structures for statistical computing in python. Proceedings of the 9th Python in Science Conference, Austin, TX, USA.","DOI":"10.25080\/Majora-92bf1922-00a"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1109\/MCSE.2007.55","article-title":"Matplotlib: A 2D graphics environment","volume":"9","author":"Hunter","year":"2007","journal-title":"Comput. Sci. Eng."},{"key":"ref_46","unstructured":"Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, C.S., Davis, A., Dean, J., and Devin, M. (2020, March 12). TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Available online: https:\/\/www.tensorflow.org\/."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Garc\u00eda, S., Luengo, J., and Herrera, F. (2015). Data Preprocessing in Data Mining, Springer.","DOI":"10.1007\/978-3-319-10247-4"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"636","DOI":"10.1016\/j.procs.2018.07.298","article-title":"A multivariate fuzzy time series resource forecast model for clouds using LSTM and data correlation analysis","volume":"126","author":"Tran","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1016\/j.engappai.2014.08.011","article-title":"Joint mutual information-based input variable selection for multivariate time series modeling","volume":"37","author":"Han","year":"2015","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Boussaada, Z., Curea, O., Remaci, A., Camblong, H., and Mrabet Bellaaj, N. (2018). A nonlinear autoregressive exogenous (NARX) neural network model for the prediction of the daily direct solar radiation. Energies, 11.","DOI":"10.3390\/en11030620"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1016\/j.ins.2011.12.028","article-title":"On the use of cross-validation for time series predictor evaluation","volume":"191","author":"Bergmeir","year":"2012","journal-title":"Inf. Sci."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.csda.2017.11.003","article-title":"A note on the validity of cross-validation for evaluating autoregressive time series prediction","volume":"120","author":"Bergmeir","year":"2018","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"3235","DOI":"10.1109\/TII.2018.2809730","article-title":"Deep learning-based feature representation and its application for soft sensor modeling with variable-wise weighted SAE","volume":"14","author":"Yuan","year":"2018","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Coto-Jim\u00e9nez, M., Goddard-Close, J., and Mart\u00ednez-Licona, F. (2016, January 23\u201327). Improving automatic speech recognition containing additive noise using deep denoising autoencoders of LSTM networks. Proceedings of the International Conference on Speech and Computer, Budapest, Hungary.","DOI":"10.1007\/978-3-319-43958-7_42"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Cadenas, E., Rivera, W., Campos-Amezcua, R., and Heard, C. (2016). Wind speed prediction using a univariate ARIMA model and a multivariate NARX model. Energies, 9.","DOI":"10.3390\/en9020109"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/13\/3743\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:47:16Z","timestamp":1760176036000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/13\/3743"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,4]]},"references-count":55,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2020,7]]}},"alternative-id":["s20133743"],"URL":"https:\/\/doi.org\/10.3390\/s20133743","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,4]]}}}