{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T20:50:25Z","timestamp":1773953425468,"version":"3.50.1"},"reference-count":73,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2023,10,19]],"date-time":"2023-10-19T00:00:00Z","timestamp":1697673600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Currently, e-noses are used for measuring odorous compounds at wastewater treatment plants. These devices mimic the mammalian olfactory sense, comprising an array of multiple non-specific gas sensors. An array of sensors creates a unique set of signals called a \u201cgas fingerprint\u201d, which enables it to differentiate between the analyzed samples of gas mixtures. However, appropriate advanced analyses of multidimensional data need to be conducted for this purpose. The failures of the wastewater treatment process are directly connected to the odor nuisance of bioreactors and are reflected in the level of pollution indicators. Thus, it can be assumed that using the appropriately selected methods of data analysis from a gas sensors array, it will be possible to distinguish and classify the operating states of bioreactors (i.e., phases of normal operation), as well as the occurrence of malfunction. This work focuses on developing a complete protocol for analyzing and interpreting multidimensional data from a gas sensor array measuring the properties of the air headspace in a bioreactor. These methods include dimensionality reduction and visualization in two-dimensional space using the principal component analysis (PCA) method, application of data clustering using an unsupervised method by Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, and at the last stage, application of extra trees as a supervised machine learning method to achieve the best possible accuracy and precision in data classification.<\/jats:p>","DOI":"10.3390\/s23208578","type":"journal-article","created":{"date-parts":[[2023,10,19]],"date-time":"2023-10-19T07:15:36Z","timestamp":1697699736000},"page":"8578","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Rapid Method of Wastewater Classification by Electronic Nose for Performance Evaluation of Bioreactors with Activated Sludge"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6531-6562","authenticated-orcid":false,"given":"Magdalena","family":"Pi\u0142at-Ro\u017cek","sequence":"first","affiliation":[{"name":"Faculty of Mathematics and Information Technology, Lublin University of Technology, 20-618 Lublin, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0506-6653","authenticated-orcid":false,"given":"Marcin","family":"Dziadosz","sequence":"additional","affiliation":[{"name":"Faculty of Mathematics and Information Technology, Lublin University of Technology, 20-618 Lublin, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0035-7187","authenticated-orcid":false,"given":"Dariusz","family":"Majerek","sequence":"additional","affiliation":[{"name":"Faculty of Mathematics and Information Technology, Lublin University of Technology, 20-618 Lublin, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6015-3477","authenticated-orcid":false,"given":"Katarzyna","family":"Jaromin-Gle\u0144","sequence":"additional","affiliation":[{"name":"Institute of Agrophysics, Polish Academy of Sciences, 20-290 Lublin, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0559-5475","authenticated-orcid":false,"given":"Bartosz","family":"Szel\u0105g","sequence":"additional","affiliation":[{"name":"Institute of Environmental Engineering, Warsaw University of Life Sciences\u2014SGGW, 02-797 Warsaw, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"\u0141ukasz","family":"Guz","sequence":"additional","affiliation":[{"name":"Faculty of Environmental Engineering, Lublin University of Technology, 20-618 Lublin, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1442-4176","authenticated-orcid":false,"given":"Adam","family":"Piotrowicz","sequence":"additional","affiliation":[{"name":"Faculty of Environmental Engineering, Lublin University of Technology, 20-618 Lublin, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0621-7222","authenticated-orcid":false,"given":"Grzegorz","family":"\u0141ag\u00f3d","sequence":"additional","affiliation":[{"name":"Faculty of Environmental Engineering, Lublin University of Technology, 20-618 Lublin, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"137019","DOI":"10.1016\/j.jclepro.2023.137019","article-title":"Predicting Quality Parameters of Wastewater Treatment Plants Using Artificial Intelligence Techniques","volume":"405","author":"Aghdam","year":"2023","journal-title":"J. Clean. Prod."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"137878","DOI":"10.1016\/j.scitotenv.2020.137878","article-title":"Optimized Fuzzy Inference System to Enhance Prediction Accuracy for Influent Characteristics of a Sewage Treatment Plant","volume":"722","author":"Ansari","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Henze, M., van Loosdrecht, M.C.M., Ekama, G.A., and Brdjanovic, D. (2008). Biological Wastewater Treatment: Principles, Modelling and Design, IWA Publishing.","DOI":"10.2166\/9781780401867"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"56","DOI":"10.5004\/dwt.2022.28638","article-title":"Rapid On-Line Method of Wastewater Parameters Estimation by Electronic Nose for Control and Operating Wastewater Treatment Plants toward Green Deal Implementation","volume":"275","author":"Drewnowski","year":"2022","journal-title":"Desalin. Water Treat."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.envsoft.2017.11.023","article-title":"Transforming Data into Knowledge for Improved Wastewater Treatment Operation: A Critical Review of Techniques","volume":"106","author":"Corominas","year":"2018","journal-title":"Environ. Model. Softw."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.watres.2013.10.066","article-title":"Methods for Assessing Biochemical Oxygen Demand (BOD): A Review","volume":"49","author":"Jouanneau","year":"2014","journal-title":"Water Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"23","DOI":"10.3844\/ajbbsp.2022.23.32","article-title":"A Review of Detection Techniques for Chemical Oxygen Demand in Wastewater","volume":"18","author":"Wu","year":"2022","journal-title":"Am. J. Biochem. Biotechnol."},{"key":"ref_8","unstructured":"Baird, R., Rice, E.W., and A.D. Eaton, L.B. (2017). Standard Methods for the Examination of Water and Wastewater, American Health Association. [23rd ed.]."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"486","DOI":"10.1016\/S0165-9936(96)00061-1","article-title":"Electronic Noses\u2014Development and Future Prospects","volume":"15","author":"Craven","year":"1996","journal-title":"TrAC Trends Anal. Chem."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1108\/02602280410525977","article-title":"A Review of Gas Sensors Employed in Electronic Nose Applications","volume":"24","author":"Arshak","year":"2004","journal-title":"Sens. Rev."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Bieganowski, A., J\u00f3zefaciuk, G., Bandura, L., Guz, \u0141., \u0141ag\u00f3d, G., and Franus, W. (2018). Evaluation of Hydrocarbon Soil Pollution Using E-Nose. Sensors, 18.","DOI":"10.3390\/s18082463"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Garbacz, M., Malec, A., Duda-Saternus, S., Suchorab, Z., Guz, \u0141., and \u0141ag\u00f3d, G. (2020). Methods for Early Detection of Microbiological Infestation of Buildings Based on Gas Sensor Technologies. Chemosensors, 8.","DOI":"10.3390\/chemosensors8010007"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1089\/ees.2016.0044","article-title":"Application of Classification Trees for Predicting Disinfection By-Product Formation Targets from Source Water Characteristics","volume":"33","author":"Bergman","year":"2016","journal-title":"Environ. Eng. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.chemolab.2008.07.010","article-title":"Comparison of Performance of Five Common Classifiers Represented as Boundary Methods: Euclidean Distance to Centroids, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Learning Vector Quantization and Support Vector Machines, as Dependent On","volume":"95","author":"Dixon","year":"2009","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Pi\u0142at-Ro\u017cek, M., \u0141azuka, E., Majerek, D., Szel\u0105g, B., Duda-Saternus, S., and \u0141ag\u00f3d, G. (2023). Application of Machine Learning Methods for an Analysis of E-Nose Multidimensional Signals in Wastewater Treatment. Sensors, 23.","DOI":"10.3390\/s23010487"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2818","DOI":"10.3390\/s120302818","article-title":"Pattern Classification Using an Olfactory Model with PCA Feature Selection in Electronic Noses: Study and Application","volume":"12","author":"Fu","year":"2012","journal-title":"Sensors"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2475","DOI":"10.1016\/S0043-1354(00)00530-3","article-title":"The Electronic Nose as a Rapid Sensor for Volatile Compounds in Treated Domestic Wastewater","volume":"35","author":"Dewettinck","year":"2001","journal-title":"Water Res."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3390\/s150100001","article-title":"Application of Gas Sensor Arrays in Assessment of Wastewater Purification Effects","volume":"15","author":"Guz","year":"2015","journal-title":"Sensors"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"843","DOI":"10.1016\/j.envsoft.2004.04.012","article-title":"Determination of the Relationship between Sewage Odour and BOD by Neural Networks","volume":"20","author":"Demir","year":"2005","journal-title":"Environ. Model. Softw."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1016\/S0043-1354(98)00245-0","article-title":"Characterisation of Wastewater Using an Electronic Nose","volume":"33","author":"Stuetz","year":"1999","journal-title":"Water Res."},{"key":"ref_21","unstructured":"Ko\u015bmider, J., Mazur-Chrzanowska, B., and Wyszy\u0144ski, B. (2012). Odory, Wydawnictwo Naukowe PWN."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"733","DOI":"10.1016\/j.scitotenv.2012.11.037","article-title":"Modelling of Micropollutant Removal in Biological Wastewater Treatments: A Review","volume":"443","author":"Choubert","year":"2013","journal-title":"Sci. Total Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1002\/ep.670100111","article-title":"Integrated Model for Predicting the Fate of Organics in Wastewater Treatment Plants","volume":"10","author":"Govind","year":"1991","journal-title":"Environ. Prog."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2523","DOI":"10.1016\/S0043-1354(00)00529-7","article-title":"The Fate of Xenobiotic Organic Compounds in Wastewater Treatment Plants","volume":"35","author":"Byrns","year":"2001","journal-title":"Water Res."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"891","DOI":"10.1016\/0043-1354(91)90170-U","article-title":"A Spreadsheet-Based Box Model to Predict the Fate of Xenobiotics in a Municipal Wastewater Treatment Plant","volume":"25","author":"Struijs","year":"1991","journal-title":"Water Res."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1118","DOI":"10.2175\/106143098X123480","article-title":"Advanced Steady-State Model for the Fate of Hydrophobic and Volatile Compounds in Activated Sludge","volume":"70","author":"Lee","year":"1998","journal-title":"Water Environ. Res."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.snb.2007.12.004","article-title":"Electronic Noses for the Continuous Monitoring of Odours from a Wastewater Treatment Plant at Specific Receptors: Focus on Training Methods","volume":"131","author":"Capelli","year":"2008","journal-title":"Sens. Actuators B Chem."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.snb.2004.05.034","article-title":"Outdoor in Situ Monitoring of Volatile Emissions from Wastewater Treatment Plants with Two Portable Technologies of Electronic Noses","volume":"106","author":"Nake","year":"2005","journal-title":"Sens. Actuators B Chem."},{"key":"ref_29","unstructured":"Giuliani, S., Zarra, T., Nicolas, J., Naddeo, V., Belgiorno, V., and Romain, A.C. (2012). An Alternative Approach of the E-Nose Training Phase in Odour Impact Assessment. Chem. Eng. Trans., 30."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1016\/j.wasman.2006.03.011","article-title":"Environmental Odours Assessment from Waste Treatment Plants: Dynamic Olfactometry in Combination with Sensorial Analysers \u201cElectronic Noses\u201d","volume":"27","author":"Littarru","year":"2007","journal-title":"Waste Manag."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1016\/S0043-1354(98)00246-2","article-title":"Assessment of Odours from Sewage Treatment Works by an Electronic Nose, H2S Analysis and Olfactometry","volume":"33","author":"Stuetz","year":"1999","journal-title":"Water Res."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Mas\u0142o\u0144, A. (2022). Impact of Uneven Flow Wastewater Distribution on the Technological Efficiency of a Sequencing Batch Reactor. Sustainability, 14.","DOI":"10.3390\/su14042405"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"9149","DOI":"10.1038\/s41598-023-36333-8","article-title":"Development and Application of Random Forest Regression Soft Sensor Model for Treating Domestic Wastewater in a Sequencing Batch Reactor","volume":"13","author":"Cheng","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1007\/s40726-015-0016-y","article-title":"Sequencing Batch Reactor for Wastewater Treatment: Recent Advances","volume":"1","author":"Dutta","year":"2015","journal-title":"Curr. Pollut. Rep."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Wilderer, P.A., Irvine, R.L., and Goronszy, M.C. (2007). Sequencing Batch Reactor Technology, IWA Publishing.","DOI":"10.2166\/9781780402246"},{"key":"ref_36","first-page":"405","article-title":"Detection of Wastewater Treatment Process Disturbances in Bioreactors Using the E-Nose Technology","volume":"25","author":"Guz","year":"2018","journal-title":"Ecol. Chem. Eng. S"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1327","DOI":"10.1080\/19443994.2014.1002279","article-title":"Assessment of Batch Bioreactor Odour Nuisance Using an E-Nose","volume":"57","author":"Guz","year":"2016","journal-title":"Desalin. Water Treat."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"9373","DOI":"10.1016\/j.eswa.2011.01.135","article-title":"A New Hybrid Method Based on Partitioning-Based DBSCAN and Ant Clustering","volume":"38","author":"Jiang","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1002\/wics.101","article-title":"Principal Component Analysis","volume":"2","author":"Abdi","year":"2010","journal-title":"Wiley Interdiscip. Rev. Comput. Stat."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1080\/14786440109462720","article-title":"On Lines and Planes of Closest Fit to Systems of Points in Space","volume":"2","author":"Pearson","year":"1901","journal-title":"Dublin Philos. Mag. J. Sci."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"498","DOI":"10.1037\/h0070888","article-title":"Analysis of a Complex of Statistical Variables into Principal Components","volume":"24","author":"Hotelling","year":"1933","journal-title":"J. Educ. Psychol."},{"key":"ref_42","unstructured":"Mardia, K.V., Kent, T., and Bibby, J. (1979). Multivariate Analysis, Academic Press Limited."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1177\/001316446002000116","article-title":"The Application of Electronic Computers to Factor Analysis","volume":"20","author":"Kaiser","year":"1960","journal-title":"Educ. Psychol. Meas."},{"key":"ref_44","unstructured":"Jolliffe, I.T. (2002). Principal Component Analysis, Springer."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"\u0141ag\u00f3d, G., Pi\u0142at-Ro\u017cek, M., Majerek, D., \u0141azuka, E., Suchorab, Z., Guz, \u0141., Ko\u010d\u00ed, V., and \u010cern\u00fd, R. (2023). Application of Dimensionality Reduction and Machine Learning Methods for the Interpretation of Gas Sensor Array Readouts from Mold-Threatened Buildings. Appl. Sci., 13.","DOI":"10.3390\/app13158588"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"4566","DOI":"10.1016\/j.watres.2007.06.030","article-title":"Comparison of Self-Organizing Maps Classification Approach with Cluster and Principal Components Analysis for Large Environmental Data Sets","volume":"41","author":"Astel","year":"2007","journal-title":"Water Res."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1016\/j.envsoft.2006.02.001","article-title":"Assessment of Surface Water Quality Using Multivariate Statistical Techniques: A Case Study of the Fuji River Basin, Japan","volume":"22","author":"Shrestha","year":"2007","journal-title":"Environ. Model. Softw."},{"key":"ref_48","unstructured":"Ester, M., Kriegel, H.-P., Sander, J., and Xu, X. (1996, January 2\u20134). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, Portland, OR, USA."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Hahsler, M., Piekenbrock, M., and Doran, D. (2019). Dbscan: Fast Density-Based Clustering with R. J. Stat. Softw., 91.","DOI":"10.18637\/jss.v091.i01"},{"key":"ref_50","first-page":"2825","article-title":"Scikit-Learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_51","unstructured":"Rosenberg, A., and Hirschberg, J. (2007, January 28\u201330). V-Measure: A Conditional Entropy-Based External Cluster Evaluation Measure. Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), Prague, Czech Republic."},{"key":"ref_52","first-page":"2837","article-title":"Information Theoretic Measures for Clusterings Comparison: Variants, Properties, Normalization and Correction for Chance","volume":"11","author":"Vinh","year":"2010","journal-title":"J. Mach. Learn. Res."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1007\/BF01908075","article-title":"Comparing Partitions","volume":"2","author":"Hubert","year":"1985","journal-title":"J. Classif."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1007\/s11634-022-00491-w","article-title":"Minimum Adjusted Rand Index for Two Clusterings of a given Size","volume":"17","author":"Rastrojo","year":"2023","journal-title":"Adv. Data Anal. Classif."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/0377-0427(87)90125-7","article-title":"Silhouettes: A Graphical Aid to the Interpretation and Validation of Cluster Analysis","volume":"20","author":"Rousseeuw","year":"1987","journal-title":"J. Comput. Appl. Math."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"123611","DOI":"10.1016\/j.jclepro.2020.123611","article-title":"Novel Leakage Detection and Water Loss Management of Urban Water Supply Network Using Multiscale Neural Networks","volume":"278","author":"Hu","year":"2021","journal-title":"J. Clean. Prod."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Syafrudin, M., Alfian, G., Fitriyani, N., and Rhee, J. (2018). Performance Analysis of IoT-Based Sensor, Big Data Processing, and Machine Learning Model for Real-Time Monitoring System in Automotive Manufacturing. Sensors, 18.","DOI":"10.3390\/s18092946"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Iyer, S., Thakur, S., Dixit, M., Katkam, R., Agrawal, A., and Kazi, F. (2019, January 6\u20138). Blockchain and Anomaly Detection Based Monitoring System for Enforcing Wastewater Reuse. Proceedings of the 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Kanpur, India.","DOI":"10.1109\/ICCCNT45670.2019.8944586"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s10994-006-6226-1","article-title":"Extremely Randomized Trees","volume":"63","author":"Geurts","year":"2006","journal-title":"Mach. Learn."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"126401","DOI":"10.1016\/j.cej.2020.126401","article-title":"Non-Intrusive Classification of Gas-Liquid Flow Regimes in an S-Shaped Pipeline Riser Using a Doppler Ultrasonic Sensor and Deep Neural Networks","volume":"403","author":"Kuang","year":"2021","journal-title":"Chem. Eng. J."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"S441","DOI":"10.1002\/ep.13128","article-title":"Decision Tree-Based Modeling of CO 2 Equilibrium Absorption in Different Aqueous Solutions of Absorbents","volume":"38","author":"Yarveicy","year":"2019","journal-title":"Environ. Prog. Sustain. Energy"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"107927","DOI":"10.1016\/j.buildenv.2021.107927","article-title":"A Machine Learning and Deep Learning Based Approach to Predict the Thermal Performance of Phase Change Material Integrated Building Envelope","volume":"199","author":"Bhamare","year":"2021","journal-title":"Build. Environ."},{"key":"ref_63","unstructured":"(2023, August 29). TGS Figaro Sensors Datasheets for: TGSTGS 2602, TGS 2610, TGS 2611, TGS 2612, TGS 2620. Available online: https:\/\/www.figarosensor.com\/product\/."},{"key":"ref_64","unstructured":"(2023, August 29). Dallas Semiconductor DS18B20 Datasheet. Available online: www.dalsemi.com."},{"key":"ref_65","unstructured":"(2023, August 29). Sensing and Control Honeywell Honeywell HIH-4000 Datasheet. Available online: www.honeywell.com."},{"key":"ref_66","first-page":"87","article-title":"Jupyter Notebooks\u2014A Publishing Format for Reproducible Computational Workflows","volume":"26","author":"Kluyver","year":"2016","journal-title":"Elpub"},{"key":"ref_67","unstructured":"Van Rossum, G., and Drake, F.L. (1995). Python Reference Manual, Centrum voor Wiskunde en Informatica Amsterdam."},{"key":"ref_68","unstructured":"McKinney, W. (July, January 28). Data Structures for Statistical Computing in Python. Proceedings of the 9th Python in Science Conference, Austin, TX, USA."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1038\/s41586-020-2649-2","article-title":"Array Programming with NumPy","volume":"585","author":"Harris","year":"2020","journal-title":"Nature"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"3021","DOI":"10.21105\/joss.03021","article-title":"Seaborn: Statistical Data Visualization","volume":"6","author":"Waskom","year":"2021","journal-title":"J. Open Source Softw."},{"key":"ref_71","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_72","unstructured":"Plotly Technologies Inc. (2023, August 16). Collaborative Data Science. Available online: https:\/\/plot.ly."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1016\/S0925-4005(02)00377-5","article-title":"Development of a Sensor Array Based Measurement System for Continuous Monitoring of Water and Wastewater","volume":"88","author":"Bourgeois","year":"2003","journal-title":"Sens. Actuators B Chem."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/20\/8578\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:09:43Z","timestamp":1760130583000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/20\/8578"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,19]]},"references-count":73,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2023,10]]}},"alternative-id":["s23208578"],"URL":"https:\/\/doi.org\/10.3390\/s23208578","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,19]]}}}