{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T16:47:28Z","timestamp":1777049248844,"version":"3.51.4"},"reference-count":160,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T00:00:00Z","timestamp":1758067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Foundation for Science and Technology (FCT) and the Ministry of Science, Technology and Higher Education (MCTES)","award":["PRT\/BD\/154543\/2022"],"award-info":[{"award-number":["PRT\/BD\/154543\/2022"]}]},{"name":"Foundation for Science and Technology (FCT) and the Ministry of Science, Technology and Higher Education (MCTES)","award":["UIDB\/00511\/2020"],"award-info":[{"award-number":["UIDB\/00511\/2020"]}]},{"name":"Foundation for Science and Technology (FCT) and the Ministry of Science, Technology and Higher Education (MCTES)","award":["UIDP\/00511\/2020"],"award-info":[{"award-number":["UIDP\/00511\/2020"]}]},{"name":"Foundation for Science and Technology (FCT) and the Ministry of Science, Technology and Higher Education (MCTES)","award":["LA\/P\/0045\/2020"],"award-info":[{"award-number":["LA\/P\/0045\/2020"]}]},{"name":"Foundation for Science and Technology (FCT) and the Ministry of Science, Technology and Higher Education (MCTES)","award":["UIDP\/04378\/2020"],"award-info":[{"award-number":["UIDP\/04378\/2020"]}]},{"name":"Foundation for Science and Technology (FCT) and the Ministry of Science, Technology and Higher Education (MCTES)","award":["UIDB\/04378\/2020"],"award-info":[{"award-number":["UIDB\/04378\/2020"]}]},{"name":"Foundation for Science and Technology (FCT) and the Ministry of Science, Technology and Higher Education (MCTES)","award":["LA\/P\/0140\/2020"],"award-info":[{"award-number":["LA\/P\/0140\/2020"]}]},{"name":"LEPABE (Laboratory for Process Engineering, Environment, Biotechnology and Energy, Faculty of Engineering, University of Porto","award":["PRT\/BD\/154543\/2022"],"award-info":[{"award-number":["PRT\/BD\/154543\/2022"]}]},{"name":"LEPABE (Laboratory for Process Engineering, Environment, Biotechnology and Energy, Faculty of Engineering, University of Porto","award":["UIDB\/00511\/2020"],"award-info":[{"award-number":["UIDB\/00511\/2020"]}]},{"name":"LEPABE (Laboratory for Process Engineering, Environment, Biotechnology and Energy, Faculty of Engineering, University of Porto","award":["UIDP\/00511\/2020"],"award-info":[{"award-number":["UIDP\/00511\/2020"]}]},{"name":"LEPABE (Laboratory for Process Engineering, Environment, Biotechnology and Energy, Faculty of Engineering, University of Porto","award":["LA\/P\/0045\/2020"],"award-info":[{"award-number":["LA\/P\/0045\/2020"]}]},{"name":"LEPABE (Laboratory for Process Engineering, Environment, Biotechnology and Energy, Faculty of Engineering, University of Porto","award":["UIDP\/04378\/2020"],"award-info":[{"award-number":["UIDP\/04378\/2020"]}]},{"name":"LEPABE (Laboratory for Process Engineering, Environment, Biotechnology and Energy, Faculty of Engineering, University of Porto","award":["UIDB\/04378\/2020"],"award-info":[{"award-number":["UIDB\/04378\/2020"]}]},{"name":"LEPABE (Laboratory for Process Engineering, Environment, Biotechnology and Energy, Faculty of Engineering, University of Porto","award":["LA\/P\/0140\/2020"],"award-info":[{"award-number":["LA\/P\/0140\/2020"]}]},{"name":"ALiCE (Associate Laboratory in Chemical Engineering, Faculty of Engineering, University of Porto","award":["PRT\/BD\/154543\/2022"],"award-info":[{"award-number":["PRT\/BD\/154543\/2022"]}]},{"name":"ALiCE (Associate Laboratory in Chemical Engineering, Faculty of Engineering, University of Porto","award":["UIDB\/00511\/2020"],"award-info":[{"award-number":["UIDB\/00511\/2020"]}]},{"name":"ALiCE (Associate Laboratory in Chemical Engineering, Faculty of Engineering, University of Porto","award":["UIDP\/00511\/2020"],"award-info":[{"award-number":["UIDP\/00511\/2020"]}]},{"name":"ALiCE (Associate Laboratory in Chemical Engineering, Faculty of Engineering, University of Porto","award":["LA\/P\/0045\/2020"],"award-info":[{"award-number":["LA\/P\/0045\/2020"]}]},{"name":"ALiCE (Associate Laboratory in Chemical Engineering, Faculty of Engineering, University of Porto","award":["UIDP\/04378\/2020"],"award-info":[{"award-number":["UIDP\/04378\/2020"]}]},{"name":"ALiCE (Associate Laboratory in Chemical Engineering, Faculty of Engineering, University of Porto","award":["UIDB\/04378\/2020"],"award-info":[{"award-number":["UIDB\/04378\/2020"]}]},{"name":"ALiCE (Associate Laboratory in Chemical Engineering, Faculty of Engineering, University of Porto","award":["LA\/P\/0140\/2020"],"award-info":[{"award-number":["LA\/P\/0140\/2020"]}]},{"name":"UCIBIO (Research Unit on Applied Molecular Biosciences","award":["PRT\/BD\/154543\/2022"],"award-info":[{"award-number":["PRT\/BD\/154543\/2022"]}]},{"name":"UCIBIO (Research Unit on Applied Molecular Biosciences","award":["UIDB\/00511\/2020"],"award-info":[{"award-number":["UIDB\/00511\/2020"]}]},{"name":"UCIBIO (Research Unit on Applied Molecular Biosciences","award":["UIDP\/00511\/2020"],"award-info":[{"award-number":["UIDP\/00511\/2020"]}]},{"name":"UCIBIO (Research Unit on Applied Molecular Biosciences","award":["LA\/P\/0045\/2020"],"award-info":[{"award-number":["LA\/P\/0045\/2020"]}]},{"name":"UCIBIO (Research Unit on Applied Molecular Biosciences","award":["UIDP\/04378\/2020"],"award-info":[{"award-number":["UIDP\/04378\/2020"]}]},{"name":"UCIBIO (Research Unit on Applied Molecular Biosciences","award":["UIDB\/04378\/2020"],"award-info":[{"award-number":["UIDB\/04378\/2020"]}]},{"name":"UCIBIO (Research Unit on Applied Molecular Biosciences","award":["LA\/P\/0140\/2020"],"award-info":[{"award-number":["LA\/P\/0140\/2020"]}]},{"name":"i4HB (Associate Laboratory Institute for Health and Bioeconomy","award":["PRT\/BD\/154543\/2022"],"award-info":[{"award-number":["PRT\/BD\/154543\/2022"]}]},{"name":"i4HB (Associate Laboratory Institute for Health and Bioeconomy","award":["UIDB\/00511\/2020"],"award-info":[{"award-number":["UIDB\/00511\/2020"]}]},{"name":"i4HB (Associate Laboratory Institute for Health and Bioeconomy","award":["UIDP\/00511\/2020"],"award-info":[{"award-number":["UIDP\/00511\/2020"]}]},{"name":"i4HB (Associate Laboratory Institute for Health and Bioeconomy","award":["LA\/P\/0045\/2020"],"award-info":[{"award-number":["LA\/P\/0045\/2020"]}]},{"name":"i4HB (Associate Laboratory Institute for Health and Bioeconomy","award":["UIDP\/04378\/2020"],"award-info":[{"award-number":["UIDP\/04378\/2020"]}]},{"name":"i4HB (Associate Laboratory Institute for Health and Bioeconomy","award":["UIDB\/04378\/2020"],"award-info":[{"award-number":["UIDB\/04378\/2020"]}]},{"name":"i4HB (Associate Laboratory Institute for Health and Bioeconomy","award":["LA\/P\/0140\/2020"],"award-info":[{"award-number":["LA\/P\/0140\/2020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Processes"],"abstract":"<jats:p>Despite the extensive research work on microalgae systems over the last decades, there is still a poor understanding of critical cultivation factors that could boost microalgae production economics. Extensive and systematic analysis of microalgae pilot and industrial production data could bring new insights into mechanisms and operational strategies for enhancing microalgae production systems. Recently, various machine learning methods have been employed within data mining workflows to accurately model microalgae growth under various process conditions. This review article provides a comprehensive analysis of data mining and machine learning methods in microalgae systems, with a focus on the effective application of artificial neural networks and deep learning models. It also highlights the importance of data acquisition techniques and real-time data availability that could foster the development of robust machine learning models. In addition, this paper delves into the field of hybrid modeling, a distinct approach that integrates the prior knowledge of mechanistic models with the descriptive power and adaptability of data-driven models. This synergy offers a robust framework to enhance production strategies, addressing critical challenges in scalability and efficiency, eventually paving the way for more sustainable and economical microalgae production systems.<\/jats:p>","DOI":"10.3390\/pr13092956","type":"journal-article","created":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T11:00:00Z","timestamp":1758106800000},"page":"2956","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Review of Intelligent Modeling for Microalgae Systems: Integrating Data Mining, Machine Learning, and Hybrid Approaches"],"prefix":"10.3390","volume":"13","author":[{"given":"Geovani R.","family":"Freitas","sequence":"first","affiliation":[{"name":"LEPABE, Laboratory for Process Engineering, Environment, Biotechnology and Energy, Chemical Engineering Department, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal"},{"name":"ALiCE, Associate Laboratory in Chemical Engineering, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal"},{"name":"A4F\u2014Algae for Future, Campus do Lumiar, Estrada do Pa\u00e7o do Lumiar, Edif. E, R\/C, 1649-038 Lisbon, Portugal"},{"name":"UCIBIO, Research Unit on Applied Molecular Biosciences, Department of Chemistry, NOVA School of Science and Technology, NOVA University Lisbon, 2829-516 Caparica, Portugal"},{"name":"Associate Laboratory i4HB, Institute for Health and Bioeconomy, NOVA School of Science and Technology, NOVA University Lisbon, 2829-516 Caparica, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6507-536X","authenticated-orcid":false,"given":"Sara","family":"Badenes","sequence":"additional","affiliation":[{"name":"A4F\u2014Algae for Future, Campus do Lumiar, Estrada do Pa\u00e7o do Lumiar, Edif. E, R\/C, 1649-038 Lisbon, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8077-4177","authenticated-orcid":false,"given":"Rui","family":"Oliveira","sequence":"additional","affiliation":[{"name":"UCIBIO, Research Unit on Applied Molecular Biosciences, Department of Chemistry, NOVA School of Science and Technology, NOVA University Lisbon, 2829-516 Caparica, Portugal"},{"name":"Associate Laboratory i4HB, Institute for Health and Bioeconomy, NOVA School of Science and Technology, NOVA University Lisbon, 2829-516 Caparica, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0960-4620","authenticated-orcid":false,"given":"Fernando G.","family":"Martins","sequence":"additional","affiliation":[{"name":"LEPABE, Laboratory for Process Engineering, Environment, Biotechnology and Energy, Chemical Engineering Department, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal"},{"name":"ALiCE, Associate Laboratory in Chemical Engineering, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Richmond, A., and Hu, Q. (2013). Dunaliella: Biology, Production, and Markets. Handbook of Microalgal Culture: Applied Phycology and Biotechnology, Wiley Blackwell.","DOI":"10.1002\/9781118567166"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Ben-Amotz, A., Polle, J.E.W., and Rao, D.V.S. (2009). The Alga Dunaliella: Biodiversity, Physiology, Genomics and Biotechnology, Science Publishers. [1st ed.].","DOI":"10.1201\/b10300"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"102200","DOI":"10.1016\/j.algal.2021.102200","article-title":"Microalgae, Soil and Plants: A Critical Review of Microalgae as Renewable Resources for Agriculture","volume":"54","author":"Alvarez","year":"2021","journal-title":"Algal Res."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Aghaalipour, E., Akbulut, A., and G\u00fcll\u00fc, G. (2020). Carbon Dioxide Capture with Microalgae Species in Continuous Gas-Supplied Closed Cultivation Systems. Biochem. Eng. J., 163.","DOI":"10.1016\/j.bej.2020.107741"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Leong, Y.K., and Chang, J.S. (2020). Bioremediation of Heavy Metals Using Microalgae: Recent Advances and Mechanisms. Bioresour. Technol., 303.","DOI":"10.1016\/j.biortech.2020.122886"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"175460","DOI":"10.1016\/j.scitotenv.2024.175460","article-title":"Microalgal Metabolic Engineering Facilitates Precision Nutrition and Dietary Regulation","volume":"951","author":"Zhao","year":"2024","journal-title":"Sci. Total Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"102189","DOI":"10.1016\/j.algal.2021.102189","article-title":"Harvesting Microalgae for Health Beneficial Dietary Supplements","volume":"54","author":"Laamanen","year":"2021","journal-title":"Algal Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1121","DOI":"10.1016\/j.scitotenv.2017.01.172","article-title":"Carbon Dioxide (CO2) Biofixation by Microalgae and Its Potential for Biorefinery and Biofuel Production","volume":"584\u2013585","author":"Kassim","year":"2017","journal-title":"Sci. Total Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.rser.2019.04.050","article-title":"Progress, Challenges and Solutions of Research on Photosynthetic Carbon Sequestration Efficiency of Microalgae","volume":"110","author":"Xu","year":"2019","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1016\/j.fuel.2019.04.138","article-title":"Potential of Two-Stage Cultivation in Microalgae Biofuel Production","volume":"252","author":"Nagappan","year":"2019","journal-title":"Fuel"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1007\/s10311-019-00939-0","article-title":"Biofuel Production from Microalgae: A Review","volume":"18","author":"Peng","year":"2020","journal-title":"Environ. Chem. Lett."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1016\/j.biombioe.2018.10.016","article-title":"Statistical Analysis of Sustainable Production of Algal Biomass from Wastewater Treatment Process","volume":"120","author":"Ambat","year":"2019","journal-title":"Biomass Bioenergy"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"109943","DOI":"10.1016\/j.jece.2023.109943","article-title":"Symbiosis of Microalgae and Bacteria Consortium for Heavy Metal Remediation in Wastewater","volume":"11","author":"Zhao","year":"2023","journal-title":"J. Environ. Chem. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2085","DOI":"10.1007\/s10311-023-01609-y","article-title":"Carbon Capture, Storage, and Usage with Microalgae: A Review","volume":"21","author":"Tripathi","year":"2023","journal-title":"Environ. Chem. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"622","DOI":"10.1016\/j.rser.2013.05.063","article-title":"Integrated CO2 Capture, Wastewater Treatment and Biofuel Production by Microalgae Culturing\u2014A Review","volume":"27","author":"Razzak","year":"2013","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1016\/j.rser.2017.05.064","article-title":"Photobioreactor Design for Microalgae Production through Computational Fluid Dynamics: A Review","volume":"79","author":"Pires","year":"2017","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1007\/s42979-021-00592-x","article-title":"Machine Learning: Algorithms, Real-World Applications and Research Directions","volume":"2","author":"Sarker","year":"2021","journal-title":"SN Comput. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Bock, F.E., Aydin, R.C., Cyron, C.J., Huber, N., Kalidindi, S.R., and Klusemann, B. (2019). A Review of the Application of Machine Learning and Data Mining Approaches in Continuum Materials Mechanics. Front. Mater., 6.","DOI":"10.3389\/fmats.2019.00110"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"S\u00f6derg\u00e5rd, C., Mildorf, T., Habyarimana, E., Berre, A.J., Fernandes, J.A., and Zinke-Wehlmann, C. (2021). Data Analytics and Machine Learning. Big Data in Bioeconomy\u2014Results from the European DataBio Project, Springer.","DOI":"10.1007\/978-3-030-71069-9"},{"key":"ref_20","unstructured":"Han, J., Kamber, M., and Pei, J. (2012). Data Mining: Concepts and Techniques, Morgan Kaufmann Publishers\u2014Elsevier. [3rd ed.]."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Mondal, P.P., Galodha, A., Verma, V.K., Singh, V., Show, P.L., Awasthi, M.K., Lall, B., Anees, S., Pollmann, K., and Jain, R. (2023). Review on Machine Learning-Based Bioprocess Optimization, Monitoring, and Control Systems. Bioresour. Technol., 370.","DOI":"10.1016\/j.biortech.2022.128523"},{"key":"ref_22","unstructured":"Dietterich, T. (2010). Introduction to Machine Learning, MIT Press. [2nd ed.]."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3356","DOI":"10.1002\/bit.27512","article-title":"Combining Model Structure Identification and Hybrid Modelling for Photo-Production Process Predictive Simulation and Optimisation","volume":"117","author":"Zhang","year":"2020","journal-title":"Biotechnol. Bioeng."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1499","DOI":"10.1002\/aic.690381003","article-title":"A Hybrid Neural Network-First Principles Approach to Process Modeling","volume":"38","author":"Psichogios","year":"1992","journal-title":"AIChE J."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"755","DOI":"10.1016\/j.compchemeng.2004.02.014","article-title":"Combining First Principles Modelling and Artificial Neural Networks: A General Framework","volume":"28","author":"Oliveira","year":"2004","journal-title":"Comput. Chem. Eng."},{"key":"ref_26","unstructured":"(2025, July 30). VOSviewer, Version 1.6.20; 31 October 2023. Available online: http:\/\/www.vosviewer.com\/."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.algal.2018.08.005","article-title":"Microalgae Research Worldwide","volume":"35","year":"2018","journal-title":"Algal Res."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Aggarwal, C.C. (2015). Data Mining, Springer International Publishing.","DOI":"10.1007\/978-3-319-14142-8"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Witten, I.H., Frank, E., and Hall, M.A. (2011). What\u2019s It All About?. Data Mining: Practical Machine Learning Tools and Techniques, Elsevier.","DOI":"10.1016\/B978-0-12-374856-0.00001-8"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.ijmedinf.2006.11.006","article-title":"Predictive Data Mining in Clinical Medicine: Current Issues and Guidelines","volume":"77","author":"Bellazzi","year":"2008","journal-title":"Int. J. Med. Inform."},{"key":"ref_31","unstructured":"Tan, P.-N., Steinbach, M., Karpatne, A., and Kumar, V. (2018). Introduction to Data Mining, Addison Wesley. [2nd ed.]."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"12012","DOI":"10.1088\/1742-6596\/1142\/1\/012012","article-title":"Machine Learning from Theory to Algorithms: An Overview","volume":"1142","author":"Alzubi","year":"2018","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_33","first-page":"395","article-title":"A Review on Machine Learning Techniques","volume":"4","author":"Dhage","year":"2016","journal-title":"Int. J. Recent Innov. Trends Comput. Commun."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.btre.2015.05.001","article-title":"Effects of Light Intensity and Temperature on Photoautotrophic Growth of a Green Microalga, Chlorococcum Littorale","volume":"7","author":"Ota","year":"2015","journal-title":"Biotechnol. Rep."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"12307","DOI":"10.1021\/jf403086f","article-title":"High-Throughput Quantitative Biochemical Characterization of Algal Biomass by NIR Spectroscopy; Multiple Linear Regression and Multivariate Linear Regression Analysis","volume":"61","author":"Laurens","year":"2013","journal-title":"J. Agric. Food Chem."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Yan, Q., Yang, C., and Wan, Z. (2023). A Comparative Regression Analysis between Principal Component and Partial Least Squares Methods for Flight Load Calculation. Appl. Sci., 13.","DOI":"10.3390\/app13148428"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1823","DOI":"10.1007\/s10811-014-0494-0","article-title":"Real-Time Monitoring, Diagnosis, and Time-Course Analysis of Microalgae Scenedesmus AMDD Cultivation Using Dual Excitation Wavelength Fluorometry","volume":"27","author":"Karakach","year":"2015","journal-title":"J. Appl. Phycol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1366\/10-06122","article-title":"Fourier Transform Infrared (FT-IR) Spectroscopy and Improved Principal Component Regression (PCR) for Quantification of Solid Analytes in Microalgae and Bacteria","volume":"65","author":"Horton","year":"2011","journal-title":"Appl. Spectrosc."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Lee, J.H., Park, J.J., and Yoon, H. (2020). Automatic Bridge Design Parameter Extraction for Scan-to-BIM. Appl. Sci., 10.","DOI":"10.3390\/app10207346"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"101908","DOI":"10.1016\/j.algal.2020.101908","article-title":"Classification of Dead and Living Microalgae Chlorella Vulgaris by Bioimage Informatics and Machine Learning","volume":"48","author":"Reimann","year":"2020","journal-title":"Algal Res."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"103400","DOI":"10.1016\/j.algal.2024.103400","article-title":"Artificial Intelligence-Driven Microalgae Autotrophic Batch Cultivation: A Comparative Study of Machine and Deep Learning-Based Image Classification Models","volume":"79","author":"Chong","year":"2024","journal-title":"Algal Res."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Anuntakarun, S., Lertampaiporn, S., Laomettachit, T., Wattanapornprom, W., and Ruengjitchatchawalya, M. (2022). MSRFR: A Machine Learning Model Using Microalgal Signature Features for NcRNA Classification. BioData Min., 15.","DOI":"10.1186\/s13040-022-00291-0"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"131677","DOI":"10.1016\/j.energy.2024.131677","article-title":"Thermo-Kinetics Study of Microalgal Biomass in Oxidative Torrefaction Followed by Machine Learning Regression and Classification Approaches","volume":"301","author":"Chen","year":"2024","journal-title":"Energy"},{"key":"ref_44","first-page":"103670","article-title":"Data-Driven Model Development for Prediction and Optimization of Biomass Yield of Microalgae-Based Wastewater Treatment","volume":"63","author":"Meenatchisundaram","year":"2024","journal-title":"Sustain. Energy Technol. Assess."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"126230","DOI":"10.1016\/j.cej.2020.126230","article-title":"Chlorella Vulgaris FSP-E Cultivation in Waste Molasses: Photo-to-Property Estimation by Artificial Intelligence","volume":"402","author":"Yew","year":"2020","journal-title":"Chem. Eng. J."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Jung, W.S., Jo, B.G., and Kim, Y. (2023). Do A Study on the Occurrence Characteristics of Harmful Blue-Green Algae in Stagnant Rivers Using Machine Learning. Appl. Sci., 13.","DOI":"10.3390\/app13063699"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"614","DOI":"10.1016\/j.cherd.2024.09.004","article-title":"Tree-Based Machine Learning for Predicting Neochloris Oleoabundans Biomass Growth and Biological Nutrient Removal from Tertiary Municipal Wastewater","volume":"210","author":"Razzak","year":"2024","journal-title":"Chem. Eng. Res. Des."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Singh, V., and Mishra, V. (2021). Exploring the Effects of Different Combinations of Predictor Variables for the Treatment of Wastewater by Microalgae and Biomass Production. Biochem. Eng. J., 174.","DOI":"10.1016\/j.bej.2021.108129"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"111387","DOI":"10.1016\/j.jece.2023.111387","article-title":"Machine Learning-Based Optimisation of Microalgae Biomass Production by Using Wastewater","volume":"11","author":"Singh","year":"2023","journal-title":"J. Environ. Chem. Eng."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1299","DOI":"10.1016\/j.renene.2020.09.034","article-title":"Exploring the Critical Factors of Algal Biomass and Lipid Production for Renewable Fuel Production by Machine Learning","volume":"163","year":"2021","journal-title":"Renew. Energy"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"30686","DOI":"10.1364\/OE.406036","article-title":"Classification, Identification, and Growth Stage Estimation of Microalgae Based on Transmission Hyperspectral Microscopic Imaging and Machine Learning","volume":"28","author":"Xu","year":"2020","journal-title":"Opt. Express"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"112252","DOI":"10.1016\/j.enconman.2019.112252","article-title":"Is Hydrothermal Treatment Coupled with Carbon Capture and Storage an Energy-Producing Negative Emissions Technology?","volume":"203","author":"Cheng","year":"2020","journal-title":"Energy Convers. Manag."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1016\/j.algal.2018.12.007","article-title":"Direct Estimation of Microalgal Flocs Fractal Dimension through Laser Reflectance and Machine Learning","volume":"37","author":"Negro","year":"2019","journal-title":"Algal Res."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.jbiotec.2016.12.020","article-title":"Laser Reflectance Measurement for the Online Monitoring of Chlorella Sorokiniana Biomass Concentration","volume":"243","year":"2017","journal-title":"J. Biotechnol."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Ning, H., Li, R., and Zhou, T. (2022). Machine Learning for Microalgae Detection and Utilization. Front. Mar. Sci., 9.","DOI":"10.3389\/fmars.2022.947394"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1364\/OSAC.387523","article-title":"Accurate Classification of Microalgae by Intelligent Frequency-Division-Multiplexed Fluorescence Imaging Flow Cytometry","volume":"3","author":"Harmon","year":"2020","journal-title":"OSA Contin."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"103642","DOI":"10.1016\/j.algal.2024.103642","article-title":"Digitalised Prediction of Blue Pigment Content from Spirulina Platensis: Next-Generation Microalgae Bio-Molecule Detection","volume":"83","author":"Chong","year":"2024","journal-title":"Algal Res."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Yeh, Y.C., Syed, T., Brinitzer, G., Frick, K., Schmid-Staiger, U., Haasdonk, B., Tovar, G.E.M., Krujatz, F., M\u00e4dler, J., and Urbas, L. (2023). Improving Microalgae Growth Modeling of Outdoor Cultivation with Light History Data Using Machine Learning Models: A Comparative Study. Bioresour. Technol., 390.","DOI":"10.1016\/j.biortech.2023.129882"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"945","DOI":"10.1016\/j.renene.2021.07.085","article-title":"Fault Detection by an Ensemble Framework of Extreme Gradient Boosting (XGBoost) in the Operation of Offshore Wind Turbines","volume":"179","author":"Trizoglou","year":"2021","journal-title":"Renew. Energy"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"136819","DOI":"10.1016\/j.snb.2024.136819","article-title":"A Portable and Low-Cost Optical Device for Pigment-Based Taxonomic Classification of Microalgae Using Machine Learning","volume":"423","author":"Pinto","year":"2025","journal-title":"Sens. Actuators B Chem."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1613","DOI":"10.1007\/s00477-023-02648-1","article-title":"Comparative Evaluation of Performances of Algae Indices, Pixel- and Object-Based Machine Learning Algorithms in Mapping Floating Algal Blooms Using Sentinel-2 Imagery","volume":"38","author":"Colkesen","year":"2024","journal-title":"Stoch. Environ. Res. Risk Assess"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"115406","DOI":"10.1016\/j.jece.2025.115406","article-title":"Predicting Harvesting Efficiency of Microalgae with Magnetic Nanoparticles Using Machine Learning Models","volume":"13","author":"Fu","year":"2025","journal-title":"J. Environ. Chem. Eng."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"106845","DOI":"10.1016\/j.psep.2025.106845","article-title":"Time Series Forecasting of Microalgae Cultivation for a Sustainable Wastewater Treatment","volume":"196","author":"Kumar","year":"2025","journal-title":"Process Saf. Environ. Prot."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Syed, T., Krujatz, F., Ihadjadene, Y., M\u00fchlst\u00e4dt, G., Hamedi, H., M\u00e4dler, J., and Urbas, L. (2024). A Review on Machine Learning Approaches for Microalgae Cultivation Systems. Comput. Biol. Med., 172.","DOI":"10.1016\/j.compbiomed.2024.108248"},{"key":"ref_65","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"154202","DOI":"10.1016\/j.cej.2024.154202","article-title":"Multi-Criteria Analysis of the Continuous Operation of a Membrane Photobioreactor to Treat Sewage: Modeling and Sensitivity Analysis","volume":"496","author":"Szelag","year":"2024","journal-title":"Chem. Eng. J."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.wasman.2017.03.044","article-title":"Comparison of ANN (MLP), ANFIS, SVM, and RF Models for the Online Classification of Heating Value of Burning Municipal Solid Waste in Circulating Fluidized Bed Incinerators","volume":"68","author":"You","year":"2017","journal-title":"Waste Manag."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"2960","DOI":"10.1002\/bit.27128","article-title":"Increasing Productivity of Spirulina Platensis in Photobioreactors Using Artificial Neural Network Modeling","volume":"116","author":"Susanna","year":"2019","journal-title":"Biotechnol. Bioeng."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1251","DOI":"10.1364\/AO.46.001251","article-title":"Retrieval of Pigment Concentrations and Size Structure of Algal Populations from Their Absorption Spectra Using Multilayered Perceptrons","volume":"46","author":"Bricaud","year":"2007","journal-title":"Appl. Opt."},{"key":"ref_70","unstructured":"G\u00e9ron, A. (2018). Hands-on Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, O\u2019Reilly."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Chong, J.W.R., Khoo, K.S., Chew, K.W., Vo, D.V.N., Balakrishnan, D., Banat, F., Munawaroh, H.S.H., Iwamoto, K., and Show, P.L. (2023). Microalgae Identification: Future of Image Processing and Digital Algorithm. Bioresour. Technol., 369.","DOI":"10.1016\/j.biortech.2022.128418"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"2202","DOI":"10.1007\/s00343-022-1312-1","article-title":"Identification of Paralytic Shellfish Toxin-Producing Microalgae Using Machine Learning and Deep Learning Methods","volume":"40","author":"Xu","year":"2022","journal-title":"J. Oceanol. Limnol."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"102018","DOI":"10.1016\/j.algal.2020.102018","article-title":"Deep Learning-Based ResNeXt Model in Phycological Studies for Future","volume":"50","author":"Yadav","year":"2020","journal-title":"Algal Res."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"101932","DOI":"10.1016\/j.algal.2020.101932","article-title":"ResNeXt Convolution Neural Network Topology-Based Deep Learning Model for Identification and Classification of Pediastrum","volume":"48","author":"Pant","year":"2020","journal-title":"Algal Res."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-020-71165-w","article-title":"Deep Learning-Based Diatom Taxonomy on Virtual Slides","volume":"10","author":"Kloster","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Zhuo, Z., Wang, H., Liao, R., and Ma, H. (2022). Machine Learning Powered Microalgae Classification by Use of Polarized Light Scattering Data. Appl. Sci., 12.","DOI":"10.3390\/app12073422"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Park, J., Baek, J., Kim, J., You, K., and Kim, K. (2022). Deep Learning-Based Algal Detection Model Development Considering Field Application. Water, 14.","DOI":"10.3390\/w14081275"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"102256","DOI":"10.1016\/j.algal.2021.102256","article-title":"Microalgae Classification Based on Machine Learning Techniques","volume":"55","author":"Berenguel","year":"2021","journal-title":"Algal Res."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"106395","DOI":"10.1016\/j.ecolind.2020.106395","article-title":"Identification and Enumeration of Cyanobacteria Species Using a Deep Neural Network","volume":"115","author":"Baek","year":"2020","journal-title":"Ecol. Indic."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Park, J., Lee, H., Park, C.Y., Hasan, S., Heo, T.Y., and Lee, W.H. (2019). Algal Morphological Identification in Watersheds for Drinking Water Supply Using Neural Architecture Search for Convolutional Neural Network. Water, 11.","DOI":"10.3390\/w11071338"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.culher.2024.08.012","article-title":"Automatic Monitoring of the Bio-Colonisation of Historical Building\u2019s Facades through Convolutional Neural Networks (CNN)","volume":"70","author":"Gianangeli","year":"2024","journal-title":"J. Cult. Herit."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"103506","DOI":"10.1016\/j.micron.2023.103506","article-title":"Deep Learning-Based Classification of Microalgae Using Light and Scanning Electron Microscopy Images","volume":"172","author":"Sonmez","year":"2023","journal-title":"Micron"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"146956","DOI":"10.1016\/j.scitotenv.2021.146956","article-title":"Detection of Morphological Changes Caused by Chemical Stress in the Cyanobacterium Planktothrix Agardhii Using Convolutional Neural Networks","volume":"784","author":"Carloto","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"100653","DOI":"10.1016\/j.prime.2024.100653","article-title":"Convolutional Neural Network Regression for Low-Cost Microalgal Density Estimation","volume":"9","author":"Nguyen","year":"2024","journal-title":"e-Prime Adv. Electr. Eng. Electron. Energy"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"915","DOI":"10.1002\/aic.16473","article-title":"Deep Learning-Based Surrogate Modeling and Optimization for Microalgal Biofuel Production and Photobioreactor Design","volume":"65","author":"Wagner","year":"2019","journal-title":"AIChE J."},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Yang, C.T., Kristiani, E., Leong, Y.K., and Chang, J.S. (2024). Machine Learning in Microalgae Biotechnology for Sustainable Biofuel Production: Advancements, Applications, and Prospects. Bioresour. Technol., 413.","DOI":"10.1016\/j.biortech.2024.131549"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"109049","DOI":"10.1016\/j.compchemeng.2025.109049","article-title":"Advancing Algal Biofuel Production through Data-Driven Insights: A Comprehensive Review of Machine Learning Applications","volume":"196","author":"Omole","year":"2025","journal-title":"Comput. Chem. Eng."},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Rodr\u00edguez-Rangel, H., Arias, D.M., Morales-Rosales, L.A., Gonzalez-Huitron, V., Partida, M.V., and Garc\u00eda, J. (2022). Machine Learning Methods Modeling Carbohydrate-Enriched Cyanobacteria Biomass Production in Wastewater Treatment Systems. Energies, 15.","DOI":"10.3390\/en15072500"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"3145","DOI":"10.1016\/B978-0-443-28824-1.50525-1","article-title":"LSTM-Based Soft Sensor for the Prediction of Microalgae Growth","volume":"Volume 53","author":"Syed","year":"2024","journal-title":"Computer Aided Chemical Engineering"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"101663","DOI":"10.1016\/j.algal.2019.101663","article-title":"Multivariate Modeling for Microalgae Growth in Outdoor Photobioreactors","volume":"45","author":"Mazzelli","year":"2020","journal-title":"Algal Res."},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Davani, L., Terenzi, C., Tumiatti, V., De Simone, A., Andrisano, V., and Montanari, S. (2022). Integrated Analytical Approaches for the Characterization of Spirulina and Chlorella Microalgae. J. Pharm. Biomed. Anal., 219.","DOI":"10.1016\/j.jpba.2022.114943"},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Hegazi, N., Khattab, A.R., Saad, H.H., Abib, B., and Farag, M.A. (2024). A Multiplex Metabolomic Approach for Quality Control of Spirulina Supplement and Its Allied Microalgae (Amphora & Chlorella) Assisted by Chemometrics and Molecular Networking. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-53219-5"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.ymeth.2022.03.017","article-title":"Promoter Prediction in Nannochloropsis Based on Densely Connected Convolutional Neural Networks","volume":"204","author":"Wei","year":"2022","journal-title":"Methods"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"15842","DOI":"10.1073\/pnas.1902322116","article-title":"Label-Free Chemical Imaging Flow Cytometry by High-Speed Multicolor Stimulated Raman Scattering","volume":"116","author":"Suzuki","year":"2019","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"108444","DOI":"10.1016\/j.jece.2022.108444","article-title":"Analysing the Effects of Culture Parameters on Wastewater Treatment Capability of Microalgae through Association Rule Mining","volume":"10","author":"Singh","year":"2022","journal-title":"J. Environ. Chem. Eng."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"107633","DOI":"10.1016\/j.compchemeng.2021.107633","article-title":"Cluster Analysis of Crude Oils with K-Means Based on Their Physicochemical Properties","volume":"157","author":"Sancho","year":"2022","journal-title":"Comput. Chem. Eng."},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"S\u00e1nchez, C., V\u00e1llez, N., Bueno, G., and Crist\u00f3bal, G. (2019, January 1\u20134). Diatom Classification Including Morphological Adaptations Using CNNs. Proceedings of the Iberian Conference on Pattern Recognition and Image Analysis, Madrid, Spain.","DOI":"10.1007\/978-3-030-31332-6_28"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"2967","DOI":"10.1007\/s10811-020-02180-7","article-title":"Machine Learning Processing of Microalgae Flow Cytometry Readings: Illustrated with Chlorella Vulgaris Viability Assays","volume":"32","author":"Pozzobon","year":"2020","journal-title":"J. Appl. Phycol."},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Manian, V., Alfaro-Mej\u00eda, E., and Tokars, R.P. (2022). Hyperspectral Image Labeling and Classification Using an Ensemble Semi-Supervised Machine Learning Approach. Sensors, 22.","DOI":"10.3390\/s22041623"},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1007\/s13173-013-0121-y","article-title":"Microalgae Classification Using Semi-Supervised and Active Learning Based on Gaussian Mixture Models","volume":"19","author":"Colares","year":"2013","journal-title":"J. Braz. Comput. Soc."},{"key":"ref_101","unstructured":"Sutton, R.S., and Barto, A.G. (2018). Reinforcement Learning: An Introduction, Bradford Books. [2nd ed.]."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"106649","DOI":"10.1016\/j.compchemeng.2019.106649","article-title":"Reinforcement Learning for Batch Bioprocess Optimization","volume":"133","author":"Petsagkourakis","year":"2020","journal-title":"Comput. Chem. Eng."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.jprocont.2018.07.013","article-title":"Control of a Bioreactor Using a New Partially Supervised Reinforcement Learning Algorithm","volume":"69","author":"Pandian","year":"2018","journal-title":"J. Process Control"},{"key":"ref_104","doi-asserted-by":"crossref","unstructured":"Treloar, N.J., Fedorec, A.J.H., Ingalls, B., and Barnes, C.P. (2020). Deep Reinforcement Learning for the Control of Microbial Co-Cultures in Bioreactors. PLoS Comput. Biol., 16.","DOI":"10.1371\/journal.pcbi.1007783"},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"2733","DOI":"10.1007\/s10811-021-02488-y","article-title":"Optimization of Spirulina Sp. Cultivation Using Reinforcement Learning with State Prediction Based on LSTM Neural Network","volume":"33","author":"Doan","year":"2021","journal-title":"J. Appl. Phycol."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/j.neucom.2018.08.064","article-title":"Unsupervised Pixel-Wise Classification for Chaetoceros Image Segmentation","volume":"318","author":"Tang","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.compchemeng.2013.08.008","article-title":"Hybrid Semi-Parametric Modeling in Process Systems Engineering: Past, Present and Future","volume":"60","author":"Oliveira","year":"2014","journal-title":"Comput. Chem. Eng."},{"key":"ref_108","unstructured":"Baughman, D.R., and Liu, Y.A. (2014). Neural Networks in Bioprocessing and Chemical Engineering, Academic Press."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"2919","DOI":"10.1002\/bit.27120","article-title":"Hybrid Physics-Based and Data-Driven Modeling for Bioprocess Online Simulation and Optimization","volume":"116","author":"Zhang","year":"2019","journal-title":"Biotechnol. Bioeng."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"113926","DOI":"10.1016\/j.rser.2023.113926","article-title":"Microalgae Biomass and Biomolecule Quantification: Optical Techniques, Challenges and Prospects","volume":"189","author":"Thiviyanathan","year":"2024","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_111","doi-asserted-by":"crossref","unstructured":"Allouzi, M.M.A., Allouzi, S., Al-Salaheen, B., Khoo, K.S., Rajendran, S., Sankaran, R., Sy-Toan, N., and Show, P.L. (2022). Current Advances and Future Trend of Nanotechnology as Microalgae-Based Biosensor. Biochem. Eng. J., 187.","DOI":"10.1016\/j.bej.2022.108653"},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Chuong, J.J.C.C., Rahman, M., Ibrahim, N., Heng, L.Y., Tan, L.L., and Ahmad, A. (2022). Harmful Microalgae Detection: Biosensors versus Some Conventional Methods. Sensors, 22.","DOI":"10.3390\/s22093144"},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"100679","DOI":"10.1016\/j.crfs.2024.100679","article-title":"Machine Learning Assisted Biosensing Technology: An Emerging Powerful Tool for Improving the Intelligence of Food Safety Detection","volume":"8","author":"Zhou","year":"2024","journal-title":"Curr. Res. Food Sci."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1080\/21655979.2022.2095089","article-title":"A Review of Biosensor for Environmental Monitoring: Principle, Application, and Corresponding Achievement of Sustainable Development Goals","volume":"14","author":"Huang","year":"2023","journal-title":"Bioengineered"},{"key":"ref_115","doi-asserted-by":"crossref","unstructured":"Naresh, V., and Lee, N. (2021). A Review on Biosensors and Recent Development of Nanostructured Materials-Enabled Biosensors. Sensors, 21.","DOI":"10.3390\/s21041109"},{"key":"ref_116","doi-asserted-by":"crossref","unstructured":"Jin, X., Liu, C., Xu, T., Su, L., and Zhang, X. (2020). Artificial Intelligence Biosensors: Challenges and Prospects. Biosens. Bioelectron., 165.","DOI":"10.1016\/j.bios.2020.112412"},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"114123","DOI":"10.1016\/j.measurement.2024.114123","article-title":"A Topical Review on AI-Interlinked Biodomain Sensors for Multi-Purpose Applications","volume":"227","author":"Thapa","year":"2024","journal-title":"Measurement"},{"key":"ref_118","doi-asserted-by":"crossref","unstructured":"Wieser, W., Assaf, A.A., Le Gouic, B., Dechandol, E., Herve, L., Louineau, T., Dib, O.H., Gon\u00e7alves, O., Titica, M., and Couzinet-Mossion, A. (2023). Development and Application of an Automated Raman Sensor for Bioprocess Monitoring: From the Laboratory to an Algae Production Platform. Sensors, 23.","DOI":"10.3390\/s23249746"},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"103071","DOI":"10.1016\/j.algal.2023.103071","article-title":"Seeing Good and Bad: Optical Sensing of Microalgal Culture Condition","volume":"71","author":"Solovchenko","year":"2023","journal-title":"Algal Res."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.algal.2017.11.020","article-title":"Microfluidic Systems for Microalgal Biotechnology: A Review","volume":"30","author":"Kim","year":"2018","journal-title":"Algal Res."},{"key":"ref_121","doi-asserted-by":"crossref","unstructured":"Sebasti\u00e1-Frasquet, M.T., Aguilar-Maldonado, J.A., Herrero-Dur\u00e1, I., Santamar\u00eda-Del-\u00e1ngel, E., Morell-Monz\u00f3, S., and Estornell, J. (2020). Advances in the Monitoring of Algal Blooms by Remote Sensing: A Bibliometric Analysis. Appl. Sci., 10.","DOI":"10.3390\/app10217877"},{"key":"ref_122","doi-asserted-by":"crossref","unstructured":"Stauffer, B.A., Bowers, H.A., Buckley, E., Davis, T.W., Johengen, T.H., Kudela, R., McManus, M.A., Purcell, H., Smith, G.J., and Woude, A.V. (2019). Considerations in Harmful Algal Bloom Research and Monitoring: Perspectives from a Consensus-Building Workshop and Technology Testing. Front. Mar. Sci., 6.","DOI":"10.3389\/fmars.2019.00399"},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"741351","DOI":"10.1016\/j.aquaculture.2024.741351","article-title":"A Review on Monitoring, Forecasting, and Early Warning of Harmful Algal Bloom","volume":"593","author":"Zahir","year":"2024","journal-title":"Aquaculture"},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"103649","DOI":"10.1016\/j.algal.2024.103649","article-title":"Advanced Imaging for Microalgal Biotechnology","volume":"82","author":"Plouviez","year":"2024","journal-title":"Algal Res."},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"1443","DOI":"10.1039\/D1EM00258A","article-title":"Water Monitoring by Means of Digital Microscopy Identification and Classification of Microalgae","volume":"23","author":"Barsanti","year":"2021","journal-title":"Environ. Sci. Process Impacts"},{"key":"ref_126","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Li, J., Zhou, Y., Zhang, X., and Liu, X. (2024). Artificial Intelligence-Based Microfluidic Platform for Detecting Contaminants in Water: A Review. Sensors, 24.","DOI":"10.3390\/s24134350"},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"103662","DOI":"10.1016\/j.algal.2024.103662","article-title":"Prediction of Product Properties and Identification of Key Influencing Parameters in Microwave Pyrolysis of Microalgae Using Machine Learning","volume":"82","author":"Hou","year":"2024","journal-title":"Algal Res."},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"16223","DOI":"10.1109\/ACCESS.2024.3436838","article-title":"AlgaeClass_Net: Optimizing Few-Shot Marine Microalgae Classification with Multi-Scale Feature Enhancement Network","volume":"13","author":"Liu","year":"2024","journal-title":"IEEE Access"},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"102568","DOI":"10.1016\/j.algal.2021.102568","article-title":"Convolutional Neural Network\u2014Support Vector Machine Based Approach for Classification of Cyanobacteria and Chlorophyta Microalgae Groups","volume":"61","author":"Sonmez","year":"2022","journal-title":"Algal Res."},{"key":"ref_130","doi-asserted-by":"crossref","unstructured":"Correa, I., Drews, P., Botelho, S., De Souza, M.S., and Tavano, V.M. (2017, January 18\u201321). Deep Learning for Microalgae Classification. Proceedings of the 16th IEEE International Conference on Machine Learning and Applications (ICMLA), Cancun, Mexico.","DOI":"10.1109\/ICMLA.2017.0-183"},{"key":"ref_131","doi-asserted-by":"crossref","unstructured":"Ali, S., Khan, Z., Hussain, A., Athar, A., and Kim, H.C. (2022). Computer Vision Based Deep Learning Approach for the Detection and Classification of Algae Species Using Microscopic Images. Water, 14.","DOI":"10.3390\/w14142219"},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"S521","DOI":"10.1002\/cjce.24020","article-title":"Synthesizing Data by Transferring Information in Data-Intensive Regions to Enhance Process Monitoring Performance in Data-Scarce Region","volume":"99","author":"Lyu","year":"2021","journal-title":"Can. J. Chem. Eng."},{"key":"ref_133","doi-asserted-by":"crossref","unstructured":"Hao, X., Liu, L., Yang, R., Yin, L., Zhang, L., and Li, X. (2023). A Review of Data Augmentation Methods of Remote Sensing Image Target Recognition. Remote Sens., 15.","DOI":"10.3390\/rs15030827"},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"174462","DOI":"10.1016\/j.scitotenv.2024.174462","article-title":"Assessing Global Carbon Sequestration and Bioenergy Potential from Microalgae Cultivation on Marginal Lands Leveraging Machine Learning","volume":"948","author":"Chen","year":"2024","journal-title":"Sci. Total Environ."},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"174767","DOI":"10.1016\/j.scitotenv.2024.174767","article-title":"Assessing the Global Distribution and Risk of Harmful Microalgae: A Focus on Three Toxic Alexandrium Dinoflagellates","volume":"948","author":"Hu","year":"2024","journal-title":"Sci. Total Environ."},{"key":"ref_136","doi-asserted-by":"crossref","unstructured":"Andriopoulos, V., and Kornaros, M. (2023). LASSO Regression with Multiple Imputations for the Selection of Key Variables Affecting the Fatty Acid Profile of Nannochloropsis Oculata. Mar. Drugs, 21.","DOI":"10.3390\/md21090483"},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"2283","DOI":"10.1007\/s10098-022-02321-1","article-title":"Early Prediction of Spirulina Platensis Biomass Yield for Biofuel Production Using Machine Learning","volume":"24","author":"Ching","year":"2022","journal-title":"Clean. Technol. Environ. Policy"},{"key":"ref_138","doi-asserted-by":"crossref","unstructured":"Teixeira, A.P., Alves, C., Alves, P.M., Carrondo, M.J.T., and Oliveira, R. (2007). Hybrid Elementary Flux Analysis\/Nonparametric Modeling: Application for Bioprocess Control. BMC Bioinform., 8.","DOI":"10.1186\/1471-2105-8-30"},{"key":"ref_139","doi-asserted-by":"crossref","unstructured":"Benner, P., Findeisen, R., Flockerzi, D., Reichl, U., and Sundmacher, K. (2014). Chapter 7\u2014Hybrid Modeling for Systems Biology: Theory and Practice. Large-Scale Networks in Engineering and Life Sciences, Springer International Publishing.","DOI":"10.1007\/978-3-319-08437-4"},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"1171","DOI":"10.1016\/j.jprocont.2012.05.004","article-title":"A General Hybrid Semi-Parametric Process Control Framework","volume":"22","author":"Oliveira","year":"2012","journal-title":"J. Process Control"},{"key":"ref_141","doi-asserted-by":"crossref","unstructured":"H\u00e4rdle, W., M\u00fcller, M., Sperlich, S., and Werwatz, A. (2004). Nonparametric and Semiparametric Models, Springer.","DOI":"10.1007\/978-3-642-17146-8"},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1016\/0005-1098(93)90124-C","article-title":"Grey-Box Modelling and Identification Using Physical Knowledge and Bayesian Techniques","volume":"29","author":"Tulleken","year":"1993","journal-title":"Automatica"},{"key":"ref_143","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/B978-0-443-15274-0.50014-7","article-title":"Investigating Physics-Informed Neural Networks for Bioprocess Hybrid Model Construction","volume":"52","author":"Rogers","year":"2023","journal-title":"Comput. Aided Chem. Eng."},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1016\/0167-7799(94)90048-5","article-title":"Neural-Network Contributions in Biotechnology Artificial Neural Network Representations The Problem of Identifying the Parameters of a Model Structure Essentially Reduces to the Determi","volume":"12","author":"Montague","year":"1994","journal-title":"Trends Biotechnol."},{"key":"ref_145","doi-asserted-by":"crossref","first-page":"107952","DOI":"10.1016\/j.compchemeng.2022.107952","article-title":"A General Deep Hybrid Model for Bioreactor Systems: Combining First Principles with Deep Neural Networks","volume":"165","author":"Pinto","year":"2022","journal-title":"Comput. Chem. Eng."},{"key":"ref_146","doi-asserted-by":"crossref","first-page":"1554","DOI":"10.1002\/bit.28668","article-title":"Deep Hybrid Modeling of a HEK293 Process: Combining Long Short-Term Memory Networks with First Principles Equations","volume":"121","author":"Ramos","year":"2024","journal-title":"Biotechnol. Bioeng."},{"key":"ref_147","doi-asserted-by":"crossref","unstructured":"Pinto, J., Ramos, J.R.C., Costa, R.S., Rossell, S., Dumas, P., and Oliveira, R. (2023). Hybrid Deep Modeling of a CHO-K1 Fed-Batch Process: Combining First-Principles with Deep Neural Networks. Front. Bioeng. Biotechnol., 11.","DOI":"10.3389\/fbioe.2023.1237963"},{"key":"ref_148","doi-asserted-by":"crossref","first-page":"103230","DOI":"10.1016\/j.algal.2023.103230","article-title":"Design and Validation of a Microalgae Biorefinery Using Machine Learning-Assisted Modeling of Hydrothermal Liquefaction","volume":"74","author":"Wu","year":"2023","journal-title":"Algal Res."},{"key":"ref_149","doi-asserted-by":"crossref","first-page":"1328","DOI":"10.1002\/aic.690400806","article-title":"Modeling Chemical Processes Using Prior Knowledge and Neural Networks","volume":"40","author":"Thompson","year":"1994","journal-title":"AIChE J."},{"key":"ref_150","doi-asserted-by":"crossref","first-page":"670","DOI":"10.1002\/bit.10247","article-title":"Hybrid Neural Network Modeling of a Full-Scale Industrial Wastewater Treatment Process","volume":"78","author":"Lee","year":"2002","journal-title":"Biotechnol. Bioeng."},{"key":"ref_151","doi-asserted-by":"crossref","first-page":"2013593","DOI":"10.1080\/19420862.2021.2013593","article-title":"Harnessing the Potential of Machine Learning for Advancing \u201cQuality by Design\u201d in Biomanufacturing","volume":"14","author":"Walsh","year":"2022","journal-title":"MAbs"},{"key":"ref_152","doi-asserted-by":"crossref","first-page":"2072","DOI":"10.1002\/bit.28503","article-title":"Hybrid Modeling in Bioprocess Dynamics: Structural Variabilities, Implementation Strategies, and Practical Challenges","volume":"120","author":"Mahanty","year":"2023","journal-title":"Biotechnol. Bioeng."},{"key":"ref_153","doi-asserted-by":"crossref","first-page":"2595","DOI":"10.1016\/B978-0-443-15274-0.50412-1","article-title":"Integrating Hybrid Modelling and Transfer Learning for New Bioprocess Predictive Modelling","volume":"Volume 52","author":"Kay","year":"2023","journal-title":"Proceedings of the 33rd European Symposium on Computer Aided Chemical Engineering (ESCAPE33)"},{"key":"ref_154","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1016\/B978-0-443-15274-0.50075-5","article-title":"A Hybrid Modelling Framework for Dynamic Modelling of Bioprocesses","volume":"Volume 52","author":"Wang","year":"2023","journal-title":"Proceedings of the 33rd European Symposium on Computer Aided Chemical Engineering (ESCAPE33)"},{"key":"ref_155","doi-asserted-by":"crossref","first-page":"103750","DOI":"10.1016\/j.algal.2024.103750","article-title":"A Reduced-Order Hybrid Model for Photobioreactor Performance and Biomass Prediction","volume":"84","author":"Shahhoseyni","year":"2024","journal-title":"Algal Res."},{"key":"ref_156","doi-asserted-by":"crossref","first-page":"2546","DOI":"10.1002\/aic.15667","article-title":"Kinetic Modeling and Process Analysis for Desmodesmus Sp. Lutein Photo-Production","volume":"63","author":"Ahmed","year":"2017","journal-title":"AIChE J."},{"key":"ref_157","doi-asserted-by":"crossref","first-page":"6334","DOI":"10.1021\/acs.iecr.5b00612","article-title":"Optimal Operation Strategy for Biohydrogen Production","volume":"54","author":"Dechatiwongse","year":"2015","journal-title":"Ind. Eng. Chem. Res."},{"key":"ref_158","doi-asserted-by":"crossref","first-page":"162797","DOI":"10.1016\/j.scitotenv.2023.162797","article-title":"Artificial Intelligence and Machine Learning Tools for High-Performance Microalgal Wastewater Treatment and Algal Biorefinery: A Critical Review","volume":"876","author":"Oruganti","year":"2023","journal-title":"Sci. Total Environ."},{"key":"ref_159","doi-asserted-by":"crossref","first-page":"1","DOI":"10.4018\/IJDA.343311","article-title":"Integrating Unsupervised and Supervised ML Models for Analysis of Synthetic Data From VAE, GAN, and Clustering of Variables","volume":"5","author":"Prayaga","year":"2024","journal-title":"Int. J. Data Anal."},{"key":"ref_160","doi-asserted-by":"crossref","first-page":"11651","DOI":"10.1109\/ACCESS.2019.2891360","article-title":"Generating Synthetic Missing Data: A Review by Missing Mechanism","volume":"7","author":"Santos","year":"2019","journal-title":"IEEE Access"}],"container-title":["Processes"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2227-9717\/13\/9\/2956\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:46:54Z","timestamp":1760035614000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2227-9717\/13\/9\/2956"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,17]]},"references-count":160,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["pr13092956"],"URL":"https:\/\/doi.org\/10.3390\/pr13092956","relation":{},"ISSN":["2227-9717"],"issn-type":[{"value":"2227-9717","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,17]]}}}