Machine learning approaches for smart cultivation and processing of microalgae

Keywords

Real-time monitoring / Neural network / Cultivation / Harvesting / Hyperspectral imaging

References

[1] Agatonovic-Kustrin S, Beresford R. Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. J Pharm Biomed Anal. 2000, 22(5): 717-27.

[2] Ajala SO, Alexander ML. Multi-objective optimization studies of microalgae dewatering by utilizing bio-based alkali: a case study of response surface methodology (RSM) and genetic algorithm (GA). SN Appl Sci. 2020, 2: 387.

[3] Al-Huqail A, Mohammed KJ, Suhatril M, Almujibah H, Toghroli S, Alnahdi SS, Ponnore JJ. Optimizing microalgal biomass conversion into carbon materials and their application in water treatment: a machine learning approach. Carbon Lett. 2025, 351-20.

[4] Alhattab M, Kermanshahi-Pour A, Brooks MSL. Microalgae disruption techniques for product recovery: influence of cell wall composition. J ApplPhycol. 2019, 31(1): 61-88

[5] Álvarez E, Moyano M, López-Urrutia Á, Nogueira E, Scharek R. Routine determination of plankton community composition and size structure: a comparison between flowcam and light microscopy. J Plankton Res. 2014, 36(1): 170-84.

[6] Araujo LN, Belotti JT, Alves TA, de Souza Tadano Y, Siqueira H. Ensemble method based on artificial neural networks to estimate air pollution health risks. Environ Model Softw. 2020, 123: 104567.

[7] Baba NM, Makhtar M, Fadzli SA, Awang MK. Current issues in ensemble methods and its applications. J Theoretical Appl Inf Technol. 2015, 81(2): 266-76

[8] Balan V, Pierson J, Husain H, Kumar S, Saffron C, Kumar V. Potential of using microalgae to sequester carbon dioxide and processing to bioproducts. Green Chem. 2023, 25207934-51.

[9] Barbierato E, Gatti A. The challenges of machine learning: A critical review. Electronics. 2024, 13(2): 416.

[10] Barros AI, Gonçalves AL, Sim˜oes M, Pires JCM. Harvesting techniques applied to microalgae: A review. Renew Sustain Energy Rev. 2015, 41: 1489-500.

[11] Benavides M, Mailier J, Hantson A-L, Muñoz G, Vargas A, Van Impe J, Wouwer AV. Design and test of a low-cost RGB sensor for online measurement of microalgae concentration within a photo-bioreactor. Sensors. 2015, 1534766-80.

[12] Bilad MR, Discart V, Vandamme D, Foubert I, Muylaert K, Vankelecom IFJ. Harvesting microalgal biomass using a magnetically induced membrane vibration (MMV) system: filtration performance and energy consumption. Bioresour Technol. 2013, 138: 329-38.

[13] Boli E, Savvidou M, Logothetis D, Louli V, Pappa G, Voutsas E. Magnetic harvesting of marine algae Nannochloropsis oceanica. Sep Sci Technol. 2017, 54(4): 730-7.

[14] Cho SJ, Sung YJ, Lee JS, Yu BS, Sim SJ. Robust cyst germination induction in haematococcus pluvialis to enhance Astaxanthin productivity in a semi-continuous outdoor culture system using power plant flue gas. Bioresour Technol. 2021, 338: 125533.

[15] Choi HI, Sung YJ, Hong ME, Han J, Min BK, Sim SJ. Reconsidering the potential of direct microalgal biomass utilization as end-products: a review. Renew Sustain Energy Rev. 2021, 155111930.

[16] Chong JWR, Khoo KS, Chew KW, Ting HY, Iwamoto K, Ruan R, Show PL. Artificial intelligence-driven microalgae autotrophic batch cultivation: a comparative study of machine and deep learning-based image classification models. Algal Res. 2024, 79103400.

[17] Coltelli P, Barsanti L, Evangelista V, Frassanito AM, Passarelli V, Gualtieri P. Automatic and real time recognition of microalgae by means of pigment signature and shape. Environ Science: Processes Impacts. 2013, 15(7): 1397-410

[18] Correa I, Drews P, Botelho S, de Souza MS, Tavano VM. 2017. Deep learning for microalgae classification. In: 2017 16th IEEE International conference on machine learning and applications (ICMLA). IEEE. pp. 20–25.

[19] Coşgun A, Günay ME, Yıldırım R. Exploring the critical factors of algal biomass and lipid production for renewable fuel production by machine learning. Renewable Energy. 2021, 163: 1299-317.

[20] Cunningham P, Cord M, Delany SJ. Supervised learning. Machine learning techniques for multimedia. Springer; 2008. pp. 21–49.

[21] Díaz-Santos E, Vila M, de la Vega M, León R, Vigara J. Study of bioflocculation induced by Saccharomyces bayanus var. Uvarum and flocculating protein factors in microalgae. Algal Res. 2015, 8: 23-9.

[22] El Naqa I, Murphy MJ. What is machine learning? In: machine learning in radiation oncology. Springer; 2015. pp. 3–11.

[23] Fabiyi SD. A review of unsupervised artificial neural networks with applications. Int J Comput Appl. 2019, 1814022-6

[24] Figueroa J, Rivas-Villar D, Rouco J, Novo J. Phytoplankton detection and recognition in freshwater digital microscopy images using deep learning object detectors. Heliyon. 2024, 10(3): e25367.

[25] Fraga-García P, Kubbutat P, Brammen M, Schwaminger S, Berensmeier S. 2018. Bare iron oxide nanoparticles for magnetic harvesting of microalgae: from interaction behavior to process realization. In: Nanomaterials (Basel, Switzerland), p. 5.

[26] Franco B, Navas L, Gómez C, Sepúlveda C, Acién F. Monoalgal and mixed algal cultures discrimination by using an artificial neural network. Algal Res. 2019, 38: 101419.

[27] Freitas GR, Badenes S, Oliveira R, Martins FG. A review of intelligent modeling for microalgae systems: integrating data Mining, machine Learning, and hybrid approaches. Processes. 2025, 1392956.

[28] Fu Y, Hu F, Li H, Cui L, Qian G, Zhang D, Xu Y. Application and mechanisms of microalgae harvesting by magnetic nanoparticles (MNPs). Separ Puri Technol. 2021, 265118519.

[29] Fu Y, Zhang Q, Tan Z, Yu S, Zhang Y. Predicting harvesting efficiency of microalgae with magnetic nanoparticles using machine learning models. J Environ Chem Eng. 2025, 132115406.

[30] Gallego I, Medic N, Pedersen JS, Ramasamy PK, Robbens J, Vereecke E, Romeis J. The microalgal sector in europe: towards a sustainable bioeconomy. New Biotechnol. 2025, 861-13.

[31] Ghahramani Z. 2003. Unsupervised learning. Summer school on machine learning. Springer. pp. 72–112.

[32] Grima EM, Belarbi E-H, Fern´andez FA, Medina AR, Chisti Y. Recovery of microalgal biomass and metabolites: process options and economics. Biotechnol Adv. 2003, 207–8491-515.

[33] Guo SL, Zhao XQ, Wan C, Huang ZY, Yang YL, Alam MA, Ho SH, Bai FW, Chang JS. Characterization of flocculating agent from the self-flocculating microalga scenedesmusobliquus AS-6-1 for effcient biomass harvest. Bioresour Technol. 2013, 145: 285-9.

[34] Gutarowska B, Czyzowska A. The ability of filamentous fungi to produce acids on indoor Building materials. Ann Microbiol. 2009, 59: 807-13.

[35] Guzmán J, Acién F, Berenguel M. Modelling and control of microalgae production in industrial photobioreactors. Revista Iberoamericana De Automática E Informática Industrial. 2021, 18(1): 1-18.

[36] Heimann K, Huerlimann R. Microalgal classification: major classes and genera of commercial microalgal species. Handbook of marine microalgae. Elsevier; 2015. pp. 25–41.

[37] von Helleckes LM, Hemmerich J, Wiechert W, Lieres E, Grünberger A. Machine learning in bioprocess development: from promise to practice. Trends Biotech. 2022, 41(6): 817-35.

[38] Hiller J, Franco-Lara E, Papaioannou V, Weuster-Botz D. Fast sampling and quenching procedures for microbial metabolic profiling. Biotechnol Lett. 2007, 29(8): 1161-7.

[39] Hoang AT, Sirohi R, Pandey A, Nižetić S, Lam SS, Chen W-H, Luque R, Thomas S, Arıcı M, Pham VV. Biofuel production from microalgae: challenges and chances. Phytochem Rev. 2023, 22(4): 1089-126.

[40] Huang Z, Shuai S, Wang H, Liu R, Zhang S, Cheng C, Hu Y, Yu X, He G, Fu W. Froth flotation separation of lepidolite ore using a using a new gemini surfactant as the flotation collector. Sep Purif Technol. 2021, 282: 119122.

[41] Huesemann MH, Knoshaug EP, Laurens LM, Dale T, Lane TW, McGowen J. Development of integrated screening, cultivar optimization, and verification research (DISCOVR): A coordinated research-driven approach to improve microalgal productivity, composition, and culture stability for commercially viable biofuels production. Algal Res. 2024, 79102961.

[42] Imamoglu E. Artificial intelligence and/or machine learning algorithms in microalgae bioprocesses. Bioengineering. 2024, 11(11): 1143.

[43] Inostroza C, Bahraoui NE, Rivera-Tinoco R, Acién FG. Uses of electro-coagulation-flocculation (ECF) for the pre-concentration of microalgae biomass. Process BiocheM. 2022, 12221-7.

[44] Jiang J, Jin W, Tu R, Han S, Ji Y, Zhou X. Harvesting of microalgae Chlorella pyrenoidosa by Bio-flocculation with bacteria and filamentous fungi. Waste Biomass Valori. 2021, 12: 145-54.

[45] Jordan MI, Mitchell TM. Machine learning: Trends, perspectives, and prospects. Science. 2015, 3496245255-60.

[46] Joun J, Hong ME, Sirohi R, Sim SJ. Enhanced biomass production through a repeated sequential auto-and heterotrophic culture mode in chlorella protothecoides. Bioresour Technol. 2021, 338: 125532.

[47] Jung N-C, Popescu I, Kelderman P, Solomatine DP, Price RK. Application of model trees and other machine learning techniques for algal growth prediction in Yongdam reservoir, Republic of Korea. J Hydroinformatics. 2010, 12(3): 262-74.

[48] Kaelbling LP, Littman ML, Moore AW. Reinforcement learning: A survey. J Artif Intell Res. 1996, 4: 237-85.

[49] Kim JYH, Kwak HS, Sung YJ, Choi HI, Hong ME, Lim HS, Lee J-H, Lee SY, Sim SJ. Microfluidic high-throughput selection of microalgal strains with superior photosynthetic productivity using competitive phototaxis. Sci Rep. 2016, 611-11

[50] Kumar N, Banerjee C, Jagadevan S. Cationically functionalized dextrin polymer as an efficient flocculant for harvesting microalgae. Energy Rep. 2020, 6: 2803-15.

[51] Kurniawati HA, Ismadji S, Liu JC. Microalgae Harvesting Flotat Using Nat Saponin Chitosan Bioresour Technol. 2014, 166: 429-34

[52] Kwak DH, Kim MS. Flotation of algae for water reuse and biomass production: role of zeta potential and surfactant to separate algal particles. Water Sci Technol. 2015, 72(5): 762-9.

[53] Lee JS, Sung YJ, Sim SJ. Kinetic analysis of microalgae cultivation utilizing 3D-printed real-time monitoring system reveals potential of biological CO2 conversion. Bioresour Technol. 2022, 364: 128014.

[54] Lei X, Zheng W, Ding H, Zhu X, Chen Q, Xu X, Zheng T, Tian Y. Effective harvesting of the marine microalga Thalassiosira Pseudonana by Marinobacter sp. FL06. Bioresour Technol. 2018, 269127-33.

[55] Levasseur W, Pozzobon V, Perré P. Green microalgae in intermittent light: a meta-analysis assisted by machine learning. J Appl Phycol. 2021, 34(1): 1-24

[56] Li SE. Deep reinforcement learning. Reinforcement learning for sequential decision and optimal control. Springer; 2023. pp. 365–402.

[57] Li H, Zhang E. A coupled sampling design for parameter Estimation in microalgae growth experiment: maximizing the benefits of uniform and non-uniform sampling. Water. 2021, 13(21): 2996.

[58] Li T, Jiang L, Hu Y, Paul JT, Zuniga C, Zengler K, Betenbaugh MJ. Creating a synthetic lichen: mutualistic co-culture of fungi and extracellular polysaccharide secreting Cyanobacterium Nostoc PCC 7413. Algal Res. 2020, 45: 101755.

[59] Li H, Chen L, Zhang F, Cai Z. Graph-learning-based machine learning improves prediction and cultivation of commercial-grade marine microalgae porphyridium. Bioresour Technol. 2025, 416131728.

[60] Liakos KG, Busato P, Moshou D, Pearson S, Bochtis D. Machine learning in agriculture: A review. Sensors. 2018, 1882674.

[61] Lim HR, Khoo KS, Chia WY, Chew KW, Ho S-H, Show PL. Smart microalgae farming with internet-of-things for sustainable agriculture. Biotechnol Adv. 2022, 57107931.

[62] Lin W, Chen L, Tan Z, Deng Z, Liu H. Application of filamentous fungi in microalgae-based wastewater remediation for biomass harvesting and utilization: from mechanisms to practical application. Algal Res. 2022, 62102614.

[63] Lopez-Exposito P, Negro C, Blanco A. Direct Estimation of microalgal Flocs fractal dimension through laser reflectance and machine learning. Algal Res. 2019, 37240-7.

[64] Markeb AA, Llimos-Turet J, Ferrer I, Blanquez P, Alonso A, Sanchez A, Moral-Vico J, Font X. The use of magnetic iron oxide based nanoparticles to improve microalgae harvesting in real wastewater. Water Res. 2019, 159: 490-500.

[65] Mathur M, Kumar A, Ariyadasa TU, Malik M. Yeast assisted algal flocculation for enhancing nutraceutical potential of Chlorella pyrenoidosa. Bioresour Technol. 2021, 340: 125670.

[66] Milić M, Petković B, Selmi A, Petković D, Jermsittiparsert K, Radivojević A, Milovancevic M, Khan A, Vidosavljević ST, Denić N. Computational evaluation of microalgae biomass conversion to biodiesel. Biomass Conv Bioref. 2021, 1341-8

[67] Min KH, Kim DH, Ki MR, Pack SP. Recent progress in flocculation, dewatering, and drying technologies for microalgae utilization: scalable and low-cost harvesting process development. BioresourTechnol. 2022, 344: 126404.

[68] Mondal PP, Galodha A, Verma VK, Singh V, Show PL, Awasthi MK, Jain R. Review on machine learning-based bioprocess optimization, monitoring, and control systems. Bioresour Technol. 2023, 370128523.

[69] NASA. (2025). EarthData. Retrieved from https://www.earthdata.nasa.gov/data/projects/cyan?utm_source=chatgpt.com (Accessed 10 Dec 2025).

[70] Nasir NM, Jusoh A, Manan H, Kasan NA, Kamaruzzan AS, Ghani, W.A.W.A.K.G.,Kurniawan SB, Lananan F. Utilization of microalgae, Chlorella Sp UMT LF2 for bioremediation of Litopenaeusvannamei culture system and harvesting using bio-flocculant, Aspergillus Niger. Biocatal Agri Biotechnol. 2023, 47102596.

[71] Ndikubwimana T, Zeng X, He N, Xiao Z, Xie Y, S Chang J, Lin L, Lu Y. Microalgae biomass harvesting by bioflocculation-interpretation by classical DLVO theory. Biochem Eng. 2015, 101160-7.

[72] Nguyen LN, Labeeuw L, Commault AS, Emmerton B, Ralph PJ, Johir MAH, Guo W, Ngo HH, Nghiem LD. Validation of a cationic polyacrylamide flocculant for the harvesting fresh and seawater microalgal biomass.Environ technol. Innovation. 2019, 16: 100466

[73] Noguchi R, Ahamed T, Mikihide D, Watanabe MM. A decision tree approach to estimate the microalgae production in open raceway pond. IOP Conf Series: Earth Environ Sci. 2018, IOP Publishingpp012050

[74] Noguchi R, Ahamed T, Rani DS, Sakurai K, Nasution MA, Wibawa DS, Demura M, Watanabe MM. Artificial neural networks model for estimating growth of polyculture microalgae in an open raceway pond. Biosyst Eng. 2019, 177122-9.

[75] Nwoba EG, Chuka-Ogwude D, Vadiveloo A, Ogbonna JC. Process control strategies applied to microalgae-based biofuel production. 3rd generation biofuels. Elsevier; 2022. pp. 105–34.

[76] Orenstein EC, Beijbom O, Peacock EE, Sosik HM. (2015). Whoi-plankton-a large scale fine grained visual recognition benchmark dataset for plankton classification. arXiv preprint arXiv:1510.00745.

[77] Otálora P, Guzmán J, Acién F, Berenguel M, Reul A. Microalgae classification based on machine learning techniques. Algal Res. 2021, 55: 102256.

[78] Otálora P, Guzmán JL, Acién FG, Berenguel M, Reul A. An artificial intelligence approach for identification of microalgae cultures. New Biotechnol. 2023, 7758-67.

[79] Pahl SL, Lee AK, Kalaitzidis T, Ashman PJ, Sathe S, Lewis DM. Harvesting, thickening and dewatering microalgae biomass. Algae for biofuels and energy. Springer; 2013. pp. 165–85.

[80] Parmentier D, Manhaeghe D, Baccini L, Van Meirhaeghe R, Rousseau DPL, Van Hulle S. A new reactor design for harvesting algae through electrocoagulation-flotation in a continuous mode. Algal Res. 2020, 47: 101828.

[81] Pedrycz W, Succi G, Chun M-G. Association analysis of software measures. Int J Software Eng Knowl Eng. 2002, 1203291-316.

[82] Pozzobon V, Levasseur W, Viau E, Michiels E, Clément T, Perré P. Machine learning processing of microalgae flow cytometry readings: illustrated with chlorella vulgaris viability assays. J Appl Phycol. 2020, 3252967-76.

[83] Promdaen S, Wattuya P, Sanevas N. Automated microalgae image classification. Procedia Comput Sci. 2014, 29: 1981-92.

[84] Qi J, Lan HC, Liu PP, Liu HJ, Qu JH. Efficient Microcystis aeruginosa removal by moderate photocatalysis-enhanced coagulation with magnetic Zn-doped Fe3O4 particles. Water Res. 2020, 171: 115448.

[85] Qu K, Guo F, Liu X, Lin Y, Zou Q. Application of machine learning in microbiology. Front Microbiol. 2019, 10827.

[86] Radha S, Fathima AA, Iyappan S, Ramya M. Direct colony PCR for rapid identification of varied microalgae from freshwater environment. J Appl Phycol. 2013, 25(2): 609-13.

[87] Rodríguez-Rángel H, Arias DM, Morales-Rosales LA, Gonzalez-Huitron V, Valenzuela Partida M, García J. Machine learning methods modeling Carbohydrate-Enriched cyanobacteria biomass production in wastewater treatment systems. Energies. 2022, 1572500.

[88] Salam S, Verma TN. Appending empirical modelling to numerical solution for behaviourcharacterisation of microalgae biodiesel. Energy Conv Manag. 2019, 180: 496-510.

[89] Salim S, Kosterink NR, Wacka NT, Vermuë MH, Wijffels RH. Mechanism behind autoflocculation of unicellular green microalgae Ettliatexensis. J Biotechnol. 2014, 174: 34-8.

[90] Sandip A, Smith VH, Faddis TN. An experimental investigation of microalgal dewatering efficiency of belt filter system. Energy Rep. 2015, 1: 169-74.

[91] Schobesberger M, Real BK, Meijer D, Berensmeier S, Fraga- García P. Natural magnetite ore as a harvesting agent for saline microalgae Microchloropsis Salina. Bioresour Technol Rep. 2021, 15: 100798.

[92] Shim SJ, Hong ME, Chang WS, Sim SJ. Repeated-batch production of omega-3 enriched biomass of Chlorella Sorokiniana via calcium-induced homeoviscous adaptation. Bioresour Technol. 2020, 303122944.

[93] Singh G, Patidar SK. Microalgae harvesting techniques: a review. J Environ Manage. 2018, 217499-508.

[94] Singh V, Verma M, Chivate MS, Mishra V. Machine learning-based optimisation of microalgae biomass production by using wastewater. J Environ Chem Eng. 2023, 116111387.

[95] Solomatine DP, Dulal KN. Model trees as an alternative to neural networks in rainfall—runoff modelling. Hydrol Sci J. 2003, 483399-411.

[96] Soo A, Gao L, Shon HK. Machine learning and nutrient circular economy recovery for microalgae chlorella vulgaris for biomass production. Desalination. 2026, 622: 119735.

[97] Steinwandter V, Borchert D, Herwig C. Data science tools and applications on the way to pharma 4.0. Drug Discovery Today. 2019, 24(9): 1795-805.

[98] Sunde M, Kwan AHY, Templeton MD, Beever RE, Mackay JP. Structural analysis of hydrophobins. Micron. 2008, 39: 773-84.

[99] Sung YJ, Sim SJ. Multifaceted strategies for economic production of microalgae haematococcus pluvialis-derived Astaxanthin via direct conversion of CO2. Bioresour Technol. 2022, 344: 126255.

[100] Sung YJ, Choi HI, Lee JS, Hong ME, Sim SJ. Screening of oleaginous algal strains from Chlamydomonas reinhardtii mutant libraries via density gradient centrifugation. Biotechnol Bioeng. 2019, 116123179-88.

[101] Sung YJ, Patel AK, Yu BS, Choi HI, Kim J, Jin E, Sim SJ. Sedimentation rate-based screening of oleaginous microalgae for utilization as a direct combustion fuel. Bioresour Technol. 2019, 293: 122045.

[102] Sung YJ, Lee JS, Sim SJ. Accelerated sunlight-driven conversion of industrial flue gas into biofuels by microfluidic high-throughput screening towards improving photosynthesis in microalgae under fluctuating light. Chem Eng J. 2022, 443136487.

[103] Sung YJ, Yoon HK, Lee JS, Joun J, Yu BS, Sirohi R, Sim SJ. Novel 3D-printed buoyant structures for improvement in flue gas CO2-derived microalgal biomass production by enhancing anti-biofouling on vertical polymeric photobioreactor. J Clean Prod. 2022, 366133030.

[104] Sung YJ, Yu BS, Yang HE, Kim DH, Lee JY, Sim SJ. Microalgae-derived hydrogen production towards low carbon emissions via large-scale outdoor systems. Bioresour Technol. 2022, 364128134.

[105] Suparmaniam U, Shaik NB, Lam MK, Lim JW, Uemura Y, Shuit SH, Show PL, Tan IS, Lee KT. Valorization of fish bone waste as novel bioflocculant for rapid microalgae harvesting: experimental evaluation and modelling using back propagation artificial neural network. J Water Process Eng. 2022, 47102808.

[106] Suparmaniam U, Shaik NB, Lam MK, Lim JW, Uemura Y, Shuit SH, Show PL, Tan IS, Lee KT. Valorization of fish bone waste as novel bioflocculant for rapid microalgae harvesting: experimental evaluation and modelling using back propagation artificial neural network. J Water Process Eng. 2022, 47102808.

[107] Supriyanto, Noguchi R, Ahamed T, Rani DS, Sakurai K, Nasution MA, Wibawa DS, Demura M, Watanabe MM. Artificial neural networks model for estimating growth of polyculture microalgae in an open raceway pond. Biosyst Eng. 2019, 177122-9.

[108] Teixeira MS, da Silva SLG, Moruzzi IC, Silva RB. Tannin-based coagulant for harvesting microalgae cultivated in wastewater: Efficiency, Floc morphology and products characterization. Sci Total Environ. 2022, 807: 150776.

[109] Tufail S, Riggs H, Tariq M, Sarwat AI. Advancements and challenges in machine learning: A comprehensive review of models, libraries, applications, and algorithms. Electronics. 2023, 1281789.

[110] Uguz S, Sahin YS, Kumar P, Yang X, Anderson G. Real-Time algal monitoring using novel machine learning approaches. Big Data Cogn Comput. 2025, 96153.

[111] Jacob-Lopes E, Zepka LQ, Deprá MC, editors. Chapter 7 - Recent advancements of coagulation–flocculation in microalgal downstream processes Editor(s):Gagnon,Y. Woodhead Series in Bioenergy. Algal Bioreactors. Elsevier Science Ltd; 2025. pp. 115–24. https://doi.org/10.1016/B978-0-443-14059-4.00009-X

[112] Ummalyma SB, Mathew A, Sukumaran RK. Harvesting of microalgal biomass: efficient method for flocculation through pH modulation. Bioresour Technol. 2016, 223216-21.

[113] Ummalyma SB, Gnansounou E, Sukumaran RK, Sindhu R, Pandey A, Sahoo D. Bioflocculation: an alternative strategy for harvesting of microalgae – an overview. Bioresour Technol. 2017, 242: 227-35.

[114] Vadiveloo A, Nwoba EG, Ogbonna C, Mehta P. Sustainable production of bioproducts from wastewater-grown microalgae. In: Sustainable downstream processing of microalgae for industrial application. CRC; 2019. pp. 165–200.

[115] Wang QRHY, Li LH. Insights into differences between spore-assisted and pellet-assisted microalgae harvesting using a highly efficient fungus: Efficiency, high-value substances, and mechanisms. Sci Total Environ. 2023, 877: 162945.

[116] Wang J, Zhao J, Wang Y, Wang W, Gao Y, Xu R, Zhao W. A new microfluidic device for classification of microalgae cells based on simultaneous analysis of chlorophyll fluorescence, side light scattering, resistance pulse sensing. Micromachines. 2016, 711198.

[117] Wen H, Zhang H, He M, Zhang X. A novel approach for harvesting of the microalgae Chlorella vulgaris with Moringa Oleifera extracts microspheres by Buoy-bead flotation method. Algal Res. 2021, 60102479.

[118] Wolfrum E, Knoshaug E, Laurens L, Harmon V, Dempster T, McGowan J, Hu Z. (2018). ATP3 Unified Field Study Data (No. 76). National Renewable Energy Laboratory-Data (NREL-DATA), Golden, CO (United States); National Renewable Energy Lab.(NREL), Golden, CO (United States).

[119] Xie B, Gong W, Yu H, Tang X, Yan Z, Luo X, Gan Z, Wang T, Li G, Liang H. Immobilized microalgae for anaerobic digestion effluent treatment in a photobioreactor-ultrafiltration system: algal harvest and membrane fouling control. Bioresour Technol. 2018, 268139-48.

[120] Xu Z, Jiang Y, Ji J, Forsberg E, Li Y, He S. Classification, identification, and growth stage Estimation of microalgae based on transmission hyperspectral microscopic imaging and machine learning. Opt Express. 2020, 282130686-700.

[121] Xu K, Zou X, Chang W, Qu Y, Li Y. Microalgae harvesting technique using ballasted flotation: A review. SeparPuri Technol. 2021, 276: 119439

[122] Xu M, Harmon J, Hasunuma T, Isozaki A, Goda K. 2021b. AI on a chip for identifying microalgal cells with high heavy metal removal efficiency. In: 21st International conference on solid-state sensors, actuators and microsystems (Transducers). IEEE. pp. 385–388.

[123] Xu K, Zhu Z, Zou YH. Prediction of microalgae harvesting efficiency and identification of important parameters for ballasted flotation using an optimized machine learning model. Algal Res. 2025.

[124] Xu K, Zhu Z, Yu H, Zou X. Prediction of microalgae harvesting efficiency and identification of important parameters for ballasted flotation using an optimized machine learning model. Algal Res. 2025, 87103985.

[125] Xue K, Bradshaw LP. A semi-supervised learning-based diagnostic classification method using artificial neural networks. Front Psychol. 2021, 11618336.

[126] Yadav D, Jalal A, Garlapati D, Hossain K, Goyal A, Pant G. Deep learning-based resnext model in phycological studies for future. Algal Res. 2020, 50102018.

[127] Yeh YC, Syed T, Brinitzer G, Frick K, Schmid-Staiger U, Haasdonk B, Urbas L. Improving microalgae growth modeling of outdoor cultivation with light history data using machine learning models: A comparative study. Bioresour Technol. 2023, 390129882.

[128] Zhang JG, Zhang JN. The filamentous fungal pellet and forces driving its formation. Crit Rev Biotechnol. 2016, 361066-77.

[129] Zhang H, Zhang X. Microalgal harvesting using foam flotation: a critical review. Biomass Bioenerg. 2019, 120: 176-88.

[130] Zhao Y, Guo G, Sun S, Hu C, Liu J. Co-pelletization of microalgae and fungi for efficient nutrient purification and biogas upgrading. Bioresour Technol. 2019;289:121656.

[131] Zhao Y, Han F, GuoL,SinghS,ZhangH,Zhang J. Flotation separation of hazardous Polyvinyl chloride from waste plastics based on green plasma modification. J Clean Prod. 2021, 318: 128569.

[132] Zheng J, Cole T, Zhang Y, Yuan D, Tang SY. An automated and intelligent microfluidic platform for microalgae detection and monitoring. Lab Chip. 2024, 242244-53.

[133] Zhou Z-H. Ensemble learning. In: Machine learning; 2021. pp. 181–210.

[134] Zhuo Z, Wang H, Liao R, Ma H. Machine learning powered microalgae classification by use of polarized light scattering data. Appl Sci. 2022, 1273422.

[135] Zou Q, Qu K, Luo Y, Yin D, Ju Y, Tang H. Predicting diabetes mellitus with machine learning techniques. Front Genet. 2018, 9515.

Funding

Sookmyung Women's University(1-0000-0000)

DST BRICS(TPN No-102445)

RIGHTS & PERMISSIONS

Jiangnan University