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Artificial intelligence and machine learning tools for high-performance microalgal wastewater treatment and algal biorefinery: A critical review
Abstract
The increased water scarcity, depletion of freshwater resources, and rising environmental awareness are stressing for the development of sustainable wastewater treatment processes. Microalgae-based wastewater treatment has resulted in a paradigm shift in our approach toward nutrient removal and simultaneous resource recovery from wastewater. Wastewater treatment and the generation of biofuels and bioproducts from microalgae can be coupled to promote the circular economy synergistically. A microalgal biorefinery transforms microalgal biomass into biofuels, bioactive chemicals, and biomaterials. The large-scale cultivation of microalgae is essential for the commercialization and industrialization of microalgae biorefinery. However, the inherent complexity of microalgal cultivation parameters regarding physiological and illumination parameters renders it challenging to facilitate a smooth and cost-effective operation. Artificial intelligence (AI)/machine learning algorithms (MLA) offer innovative strategies for assessing, predicting, and regulating uncertainties in algal wastewater treatment and biorefinery. The current study presents a critical review of the most promising AI/MLAs that demonstrate a potential to be applied in microalgal technologies. The most commonly used MLAs include artificial neural networks, support vector machine, genetic algorithms, decision tree, and random forest algorithms. Recent developments in AI have made it possible to combine cutting-edge techniques from AI research fields with microalgae for accurate analysis of large datasets. MLAs have been extensively studied for their potential in microalgae detection and classification. However, the ML application in microalgal industries, such as optimizing microalgae cultivation for increased biomass productivity, is still in its infancy. Incorporating smart AI/ML-enabled Internet of Things (IoT) based technologies can help the microalgal industries to operate effectively with minimum resources. Future research directions are also highlighted, and some of the challenges and perspectives of AI/ML are outlined. As the world is entering the digitalized industrial era, this review provides an insightful discussion about intelligent microalgal wastewater treatment and biorefinery for researchers in the field of microalgae.
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In the citing paper
“Studies indicate that these methods significantly improve the efficiency of biodiesel production by precisely regulating the nitrogen and phosphorus content in the culture medium [243].”
From Multi-Sensing Monitoring of the Microalgae Biomass Cultivation Systems for Biofuels and Added Value Products Synthesis—Challenges and Opportunities (2025) · cites this paper as [243]
In the citing paper
“An integrated, AI-driven cultivation framework offers the potential to improve biomass yield by 30–50%, reduce nutrient and energy consumption, and enhance process control accuracy for large-scale production [15,16,17].”
From Harnessing Artificial Intelligence to Revolutionize Microalgae Biotechnology: Unlocking Sustainable Solutions for High-Value Compounds and Carbon Neutrality (2025) · cites this paper as [15]
In the citing paper
“An integrated, AI-driven cultivation framework offers the potential to improve biomass yield by 30–50%, reduce nutrient and energy consumption, and enhance process control accuracy for large-scale production [15,16,17].”
From Harnessing Artificial Intelligence to Revolutionize Microalgae Biotechnology: Unlocking Sustainable Solutions for High-Value Compounds and Carbon Neutrality (2025) · cites this paper as [15]
In the citing paper
“An integrated, AI-driven cultivation framework offers the potential to improve biomass yield by 30–50%, reduce nutrient and energy consumption, and enhance process control accuracy for large-scale production [15,16,17].”
From Harnessing Artificial Intelligence to Revolutionize Microalgae Biotechnology: Unlocking Sustainable Solutions for High-Value Compounds and Carbon Neutrality (2025) · cites this paper as [15]
Citations
Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570
Chen, C.-Y., Yeh, K.-L., Aisyah, R., Lee, D.-J., & Chang, J.-S. (2011) · Bioresource Technology · doi:10.1016/j.biortech.2010.06.159 · Cited by 1,860
Production and harvesting of microalgae for wastewater treatment, biofuels, and bioproducts
Christenson, L., & Sims, R. C. (2011) · Biotechnology Advances · doi:10.1016/j.biotechadv.2011.05.015 · Cited by 1,386
Microalgae biorefinery: High value products perspectives
Chew, K. W., Yap, J. Y., Show, P. L., Suan, N. H., Juan, J. C., Ling, T. C., Lee, D.-J., & Chang, J.-S. (2017) · Bioresource Technology · doi:10.1016/j.biortech.2017.01.006 · Cited by 1,336
Self-citationA comprehensive review on automation in agriculture using artificial intelligence
Jha, K., Doshi, A., Patel, P., & Shah, M. (2019) · Artificial Intelligence in Agriculture · doi:10.1016/j.aiia.2019.05.004 · Cited by 885
Microalgae-Based Wastewater Treatment for Nutrients Recovery: A Review
Li, K., Liu, Q., Fang, F., Luo, R., Lu, Q., Zhou, W., Huo, S., Cheng, P., Liu, J., Addy, M., et al. (2019) · Bioresource Technology · doi:10.1016/j.biortech.2019.121934 · Cited by 737
Conversion of Microalgae to Biofuel
Suali, E., & Sarbatly, R. (2012) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2012.03.047 · Cited by 456
Guo, H., Wu, S., Tian, Y., Zhang, J., & Liu, H. (2020) · Bioresource Technology · doi:10.1016/j.biortech.2020.124114 · Cited by 395
Third generation biofuels from microalgae
Dragone, G., Fernandes, B. D., Vicente, A. A., & Teixeira, J. A. (2010) · Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) · Cited by 329
Kannangara, M., Dua, R., Ahmadi, L., & Bensebaa, F. (2017) · Waste Management · doi:10.1016/j.wasman.2017.11.057 · Cited by 327
Application of machine learning in anaerobic digestion: Perspectives and challenges
Cruz, I. A., Chuenchart, W., Long, F., Surendra, K. C., Andrade, L. R. S., Bilal, M., Liu, H., Figueiredo, R. T., Khanal, S. K., & Ferreira, L. F. R. (2021) · Bioresource Technology · doi:10.1016/j.biortech.2021.126433 · Cited by 284
Role of Microalgae in Achieving Sustainable Development Goals and Circular Economy
Olabi, A. G., Shehata, N., Sayed, E. T., Rodriguez, C., Anyanwu, R. C., Russell, C., & Abdelkareem, M. A. (2023) · Science of The Total Environment · doi:10.1016/j.scitotenv.2022.158689 · Cited by 273
Bagherzadeh, F., Mehrani, M.-J., Basirifard, M., & Roostaei, J. (2021) · Journal of Water Process Engineering · doi:10.1016/j.jwpe.2021.102033 · Cited by 269
Algae biorefinery: Review on a broad spectrum of downstream processes and products
Khoo, C. G., Dasan, Y. K., Lam, M. K., & Lee, K. T. (2019) · Bioresource Technology · doi:10.1016/j.biortech.2019.121964 · Cited by 218
Machine learning predicts and optimizes hydrothermal liquefaction of biomass
Shafizadeh, A., Shahbeik, H., Nadian, M. H., Mobli, H., Dowlati, M., Gupta, V. K., Peng, W., Lam, S. S., Tabatabaei, M., & Aghbashlo, M. (2022) · Chemical Engineering Journal · doi:10.1016/j.cej.2022.136579 · Cited by 209
Zhang, W., Li, J., Liu, T., Leng, S., Yang, L., Peng, H., Jiang, S., Zhou, W., Leng, L., & Li, H. (2021) · Bioresource Technology · doi:10.1016/j.biortech.2021.126011 · Cited by 188
The role of machine learning to boost the bioenergy and biofuels conversion
Wang, Z., Peng, X., Xia, A., Shah, A. A., Huang, Y. Y., Zhu, X., Zhu, X., & Liao, Q. (2021) · Bioresource Technology · doi:10.1016/j.biortech.2021.126099 · Cited by 187
Chen, W., Chu, Y.-S., Liu, J.-L., & Chang, J. (2018) · Energy Conversion and Management · doi:10.1016/j.enconman.2018.01.036 · Cited by 178
Sundui, B., Calderon, O. A. R., Abdeldayem, O. M., Gil, J. L., Rene, E. R., & Sambuu, U. (2021) · Clean Technologies and Environmental Policy · doi:10.1007/s10098-020-01993-x · Cited by 169
Li, J., Zhang, W., Liu, T., Yang, L., Li, H., Peng, H., Jiang, S., Wang, X., & Leng, L. (2021) · Chemical Engineering Journal · doi:10.1016/j.cej.2021.130649 · Cited by 169
Nayak, M., Dhanarajan, G., Dineshkumar, R., & Sen, R. (2018) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2018.08.048 · Cited by 166
Tang, Q., Chen, Y., Yang, H., Liu, M., Xiao, H., Wu, Z., Chen, H., & Naqvi, S. R. (2020) · Energy & Fuels · doi:10.1021/acs.energyfuels.0c01893 · Cited by 164
Smart microalgae farming with internet-of-things for sustainable agriculture
Lim, H. R., Khoo, K. S., Chia, W. Y., Chew, K. W., Ho, S.-H., & Show, P. L. (2022) · Biotechnology Advances · doi:10.1016/j.biotechadv.2022.107931 · Cited by 155
Self-citationYou, H., Ma, Z., Tang, Y., Wang, Y., Yan, J., Ni, M., Cen, K., & Huang, Q. (2017) · Waste Management · doi:10.1016/j.wasman.2017.03.044 · Cited by 149
Sultana, N. N., Hossain, S. M. Z., Abusaad, M., Alanbar, N., Senan, Y., & Razzak, S. A. (2021) · Fuel · doi:10.1016/j.fuel.2021.122184 · Cited by 148
Techniques of lipid extraction from microalgae for biofuel production: a review
Lee, S. Y., Khoiroh, I., Vo, D.-V. N., Senthil Kumar, P., & Show, P. L. (2021) · Environmental Chemistry Letters · doi:10.1007/s10311-020-01088-5 · Cited by 133
Self-citationAl‐Rashed, A. A. A. A., Karimipour, A., Bagherzadeh, S. A., Safaei, M. R., & Afrand, M. (2018) · International Journal of Heat and Mass Transfer · doi:10.1016/j.ijheatmasstransfer.2018.07.123 · Cited by 132
Khoo, C. G., Lam, M. K., Mohamed, A. R., & Lee, K. T. (2020) · Environmental Research · doi:10.1016/j.envres.2020.109828 · Cited by 130
del Rio‐Chanona, E. A., Wagner, J. L., Ali, H., Fiorelli, F., Zhang, D., & Hellgardt, K. (2018) · AIChE Journal · doi:10.1002/aic.16473 · Cited by 127
Semantic versus instance segmentation in microscopic algae detection
Ruiz-Santaquiteria, J., Bueno, G., Déniz, Ó., Vállez, N., & Cristóbal, G. (2019) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2019.103271 · Cited by 126
Microalgae as a Source of Lutein: Chemistry, Biosynthesis, and Carotenogenesis
Sun, Z., Li, T., Zhou, Z.-G., & Jiang, Y. (2015) · Advances in Biochemical Engineering/Biotechnology · doi:10.1007/10_2015_331 · Cited by 117
Wu, Z., & Shi, X. (2007) · Letters in Applied Microbiology · doi:10.1111/j.1472-765x.2006.02038.x · Cited by 115
Microalgae classification based on machine learning techniques
Otálora, P., Guzmán, J. L., Acién, F. G., Berenguel, M., & Reul, A. (2021) · Algal Research · doi:10.1016/j.algal.2021.102256 · Cited by 115
Muhammad, G., Ngatcha, A. D. P., Lv, Y., Xiong, W., El-Badry, Y. A.-M., Asmatulu, E., Xu, J., & Alam, M. A. (2021) · Renewable Energy · doi:10.1016/j.renene.2021.11.091 · Cited by 111
Coşgun, A., Günay, M. E., & Yıldırım, R. (2021) · Renewable Energy · doi:10.1016/j.renene.2020.09.034 · Cited by 110
Cheah, W. Y., Show, P. L., Juan, J. C., Chang, J., & Ling, T. C. (2018) · Energy Conversion and Management · doi:10.1016/j.enconman.2018.02.094 · Cited by 105
Self-citationSutherland, D. L., McCauley, J. I., Labeeuw, L., Ray, P., Kuzhiumparambil, U., Hall, C., Doblin, M. A., Nguyen, L. N., & Ralph, P. J. (2021) · Current Research in Environmental Sustainability · doi:10.1016/j.crsust.2021.100050 · Cited by 105
Saboe, D., Ghasemi, H., Gao, M. M., Samardzic, M., Hristovski, K. D., Boscovic, D., Burge, S. R., Burge, R. G., & Hoffman, D. A. (2021) · Science of The Total Environment · doi:10.1016/j.scitotenv.2020.142876 · Cited by 99
Faris, H., Mirjalili, S., & Aljarah, I. (2019) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-018-00913-2 · Cited by 99
Srivastava, G. P., Paul, A. K., & Goud, V. V. (2017) · Energy Conversion and Management · doi:10.1016/j.enconman.2017.10.093 · Cited by 97
Dallora, A. L., Eivazzadeh, S., Mendes, E., Berglund, J. S., & Anderberg, P. (2017) · PLoS ONE · doi:10.1371/journal.pone.0179804 · Cited by 86
Salam, S., & Verma, T. N. (2018) · Energy Conversion and Management · doi:10.1016/j.enconman.2018.11.014 · Cited by 85
Nassef, A. M., Sayed, E. T., Rezk, H., Abdelkareem, M. A., Rodríguez, C., & Olabi, A. G. (2018) · Energy Sources Part A Recovery Utilization and Environmental Effects · doi:10.1080/15567036.2018.1549171 · Cited by 85
Reimann, R., Zeng, B., Jakopec, M., Burdukiewicz, M., Petrick, I., Schierack, P., & Rödiger, S. (2020) · Algal Research · doi:10.1016/j.algal.2020.101908 · Cited by 84
Deep Learning for Microalgae Classification
Correa, I., Drews, P., Botelho, S. S. D. C., de Souza, M. S., & Tavano, V. M. (2017) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2017.0-183 · Cited by 84
Kushwaha, O. S., Uthayakumar, H., & Kumaresan, K. (2022) · Environmental Science and Pollution Research · doi:10.1007/s11356-022-19683-0 · Cited by 83
He, L., Subramanian, V. R., & Tang, Y. (2012) · Biomass and Bioenergy · doi:10.1016/j.biombioe.2012.02.025 · Cited by 82
Ansari, F. A., Nasr, M., Rawat, I., & Bux, F. (2020) · Journal of Water Process Engineering · doi:10.1016/j.jwpe.2020.101761 · Cited by 81
Raj, J. V. A., Kumar, R. P., Badathala, V., Gnansounou, E., & Bharathiraja, B. (2021) · Bioresource Technology · doi:10.1016/j.biortech.2021.124872 · Cited by 80
Chen, J., Dai, L., Mataya, D., Cobb, K., Chen, P., & Ruan, R. (2022) · Bioresource Technology · doi:10.1016/j.biortech.2022.128188 · Cited by 79
Serive, B., Nicolau, É., Bérard, J., Kaas, R., Pasquet, V., Picot, L., & Cadoret, J. (2017) · PLoS ONE · doi:10.1371/journal.pone.0171872 · Cited by 75
Saini, D. K., Rai, A., Devi, A., Pabbi, S., Chhabra, D., Chang, J.-S., & Shukla, P. (2021) · Bioresource Technology · doi:10.1016/j.biortech.2021.124908 · Cited by 75
Tham, P. E., Ng, Y. J., Vadivelu, N., Lim, H. R., Khoo, K. S., Chew, K. W., & Show, P. L. (2021) · Bioresource Technology · doi:10.1016/j.biortech.2021.126558 · Cited by 70
Self-citationArtificial neural network based modelling and optimization of microalgae microbial fuel cell
Sayed, E. T., Rezk, H., Abdelkareem, M. A., & Olabi, A. G. (2023) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2022.12.081 · Cited by 70
Dong, C., & Chen, J. (2018) · Bioresource Technology · doi:10.1016/j.biortech.2018.09.085 · Cited by 69
Artificial neural network modeling for predicting the growth of the microalga Karlodinium veneficum
García-Camacho, F., López-Rosales, L., Sánchez-Mirón, A., Belarbi, E. H., Chisti, Y., & Molina-Grima, E. (2016) · Algal Research · doi:10.1016/j.algal.2016.01.002 · Cited by 67
Monoalgal and mixed algal cultures discrimination by using an artificial neural network
Franco, B. M., Navas, L. M., Gómez, C., Sepúlveda, C., & Acién, F. G. (2019) · Algal Research · doi:10.1016/j.algal.2019.101419 · Cited by 61
Hossain, S. M. Z., Sultana, N., Jassim, M. S., Coskuner, G., Hazin, L. M., Razzak, S. A., & Hossain, M. M. (2022) · Journal of Water Process Engineering · doi:10.1016/j.jwpe.2021.102490 · Cited by 61
Malik, S., Shahid, A., Betenbaugh, M. J., Liu, C., & Mehmood, M. A. (2022) · Energy Conversion and Management · doi:10.1016/j.enconman.2022.115360 · Cited by 61
Peter, A. P., Chew, K. W., Pandey, A., Lau, S. Y., Rajendran, S., Ting, H. Y., Munawaroh, H. S. H., Phuong, N. V., & Show, P. L. (2022) · Fuel · doi:10.1016/j.fuel.2022.126438 · Cited by 59
Self-citationLiu, J.-Y., Zeng, L.-H., Ren, Z.-H., Du, T.-M., & Liu, X. (2020) · Algal Research · doi:10.1016/j.algal.2019.101739 · Cited by 55
Catalytic thermal degradation of Chlorella vulgaris: Evolving deep neural networks for optimization
Teng, S. Y., Loy, A. C. M., Leong, W. D., How, B. S., Chin, B. L. F., & Máša, V. (2019) · Bioresource Technology · doi:10.1016/j.biortech.2019.121971 · Cited by 54
Tang, D. Y. Y., Chew, K. W., Ting, H.-Y., Sia, Y.-H., Gentili, F. G., Park, Y., BANAT, F. A., Culaba, A. B., Ma, Z., & Show, P. L. (2022) · Bioresource Technology · doi:10.1016/j.biortech.2022.128503 · Cited by 54
Self-citationAutomatic identification of Scenedesmus polymorphic microalgae from microscopic images
Giraldo-Zuluaga, J.-H., Salazar, A., Diez, G., Gomez-Villa, A., Martínez, T., Vargas, J. F., & Peñuela, M. (2017) · Pattern Analysis and Applications · doi:10.1007/s10044-017-0662-3 · Cited by 52
Singh, V., & Mishra, V. (2022) · Water Research · doi:10.1016/j.watres.2022.118165 · Cited by 52
Microalgae classification using semi-supervised and active learning based on Gaussian mixture models
Drews, P., Colares, R. G., Machado, P., de Faria, M., Detoni, A. M. S., & Tavano, V. M. (2013) · Journal of the Brazilian Computer Society · doi:10.1007/s13173-013-0121-y · Cited by 51
Singh, V., & Mishra, V. (2021) · Biochemical Engineering Journal · doi:10.1016/j.bej.2021.108129 · Cited by 46
Hu, D., Liu, H., Yang, C., & Hu, E. (2008) · Acta Astronautica · doi:10.1016/j.actaastro.2008.02.008 · Cited by 45
Trends on CO2 Capture with Microalgae: A Bibliometric Analysis
Miranda, A. M., Hernández-Tenorio, F., Ocampo, D., Vargas, G. J., & Sáez, A. A. (2022) · Molecules · doi:10.3390/molecules27154669 · Cited by 44
Rodríguez‐Miranda, E., Acién, F. G., Guzmán, J. L., Berenguel, M., & Visioli, A. (2020) · Biotechnology and Bioengineering · doi:10.1002/bit.27617 · Cited by 44
Camacho-Rodríguez, J., Cerón-García, M. C., Fernández-Sevilla, J. M., & Grima, E. M. (2014) · Bioresource Technology · doi:10.1016/j.biortech.2014.11.057 · Cited by 42
Banerjee, A., Guria, C., & Maiti, S. K. (2016) · Energy · doi:10.1016/j.energy.2016.09.066 · Cited by 41
Zhu, C., Ji, Y., Du, X., Kong, F., Chi, Z., & Zhao, Y. (2022) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2022.158515 · Cited by 41
del Rio‐Chanona, E. A., Ahmed, N. R., Wagner, J. L., Lu, Y., Zhang, D., & Jing, K. (2019) · Biotechnology and Bioengineering · doi:10.1002/bit.27131 · Cited by 40
Metekia, W. A., Usman, A. G., Ulusoy, B. H., Abba, S. I., & Chirkena, K. (2021) · Saudi Journal of Biological Sciences · doi:10.1016/j.sjbs.2021.09.055 · Cited by 38
Kumar, S., Jain, S., & Kumar, H. (2018) · Energy Sources Part A Recovery Utilization and Environmental Effects · doi:10.1080/15567036.2018.1515277 · Cited by 37
Optimization of Microalgae-to-Biodiesel Production Process Using a Metaheuristic Technique
Hernández-Pérez, L. G., Sánchez-Tuirán, E. L., Ojeda, K. A., El‐Halwagi, M. M., & Ponce‐Ortega, J. M. (2019) · ACS Sustainable Chemistry & Engineering · doi:10.1021/acssuschemeng.9b00274 · Cited by 36
Lopez-Exposito, P., Negro, C., & Blanco, Á. (2018) · Algal Research · doi:10.1016/j.algal.2018.12.007 · Cited by 36
Wang, S., Wang, J., Shang, F., Wang, Y., Cheng, Q., & Liu, N. (2019) · Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy · doi:10.1016/j.saa.2019.117396 · Cited by 32
Harmon, J., Mikami, H., Kanno, H., Ito, T., & Goda, K. (2020) · OSA Continuum · doi:10.1364/osac.387523 · Cited by 31
Shokrkar, H., Ebrahimi, S., & Zamani, M. (2017) · Chemical Engineering Communications · doi:10.1080/00986445.2017.1356291 · Cited by 29
López‐Rosales, L., Gallardo-Rodríguez, J. J., Sánchez‐Mirón, A., Contreras‐Gómez, A., García-Camacho, F., & Grima, E. M. (2013) · Bioresource Technology · doi:10.1016/j.biortech.2013.07.141 · Cited by 28
Susanna, D., Dhanapal, R., Mahalingam, R., & Ramamurthy, V. (2019) · Biotechnology and Bioengineering · doi:10.1002/bit.27128 · Cited by 27
Ching, P. M. L., Mayol, A. P., Juan, J. L. S., Calapatia, A. M. A., So, R. H. Y., Sy, C. L., Ubando, A. T., & Culaba, A. B. (2021) · Process Integration and Optimization for Sustainability · doi:10.1007/s41660-020-00145-4 · Cited by 25
Zenooz, A. M., Ashtiani, F. Z., Ranjbar, R., Nikbakht, F., & Bolouri, O. (2017) · Preparative Biochemistry & Biotechnology · doi:10.1080/10826068.2016.1275013 · Cited by 24
Gruber, Z., Tóth, A. J., Menyhárd, A., Mizsey, P., Owsianiak, M., & Fózer, D. (2022) · Bioresource Technology · doi:10.1016/j.biortech.2022.128071 · Cited by 24
Bi, X., Lin, S., Zhu, S., Yin, H., Li, Z., & Chen, Z. (2018) · Optik · doi:10.1016/j.ijleo.2018.09.077 · Cited by 23
A Decision Tree Approach to Estimate the Microalgae Production in Open Raceway Pond
Supriyanto, S., Noguchi, R., Ahamed, T., Demura, M., & Watanabe, M. M. (2018) · IOP Conference Series Earth and Environmental Science · doi:10.1088/1755-1315/209/1/012050 · Cited by 21
Predicting Drying Curves in Algal Biorefineries using Gaussian Process Autoregressive Models
Pilario, K. E., Ching, P. M. L., Calapatia, A. M. A., & Culaba, A. B. (2022) · Digital Chemical Engineering · doi:10.1016/j.dche.2022.100036 · Cited by 12
Lee, J. S., Sung, Y. J., & Sim, S. J. (2022) · Bioresource Technology · doi:10.1016/j.biortech.2022.128014 · Cited by 12
Mahfouz, A. B., Ali, A., Crocker, M., Ahmed, A., Nasir, R., & Show, P. L. (2023) · Fermentation · doi:10.3390/fermentation9010047 · Cited by 8
Self-citationDeep Learning Development Review
Lv, W.-H., & Lei, J. (2020) · International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE) · doi:10.1109/aemcse50948.2020.00043 · Cited by 8
Sonkar, S., Shibani, & Mallick, N. (2020) · doi:10.22541/au.160372833.38766717/v1 · Cited by 4
Research works printed but not linked: 3
- Alzubi, 2018, Machine learning from theory to algorithms : an overview machine learning from theory to algorithms : an overview, J. Phys.
- Beltramo, 2019, Engineering in agriculture, environment and food evaluation of the linear and non-linear prediction models optimized with metaheuristics : application to anaerobic digestion processes, Eng. Agric. Environ. Food, 1–7
- Morowvat, 2016, Medium optimization by artificial neural networks for maximizing the triglycerides-rich lipids from biomass of chlorella vulgaris, Int. J. Pharm. Clin. Res., 8, 1414
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Automatic Identification of Individual Nanoplastics by Raman Spectroscopy Based on Machine Learning
Xie, L., Luo, S.-H., Liu, Y., Ruan, X., Gong, K., Ge, Q., Li, K., Valev, V. K., Liu, G., & Zhang, L. (2023) · Environmental Science & Technology · doi:10.1021/acs.est.3c03210 · Cited by 160
Samoraj, M., Çalış, D., Trzaska, K., Mironiuk, M., & Chojnacka, K. (2024) · Biocatalysis and Agricultural Biotechnology · doi:10.1016/j.bcab.2024.103224 · Cited by 152
Metadata
- Authors
- Oruganti, R. K., Biji, A. P., Lanuyanger, T., Show, P. L., Sriariyanun, M., Upadhyayula, V. K. K., Gadhamshetty, V., & Bhattacharyya, D.
- Affiliations
- Indian Institute of Technology Hyderabad (IN)
- Khalifa University of Science and Technology (AE)
- King Mongkut's University of Technology North Bangkok (TH)
- Umeå University (SE)
- South Dakota School of Mines and Technology (US)
- Corresponding author
- Bhattacharyya, Debraj
- ORCID
- Venue
- Science of The Total Environment
- Publisher
- Elsevier BV
- Published
- 2023-06
- Volume
- 876
- Pages
- 162797
- ISSN
- 0048-9697 (ISSN-L)
- OpenAlex
- W4323923505
- Cited by
- 211
- References
- 95
- Citations by year
- 2026: 56 · 2025: 83 · 2024: 49 · 2023: 23
- FWCI
- 3.23
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- The Science of The Total Environment (publishedVersion)
- PubMed (publishedVersion)
- Research Archive of Indian Institute of Technology Hyderabad (Indian Institute of Technology Hyderabad) (acceptedVersion)
- Topics
- Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Algal biology and biofuel production
- Physical Sciences › Environmental Science › Waste Management and Disposal › Wastewater Treatment and Reuse
- Physical Sciences › Environmental Science › Environmental Chemistry › Aquatic Ecosystems and Phytoplankton Dynamics
- Keywords
- microalgae-based wastewater treatment, microalgae biorefinery, artificial intelligence, machine learning, nutrient removal, resource recovery, microalgae cultivation optimization, biomass productivity, artificial neural networks, random forest, support vector machine, genetic algorithm, decision tree, Internet of Things, circular economy, biofuels, biomaterials
- MeSH terms
- Machine Learning, Artificial Intelligence, Biotechnology, Waardenburg Syndrome, Water Purification, Biomass, Biofuels, Microalgae
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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