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Bioresource Technology 369 (2023) 128451 Available online 9 December 2022 0960-8524/© 2022 Elsevier Ltd. All rights reserved. Artificial intelligence technologies in bioprocess: Opportunities and challenges Yang Chenga,b, Xinyu Bia,b, Yameng Xua,b, Yanfeng Liua,b, Jianghua Lia,b, Guocheng Dua,b, Xueqin Lva,b, Long Liua... | 1-s2.0-S0960852422017849-main.pdf |
Bioresource Technology 369 (2023) 128451 21. Introduction As the global transition from a fossil-based to a bio-based economy continues, the number of industrial bioprocesses employed to produce biofuels, materials, and healthcare products is steadily growing (Mol et al., 2021 ). Bioprocess is any process that uses liv... | 1-s2.0-S0960852422017849-main.pdf |
Bioresource Technology 369 (2023) 128451 3enrichment of microbial communities in co-culture systems (Xu et al., 2022 ). More than this, kinetic models could be further exploited by ANN. In a recent study, pyrolysis kinetics were generated by Chemistry- Informed Neural Networks (CINN), where a database containing ther-m... | 1-s2.0-S0960852422017849-main.pdf |
Bioresource Technology 369 (2023) 128451 4hyperplane maximizes the distance between the nearest data points and the hyperplane (Fig. 1). The sample point closest to the hyperplane is called the support vector (Guo et al., 2021). SVM uses the structural risk minimization (SRM) approach, which is opposed to the empirical... | 1-s2.0-S0960852422017849-main.pdf |
Bioresource Technology 369 (2023) 128451 5be simulated by choosing individuals with different possibilities. The one with the highest fitness value will be selected through constant iteration and evolution. Several solutions could be simultaneously evaluated by using this GA when dealing with complicated combinatorial ... | 1-s2.0-S0960852422017849-main.pdf |
Bioresource Technology 369 (2023) 128451 6methane potential (BMP) of feedstocks in anaerobic digestion, a multi-variate regression model was established between BMP and NIRS. The result show that predicted accuracy of NIRS model based on charac-teristic wavelengths outperformed all regression models based on the physic... | 1-s2.0-S0960852422017849-main.pdf |
Bioresource Technology 369 (2023) 128451 7Rapid detection technology of CPPs is an essential part in bioprocess control and optimization technology. In recent years, rapid detection technology has huge advancement due to the emergence of image recognition and AI technologies. However, there are still some limita-tions ... | 1-s2.0-S0960852422017849-main.pdf |
Bioresource Technology 369 (2023) 128451 8time warping (DTW) technology was applied to cope with these prob-lems and to optimize the control strategy by overcoming the time scale variability (Gollmer and Posten, 1996 ). Additionally, phase changes of bioprocess could be detected by a sliding window-based approach (Mait... | 1-s2.0-S0960852422017849-main.pdf |
Bioresource Technology 369 (2023) 128451 9example, a combination of positive and negative rules was employed to build integrated FL control strategies for yeast propagation (Birle et al., 2016 ). Under the guidance of a reference trajectory, RMSE generated by the integrated FL models could be reduced by 62. 8 % compare... | 1-s2.0-S0960852422017849-main.pdf |
Bioresource Technology 369 (2023) 128451 10optimizing the whole bioprocess have matured. In order to further enhance bioprocess, machine vison, spectroscopy and soft sensors have been gradually employed to achieve more accurate monitoring of bio-process. Meanwhile, smart control strategies have been proposed based on a... | 1-s2.0-S0960852422017849-main.pdf |
Bioresource Technology 369 (2023) 128451 11conditions. Bioresour. Technol. 157, 293-304. https://doi. org/10. 1016/j. biortech. 2014. 01. 032. Akinade, O. O., Oyedele, L. O., 2019. Integrating construction supply chains within a circular economy: An ANFIS-based waste analytics system (A-WAS). J. Clean. Prod. 229, 863-8... | 1-s2.0-S0960852422017849-main.pdf |
Bioresource Technology 369 (2023) 128451 12Luttmann, R., Bracewell, D. G., Cornelissen, G., Gernaey, K. V., Glassey, J., Hass, V. C., Kaiser, C., Preusse, C., Striedner, G., Mandenius, C.-F., 2012. Soft sensors in bioprocessing: A status report and recommendations. Biotechnol. J. 7 (8), 1040-1048. Mahlein, A.-K., 2016.... | 1-s2.0-S0960852422017849-main.pdf |
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