Artificial neural network TSR for optimization of actinomycin production
The optimization of industrial fermentation processes, particularly for the production of bioactive compounds like Actinomycin V, is essential for maximizing yield and cost-efficiency. This study introduces a hybrid approach integrating Artificial Neural Networks (ANNs) with the Trees Social Relatio...
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| Format: | Article |
| Language: | English |
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REA Press
2024-03-01
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| Series: | Big Data and Computing Visions |
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| Online Access: | https://www.bidacv.com/article_205624_854f7edf845d7ba3413333f261d08df4.pdf |
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| author | Marzieh Lamtar-Gholipoor Soheil Fakheri Mahmoud Alimoradi |
| author_facet | Marzieh Lamtar-Gholipoor Soheil Fakheri Mahmoud Alimoradi |
| author_sort | Marzieh Lamtar-Gholipoor |
| collection | DOAJ |
| description | The optimization of industrial fermentation processes, particularly for the production of bioactive compounds like Actinomycin V, is essential for maximizing yield and cost-efficiency. This study introduces a hybrid approach integrating Artificial Neural Networks (ANNs) with the Trees Social Relations Optimization Algorithm (TSR) to optimize medium composition for the production of Actinomycin V by Streptomyces triostinicus. Traditional optimization techniques, such as Response Surface Methodology (RSM), often fall short of capturing the complex, non-linear interactions between medium components. By contrast, the ANN-TSR hybrid approach leverages the predictive power of neural networks and the robust optimization capabilities of TSR, inspired by the resource-sharing behaviors of tree communities. The study employed a Central Composite Design (CCD) to systematically vary concentrations of key medium components, with experimental data used to train the ANN. The TSR algorithm then iteratively refined the ANN model to identify optimal conditions, significantly increasing Actinomycin V yield from an initial 110 mg/L to 443 mg/L. This fourfold enhancement underscores the potential of combining advanced machine learning techniques with nature-inspired optimization algorithms to optimize complex bioprocesses. The methodology presented here offers a generalizable framework applicable to various industrial bioprocesses. |
| format | Article |
| id | doaj-art-3dd71863c9c24d62ad5ecf0d0ed629ce |
| institution | Kabale University |
| issn | 2783-4956 2821-014X |
| language | English |
| publishDate | 2024-03-01 |
| publisher | REA Press |
| record_format | Article |
| series | Big Data and Computing Visions |
| spelling | doaj-art-3dd71863c9c24d62ad5ecf0d0ed629ce2025-01-30T12:23:17ZengREA PressBig Data and Computing Visions2783-49562821-014X2024-03-0141576610.22105/bdcv.2024.474793.1184205624Artificial neural network TSR for optimization of actinomycin productionMarzieh Lamtar-Gholipoor0Soheil Fakheri1Mahmoud Alimoradi2Department of Chemical Engineering, Quchan Branch, Islamic Azad University, Quchan, Iran.Department of Computer Engineering and Information Technology, Lahijan Branch, Islamic Azad University, Lahijan, Iran.Department of Computer Engineering and Information Technology, Lahijan Branch, Islamic Azad University, Lahijan, Iran.The optimization of industrial fermentation processes, particularly for the production of bioactive compounds like Actinomycin V, is essential for maximizing yield and cost-efficiency. This study introduces a hybrid approach integrating Artificial Neural Networks (ANNs) with the Trees Social Relations Optimization Algorithm (TSR) to optimize medium composition for the production of Actinomycin V by Streptomyces triostinicus. Traditional optimization techniques, such as Response Surface Methodology (RSM), often fall short of capturing the complex, non-linear interactions between medium components. By contrast, the ANN-TSR hybrid approach leverages the predictive power of neural networks and the robust optimization capabilities of TSR, inspired by the resource-sharing behaviors of tree communities. The study employed a Central Composite Design (CCD) to systematically vary concentrations of key medium components, with experimental data used to train the ANN. The TSR algorithm then iteratively refined the ANN model to identify optimal conditions, significantly increasing Actinomycin V yield from an initial 110 mg/L to 443 mg/L. This fourfold enhancement underscores the potential of combining advanced machine learning techniques with nature-inspired optimization algorithms to optimize complex bioprocesses. The methodology presented here offers a generalizable framework applicable to various industrial bioprocesses.https://www.bidacv.com/article_205624_854f7edf845d7ba3413333f261d08df4.pdftrees social relationship algorithmfermentationneural network modelingmetaheuristic algorithmmachine learningoptimization |
| spellingShingle | Marzieh Lamtar-Gholipoor Soheil Fakheri Mahmoud Alimoradi Artificial neural network TSR for optimization of actinomycin production Big Data and Computing Visions trees social relationship algorithm fermentation neural network modeling metaheuristic algorithm machine learning optimization |
| title | Artificial neural network TSR for optimization of actinomycin production |
| title_full | Artificial neural network TSR for optimization of actinomycin production |
| title_fullStr | Artificial neural network TSR for optimization of actinomycin production |
| title_full_unstemmed | Artificial neural network TSR for optimization of actinomycin production |
| title_short | Artificial neural network TSR for optimization of actinomycin production |
| title_sort | artificial neural network tsr for optimization of actinomycin production |
| topic | trees social relationship algorithm fermentation neural network modeling metaheuristic algorithm machine learning optimization |
| url | https://www.bidacv.com/article_205624_854f7edf845d7ba3413333f261d08df4.pdf |
| work_keys_str_mv | AT marziehlamtargholipoor artificialneuralnetworktsrforoptimizationofactinomycinproduction AT soheilfakheri artificialneuralnetworktsrforoptimizationofactinomycinproduction AT mahmoudalimoradi artificialneuralnetworktsrforoptimizationofactinomycinproduction |