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Modeling tool using neural networks for l(+)-lactic acid production by pellet-form Rhizopus oryzae NRRL 395 on biodiesel crude glycerol

机译:使用神经网络的建模工具用于在生物柴油粗甘油上以小球状米根霉NRRL 395生产l(+)-乳酸

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摘要

Most chemical reactions produce unwanted by-products. In an effort to reduce environmental problems these by-products could be used to produce valuable organic chemicals. In biodiesel industry a huge amount of glycerol is generated, approximately 10% of the final product. The research group from University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca developed opportunities to produce l(+) lactic acid from the glycerol. The team is using the Rhizopus oryzae NRRL 395 bacteria for the fermentation of the glycerol. The purpose of the research is to improve the production of l(+) lactic acid in order to optimize the process. A predictive model obtained by neural networks is useful in this case. The main objective of the present work is to present the developed user-friendly application useful in modeling this fermentation process, in order to be used by people who are inexperienced with neural networks or specific software. Besides the interface for training of a new neural network in order to develop the model in some characteristic condition, the software also provides an interface for visualization of the results, useful in interpretation and as a tool for prediction.
机译:大多数化学反应会产生有害的副产物。为了减少环境问题,这些副产物可用于生产有价值的有机化学品。在生物柴油行业中,会产生大量甘油,约占最终产品的10%。农业科学和兽医大学的研究小组克卢日-纳波卡开发了利用甘油生产l(+)乳酸的机会。该团队正在使用米根霉NRRL 395细菌对甘油进行发酵。该研究的目的是提高l(+)乳酸的产量,以优化工艺。通过神经网络获得的预测模型在这种情况下很有用。本工作的主要目的是介绍可用于对该发酵过程进行建模的开发的用户友好应用程序,以供不熟悉神经网络或特定软件的人员使用。除了用于训练新的神经网络以在某些特征条件下开发模型的界面之外,该软件还提供了用于结果可视化的界面,可用于解释并用作预测工具。

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