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基于IBA-LSSVM的光合細菌發(fā)酵軟測量
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TP301

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鎮江市重點(diǎn)研發(fā)計劃(SH2017002)(微生物關(guān)鍵參量軟測量與控制技術(shù)及裝置研發(fā))


Soft sensor of photosynthetic bacteria fermentationbased on IBA-LSSVM
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    摘要:

    針對光合細菌(PSB)發(fā)酵過(guò)程活菌濃度難以在線(xiàn)檢測,離線(xiàn)測量又存在很大延時(shí)及易染菌的問(wèn)題,提出一種基于改進(jìn)蝙蝠-最小二乘支持向量機(IBA-LSSVM)的軟測量建模方法。本文先對BA的速度更新公式進(jìn)行改進(jìn),且將差分進(jìn)化算法(DE)的變異機制引入BA,增加了種群多樣性,進(jìn)而提升了BA算法的全局及局部搜索能力,然后構建了活菌濃度的IBA-LSSVM軟測量模型,并與BA-LSSVM軟測量模型進(jìn)行對比。仿真結果表明,改進(jìn)的模型相較于BA-LSSVM模型有著(zhù)更好的學(xué)習能力和預測性能,測量誤差為0.1358,可為光合細菌發(fā)酵過(guò)程的優(yōu)化控制提供準確有效的指導,有一定的實(shí)際應用價(jià)值。

    Abstract:

    In view of the living cell concentration is difficult to measure on line in photosynthetic bacteria(PSB)fermentation process and the off-line measurement is accompanied by large time-delay error and easy to stain bacteria , a soft sensor model based on IBA-LSSVM was proposed. The velocity equation of the BA algorithm was improved and the variance mechanism of DE algorithm was introduced in the BA algorithm. Thus, the diversity of the population can be increased and the global and local searching ability of the BA algorithm can be enhanced. Furthermore, the IBA-LSSVM soft sensor model was established for the living cell concentration and compared with BA-LSSVM soft sensor model. Finally, the simulation results showed that the improved model was better learning ability and prediction performance than BA-LSSVM, the measurement error is 0.1358. The improved model could provide accurate guidance for the photosynthetic bacteria fermentation control optimization. This model has certain practical value.

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朱湘臨,陳威,丁煜函,王博,朱莉,姜哲宇,宋彥.基于IBA-LSSVM的光合細菌發(fā)酵軟測量計算機測量與控制[J].,2019,27(6):41-44.

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歷史
  • 收稿日期:2018-11-29
  • 最后修改日期:2018-12-15
  • 錄用日期:2018-12-17
  • 在線(xiàn)發(fā)布日期: 2019-06-12
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