BACKGROUND Aiming at the characteristics of nonlinear, multi-parameter, strong coupling and difficulty in direct on-line measurement of key biological parameters of marine low-temperature protease fermentation process, a soft-sensing modeling method based on artificial bee colony (ABC) and multiple least squares support vector machine (MLSSVM) inversion for marine protease fermentation process is proposed. METHODS Firstly, based on the material balance and the characteristics of the fermentation process, the dynamic "grey box" model of the fed-batch fermentation process of marine protease is established. The inverse model is constructed by analyzing the inverse system existence and introducing the characteristic information of the fermentation process. Then, the inverse model is identified off-line using MLSSVM. Meanwhile, in order to reduce the model error, the ABC algorithm is used to correct the inverse model. Finally, the corrected inverse model is connected in series to the marine alkaline protease MP fermentation process to form a composite pseudo-linear system, thus, real-time on-line prediction of key biological parameters in fermentation process can be realized. RESULTS Taking the alkaline protease MP fermentation process as an example, the simulation results demonstrate that the soft-sensing modeling method can solve the real-time prediction problem of key biological parameters in the fermentation process on-line, and has higher accuracy and generalization ability than the traditional soft-sensing method of support vector machine. CONCLUSIONS The research provides a new method for soft-sensing modeling of key biological parameters in fermentation process, which can be extended to soft-sensing modeling of general nonlinear systems.

中文翻译：

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基于ABC-MLSSVM反演的海洋低温碱性蛋白酶MP发酵过程软测量建模。

背景技术针对海洋低温蛋白酶发酵过程的关键，非线性，多参数，强耦合，难以直接在线测量关键生物学参数的特点，提出了一种基于人工蜂群（ABC）的软传感建模方法。提出了用于海洋蛋白酶发酵过程的多重最小二乘支持向量机（MLSSVM）反演。方法：首先，基于物料平衡和发酵过程的特点，建立了海洋蛋白酶补料分批发酵过程的动态“灰箱”模型。通过分析逆系统的存在并引入发酵过程的特征信息，构造逆模型。然后，使用MLSSVM离线识别逆模型。与此同时，为了减少模型误差，使用ABC算法校正逆模型。最后，将校正后的逆模型与海洋碱性蛋白酶MP发酵过程串联，形成一个复合假线性系统，从而可以实现发酵过程中关键生物学参数的实时在线预测。结果以碱性蛋白酶MP发酵过程为例，仿真结果表明，软传感建模方法可以解决发酵过程中关键生物学参数的实时预测问题，具有较高的准确性和泛化能力。比传统的支持向量机软检测方法要好。