发酵过程状态预估的BP神经网络模型实现

论述了利用改进的BP神经网络实现发酵过程状态预估模型的设计原理和方法,包括BP神经网络的拓扑结构选取、学习和测试样本的选择及处理,变步长引入动量项BP神经网络的训练方法以及全局收敛法的实现等。此外,用VC实现了发酵过程BP神经网络建模平台。经聚赖氨酸发酵过程验证,其模型具有良好的收敛性能和泛化性能,可应用于发酵过程状态参数的在线预估和测量。

谷氨酸发酵过程先进控制

阐述基于工业过程计算机和PLC的谷氨酸发酵过程控制,在若干生化过程理论模型的实际应用具有一定困难的情况下,研究了在发酵过程中对某些生化过程变量的估算方法及相应的先进过程控制策略,对谷氨酸发酵过程的主要控制回路作了介绍。通过对50m3发酵罐实时控制,表明本文所述控制系统的有效性。

动态MPCA在发酵过程监测与故障诊断中的应用

针对发酵过程非线性和时变特点,提出了一种具有实时性的动态MPCA方法,采用多模型非线性结构代替传统MPCA单模型线性化结构,克服了后者不能处理非线性过程和实时性的问题,并避免了MPCA在线应用时预报未来测量值带来的误差,提高了发酵过程性能监测和故障诊断的准确性。对头孢菌素C发酵过程的拟在线仿真研究,验证了基于动态MPCA的统计过程监测的有效性。

Dynamic soft sensor development based on Gaussian mixture regression for fermentation processes

The dynamic soft sensor based on a single Gaussian process regression(GPR) model has been developed in fermentation processes.However,limitations of single regression models,for multiphase/multimode fermentation processes,may result in large prediction errors and complexity of the soft sensor.Therefore,a dynamic soft sensor based on Gaussian mixture regression(GMR) was proposed to overcome the problems.Two structure parameters,the number of Gaussian components and the order of the model,are crucial to the soft sensor model.To achieve a simple and effective soft sensor,an iterative strategy was proposed to optimize the two structure parameters synchronously.For the aim of comparisons,the proposed dynamic GMR soft sensor and the existing dynamic GPR soft sensor were both investigated to estimate biomass concentration in a Penicillin simulation process and an industrial Erythromycin fermentation process.Results show that the proposed dynamic GMR soft sensor has higher prediction accuracy and is more suitable for dynamic multiphase/multimode fermentation processes.

An Optimal Control Strategy Combining SVM with RGA for Improving Fermentation Titer

An optimal control strategy is proposed to improve the fermentation titer,which combines the support vector machine(SVM)with real code genetic algorithm(RGA).A prediction model is established with SVM for penicillin fermentation processes,and it is used in RGA for fitting function.A control pattern is proposed to overcome the coupling problem of fermentation parameters,which describes the overall production condition.Experimental results show that the optimal control strategy improves the penicillin titer of the fermentation process by 22.88%,compared with the routine operation.

A Geometric Approach to Support Vector Regression and Its Application to Fermentation Process Fast Modeling

Support vector machine(SVM) has shown great potential in pattern recognition and regressive estima-tion.Due to the industrial development demands,such as the fermentation process modeling,improving the training performance on increasingly large sample sets is an important problem.However,solving a large optimization problem is computationally intensive and memory intensive.In this paper,a geometric interpretation of SVM re-gression(SVR) is derived,and μ-SVM is extended for both L1-norm and L2-norm penalty SVR.Further,Gilbert al-gorithm,a well-known geometric algorithm,is modified to solve SVR problems.Theoretical analysis indicates that the presented SVR training geometric algorithms have the same convergence and almost identical cost of computa-tion as their corresponding algorithms for SVM classification.Experimental results show that the geometric meth-ods are more efficient than conventional methods using quadratic programming and require much less memory.

Simultaneous hybrid modeling of a nosiheptide fermentation process using particle swarm optimization

Hybrid modeling approaches have recently been investigated as an attractive alternative to model fermentation processes. Normally, these approaches require estimation data to train the empirical model part of a hybrid model. This may result in decreasing the generalization ability of the derived hybrid model. Therefore, a simultaneous hybrid modeling approach is presented in this paper. It transforms the training of the empirical model part into a dynamic system parameter identification problem, and thus allows training the empirical model part with only measured data. An adaptive escaping particle swarm optimization(AEPSO) algorithm with escaping and adaptive inertia weight adjustment strategies is constructed to solve the resulting parameter identification problem, and thereby accomplish the training of the empirical model part. The uniform design method is used to determine the empirical model structure. The proposed simultaneous hybrid modeling approach has been used in a lab-scale nosiheptide batch fermentation process. The results show that it is effective and leads to a more consistent model with better generalization ability when compared to existing ones. The performance of AEPSO is also demonstrated.

Establishment of Kinetics Models for Batch Fermentation Process of β-mannase with Bacillus licheniformis HDYM-04

In order to improve the yield of β-mannase and to investigate the rules of fermentation production, a high-yield β-mannase producing strain, Bacillus licheniformis HDYM-04, was used to investigate the kinetics models based on the optimal fermentation conditions： HDYM-04 strain was fermented at 37℃ for 30 h with agitation speed at 300 r/min and aeration rate at 3 L/min in a 5 L fermenter, the initial addition amount of konjac flour was 2%（w/v）, the initial pH of medium was 8.0, and the inoculum concentration was 6.7%（v/v）. Three batch fermentation kinetic models were established （cell growth kinetic model, substrate consumption kinetic model, product formation kinetic model） bases on Logistic and Luedeking-Piret equations. To be specific, cell growth kinetic model was dX/dt =0.431X （1- X/ 15.522 ）, substrate consumption kinetic model was -ds/dt =1.11 dX/dt ＋0.000 2 dP/dt ＋0.000 8X, and product formation kinetic model was dP/dt=133.1 dX ＋222.87X. The correlation coefficients R^2 of the three equations were 0.990 21, 0.989 08 and 0.988 12, respectively, which indicated a good correlation between experimental values and models. Therefore, the three equations could be used to describe the processes of cell growth, enzyme synthesis and substrate consumption during batch fermentation using B. licheniformis strain HDYM-04. The establishment of batch fermentation kinetic models （cell growth kinetic model, substrate depletion kinetic model, product formation kinetic model） could lay the theoretical foundation and provide practical reference for the applica- tion of HDYM-04 in fermentation industry.