Prediction of product concentration in glutamate fermentation process using partial least squares and least square support vector machine

doi:10.11949/j.issn.0438-1157.20161533

Abstract

Abstract:

Considered that key variables in glutamate fermentation process could not be measured inline, which would make it difficult to control and optimize the fermentation process, a model for glutamate concentration prediction in a 5 L fermentation tank was established on the basis of partial least squares (PLS) and least square support vector machine (LSSVM). PLS was applied first to extract features of input variables, to reduce number of variable dimensions, and to eliminate correlations such that model complexity was simplified and performance was improved. Coupled simulated annealing (CSA) arithmetic was later combined with grid search to determine model parameter values of LSSVM for improved prediction accuracy. Further model simplification was completed by deleting parameters with weak correlation to glutamate concentration. The simplified model was compared to kinetic model in order to select the best model of glutamate fermentation. Experimental results showed that the simplified LSSVM model equipped with CSA parameter optimization outperformed both PLS and kinetic models,which root mean square errors (RMSE) were 1.597, 8.49 and 2.934 respectively. The LSSVM prediction model had excellent performance with high accuracy, so it would be more suitable for online prediction of glutamate concentration and offer an effective guidance for control and optimization of the glutamate fermentation process.

Key words: glutamate, fermentation, prediction, partial least squares, least squares support vector machine, reaction kinetics, model simplification

摘要：

针对谷氨酸发酵过程关键生化参数难以在线检测给发酵优化控制带来困难问题，基于谷氨酸5 L发酵罐发酵过程，建立基于偏最小二乘（PLS）和最小二乘向量机（LSSVM）相结合的谷氨酸浓度预测模型；利用PLS对输入变量进行特征提取降低维数和消除相关性，以简化模型和提高模型精度。为确定谷氨酸发酵最佳预测模型，简化后的预测模型与发酵动力学模型进行比较；实验结果表明，简化后的耦合模拟退火（coupled simulated annealing，CSA）对参数进行优化的LSSVM模型具有最好预测性能，相对PLS预测模型和发酵动力学模型具有明显优势，均方根误差分别为1.597、8.49和2.934，可以为谷氨酸发酵过程操作及时调整及优化控制提供有效指导。

关键词: 谷氨酸, 发酵, 预测, 偏最小二乘, 最小二乘向量机, 反应动力学, 模型简化

CLC Number:

TP274

ZHENG Rongjian, PAN Feng. Prediction of product concentration in glutamate fermentation process using partial least squares and least square support vector machine[J]. CIESC Journal, 2017, 68(3): 976-983.

郑蓉建, 潘丰. 基于PLS-LSSVM的谷氨酸发酵产物浓度预测建模[J]. 化工学报, 2017, 68(3): 976-983.

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[8]	WANG T, SUN J W, ZHANG W D, et al. Prediction of product formation in 2-keto-l-gulonic acid fermentation through Bayesian combination of multiple neural networks[J]. Process Biochemistry, 2014,49:188-194.
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[10]	LIU G H, ZHOU D W, XU H X, et al. Model optimization of SVM for a fermentation soft sensor[J]. Expert Systems with Applications,2010,37(4): 2708-2713.
[11]	ACUNA G, RAMIREZ C, CURILEM M. Software sensors for biomass concentration in a SSC process using artificial neural networks and support vector machine[J]. Bioprocess Biosyst. Eng., 2014,37(1): 27-36.
[12]	SUYKENS J A K, VANDEWALLE J. Least squares support machine classifiers[J]. Neural Processing Letters,1999,9(3): 293-300.
[13]	WANG X F, CHEN J D, LIU C B, et al. Hybrid modeling of penicillin fermentation process based on least square support vector machine[J]. Chemical Engineering Research and Design, 2010,88: 415-420.
[14]	NIU D P, JIA M X, WANG F L, et al. Optimization of Nosiheptide fed-batch fermentation process based on hybrid model[J]. Industrial & Engineering Chemistry Research,2013,52:3373-3380.
[15]	熊伟丽,姚乐,徐保国. 混沌最小二乘支持向量机及其在发酵过程建模中的应用[J]. 化工学报, 2013,64(12):4585-4591. XIONG W L, YAO L, XU B G. Chaos least squares support vector machine and its application on fermentation process modeling[J]. CIESC Journal,2013,64(12): 4585-4591.
[16]	陶劲松,杨亚帆,李远华.基于PLS和SVM的纸张抗张强度建模比较[J].华南理工大学学报(自然科学版), 2014,42(7):132-137. TAO J S, YANG Y F, LI Y H. Comparison of paper tensile strength prediction models based on PLS and SVM methods[J]. Journal of South China University of Technology (Natural Science Edition) ,2014,42(7):132-137.
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[19]	KANEKO H, ARAKAWA M, FUNATSU K. Development of a new soft sensor method using independent component analysis and partial least squares[J]. AIChE J.,2009,55(1):87-98.
[20]	汤伟,于东伟,张怡真. 基于递推PLS的置换蒸煮终点软测量建模研究[J]. 中国造纸学报,2015,30(3) :47-50. TANG W,YU D W, ZHANG Y Z. Research on cooking end point modeling based on recursive PLS for displacement cooking[J]. Transactions of China Pulp and Paper,2015,30(3): 47-50.
[21]	FUJIWARA K, SAWADA H, KANO M. Input variable selection for PLS modeling using nearest correlation spectral clustering[J]. Chemometrics and Intelligent Laboratory Systems,2012,118:109-119.
[22]	赵朋程,刘彬,高伟,等.用于水泥熟料fCaO预测的多核最小二乘支持向量机模型[J].化工学报,2016,67(6): 2480-2487. ZHAO P C, LIU B, GAO W, et al. Multiple kernel least square support vector machine model for prediction of cement clinker lime content[J]. CIESC Journal,2016,67(6):2480-2487.
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[29]	吕游,刘吉臻,杨婷婷,等. 基于PLS特征提取和LS-SVM结合的NOx排放特性建模[J]. 仪器仪表学报, 2013,34(11):2418-2424. LV Y, LIU J Z, YANG T T, et al. NOx emission characteristic modeling based on feature extraction using PLS and LS-SVM[J]. Chinese Journal of Scientific Instrument, 2013,34(11):2418-2424.
[30]	熊富强,桂卫华,阳春华,等. 基于PLS-LSSVM方法的湿法炼锌过程预测建模[J].仪器仪表学报, 2011, 32(4):941-948. XIONG F Q, GUI W H, YANG C H, et al. Forecasting modeling of zinc hydrometallurgy process based on PLS-LSSVM approach[J]. Chinese Journal of Scientific Instrument, 2011, 32(4):941-948.