基于IBA-LSSVM的光合细菌发酵软测量
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摘要
针对光合细菌(PSB)发酵过程活菌浓度难以在线检测,离线测量又存在很大延时及易染菌的问题,提出一种基于改进蝙蝠-最小二乘支持向量机(IBA-LSSVM)的软测量建模方法;先对BA的速度更新公式进行改进,且将差分进化算法(DE)的变异机制引入BA,增加了种群多样性,进而提升了BA算法的全局及局部搜索能力,然后构建了活菌浓度的IBA-LSSVM软测量模型,并与BA-LSSVM软测量模型进行对比;仿真结果表明,改进的模型相较于BA-LSSVM模型有着更好的学习能力和预测性能,测量误差为0.135 8,可为光合细菌发酵过程的优化控制提供准确有效的指导,有一定的实际应用价值。
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.
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.
引文
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[2]Qi X,Ren Y,Liang P,et al.New insights in photosynthetic microbial fuel cell using anoxygenic phototrophic bacteria[J].Bioresource Technology,2018,258:310-317.
[3]Meng F,Yang A,Wang H,et al.One-step treatment and resource recovery of high-concentration non-toxic organic wastewater by photosynthetic bacteria[J].Bioresource Technology,2017,251:121-127.
[4]张健,冯学愚,刘小彬,等.光合菌发酵啤酒糟制鱼饲料工艺优化[J].农业工程学报,2011,27(8):387-392.
[5]Sasaki K,Watanabe M,Suda Y,et al.Applications of photosynthetic bacteria for medical fields[J].Journal of Bioscience&Bioengineering,2005,100(5):481-488.
[6]Zhu X,Ji X.Soft sensing of key state variables in fermentation process based on relevance vector machine with hybrid kernel function[J].Sensors&Transducers,2014,173(6):237-243.
[7]刘国海,周大为,徐海霞,等.基于SVM的微生物发酵过程软测量建模研究[J].仪器仪表学报,2009,30(6):1228-1232.
[8]Wu J Q,Sun Y K,Huang Y H,et al.Soft sensor modeling based on GRNN for biological parameters of marine protease fermentation process[A].Control Conference[C].IEEE,2014:5102-5106.
[9]Giorgi M G D,Campilongo S,Ficarella A,et al.Comparison between wind power prediction models based on wavelet decomposition with least-squares support vector machine(LSSVM)and artificial neural network(ANN)[J].Energies,2014,7(8):5251-5272.
[10]王强,田学民.基于KPCA—LSSVM的软测量建模方法[J].化工学报,2011,62(10):2813-2817.
[11]钱晓山,阳春华,徐丽莎.基于改进差分进化和最小二乘支持向量机的铝酸钠溶液浓度软测量[J].化工学报,2013,64(5):1704-1709.
[12]Li M,Chen W,Zhang T.A novel seizure diagnostic model based on kernel density estimation and least squares support vector machine[J].Biomedical Signal Processing&Control,2018,41:233-241.
[13]Sun Y M,Du N,Sun Q Y,et al.Research and application of biological potency soft sensor modeling method in the industrial fed-batch chlortetracycline fermentation process[J].Cluster Computing,2018(5):1-12.
[14]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&Design,2010,88(4):415-420.
[15]Wang X,Chen J,Liu C,et al.Hybrid modeling of penicillin fermentation process based on least square support vector machine[J].Chemical Engineering Research&Design,2010,88(4):415-420.
[16]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.
[17]Gorjaei R G,Songolzadeh R,Torkaman M,et al.A novel PSO-LSSVM model for predicting liquid rate of two phase flow through wellhead chokes[J].Journal of Natural Gas Science&Engineering,2015,24:228-237.
[18]Ahmadi M A,Lee M,Bahadori A.Prediction of a solid desiccant dehydrator performance using least squares support vector machines algorithm[J].Journal of the Taiwan Institute of Chemical Engineers,2015,50:115-122.
[19]Yang X S,Gandomi A H.Bat algorithm:a novel approach for global engineering optimization[J].Engineering Computations,2012,29(5):464-483.
[20]吴忠强,杜春奇,李峰,等.基于蝙蝠算法的永磁同步电机健康状态监测[J].仪器仪表学报,2017,38(3):695-702.
[21]Heraguemi K E,Kamei N,Drias H.Association rule mining based on bat algorithm[J]Journal of Computational and Theoretical Nanoscience,2015,12(7):1195-1200.
[22]Sun W,Liu M,Liang Y.Wind Speed Forecasting Based on FEEMD and LSSVM Optimized by the Bat Algorithm[J].Energies,2015,8(7):6585-6607.
[23]Gupta S,Sharma K.Optimization of revenue generated by hydro power plant by Bat algorithm(BA)[A].International Conference on Computing for Sustainable Global Development[C].IEEE,2016.
[24]Liu Y B.Improved bat algorithm for reliability-redundancy allocation problems[J].Journal of Security and its Applications,2016,10(2):1-12.
[25]Yuan Y,Wu X,Wang P,et al.Application of improved bat algorithm in optimal power flow problem[J].Applied Intelligence,2017(1):1-11.
[26]Suykens J A K,Vandewalle J.Least squares support vector machine classifiers[M].Kluwer Academic Publishers,1999.
[27]李金乐,王华忠,陈冬青.基于改进蝙蝠算法的工业控制系统入侵检测[J].华东理工大学学报(自然科学版),2017,43(5):662-668.
[28]Fister I.Differential evolution strategies with random forest regression in the bat algorithm[A].Conference Companion on Genetic and Evolutionary Computation[C].ACM,2013:1703-1706.
[29]Lin Y,Yan W.Study of soft sensor modeling based on deep learning[A].American Control Conference[C].IEEE,2015:5830-5835.
[30]Torgashov A,Zmeu K.Identification of nonlinear soft sensor models of industrial distillation process under uncertainty[J].IFAC-PapersOnLine,2015,48(28):45-50.
[31]李勇,邵诚.一种新的灰关联分析算法在软测量中的应用[J].自动化学报,2006,32(2):311-317.