Application of Variable Selection Method Based on Genetic Algorithm in Marine Enzyme Fermentation

Transcription

1 1741 A publcaton of CHEMICAL ENGINEERING TRANSACTIONS VOL. 61, 217 Guest Edtors: Petar S Varbanov, Rongxn Su, Hon Loong Lam, Xa Lu, Jří J Klemeš Copyrght 217, AIDIC Servz S.r.l. ISBN ; ISSN The Italan Assocaton of Chemcal Engneerng Onlne at DOI: 1.333/CET Applcaton of Varable Selecton Method Based on Genetc Algorthm n Marne Enzyme Fermentaton Guoha Lu, Pesuo Yang, Yuhan Dng*, Congl Me, Yonghong Huang, Xangln Zhu Jangsu Unversty, Zhenjang, 21213, Jangsu, Chna yhdng@ujs.edu.cn Genetc algorthm (GA), a global searchng method, s appled to select the varables n soft sensng of marne enzyme fermentaton. Compared wth tradtonal methods of MIV and PCA, the optmal varables selected by GA have clear physcal meanng and fewer numbers on the bass of varable selecton frequency. After that, a soft sensng model based on BP neural network s establshed and a BP-GA soft sensng model s realzed. Soft sensng results of enzyme actvty show that, BP-GA model can provde better non-lnear fttng ablty and hgher soft sensng accuracy, compared wth the models of BP, BP-MIV and BP-PCA. 1. Introducton Bologcal fermentaton technology s one of the key technologes to strengthen the natonal power and promote the economy development. It plays an extremely mportant role n enrchng food types, guaranteeng food safety and mprovng lvng qualty. Wth the contnuous expanson of the fermentaton ndustry scale, the requrement of on-lne measurement technology s ncreasng day by day. It s of great sgnfcance to realze the real-tme measurement of key bochemcal parameters of the fermentaton processes. At present, some key bochemcal parameters (such as enzyme actvty n marne enzyme fermentaton) are dffcult to be measured on-lne due to techncal and economc lmtatons. The soft sensng technology s an effectve way to solve the above problem (Chen et al., 217). Marne enzyme s a knd of typcal mcroorgansm fermentaton, whch s nonlnear, mult-varable and strong couplng (Huang et al., 213). As s known to all, f the soft sensng model n marne enzyme fermentaton process has massve nput varables and these varables are redundant, t wll make the establshment of the model need longer tranng tme. And the redundant varables wll also affect the soft sensng accuracy smultaneously. Therefore, to obtan a model wth a good soft sensng ablty, varable selecton s extremely mportant. At present, the frequently used varable selecton methods are based on senstvty, MIV (mean mpact value), PCA (prncpal component analyss) and so on. However, method based on senstvty gnores the nteracton between varables mpacts the output result. It also lacks stablty for senstvty coeffcents (Ca et al., 28). Method based on MIV s serously dependent on the network and the result wll get worse f the network s not properly desgned (Lu et al., 211). The dsadvantage of PCA method s that the meanng of each characterstcs dmenson of prncpal component s not as clear as that of orgnal nputs, and nonprncple components wth small varance value may also contan mportant nformaton because of sample dfferences, whch may nfluent the follow-up data processng f they are dscarded due to descendng dmenson (Lle et al., 217). Genetc algorthm (GA) s a computatonal optmzaton method by smulatng the evolutonary process of Darwn's genetc selecton and natural selecton, whch was proposed by Holland et al. (1975). GA has the followng advantages: (1) It uses the selecton, crossover, mutaton and other operatons to generate new ndvduals, so t can easly expand the search scope, whch guarantees that the optmzaton results obtaned by searchng are global optmal solutons; (2) It s a knd of ntellgent search algorthm, whch take advantages of the ftness functon to acheve the real value gradually; (3) It mproves the speed of the search because a Please cte ths artcle as: Lu G., Yang P., Dng Y., Me C., Huang Y., Zhu X., 217, Applcaton of varable selecton method based on genetc algorthm n marne enzyme fermentaton, Chemcal Engneerng Transactons, 61, DOI:1.333/CET

2 1742 set of ndvduals are calculated wth teraton at the same tme. Consequently, GA has been wdely used n varous felds (Pappu et al., 217). In ths study, GA s used to select optmal varables to smplfy the BP soft sensng model and reduce computatonal complexty. Compared wth the tradtonal methods of MIV and PCA, GA method can obtan the least varable number and make the physcal meanng of new varables clearer. The BP-GA model s then utlzed to soft sensng the value of marne enzyme actvty onlne. Smulaton results confrm that the BP-GA model has the advantages of good stablty and hgh soft sensng accuracy, compared wth the BP, BP-MIV, and BP-PCA models. 2. Genetc algorthm and BP neural network 2.1 Genetc algorthm Genetc algorthm (GA) s a knd of artfcal ntellgence optmzaton method wth the functon of hghly nonlnear mappng, self-adapton and self-organzng for global optmzaton (Chu et al., 21). It abstractly descrbes the process of evoluton as 3 operators: selecton, cross-over and mutaton. Frstly, the GA encodes a soluton vector of a problem to strngs. Secondly, the ftness functon s used to solve the ftness degree of each ndvdual n the populaton. Accordng to the survval of the fttest, ndvduals who adapt to the envronment are selected and new offsprng are generated through genetc operators (Xu et al., 211). Fnally, the most sutable ndvduals can be obtaned, whch s the optmal soluton of the problem after several generatons of evoluton. 2.2 BP neural network BP neural network s a knd of computng structure by smulatng the structure and functon of bologcal neuron system. Lke neural systems, BP neural networks contan many nodes, whch are dstrbuted herarchcally and are not connected to each other n the same layer, but nodes between layers are nterrelated. BP neural network modelng s a data-based approach that can approxmate real models n the absence of knowledge of nternal mechansms and uncertantes. The model can be contnually revsed by learnng. The commonly expresson of the BP neural network wth sngle hdden layer s: q gx (, θ ) = β + F( ω + ω x ) β (1) o j j j j= 1 k= 1 p where the expresson for x s x= [ x1 x2... x ] T p ; p s the number of nput varables (auxlary varables); β ( =,1..., q) s the connecton weght from the hdden layer to the output layer; ωj = ( ωj, ωj1..., ωjp ) s the connecton weght from the nput layer to the hdden layer. 3. Expermental research In ths study, Pa4523 (Ch et al., 26) strans are the object of study whch are solated and obtaned from Boha and the Yellow Sea. Pa4523 stran s a knd of marne bactera producng marne enzyme. The lvng marne creature and ts producton s compatble wth hgh salt, hgh pressure, low temperature, low lght, low lght, and so on. The cold-actve enzymes produced by psychrophles and psychrotrophs have the followng 3 characterstcs: low temperature and hgh catalytc effcency; hgh structural flexblty; thermal nstablty. So, t s more advantageous than the mddle temperature enzyme and hgh temperature enzyme n the applcaton. Marne mcroorgansms are very strct to the temperature of growth envronment, only n a lmted temperature, ph value, tme, amount of noculaton, ventlaton and other range of growth, whch has the mnmum, the most approprate and the maxmum crtcal value. Through the prelmnary analyss of bacteral culture experments, the optmum ph value, temperature, tme, noculaton amount and aeraton rate of the marne enzyme producng stran are 5.5, 12 C, 72 h, 7 % and 17 ml. Under the optmum fermentaton condtons, the enzyme actvty n the fermentaton tank of 5 L stran wll reach the hghest. The fermentaton tank should be sterlzed before usng. The tme of sterlzaton s about 3 mn where the sterlzaton temperature was set to 121 C and sterlzaton pressure s set to.11 MPa. Through materal feedng system, the rght amount of dextrn, soybean ol, alcohol, ammona and other nutrents are poured nto the fermentaton tank. The fermentaton wll last for about 72 h under the condton of 12 C, ph value beng about 5.5. Dfferent knds of sensors, such temperature sensor, ph sensor, dssolved oxygen sensor, tank pressure sensor, ar flow sensor, speed sensor, are connected to fermentaton tank, whch can dynamcally montor t n

3 real tme.in a fermentaton perod, the actvty of an enzyme was tested at ntervals of 4 h. a sample of 5 mn nterval s obtaned by polynomal dfference, and formed the output sample. 4 batches of expermental data are collected, among whch three batches are used as tranng samples, and the other one batch s used as the test samples. 4. Soft sensng of BP-GA model for marne enzyme fermentaton 4.1 Varable optmzaton The parameter settngs of the GA are shown n Table 1. Table 1: Parameters settng of GA Man parameters Maxmum teratons Populaton sze Crossover probablty Mutaton probablty value The varables wth pretreated raw data are selected by GA. The varable select result s as shown n Fgure Frequency of selectons after 1 runs 96 C.V. as a functon of the number of selected varables Selecton frequency correlaton coeffcent Varable number (a) Varable selecton frequency Varable number (b) The trend of correlaton coeffcent wth dfferent varables Fgure 1: The result of GA varable selecton Fgure 1(a) s the probablty map after 1 teratons and Fgure 1(b) shows the change trend of the correlaton coeffcent wth the number of selected varables. To make the varables easy to choose and to have a hgher contrbuton rates, the varable selectng crtera a * s set to.98. The cumulatve contrbuton s * rates of the selected nput varables satsfy a = α a ( s < n ), where α r are the relatve contrbuton rates r = 1 r of the auxlary varables to the domnant varable. The selected s varables can be consdered mportant varables and the remanng n s varables are elmnated because they have less mpact on the output. The comparson results of dfferent methods are shown n Table 2. Table 2: Comparson results of dfferent varable selecton methods Varable selecton methods Orgnal number Optmzed number Tme (s) MIV PCA GA As s shown n Table 2, GA method flters out the number of 9 varables, the least varable number, less than the number of MIV, 13, and PCA, 1. The tme of selecton varables of PCA s 4.46 s, tme of MIV s 8.35 s and tme of GA s 9.57 s. Although PCA s relatvely small n tme, t s acceptable for these three methods to be of the same temporal dmenson. In general, GA varable selecton method can smplfy the model, lowered the computatonal complexty and reduce the possblty of ntroducng nose.

4 Soft sensng results of BP-GA model In ths study, a three layer feed-forward BP neural network wth structure s appled. The actvatng x x e e functon of hdden layer called "Sgmod" s f( x) = and actvatng functon of output layer s lnear x x e + e functon f( x) = x. The network s traned 1 tmes wth BP tranng algorthm untl the error s reduced to a 4 very small number 1, and then the traned BP neural network parameters are preserved. The flowchart of the GA-BP method s as shown n Fgure 2. N Y Fgure 2: The flowchart of GA-BP method To show the superorty of the method proposed n ths study, BP-GA model s compared wth other soft sensng models of BP, BP-MIV and BP-PCA. The soft sensng results are shown n Fgure 3. It can be clearly seen from Fgure 3 that the ftness between the real values and soft sensng values s preferable, whch shows that the soft sensng effect s satsfactory. Compared wth the BP, BP-MIV and BP- PCA models, the soft sensng results of the BP-GA model are closer to the true values. To compare the performance of soft sensng models, RMSE (root mean square error) s appled, whch s calculated as Eq(2). RMSE = n = 1 ( y y ) ss, real, n 2 (2)

5 where y ss, represents soft sensng values, y real, s real values n the test data set, and n s the number of test data Marne Enzyme Actvty (g/l) (a)bp Soft sensng values Marne enzyme actvty (g/l) (b) MIV-BP Soft sensng values 5 5 Marne Enzyme Actvty (g/l) (c)pca-bp Soft sensng values Marne enzyme actvty (g/l) (d)ga-bp Soft sensng values Fgure 3: Comparson of dfferent models wth soft sensng results The RMSE of the 4 soft sensng models are shown n Table 3. Table 3: Soft sensng error of dfferent models Evaluaton ndex (unt) BP BP-MIV BP-PCA BP-GA RMSE (g/l) From the Table 3, we can clearly fnd that the RMSE of BP model s the largest, whch s g/l. The RMSE of BP-MIV model and BP-PCA model s relatvely small, whle BP-GA model has the smallest RMSE, whch s only g/l. Therefore, the BP-GA soft sensng model proposed n ths study can greatly mprove the precson of soft sensng of enzyme actvty and also have hgher credblty. Because GA s a stochastc method, the test set must be smulated several tmes by usng the above BP-GA model. 5 tmes of the best soft sensng accuracy s recorded n Table 4. Table 4 shows that the errors of these 5 tmes are almost same, whose average value s g/l and ts optmal value s g/l. Ths llustrates that the stochastcty of GA method has lttle nfluence on the soft sensng results and the BP-GA model shows good stablty. Table 4: Soft sensng accuracy of dfferent tmes of BP-GA Tmes Average value Optmal value RMSE (g/l) Conclusons In marne enzyme fermentaton process, the enzyme actvty s related to many auxlary varables and these varables are somehow related and redundant, so t s feasble to establsh a soft sensng model and t s necessary to select varables and elmnate the redundancy as well. In ths paper, a GA varable selectng method s proposed. Compared wth tradtonal methods of MIV and PCA, GA selects the least number of varables and the selected varables have a clearer physcal meanng. Then, a GA-BP soft sensng model s

6 1746 establshed and appled to soft sensng the enzyme actvty. The expermental results show that compared wth tradtonal models of BP, BP-MIV and BP-PCA, the soft sensng result of BP-GA model has been greatly mproved and the RMSE has been greatly reduced. Acknowledgments Ths work s supported by the Prorty Academc Program Development of Jangsu Hgher Educaton Insttutons (PAPD [211]6), Open Research Foundaton of Key Laboratory of Modern Agrcultural Equpment and Technology n Jangsu Unversty(NZ2131), and Natural Scence Foundaton of Jangsu Provnce of Chna (BK213531, BK ). References Ca Y., Xng Y., Hu D., 28, On senstvty analyss, Journal of Bejng Normal Unversty, 44(1), Chen X., Chen X., She J., Wu M., 217, A hybrd just-n-tme soft sensor for carbon effcency of ron ore snterng process based on feature extracton of cross-sectonal frames at dscharge end, Journal of Process Control, 54, Ch N., Zhang Q., Wang X., Dou S., Zhang X., 26, Study on fermentaton condtons of a marne low temperature acd protease hgh-producton stran from pseudomonas alcalgenes, Mcrobology Chna, 33(2), Chu X., Yuan H., Wang Y., Lu W., 21, Varable selecton for partal least squares modelng by genetc algorthms, Chnese Journal of Analytcal Chemstry, 29(4), Holland J., 1975, Adaptaton n natural and artfcal systems, Ann Arbor, MI, USA: Unversty of Mchgan Press. Huang Y., Sun L., Sun Y., Zhu X., 213, Soft sensor modelng based on bologcal varables of marne, Informaton & Control, 42(4), Lle A., Scarsoareanu M., Morjan L., Dutu E., Badceanu M., Mhalescu L., 217, Prncpal component analyss of raman spectra for TO2 nanopartcle characterzaton, Appled Surface Scence, 417, Lu Y., Wang W., 211, Varable selecton of fnancal dstress predcton-the SVM method based on mean mpact value, Systems Engneerng, 29(8), Pappu S., Gummad S., 217, Artfcal neural network and regresson coupled genetc algorthm to optmze parameters for enhanced xyltol producton by debaryomyces nepalenss n boreactor, Bochemcal Engneerng Journal, 12, Xu G., 211, Research for constructon and applcaton of PCA-GA-SVM model, Journal of Quanttatve & Techncal Economcs, 2,