{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,27]],"date-time":"2025-05-27T15:17:24Z","timestamp":1748359044682,"version":"3.40.3"},"publisher-location":"New York, NY","reference-count":47,"publisher":"Springer US","isbn-type":[{"type":"print","value":"9781071608258"},{"type":"electronic","value":"9781071608265"}],"license":[{"start":{"date-parts":[[2020,8,18]],"date-time":"2020-08-18T00:00:00Z","timestamp":1597708800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,8,18]],"date-time":"2020-08-18T00:00:00Z","timestamp":1597708800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-1-0716-0826-5_5","type":"book-chapter","created":{"date-parts":[[2020,8,31]],"date-time":"2020-08-31T12:47:58Z","timestamp":1598878078000},"page":"115-138","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Neuroevolutive Algorithms Applied for Modeling Some Biochemical Separation Processes"],"prefix":"10.1007","author":[{"given":"Silvia","family":"Curteanu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elena-Niculina","family":"Dragoi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexandra Cristina","family":"Blaga","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anca Irina","family":"Galaction","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dan","family":"Cascaval","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,8,18]]},"reference":[{"issue":"10","key":"5_CR1","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1002\/cem.1401","volume":"25","author":"S Curteanu","year":"2011","unstructured":"Curteanu S, Cartwright HM (2011) Neural networks applied in chemistry. I. Determination of the optimal topology of multilayer perceptron neural networks. J Chemometrics 25(10):527\u2013549","journal-title":"J Chemometrics"},{"key":"5_CR2","unstructured":"Ragg T, Gutjahr S (1997) Automatic determination of optimal network topologies based on information theory and evolution. In: EUROMICRO 97 proceedings of the 23rd EUROMICRO conference: new frontiers of information technology (cat. no. 97TB100167)"},{"issue":"36","key":"5_CR3","doi-asserted-by":"crossref","first-page":"12673","DOI":"10.1021\/ie4000954","volume":"52","author":"HM Cartwright","year":"2013","unstructured":"Cartwright HM, Curteanu S (2013) Neural networks applied in chemistry. II. Neuro-evolutionary techniques in process modeling and optimization. Ind Eng Chem Res 52(36):12673\u201312688","journal-title":"Ind Eng Chem Res"},{"issue":"2","key":"5_CR4","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1016\/j.cej.2008.08.005","volume":"145","author":"M \u0141awry\u0144czuk","year":"2008","unstructured":"\u0141awry\u0144czuk M (2008) Modelling and nonlinear predictive control of a yeast fermentation biochemical reactor using neural networks. Chem Eng J 145(2):290\u2013307","journal-title":"Chem Eng J"},{"issue":"1","key":"5_CR5","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.cej.2006.10.015","volume":"127","author":"ZK Nagy","year":"2007","unstructured":"Nagy ZK (2007) Model based control of a yeast fermentation bioreactor using optimally designed artificial neural networks. Chem Eng J 127(1):95\u2013109","journal-title":"Chem Eng J"},{"key":"5_CR6","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1186\/1472-6750-7-53","volume":"7","author":"M Basri","year":"2007","unstructured":"Basri M, Rahman RN, Ebrahimpour A, Salleh AB et al (2007) Comparison of estimation capabilities of response surface methodology (RSM) with artificial neural network (ANN) in lipase-catalyzed synthesis of palm-based wax ester. BMC Biotechnol 7:53","journal-title":"BMC Biotechnol"},{"issue":"2","key":"5_CR7","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/j.engappai.2008.06.001","volume":"22","author":"LA da Cruz Meleiro","year":"2009","unstructured":"da Cruz Meleiro LA, Von Zuben FJ, Maciel Filho R (2009) Constructive learning neural network applied to identification and control of a fuel-ethanol fermentation process. Eng Apps Artific Intellig 22(2):201\u2013215","journal-title":"Eng Apps Artific Intellig"},{"issue":"4","key":"5_CR8","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/j.ejbt.2015.05.001","volume":"18","author":"F Chen","year":"2015","unstructured":"Chen F, Li H, Xu Z, Hou S et al (2015) User-friendly optimization approach of fed-batch fermentation conditions for the production of iturin A using artificial neural networks and support vector machine. Electron J Biotechnol 18(4):273\u2013280","journal-title":"Electron J Biotechnol"},{"issue":"2","key":"5_CR9","doi-asserted-by":"crossref","first-page":"241","DOI":"10.2298\/CICEQ120210058E","volume":"19","author":"M Esfahanian","year":"2013","unstructured":"Esfahanian M, Nikzad M, Najafpour G, Ghoreyshi AA (2013) Modeling and optimization of ethanol fermentation using Saccharomyces cerevisiae: response surface methodology and artificial neural network. Chem Ind Chem Eng Quart 19(2):241\u2013252","journal-title":"Chem Ind Chem Eng Quart"},{"issue":"3\u20134","key":"5_CR10","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.jbiotec.2012.03.025","volume":"160","author":"R Silva","year":"2012","unstructured":"Silva R, Ferreira S, Bonifacio MJ, Dias JM et al (2012) Optimization of fermentation conditions for the production of human soluble catechol-O-methyltransferase by Escherichia coli using artificial neural network. J Biotechnol 160(3\u20134):161\u2013168","journal-title":"J Biotechnol"},{"key":"5_CR11","volume-title":"Differential evolution\u2013a simple and efficient adaptive scheme for global optimization over continuous spaces. Tech. Report TR-95-012","author":"R Storn","year":"1995","unstructured":"Storn R, Price KV (1995) Differential evolution\u2013a simple and efficient adaptive scheme for global optimization over continuous spaces. Tech. Report TR-95-012. International Computer Sciences Institute, Berkeley"},{"issue":"3","key":"5_CR12","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1007\/s11063-008-9077-x","volume":"27","author":"B Subudhi","year":"2008","unstructured":"Subudhi B, Jena D (2008) Differential evolution and levenberg marquardt trained neural network scheme for nonlinear system identification. Neural Proc Lett 27(3):285\u2013296","journal-title":"Neural Proc Lett"},{"issue":"3","key":"5_CR13","doi-asserted-by":"crossref","first-page":"1126","DOI":"10.1016\/j.asoc.2009.02.012","volume":"9","author":"D Zaharie","year":"2009","unstructured":"Zaharie D (2009) Influence of crossover on the behavior of differential evolution algorithms. Appl Soft Comput 9(3):1126\u20131138","journal-title":"Appl Soft Comput"},{"key":"5_CR14","volume-title":"Differential evolution: a practical approach to global optimization","author":"K Price","year":"2006","unstructured":"Price K, Storn RM, Lampinen JA (2006) Differential evolution: a practical approach to global optimization. Springer Science & Business Media, Berlin"},{"issue":"2","key":"5_CR15","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1007\/s11633-009-0137-0","volume":"6","author":"B Subudhi","year":"2009","unstructured":"Subudhi B, Jena D (2009) An improved differential evolution trained neural network scheme for nonlinear system identification. Int J Automat Comput 6(2):137\u2013144","journal-title":"Int J Automat Comput"},{"key":"5_CR16","unstructured":"Thangaraj R, Pant M, Abraham A (2009) A simple adaptive differential evolution algorithm. In: 2009 world congress on nature and biologically inspired computing (NaBIC), IEEE"},{"issue":"7","key":"5_CR17","doi-asserted-by":"crossref","first-page":"4842","DOI":"10.1016\/j.eswa.2009.12.031","volume":"37","author":"Y Lu","year":"2010","unstructured":"Lu Y, Zhou J, Qin H, Li Y et al (2010) An adaptive hybrid differential evolution algorithm for dynamic economic dispatch with valve-point effects. Expert Syst Appl 37(7):4842\u20134849","journal-title":"Expert Syst Appl"},{"issue":"1","key":"5_CR18","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1016\/j.cor.2010.06.007","volume":"38","author":"Q-K Pan","year":"2011","unstructured":"Pan Q-K, Suganthan PN, Wang L, Gao L et al (2011) A differential evolution algorithm with self-adapting strategy and control parameters. Compt Operat Res 38(1):394\u2013408","journal-title":"Compt Operat Res"},{"issue":"11","key":"5_CR19","doi-asserted-by":"crossref","first-page":"1709","DOI":"10.1016\/j.procbio.2003.07.006","volume":"39","author":"MD Kapadi","year":"2004","unstructured":"Kapadi MD, Gudi RD (2004) Optimal control of fed-batch fermentation involving multiple feeds using differential evolution. Process Biochem 39(11):1709\u20131721","journal-title":"Process Biochem"},{"issue":"1","key":"5_CR20","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1007\/s00449-005-0004-5","volume":"28","author":"S Moonchai","year":"2005","unstructured":"Moonchai S, Madlhoo W, Jariyachavalit K, Shimizu H et al (2005) Application of a mathematical model and Differential Evolution algorithm approach to optimization of bacteriocin production by Lactococcus lactis C7. Bioprocess Biosyst Eng 28(1):15\u201326","journal-title":"Bioprocess Biosyst Eng"},{"key":"5_CR21","unstructured":"Rocha M, Pinto JP, Rocha I, Ferreira EC (2007) Evaluating evolutionary algorithms and differential evolution for the online optimization of fermentation processes. In: European conference on evolutionary computation, machine learning and data mining in bioinformatics. Springer"},{"issue":"9","key":"5_CR22","doi-asserted-by":"crossref","first-page":"1423","DOI":"10.1109\/5.784219","volume":"87","author":"X Yao","year":"1999","unstructured":"Yao X (1999) Evolving artificial neural networks. Proc IEEE 87(9):1423\u20131447","journal-title":"Proc IEEE"},{"issue":"1","key":"5_CR23","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1007\/s12065-007-0002-4","volume":"1","author":"D Floreano","year":"2008","unstructured":"Floreano D, D\u00fcrr P, Mattiussi C (2008) Neuroevolution: from architectures to learning. Evol Intell 1(1):47\u201362","journal-title":"Evol Intell"},{"key":"5_CR24","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1007\/11844297_68","volume-title":"Parallel problem solving from nature\u2014PPSN IX","author":"P Durr","year":"2006","unstructured":"Durr P, Mattiussi C, Floreano D (2006) Neuroevolution with analog genetic encoding. In: Runarsson T et al (eds) Parallel problem solving from nature\u2014PPSN IX. Springer, Berlin, pp 671\u2013680"},{"issue":"3","key":"5_CR25","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1007\/s12065-008-0015-7","volume":"1","author":"J-B Mouret","year":"2008","unstructured":"Mouret J-B, Doncieux S (2008) MENNAG: a modular, regular and hierarchical encoding for neural-networks based on attribute grammars. Evolut Intell 1(3):187\u2013207","journal-title":"Evolut Intell"},{"key":"5_CR26","unstructured":"Fischer MM, Reismann M, Hlav\u00e1ckov\u00e1-Schindler K (1999) Parameter estimation in neural spatial interaction modelling by a derivative free global optimization method. In: International conference on GeoComputation, 4, Fredericksburg, Virginia, USA"},{"key":"5_CR27","unstructured":"Plagianakos V, Magoulas G, Nousis N, Vrahatis M (2001) Training multilayer networks with discrete activation functions. In: IJCNN'01. International joint conference on neural networks. Proceedings (cat. no. 01CH37222). IEEE"},{"key":"5_CR28","doi-asserted-by":"publisher","unstructured":"Lahiri SK, Khalfe N (2010) Modeling of commercial ethylene oxide reactor: a hybrid approach by artificial neural network and differential evolution. Int J Chem Reactor Eng 8(1). \nhttps:\/\/doi.org\/10.2202\/1542-6580.2019","DOI":"10.2202\/1542-6580.2019"},{"key":"5_CR29","doi-asserted-by":"crossref","unstructured":"Bhuiyan MZA (2009) An algorithm for determining neural network architecture using differential evolution. In 2009 international conference on business intelligence and financial engineering. IEEE","DOI":"10.1109\/BIFE.2009.10"},{"issue":"7","key":"5_CR30","doi-asserted-by":"crossref","first-page":"1214","DOI":"10.1016\/j.engappai.2011.06.004","volume":"24","author":"E-N Dragoi","year":"2011","unstructured":"Dragoi E-N, Curteanu S, Leon F, Galaction A-I et al (2011) Modeling of oxygen mass transfer in the presence of oxygen-vectors using neural networks developed by differential evolution algorithm. Eng Apps Artific Intellig 24(7):1214\u20131226","journal-title":"Eng Apps Artific Intellig"},{"issue":"10","key":"5_CR31","doi-asserted-by":"crossref","first-page":"1261","DOI":"10.1080\/0305215X.2011.644546","volume":"44","author":"E-N Dr\u0103goi","year":"2012","unstructured":"Dr\u0103goi E-N, Curteanu S, Lisa C (2012) A neuro-evolutive technique applied for predicting the liquid crystalline property of some organic compounds. Eng Optimiz 44(10):1261\u20131277","journal-title":"Eng Optimiz"},{"issue":"1","key":"5_CR32","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1016\/j.asoc.2012.08.004","volume":"13","author":"E-N Dragoi","year":"2013","unstructured":"Dragoi E-N, Curteanu S, Galaction A-I, Cascaval D (2013) Optimization methodology based on neural networks and self-adaptive differential evolution algorithm applied to an aerobic fermentation process. App Soft Comp 13(1):222\u2013238","journal-title":"App Soft Comp"},{"key":"5_CR33","unstructured":"Tizhoosh HR (2005) Opposition-based learning: a new scheme for machine intelligence. In: International conference on computational intelligence for modeling,control and international conference on intelligent agents, web technologies and internet commerce, Vienna"},{"key":"5_CR34","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.ces.2012.01.021","volume":"72","author":"E-N Dragoi","year":"2012","unstructured":"Dragoi E-N, Curteanu S, Fissore D (2012) Freeze-drying modeling and monitoring using a new neuro-evolutive technique. Chem Eng Sci 72:195\u2013204","journal-title":"Chem Eng Sci"},{"issue":"12","key":"5_CR35","doi-asserted-by":"crossref","first-page":"1600","DOI":"10.1080\/00986445.2016.1206892","volume":"203","author":"E-N Dragoi","year":"2016","unstructured":"Dragoi E-N, Curteanu S, Cascaval D, Galaction A-I (2016) Artificial neural network modeling of mixing efficiency in a split-cylinder gas-lift bioreactor for Yarrowia Lipolytica suspensions. Chem Eng Comms 203(12):1600\u20131608","journal-title":"Chem Eng Comms"},{"key":"5_CR36","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.cep.2016.10.003","volume":"113","author":"B Mizzi","year":"2017","unstructured":"Mizzi B, Meyer M, Prat L, Augier F et al (2017) General design methodology for reactive liquid\u2013liquid extraction: application to dicarboxylic acid recovery in fermentation broth. Chem Eng Process 113:20\u201334","journal-title":"Chem Eng Process"},{"issue":"6","key":"5_CR37","doi-asserted-by":"crossref","first-page":"1391","DOI":"10.1039\/c0gc00797h","volume":"13","author":"PG Jessop","year":"2011","unstructured":"Jessop PG (2011) Searching for green solvents. Green Chem 13(6):1391\u20131398","journal-title":"Green Chem"},{"key":"5_CR38","doi-asserted-by":"crossref","first-page":"935","DOI":"10.1016\/j.seppur.2018.10.023","volume":"211","author":"L Sprakel","year":"2018","unstructured":"Sprakel L, Schuur B (2018) Solvent developments for liquid-liquid extraction of carboxylic acids in perspective. Sep Purif Technol 211:935\u2013957","journal-title":"Sep Purif Technol"},{"key":"5_CR39","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1016\/j.cherd.2014.04.033","volume":"93","author":"AG Demesa","year":"2015","unstructured":"Demesa AG, Laari A, Tirronen E, Turunen I (2015) Comparison of solvents for the recovery of low-molecular carboxylic acids and furfural from aqueous solutions. Chem Eng Res Design 93:531\u2013540","journal-title":"Chem Eng Res Design"},{"key":"5_CR40","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1016\/j.cherd.2019.04.017","volume":"146","author":"Y Fan","year":"2019","unstructured":"Fan Y, Cai D, Yang L, Chen X et al (2019) Extraction behavior of nicotinic acid and nicotinamide in ionic liquids. Chem Eng Res Design 146:336\u2013343","journal-title":"Chem Eng Res Design"},{"key":"5_CR41","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1016\/j.seppur.2019.02.026","volume":"219","author":"F Chemarin","year":"2019","unstructured":"Chemarin F, Moussa M, Allais F, Trelea I et al (2019) Recovery of 3-hydroxypropionic acid from organic phases after reactive extraction with amines in an alcohol-type solvent. Sep Purif Technol 219:260\u2013267","journal-title":"Sep Purif Technol"},{"key":"5_CR42","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1016\/j.seppur.2018.01.014","volume":"197","author":"S Eda","year":"2018","unstructured":"Eda S, Borra A, Parthasarathy R, Bankupalli S et al (2018) Recovery of levulinic acid by reactive extraction using tri-n-octylamine in methyl isobutyl ketone: equilibrium and thermodynamic studies and optimization using Taguchi multivariate approach. Sep Purif Technol 197:314\u2013324","journal-title":"Sep Purif Technol"},{"key":"5_CR43","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.seppur.2016.05.032","volume":"169","author":"J Gorden","year":"2016","unstructured":"Gorden J, Zeiner T, Sadowski G, Brandenbusch C (2016) Recovery of cis, cis-muconic acid from organic phase after reactive extraction. Sep Purif Technol 169:1\u20138","journal-title":"Sep Purif Technol"},{"key":"5_CR44","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.seppur.2017.05.036","volume":"185","author":"T Brouwer","year":"2017","unstructured":"Brouwer T, Blahusiak M, Babic K, Schuur B (2017) Reactive extraction and recovery of levulinic acid, formic acid and furfural from aqueous solutions containing sulphuric acid. Sep Purif Technol 185:186\u2013195","journal-title":"Sep Purif Technol"},{"key":"5_CR45","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.seppur.2018.02.010","volume":"201","author":"M Djas","year":"2018","unstructured":"Djas M, Henczka M (2018) Reactive extraction of carboxylic acids using organic solvents and supercritical fluids: a review. Sep Purif Technol 201:106\u2013119","journal-title":"Sep Purif Technol"},{"issue":"4","key":"5_CR46","first-page":"895","volume":"109","author":"AI Galaction","year":"2005","unstructured":"Galaction AI, Blaga AC, Ca\u015fcaval D, Folescu E (2005) Separation of vitamins by non-conventional techniques. Facilitated pertraction of vitamin C. Rev Med Chir Soc Med Nat Iasi 109(4):895\u2013898","journal-title":"Rev Med Chir Soc Med Nat Iasi"},{"issue":"2","key":"5_CR47","doi-asserted-by":"crossref","first-page":"63","DOI":"10.2298\/CICEQ0502063G","volume":"11","author":"A-I Galaction","year":"2005","unstructured":"Galaction A-I, Blaga A-C, Cascaval D (2005) The influence of pH and solvent polarity on the mechanism and efficiency of folic acid extraction with Amberlite LA-2. Chem Ind Chem Eng Quart 11(2):63\u201368","journal-title":"Chem Ind Chem Eng Quart"}],"container-title":["Methods in Molecular Biology","Artificial Neural Networks"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-1-0716-0826-5_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,8,31]],"date-time":"2020-08-31T12:52:47Z","timestamp":1598878367000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-1-0716-0826-5_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,18]]},"ISBN":["9781071608258","9781071608265"],"references-count":47,"URL":"https:\/\/doi.org\/10.1007\/978-1-0716-0826-5_5","relation":{},"ISSN":["1064-3745","1940-6029"],"issn-type":[{"type":"print","value":"1064-3745"},{"type":"electronic","value":"1940-6029"}],"subject":[],"published":{"date-parts":[[2020,8,18]]},"assertion":[{"value":"18 August 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}