Soft-sensor development for biochemical systems using genetic programming

Abstract Soft-sensors are software based process monitoring systems/models. In real-time they estimate those process variables, which are difficult to measure online or whose measurement by analytical procedures is tedious and time-consuming. In this study, the genetic programming (GP), an artificial intelligence based data-driven modeling formalism, has been introduced for the development of soft-sensors for biochemical processes. The novelty of the GP is that given example input–output data, it searches and optimizes both the form (structure) and parameters of an appropriate linear/nonlinear data-fitting model. In this study, GP-based soft-sensors have been developed for two bioprocesses, namely extracellular production of lipase enzyme and bacterial production of poly(3-hydroxybutyrate- co -3-hydroxyvalerate) copolymer. While in case study-I, the soft-sensor predicts the time-dependent lipase activity (U/ml), in case study-II it predicts the amount of accumulated polyhydroxyalkanoates (% dcw). The prediction and generalization performance of the GP-based soft-sensors was compared with the corresponding multi-layer perceptron (MLP) neural network and support vector regression (SVR) based soft-sensors. This comparison indicates that in the first case study the GP-based soft-sensor with the training and test set correlation coefficient (root-mean-squared-error) magnitudes of >0.96 (≈0.962 U/ml) has clearly outperformed the two other soft-sensors. In case study-II involving bacterial copolymer production, the GP and SVR based soft-sensors have performed equally well (correlation coefficient ≈ 0.98) while the MLP based soft-sensor's performance was relatively inferior (correlation coefficient ≈ 0.94).

[1]  Gary Montague,et al.  Soft-sensors for process estimation and inferential control , 1991 .

[2]  Athanasios Tsakonas,et al.  Symbolic regression via genetic programming in the optimization of a controlled release pharmaceutical formulation , 2011 .

[3]  S. S. Tambe,et al.  An artificial intelligence tool for bioprocess monitoring: application to continuous production of gluconic acid by immobilized Aspergillus niger , 2001, Biotechnology Letters.

[4]  G. Thomas,et al.  Cold active microbial lipases: some hot issues and recent developments. , 2008, Biotechnology advances.

[5]  H. Treichel,et al.  Isolation and Screening of Lipase-Producing Fungi with Hydrolytic Activity , 2011 .

[6]  Sanjeev S. Tambe,et al.  Soft-sensor development for fed-batch bioreactors using support vector regression , 2006 .

[7]  Yinglong Wang,et al.  Synthesis of heat-integrated complex distillation systems via Genetic Programming , 2008, Comput. Chem. Eng..

[8]  Luiz Augusto da Cruz Meleiro,et al.  ANN-based soft-sensor for real-time process monitoring and control of an industrial polymerization process , 2009, Comput. Chem. Eng..

[9]  H. Hotelling.  The Selection of Variates for Use in Prediction with Some Comments on the General Problem of Nuisance Parameters , 1940 .

[10]  R. Sen,et al.  Process optimization for poly(3-hydroxybutyrate-co-3-hydroxyvalerate) co-polymer production by Nostoc muscorum , 2007 .

[11]  Hod Lipson,et al.  Distilling Free-Form Natural Laws from Experimental Data , 2009, Science.

[12]  Helio J. C. Barbosa,et al.  Symbolic regression via genetic programming , 2000, Proceedings. Vol.1. Sixth Brazilian Symposium on Neural Networks.

[13]  Y.-H. Zhu,et al.  Application of neural networks to lysine production , 1996 .

[14]  Jie Yu,et al.  A Bayesian inference based two-stage support vector regression framework for soft sensor development in batch bioprocesses , 2012, Comput. Chem. Eng..

[15]  R. K. Dahule,et al.  Obtaining functional form for chaotic time series evolution using genetic algorithm. , 1999, Chaos.

[16]  Kenneth Hennessy,et al.  An improved genetic programming technique for the classification of Raman spectra , 2004, Knowl. Based Syst..

[17]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[18]  J. Polster,et al.  Software FIACRE: bioprocess monitoring on the basis of flow injection analysis using simultaneously a urea optode and a glucose luminescence sensor. , 1993, Journal of biotechnology.

[19]  John R. Koza,et al.  Genetically breeding populations of computer programs to solve problems in artificial intelligence , 1990, [1990] Proceedings of the 2nd International IEEE Conference on Tools for Artificial Intelligence.

[20]  Rui Oliveira.  Combining first principles modelling and artificial neural networks: a general framework , 2004, Comput. Chem. Eng..

[21]  N. Starodub.  Biosensors for the evaluation of lipase activity , 2006 .

[22]  M. Rodrigues,et al.  Optimization of extracellular lipase production by Geotrichum sp. using factorial design. , 2004, Bioresource technology.

[23]  Geoffrey E. Hinton,et al.  Learning representations by back-propagation errors, nature , 1986 .

[24]  H. Goicoechea,et al.  Optimization of the Bacillus thuringiensis var. kurstaki HD-1 δ-endotoxins production by using experimental mixture design and artificial neural networks , 2007 .

[25]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[26]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[27]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[28]  A. Gambacorta,et al.  Effect of growth conditions on endo- and exopolymer biosynthesis in Anabaena cylindrica 10 C , 1996 .

[29]  Sanjeev S. Tambe,et al.  Hybrid process modeling and optimization strategies integrating neural networks/support vector regression and genetic algorithms: study of benzene isopropylation on Hbeta catalyst , 2004 .

[30]  Marcio A. Mazutti,et al.  A Review on Microbial Lipases Production , 2010 .

[31]  K. Arun Kumar,et al.  Simulation of biodegradation process in a fluidized bed bioreactor using genetic algorithm trained feedforward neural network , 2009 .

[32]  Hector Budman,et al.  Developmental studies of an adaptive on-line softsensor for biological wastewater treatments , 1999 .

[33]  Douglas B. Kell,et al.  Real-time vapour sensing using an OFET-based electronic nose and genetic programming , 2009 .

[34]  I. E. Nikerel,et al.  Simultaneous modeling of enzyme production and biomass growth in recombinant Escherichia coli using artificial neural networks , 2008 .

[35]  A J Morris,et al.  Enhancing bioprocess operability with generic software sensors. , 1992, Journal of biotechnology.

[36]  Christoph Herwig,et al.  Real-time estimation of biomass and specific growth rate in physiologically variable recombinant fed-batch processes , 2012, Bioprocess and Biosystems Engineering.

[37]  S. Zaidi.  Development of support vector regression (SVR)-based model for prediction of circulation rate in a vertical tube thermosiphon reboiler , 2012 .

[38]  Y. Chi,et al.  Effects of methanol on the catalytic properties of porcine pancreatic lipase , 2005 .

[39]  M. Rodrigues,et al.  Studies on lipase‐affinity adsorption using response‐surface analysis , 2001, Biotechnology and applied biochemistry.

[40]  R. Gupta,et al.  Bacterial lipases: an overview of production, purification and biochemical properties , 2004, Applied Microbiology and Biotechnology.

[41]  Sanjeev S Tambe,et al.  Genetic Programming Assisted Stochastic Optimization Strategies for Optimization of Glucose to Gluconic Acid Fermentation , 2002, Biotechnology progress.

[42]  Y.-H. Zhu,et al.  Application of neural networks in lysine fermentation control , 1996 .

[43]  D. Bassett,et al.  Developments in crystalline polymers , 1982 .

[44]  Denis Dochain,et al.  Dynamical modelling, analysis, monitoring and control design for nonlinear bioprocesses , 1997 .

[45]  Francesco Corona,et al.  Data-derived soft-sensors for biological wastewater treatment plants: An overview , 2013, Environ. Model. Softw..

[46]  Xinggao Liu,et al.  A soft sensor based on adaptive fuzzy neural network and support vector regression for industrial melt index prediction , 2013 .

[47]  Arthur K. Kordon,et al.  Robust soft sensors based on integration of genetic programming, analytical neural networks, and support vector machines , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[48]  D. Marquardt.  An Algorithm for Least-Squares Estimation of Nonlinear Parameters , 1963 .

[49]  Jian Li,et al.  The application of neural network soft sensor technology to an advanced control system of distillation operation , 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..

[50]  R. Gross,et al.  Pseudomonas oleovorans as a Source of Poly(β-Hydroxyalkanoates) for Potential Applications as Biodegradable Polyesters , 1988, Applied and environmental microbiology.

[51]  L. Sarda,et al.  Lipase-catalysed hydrolysis of short-chain substrates in solution and in emulsion: a kinetic study. , 2001, Biochimica et biophysica acta.

[52]  H. Deeth,et al.  Methods for detecting lipase activity in milk and milk products. , 2000 .

[53]  Z. Cohen,et al.  Chemicals from Microalgae , 1999 .

[54]  R. Maciel Filho,et al.  State and parameter estimation based on a nonlinear filter applied to an industrial process control of ethanol production , 2000 .