02602nas a2200229   4500008004100000022001400041245007800055210006900133260007300202300001100275490000700286520181600293653002802109653002102137653002602158653001102184653001302195653001202208100001802220700002302238856011102261       2014                        eng d  a1369-703X00aSoft-sensor development for biochemical systems using genetic programming0 aSoftsensor development for biochemical systems using genetic pro  aPO BOX 211, 1000 AE AMSTERDAM, NETHERLANDSbELSEVIER SCIENCE BVcAPR  a89-1000 v853 a<p>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 &gt;0.96 (approximate to 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 approximate to 0.98) while the MLP based soft-sensor's performance was relatively inferior (correlation coefficient approximate to 0.94). (C) 2014 Elsevier B.V. All rights reserved.</p>10aArtificial intelligence10aBatch processing10aBioprocess monitoring10aLipase10aModeling10asensors1 aSharma, Suraj1 aTambe, Sanjeev, S.  uhttps://library.ncl.res.in/content/soft-sensor-development-biochemical-systems-using-genetic-programming-0