<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sharma, Suraj</style></author><author><style face="normal" font="default" size="100%">Tambe, Sanjeev S.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Soft-sensor development for biochemical systems using genetic programming</style></title><secondary-title><style face="normal" font="default" size="100%">Biochemical Engineering Journal</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Artificial intelligence</style></keyword><keyword><style  face="normal" font="default" size="100%">Batch processing</style></keyword><keyword><style  face="normal" font="default" size="100%">Bioprocess monitoring</style></keyword><keyword><style  face="normal" font="default" size="100%">Lipase</style></keyword><keyword><style  face="normal" font="default" size="100%">Modeling</style></keyword><keyword><style  face="normal" font="default" size="100%">sensors</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">APR</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">ELSEVIER SCIENCE BV</style></publisher><pub-location><style face="normal" font="default" size="100%">PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS</style></pub-location><volume><style face="normal" font="default" size="100%">85</style></volume><pages><style face="normal" font="default" size="100%">89-100</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;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 &amp;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.&lt;/p&gt;</style></abstract><custom3><style face="normal" font="default" size="100%">Foreign</style></custom3><custom4><style face="normal" font="default" size="100%">3.03</style></custom4></record></records></xml>