@article { ISI:000334141800011, title = {Soft-sensor development for biochemical systems using genetic programming}, journal = {Biochemical Engineering Journal}, volume = {85}, year = {2014}, month = {APR}, pages = {89-100}, publisher = {ELSEVIER SCIENCE BV}, address = {PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS}, 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 (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{\textquoteright}s performance was relatively inferior (correlation coefficient approximate to 0.94). (C) 2014 Elsevier B.V. All rights reserved.

}, keywords = {Artificial intelligence, Batch processing, Bioprocess monitoring, Lipase, Modeling, sensors}, issn = {1369-703X}, doi = {10.1016/j.bej.2014.02.007}, author = {Sharma, Suraj and Tambe, Sanjeev S.} } @article { ISI:000364264800011, title = {Genetic programming based quantitative structure-retention relationships for the prediction of Kovats retention indices}, journal = {Journal of Chromatography A}, volume = {1420}, year = {2015}, month = {NOV}, pages = {98-109}, publisher = {ELSEVIER SCIENCE BV}, address = {PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS}, abstract = {

The development of quantitative structure-retention relationships (QSRR) aims at constructing an appropriate linear/nonlinear model for the prediction of the retention behavior (such as Kovats retention index) of a solute on a chromatographic column. Commonly, multi-linear regression and artificial neural networks are used in the QSRR development in the gas chromatography (GC). In this study, an artificial intelligence based data-driven modeling formalism, namely genetic programming (GP), has been introduced for the development of quantitative structure based models predicting Kovats retention indices (KRI). The novelty of the GP formalism is that given an example dataset, it searches and optimizes both the form (structure) and the parameters of an appropriate linear/nonlinear data-fitting model. Thus, it is not necessary to pre-specify the form of the data-fitting model in the GP-based modeling. These models are also less complex, simple to understand, and easy to deploy. The effectiveness of GP in constructing QSRRs has been demonstrated by developing models predicting KRIs of light hydrocarbons (case study-I) and adamantane derivatives (case study-II). In each case study, two-, three- and four-descriptor models have been developed using the KRI data available in the literature. The results of these studies clearly indicate that the GP-based models possess an excellent KRI prediction accuracy and generalization capability. Specifically, the best performing four-descriptor models in both the case studies have yielded high (\>0.9) values of the coefficient of determination (R-2) and low values of root mean squared error (RMSE) and mean absolute percent error (MAPE) for training, test and validation set data. The characteristic feature of this study is that it introduces a practical and an effective GP-based method for developing QSRRs in gas chromatography that can be gainfully utilized for developing other types of data-driven models in chromatography science. (C) 2015 Elsevier B.V. All rights reserved.

}, keywords = {Artificial intelligence, Gas chromatography, genetic programming, Kovats retention index, Molecular descriptors, Quantitative structure-retention relationships}, issn = {0021-9673}, doi = {10.1016/j.chroma.2015.09.086}, author = {Goel, Purva and Bapat, Sanket and Vyas, Renu and Tambe, Amruta and Tambe, Sanjeev S.} } @conference { ISI:000382147300038, title = {Development of genetic programming based softsensor model for styrene polymerization process and its application in model based control}, booktitle = {2016 Indian Control Conference (ICC)}, year = {2016}, note = {2nd Indian Control Conference (ICC), Indian Inst Technol, Hyderabad, INDIA, JAN 04-06, 2016}, month = {JAN}, pages = {238-244}, publisher = {IEEE Control Syst Soc; Honeywell; Mathworks; ABB R \& D Div; GE; Mahindra Ecole Centrale}, organization = {IEEE Control Syst Soc; Honeywell; Mathworks; ABB R \& D Div; GE; Mahindra Ecole Centrale}, address = {345 E 47th ST, New York, NY 10017 USA}, abstract = {

In recent years, soft sensors have been established as a valuable alternative to the traditional hardware sensors for the acquisition of critical information regarding {\textquoteleft}{\textquoteleft}difficult-to-measure{\textquoteright}{\textquoteright} process variables and/or parameters in chemical process monitoring and control. Soft sensors can also be modified as a novel process identification tool for process monitoring and model based control. Often, in polymer industries the main polymerization reaction is highly nonlinear and complex to model accurately by the conventional {\textquoteleft}{\textquoteleft}first principles{\textquoteright}{\textquoteright} approach. In such cases, genetic programming (GP)-a novel artificial intelligence-based exclusively data driven modeling technique-can be employed for process identification. In this work GP-based soft sensors have been developed for a continuous styrene polymerization reactor. The resulting GP-based models (soft sensor) showed high prediction and generalization performances. The best performing model was successfully utilized in designing a model predictive control (MPC) scheme for the polymerization reactor.

}, keywords = {Artificial intelligence, genetic programming, Model Predictive Control, process identification, Styrene Polymerization}, isbn = {978-1-4673-7993-9}, author = {Ghugare, Suhas B. and Tambe, Sanjeev S.} }