02105nas a2200265 4500008004100000022001400041245009600055210006900151260006900220300001200289490000700301520118700308653002801495653002001523653001701543653001301560653001701573653001301590100002001603700002201623700002601645700002101671700001701692856013001709 2008 eng d a1369-703X00aOptimal xylanase production using Penicilium janthinellum NCIM 1169: a model based approach0 aOptimal xylanase production using Penicilium janthinellum NCIM 1 aPO BOX 564, 1001 LAUSANNE, SWITZERLANDbELSEVIER SCIENCE SAcJUN a348-3560 v403 a

Xylanases are an industrially important class of hydrolytic enzymes that degrade xylans. Production of xylanase from a fungal culture by submerged fermentation and optimization of the operating conditions for maximum activity are the two aims of the present study. Penicillium janthinellum NCIM 1169 with Mandels-Weber medium, sugarcane bagassse (40\#) as a carbon source and beef extract as a nitrogen source were used in the experiments. We did 41 experiments to see the effect of variations in carbon, nitrogen source, pH, and inoculum on xylanase activity. This data was then used to build an input/output model using multiple linear regression, back propagation neural network and lazy learning algorithm. It was found that lazy learning model correlated well in mapping input/output data. This model was then utilized as an objective function in genetic algorithm to find the optimal combination of the operating conditions to get the maximum xylanase activity. It was observed that with carbon source, 1.63%, nitrogen source, 0.16%, pH, 4.1, and inoculum, 5.5%, maximum xylanase activity of 28.98 +/- 1.73 U/ml was achieved. (C) 2008 Elsevier B.V. All rights reserved.

10aArtificial intelligence10aEnzyme activity10aFermentation10aModeling10aOptimization10aXylanase1 aMeshram, Mukesh1 aKulkarni, Abhijit1 aJayaraman, Valadi, K.1 aKulkarni, B., D.1 aLele, S., S. uhttps://library.ncl.res.in/content/optimal-xylanase-production-using-penicilium-janthinellum-ncim-1169-model-based-approach-002882nas a2200253 4500008004100000022001400041245010300055210006900158260005700227300001200284490000600296520197700302653002802279653003102307653001802338653002402356653002502380653002602405100002302431700001502454700001802469700001802487856012302505 2014 eng d a1939-123400aPrediction of higher heating value of solid biomass fuels using artificial intelligence formalisms0 aPrediction of higher heating value of solid biomass fuels using  a233 SPRING ST, NEW YORK, NY 10013 USAbSPRINGERcJUN a681-6920 v73 a

The higher heating value (HHV) is an important property defining the energy content of biomass fuels. A number of proximate and/or ultimate analysis based predominantly linear correlations have been proposed for predicting the HHV of biomass fuels. A scrutiny of the relationships between the constituents of the proximate and ultimate analyses and the corresponding HHVs suggests that all relationships are not linear and thus nonlinear models may be more appropriate. Accordingly, a novel artificial intelligence (AI) formalism, namely genetic programming (GP) has been employed for the first time for developing two biomass HHV prediction models, respectively using the constituents of the proximate and ultimate analyses as the model inputs. The prediction and generalization performance of these models was compared rigorously with the corresponding multilayer perceptron (MLP) neural network based as also currently available high-performing linear and nonlinear HHV models. This comparison reveals that the HHV prediction performance of the GP and MLP models is consistently better than that of their existing linear and/or nonlinear counterparts. Specifically, the GP- and MLP-based models exhibit an excellent overall prediction accuracy and generalization performance with high (> 0.95) magnitudes of the coefficient of correlation and low (< 4.5 %) magnitudes of mean absolute percentage error in respect of the experimental and model-predicted HHVs. It is also found that the proximate analysis-based GP model has outperformed all the existing high-performing linear biomass HHV prediction models. In the case of ultimate analysis-based HHV models, the MLP model has exhibited best prediction accuracy and generalization performance when compared with the existing linear and nonlinear models. The AI-based models introduced in this paper due to their excellent performance have the potential to replace the existing biomass HHV prediction models.

10aArtificial intelligence10aArtificial neural networks10aBiomass fuels10agenetic programming10aHigher heating value10aMultilayer perceptron1 aGhugare, Suhas, B.1 aTiwary, S.1 aElangovan, V.1 aTambe, S., S. uhttps://library.ncl.res.in/content/prediction-higher-heating-value-solid-biomass-fuels-using-artificial-intelligence-002602nas 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

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's performance was relatively inferior (correlation coefficient approximate to 0.94). (C) 2014 Elsevier B.V. All rights reserved.

10aArtificial intelligence10aBatch processing10aBioprocess monitoring10aLipase10aModeling10asensors1 aSharma, Suraj1 aTambe, Sanjeev, S. uhttps://library.ncl.res.in/content/soft-sensor-development-biochemical-systems-using-genetic-programming-003061nas a2200265 4500008004100000022001400041245012400055210006900179260007300248300001100321490000900332520205300341653002802394653002302422653002402445653002702469653002602496653005102522100001602573700001802589700001502607700001802622700002302640856013202663 2015 eng d a0021-967300aGenetic programming based quantitative structure-retention relationships for the prediction of Kovats retention indices0 aGenetic programming based quantitative structureretention relati aPO BOX 211, 1000 AE AMSTERDAM, NETHERLANDSbELSEVIER SCIENCE BVcNOV a98-1090 v14203 a

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.

10aArtificial intelligence10aGas chromatography10agenetic programming10aKovats retention index10aMolecular descriptors10aQuantitative structure-retention relationships1 aGoel, Purva1 aBapat, Sanket1 aVyas, Renu1 aTambe, Amruta1 aTambe, Sanjeev, S. uhttps://library.ncl.res.in/content/genetic-programming-based-quantitative-structure-retention-relationships-prediction-kovats-002012nas a2200205 4500008004100000020002200041245014000063210006900203260013600272300001200408520107600420653002801496653002401524653002901548653002701577653002701604100002301631700002301654856012901677 2016 eng d a978-1-4673-7993-900aDevelopment of genetic programming based softsensor model for styrene polymerization process and its application in model based control0 aDevelopment of genetic programming based softsensor model for st a345 E 47th ST, New York, NY 10017 USAbIEEE Control Syst Soc; Honeywell; Mathworks; ABB R & D Div; GE; Mahindra Ecole CentralecJAN a238-2443 a

In recent years, soft sensors have been established as a valuable alternative to the traditional hardware sensors for the acquisition of critical information regarding ``difficult-to-measure'' 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 ``first principles'' 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.

10aArtificial intelligence10agenetic programming10aModel Predictive Control10aprocess identification10aStyrene Polymerization1 aGhugare, Suhas, B.1 aTambe, Sanjeev, S. uhttps://library.ncl.res.in/content/development-genetic-programming-based-softsensor-model-styrene-polymerization-process-and