02882nas 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-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-and02350nas a2200145 4500008004100000245015500041210006900196260000800265300001200273490000700285520173400292100002302026700002302049856013202072 2017 eng d00aGenetic programming based high performing correlations for prediction of higher heating value of coals of different ranks and from diverse geographies0 aGenetic programming based high performing correlations for predi cJUN a476-4840 v903 a

The higher heating value (HHV) is the most important indicator of a coal's potential energy yield. It is commonly used in the efficiency and optimal design calculations pertaining to the coal combustion and gasification processes. Since the experimental determination of coal's HHV is tedious and time-consuming, a number of proximate and/or ultimate analyses based correlations which are mostly linear have been proposed for its estimation. Owing to the fact that relationships between some of the constituents of the proximate/ultimate analyses and the HHV are nonlinear, the linear models make suboptimal predictions. Also, a majority of the currently available HHV models are restricted to the coals of specific ranks or particular geographical regions. Accordingly, in this study three proximate and ultimate analysis based nonlinear correlations have been developed for the prediction of HHV of coals by utilizing the computational intelligence (CI) based genetic programming (GP) formalism. Each of these correlations possesses following noteworthy characteristics: (i) the highest HHV prediction accuracy and generalization capability as compared to the existing models, (ii) wider applicability for coals of different ranks and from diverse geographies, and (iii) structurally lower complex than the other CI-based existing HHV models. It may also be noted that in this study, the GP technique has been used for the first time for developing coal specific HHV models. Owing to the stated attractive features, the GP-based models proposed here possess a significant potential to replace the existing models for predicting the HHV of coals. (C) 2016 Energy Institute. Published by Elsevier Ltd. All rights reserved.

1 aGhugare, Suhas, B.1 aTambe, Sanjeev, S. uhttps://library.ncl.res.in/content/genetic-programming-based-high-performing-correlations-prediction-higher-heating-value-coals02503nas a2200289 4500008004100000022001400041245014800055210007100203260000800274300000900282490000700291520147100298653003101769653002001800653003101820653002701851653002401878653003001902100002001932700002301952700002401975700001601999700001902015700001802034700002302052856013802075 2018 eng d a1877-264100aCo-gasification of high ash coal–biomass blends in a fluidized bed gasifier: experimental study and computational intelligence-based modeling0 aCogasification of high ash coal–biomass blends in a fluidized be cJUN a1-190 v113 aCo-gasification (COG) is a clean-coal technology that uses a binary blend of coal and biomass for generating the product gas; it is environment-friendly since it emits lesser quantities of pollutants compared to the coal gasification process. Although coals found in many countries contain high percentages of ash, co-gasification studies involving such coals, and the process modeling thereof, are rare. Accordingly, this study presents results of the co-gasification experiments conducted in a fluidized-bed gasifier (FBG) pilot plant using as a feed the blends of high ash Indian coals with three biomasses, namely, rice husk, press mud, and sawdust. Since the underlying physicochemical phenomena are complex and nonlinear, modeling of the COG process has been performed using three computational intelligence (CI)-based methods namely, genetic programming, artificial neural networks, and support vector regression. Each of these formalisms was employed separately to develop models predicting four COG performance variables, namely, total gas yield, carbon conversion efficiency, heating value of product gas, and cold gas efficiency. All the CI-based models exhibit an excellent prediction accuracy and generalization performance. The co-gasification experiments and their modeling presented here for a pilot-plant FBG can be gainfully utilized in the efficient design and operation of the corresponding commercial scale co-gasifiers utilizing high ash coals.10aArtificial neural networks10aCo-gasification10aComputational intelligence10aFluidized bed gasifier10agenetic programming10asupport vector regression1 aTiwary, Shishir1 aGhugare, Suhas, B.1 aChavan, Prakash, D.1 aSaha, Sujan1 aDatta, Sudipta1 aSahu, Gajanan1 aTambe, Sanjeev, S. uhttps://library.ncl.res.in/content/co-gasification-high-ash-coal%E2%80%93biomass-blends-fluidized-bed-gasifier-experimental-study-and02221nas a2200205 4500008004100000245008400041210007100125260000800196300001400204490000700218520153200225100002001757700002301777700002401800700001601824700001901840700001801859700002301877856011501900 2020 eng d00aCo-gasification of high ash coal–biomass blends in a fluidized bed gasifier: 0 aCogasification of high ash coal–biomass blends in a fluidized be cJAN a323–3410 v113 a

Co-gasification (COG) is a clean-coal technology that uses a binary blend of coal and biomass for generating the product gas; it is environment-friendly since it emits lesser quantities of pollutants compared to the coal gasification process. Although coals found in many countries contain high percentages of ash, co-gasification studies involving such coals, and the process modeling thereof, are rare. Accordingly, this study presents results of the co-gasification experiments conducted in a fluidized-bed gasifier (FBG) pilot plant using as a feed the blends of high ash Indian coals with three biomasses, namely, rice husk, press mud, and sawdust. Since the underlying physicochemical phenomena are complex and nonlinear, modeling of the COG process has been performed using three computational intelligence (CI)-based methods namely, genetic programming, artificial neural networks, and support vector regression. Each of these formalisms was employed separately to develop models predicting four COG performance variables, namely, total gas yield, carbon conversion efficiency, heating value of product gas, and cold gas efficiency. All the CI-based models exhibit an excellent prediction accuracy and generalization performance. The co-gasification experiments and their modeling presented here for a pilot-plant FBG can be gainfully utilized in the efficient design and operation of the corresponding commercial scale co-gasifiers utilizing high ash coals.

1 aTiwary, Shishir1 aGhugare, Suhas, B.1 aChavan, Prakash, D.1 aSaha, Sujan1 aDatta, Sudipta1 aSahu, Gajanan1 aTambe, Sanjeev, S. uhttps://library.ncl.res.in/content/co-gasification-high-ash-coal%E2%80%93biomass-blends-fluidized-bed-gasifier