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<p>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 (&gt; 0.95) magnitudes of the coefficient of correlation and low (&lt; 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.</p>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-002494nas a2200169   4500008004100000022001300041245013100054210006900185260000800254300001400262490000600276520186100282100001802143700001502161700001602176856013202192       2017                        eng d  a0975680900aComputational intelligence based models for prediction of elemental composition of solid biomass fuels from proximate analysis0 aComputational intelligence based models for prediction of elemen  cDEC  a2083-20960 v83 aBiomass is a renewable and sustainable source of “green” energy. The elemental composition comprising carbon (C), hydrogen (H) and oxygen (O) as major components, is an important measure of the biomass fuel’s energy content. Its knowledge is also valuable in: (a) computing material balance in a biomass-based process, (b) designing and operating biomass utilizing efficient and clean combustors, gasifiers and boilers, (c) fixing the quantity of oxidants required for biomass combustion/gasification, and (d) determining the volume and composition of the combustion/gasification gases. Obtaining the elemental composition of a biomass fuel via ultimateanalysis is an expensive and time-consuming task. In comparison, proximate analysis that determines fixed carbon, ash, volatile matter and moisture content is a cruder characterization of the fuel and easier to perform. Thus, there exists a need for models possessing high accuracies for predicting the elemental composition of a solid biomass fuel from its proximate analysis constituents. Accordingly, this study utilizes three computational intelligence (CI) formalisms, namely, genetic programming, artificial neural networks and support vector regression, for developing nonlinear models for the prediction of C, H and O fractions of solid biomass fuels. A large database of 830 biomasses has been used in the stated model development. A comparison of the prediction accuracy and generalization performance of the nine CI-based models (three each for C, H and O) with that of the currently available linear models indicates that the CI-based models have consistently and significantly outperformed their linear counterparts. The models developed in this study have proved to be the best models for the prediction of elemental composition of solid biomass fuels from their proximate analyses. 1 aGhugare, S.B.1 aTiwary, S.1 aTambe, S.S.  uhttps://library.ncl.res.in/content/computational-intelligence-based-models-prediction-elemental-composition-solid-biomass-fuels02348nas a2200193   4500008004100000022001300041245009300054210006900147260000800216300001200224490000600236520170200242100001601944700002001960700001501980700001801995700001602013856012502029       2018                        eng d  a2095829300aPrediction of coal ash fusion temperatures using computational intelligence based models0 aPrediction of coal ash fusion temperatures using computational i  cDEC  a486-5070 v43 a<p>In the coal-based combustion and gasification processes, the mineral matter contained in the coal (predominantly oxides), is left as an incombustible residue, termed ash. Commonly, ash deposits are formed on the heat absorbing surfaces of the exposed equipment of the combustion/gasification processes. These deposits lead to the occurrence of slagging or fouling and, consequently, reduced process efficiency. The ash fusion temperatures (AFTs) signify the temperature range over which the ash deposits are formed on the heat absorbing surfaces of the process equipment. Thus, for designing and operating the coal-based processes, it is important to have mathematical models predicting accurately the four types of AFTs namely initial deformation temperature, softening temperature, hemispherical temperature, and flow temperature. Several linear/nonlinear models with varying prediction accuracies and complexities are available for the AFT prediction. Their principal drawback is their applicability to the coals originating from a limited number of geographical regions. Accordingly, this study presents computational intelligence (CI) based nonlinear models to predict the four AFTs using the oxide composition of the coal ash as the model input. The CI methods used in the modeling are genetic programming (GP), artificial neural networks, and support vector regression. The notable features of this study are that the models with a better AFT prediction and generalization performance, a wider application potential, and reduced complexity, have been developed. Among the CI-based models, GP and MLP based models have yielded overall improved performance in predicting all four AFTs.</p>1 aTambe, S.S.1 aNaniwadekar, M.1 aTiwary, S.1 aMukherjee, A.1 aDas, T., B.  uhttps://library.ncl.res.in/content/prediction-coal-ash-fusion-temperatures-using-computational-intelligence-based-models