02720nas a2200229   4500008004100000022001400041245011900055210006900174260007100243300001600314490000700330520185000337100002402187700001902211700002002230700002402250700002202274700002502296700002302321700002102344856012502365       2014                        eng d  a0888-588500aArtificial intelligence-based modeling of high ash coal gasification in a pilot plant scale fluidized bed gasifier0 aArtificial intelligencebased modeling of high ash coal gasificat  a1155 16TH ST, NW, WASHINGTON, DC 20036 USAbAMER CHEMICAL SOCcDEC  a18678-186890 v533 a<p>The quality of coalespecially its high ash contentsignificantly affects the performance of coal-based processes. Coal gasification is a cleaner and an efficient alternative to the coal combustion for producing the syngas. The high-ash coals are found in a number of countries, and they form an important source for the gasification. Accordingly, in this study, extensive gasification experiments were conducted in a pilot-plant scale fluidized-bed coal gasifier (FBCG) using high-ash coals from India. Specifically, the effects of eight coal and gasifier process related parameters on the four gasification performance variables, namely CO+H-2 generation rate, syngas production rate, carbon conversion, and heating value of the syngas, were rigorously studied. The data collected from these experiments were used in the FBCG modeling, which was conducted by utilizing two artificial intelligence (AI) strategies namely genetic programming (GP) and artificial neural networks (ANNs). The novelty of the GP formalism is that it searches and optimizes both the form and parameters of an appropriate linear/nonlinear function that best fits the given process data. The original eight-dimensional input space of the FBCG models was reduced to three-dimensional space using the principal component analysis (PCA) and the PCA-transformed three variables were used in the AI-based FBCG modeling. A comparison of the GP and ANN-based models reveals that their output prediction accuracies and the generalization performance vary from good to excellent as indicated by the high training and test set correlation coefficient magnitudes lying between 0.92 and 0.996. This study also presents results of the sensitivity analysis performed to identify those coal and process related parameters, which significantly affect the FBCG process performance.</p>1 aPatil-Shinde, Veena1 aKukarni, Tejas1 aKulkarni, Rahul1 aChavan, Prakash, D.1 aSharma, Tripurari1 aSharma, Bijay, Kumar1 aTambe, Sanjeey, S.1 aKulkarni, B., D.  uhttps://library.ncl.res.in/content/artificial-intelligence-based-modeling-high-ash-coal-gasification-pilot-plant-scale-002624nas a2200265   4500008004100000022001400041245016000055210006900215260008600284300001400370490000800384520154200392653003901934653002501973653002401998653004102022653003002063653003202093100002402125700001602149700002202165700002302187700002602210856012202236       2016                        eng d  a0098-644500aHigh ash char gasification in thermo-gravimetric analyzer and prediction of gasification performance parameters using computational intelligence formalisms0 aHigh ash char gasification in thermogravimetric analyzer and pre  a530 WALNUT STREET, STE 850, PHILADELPHIA, PA 19106 USAbTAYLOR & FRANCIS INCcJAN  a1029-10440 v2033 a<p>The coal gasification is a cleaner and more efficient process than the coal combustion. Although high ash coals are commonly utilized in the energy generation, systematic gasification kinetic studies using chars derived from these coals are scarce. Accordingly, this paper reports the development of the data-driven models for the gasification of chars derived from the high ash coals. Specifically, the models predict two important gasification performance parameters, viz. gasification rate constant and reactivity index. These models have been constructed using three computational intelligence (CI) methods, namely genetic programming (GP), multilayer perceptron (MLP) neural network (NN), and support vector regression (SVR). The inputs to the CI-based models consist of seven parameters representing the gasification reaction conditions and properties of high ash coals and chars. The data used in the modeling were collected by performing extensive gasification experiments in the CO2 atmosphere in a thermo-gravimetric analyzer (TGA) using char samples derived from the Indian coals containing high ash content. Values of the two gasification performance parameters were obtained by fitting the experimental data to the shrinking unreacted core (SUC) model. It has been observed that all the CI-based models possess an excellent prediction accuracy and generalization capability. Accordingly, these models can be gainfully employed in the design and operation of the fixed and fluidized bed gasifiers using high ash coals.</p>10aChar gasification kinetic modeling10aData-driven modeling10agenetic programming10aMultilayer perceptron neural network10asupport vector regression10aThermo-gravimetric analyzer1 aPatil-Shinde, Veena1 aSaha, Sujan1 aSharma, Bijay, K.1 aTambe, Sanjeev, S.1 aKulkarni, Bhaskar, D.  uhttps://library.ncl.res.in/content/high-ash-char-gasification-thermo-gravimetric-analyzer-and-prediction-gasification02333nas a2200229   4500008004100000020002200041245010100063210006900164260013600233300001200369520139300381653001801774653002401792653002301816653002701839653002501866100002001891700001601911700002401927700002301951856012901974       2016                        eng d  a978-1-4673-7993-900aUse genetic programming for selecting predictor variables and modeling in process identification0 aUse genetic programming for selecting predictor variables and mo  a345 E 47th ST, New York, NY 10017 USAbIEEE Control Syst Soc; Honeywell; Mathworks; ABB R & D Div; GE; Mahindra Ecole CentralecJAN  a230-2373 a<p>Availability of an accurate and robust dynamic model is essential for implementing the model dependent process control. When first principles based modeling becomes difficult, tedious and/or costly, a dynamic model in the black-box form is obtained (process identification) by using the measured input-output process data. Such a dynamic model frequently contains a number of time delayed inputs and outputs as predictor variables. The determination of the specific predictor variables is usually done via a trial and error approach that requires an extensive computational effort. The computational intelligence (CI) based data-driven modeling technique, namely, genetic programming (GP) can search and optimize both the structure and parameters of a linear/nonlinear dynamic process model. It is also capable of choosing those predictor variables that significantly influence the model output. Thus usage of GP for process identification helps in avoiding the extensive time and efforts involved in the selection of the time delayed input-output variables. This advantageous GP feature has been illustrated in this study by conducting process identification of two chemical engineering systems. The results of the GP-based identification when compared with those obtained using the transfer function based identification clearly indicates the outperformance by the former method.</p>10adynamic model10agenetic programming10apredictor variable10aprocess identification10aSensitivity analysis1 aVerma, Devendra1 aGoel, Purva1 aPatil-Shinde, Veena1 aTambe, Sanjeev, S.  uhttps://library.ncl.res.in/content/use-genetic-programming-selecting-predictor-variables-and-modeling-process-identification02420nas a2200169   4500008004100000245015100041210006900192260000800261300001200269490000700281520175000288100001602038700001902054700002402073700002302097856013002120       2017                        eng d00aPrediction of degrees API values of crude oils by use of saturates/aromatics/resins/ asphaltenes analysis: computational-intelligence-based models0 aPrediction of degrees API values of crude oils by use of saturat  cJUN  a817-8530 v223 aThe degrees API value is an important physicochemical characteristic of crude oils often used in determining their properties and quality. There exist models-predominantly linear ones-for predicting the degrees API magnitude from the molecular composition of a crude oil. This approach is tedious and time-consuming because it requires quantitative determination of numerous crude-oil components. Usually, the hydrocarbons present in a crude oil are grouped according to their molecular average structures into saturates, aromatics, resins, and asphaltenes (SARA) fractions. An degrees API-value prediction model dependent on these four fractions is relatively easier to develop, although this approach has been rarely used. A rigorous scrutiny suggests that some of the dependencies between the individual SARA fractions and the corresponding degrees API value could be nonlinear. Accordingly, in this study, SARA-fraction-based nonlinear models have been developed for the prediction of values using three computational-intelligence (CI) formalisms: genetic programming (GP), artificialneural networks (ANNs), and support-vector regression (SVR). The SARA analyses and degrees API values of 403 crude-oil samples covering wide ranges have been used in developing these models. A comparison of the CI-based models with an existing linear model indicates that all the former class of models possess a significantly better degrees API-value prediction and generalization performance than those exhibited by the linear model. Also, the SVR-based model has been found to be the most accurate degrees API-value predictor. Because of their better prediction accuracy, CI-based models can be gainfully used to predict degrees API values of crude oils.1 aGoel, Purva1 aSaurabh, Kumar1 aPatil-Shinde, Veena1 aTambe, Sanjeev, S.  uhttps://library.ncl.res.in/content/prediction-degrees-api-values-crude-oils-use-saturatesaromaticsresins-asphaltenes-analysis02365nas a2200145   4500008004100000245008000041210006900121260000800190300001000198490000700208520184900215100002402064700002302088856010802111       2018                        eng d00aGenetic programming based models for prediction of vapor-liquid equilibrium0 aGenetic programming based models for prediction of vaporliquid e  cMAR  a68-800 v603 aThe design, operation, and control of chemical separation processes heavily rely on the knowledge of the vapor liquid equilibrium (VLE). Often, conducting experiments to gain an insight into the separation behavior becomes tedious and expensive. Thus, standard thermodynamic models are used in the VLE prediction. Sometimes, exclusively data-driven models are also used in VLE prediction although this method too possesses drawbacks such as a trial and error approach in specifying the data-fitting function. For overcoming these difficulties, this paper employs a machine learning (ML) formalism namely "genetic programming (GP)" possessing certain attractive features for the VLE prediction. Specifically, three case studies have been performed wherein GP-based models have been developed using experimental data, for predicting the vapor phase composition of a ternary, and a group of non ideal binary systems. The inputs to models consists of three pure component attributes (acentric factor, critical temperature, and critical pressure), and as many intensive thermodynamic parameters (liquid phase composition, pressure, and temperature). A comparison of the VLE prediction and generalization performance of the GP-based models with the corresponding standard thermodynamic models reveals that the former class of models possess either superior or closely comparable performance vis-a-vis thermodynamic models. Noteworthy features of this study are: (i) a single GP-based model can predict VLE of a group of binary systems, and (ii) applicability of a GP-based model trained on an alcohol-acetate series data for its higher homolog. The VLE modeling approach exemplified here can be gainfully extended to other ternary and non-ideal binary systems, and for designing corresponding experiments in different pressure and temperature ranges.1 aPatil-Shinde, Veena1 aTambe, Sanjeev, S.  uhttps://library.ncl.res.in/content/genetic-programming-based-models-prediction-vapor-liquid-equilibrium