@article { ISI:000335400800021,
	title = {Prediction of higher heating value of solid biomass fuels using artificial intelligence formalisms},
	journal = {Bioenergy Research},
	volume = {7},
	number = {2},
	year = {2014},
	month = {JUN},
	pages = {681-692},
	publisher = {SPRINGER},
	address = {233 SPRING ST, NEW YORK, NY 10013 USA},
	abstract = {<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>},
	keywords = {Artificial intelligence, Artificial neural networks, Biomass fuels, genetic programming, Higher heating value, Multilayer perceptron},
	issn = {1939-1234},
	doi = {10.1007/s12155-013-9393-5},
	author = {Ghugare, Suhas B. and Tiwary, S. and Elangovan, V. and Tambe, S. 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 = {<p>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.</p>},
	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.}
}
