Application of machine learning algorithms to predict the performance of coal gasification process

[1]  Jianren Fan,et al.  A comprehensive study on estimating higher heating value of biomass from proximate and ultimate analysis with machine learning approaches , 2019 .

[2]  John C. Platt,et al.  Fast training of support vector machines using sequential minimal optimization, advances in kernel methods , 1999 .

[3]  Saptarshi Das,et al.  Artificial neural network based modelling approach for municipal solid waste gasification in a fluidized bed reactor , 2016, Waste management.

[4]  Hasan Sadikoglu,et al.  Comparison of the different artificial neural networks in prediction of biomass gasification products , 2019, International Journal of Energy Research.

[5]  B. Oboirien,et al.  Performance evaluation of gasification system efficiency using artificial neural network , 2020 .

[6]  Yuan Yuan,et al.  Measuring urban poverty using multi-source data and a random forest algorithm: A case study in Guangzhou , 2020 .

[7]  Adam P. Piotrowski,et al.  A comparison of methods to avoid overfitting in neural networks training in the case of catchment runoff modelling , 2013 .

[8]  Li Ma,et al.  A comparison of random forest and support vector machine approaches to predict coal spontaneous combustion in gob , 2019, Fuel.

[9]  Sanjiban Sekhar Roy,et al.  Prediction of Customer Satisfaction Using Naive Bayes, MultiClass Classifier, K-Star and IBK , 2016, SOFA.

[10]  Zeynep Ceylan Assessment of agricultural energy consumption of Turkey by MLR and Bayesian optimized SVR and GPR models , 2020 .

[11]  N. Altman An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression , 1992 .

[12]  Zeynep Ceylan Estimation of municipal waste generation of Turkey using socio-economic indicators by Bayesian optimization tuned Gaussian process regression , 2020, Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA.

[13]  Hossein Moayedi,et al.  Comprehensive preference learning and feature validity for designing energy-efficient residential buildings using machine learning paradigms , 2019, Appl. Soft Comput..

[14]  Dogukan Aksu,et al.  Hydrogen production via biomass gasification, and modeling by supervised machine learning algorithms , 2019, International Journal of Hydrogen Energy.

[15]  Geoff Holmes,et al.  Generating Rule Sets from Model Trees , 1999, Australian Joint Conference on Artificial Intelligence.

[16]  Zeynep Ceylan,et al.  Estimation of coal elemental composition from proximate analysis using machine learning techniques , 2020 .

[17]  Yoav Freund,et al.  The Alternating Decision Tree Learning Algorithm , 1999, ICML.

[18]  Muhammad Faheem,et al.  Autonomic performance prediction framework for data warehouse queries using lazy learning approach , 2020, Appl. Soft Comput..

[19]  R. Gujar,et al.  Prediction and validation of alternative fillers used in micro surfacing mix-design using machine learning techniques , 2019, Construction and Building Materials.

[20]  K. Pearson Contributions to the Mathematical Theory of Evolution. II. Skew Variation in Homogeneous Material , 1895 .

[21]  S Ramani,et al.  Intrusion Detection in Computer Networks using Lazy Learning Algorithm , 2018 .

[22]  James Geller,et al.  Data Mining: Practical Machine Learning Tools and Techniques - Book Review , 2002, SIGMOD Rec..

[23]  Furkan Elmaz,et al.  Data-driven identification and model predictive control of biomass gasification process for maximum energy production , 2020 .

[24]  J. R. Quinlan Learning With Continuous Classes , 1992 .

[25]  Sanjeev S. Tambe,et al.  Artificial intelligence-based modeling of high ash coal gasification in a pilot plant scale fluidized bed gasifier , 2014 .

[26]  Walter L. Ruzzo,et al.  Tree-size bounded alternation(Extended Abstract) , 1979, J. Comput. Syst. Sci..

[27]  V. Muralidharan,et al.  Fault diagnosis of self-aligning troughing rollers in belt conveyor system using k-star algorithm , 2019, Measurement.

[28]  Jianren Fan,et al.  Predictive single-step kinetic model of biomass devolatilization for CFD applications: A comparison study of empirical correlations (EC), artificial neural networks (ANN) and random forest (RF) , 2019, Renewable Energy.

[29]  D. Bui,et al.  Feature validity during machine learning paradigms for predicting biodiesel purity , 2020 .

[30]  C. Muraleedharan,et al.  Assessment of producer gas composition in air gasification of biomass using artificial neural network model , 2018 .

[31]  John G. Cleary,et al.  K*: An Instance-based Learner Using and Entropic Distance Measure , 1995, ICML.

[32]  S. Sánchez-Delgado,et al.  Predicting the effect of bed materials in bubbling fluidized bed gasification using artificial neural networks (ANNs) modeling approach , 2020 .

[33]  Furkan Elmaz,et al.  Predictive modeling of biomass gasification with machine learning-based regression methods , 2020 .

[34]  Sotiris Kotsiantis,et al.  Machine learning and nonlinear models for the estimation of fundamental period of vibration of masonry infilled RC frame structures , 2020 .

[35]  Biomass higher heating value prediction analysis by ANFIS, PSO-ANFIS and GA-ANFIS models , 2018, Issue 3.

[36]  Neil D. Lawrence,et al.  Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models , 2005, J. Mach. Learn. Res..

[37]  Hao Li,et al.  Noninvasive fracture characterization based on the classification of sonic wave travel times , 2020 .

[38]  L. Puigjaner,et al.  Identification of a pilot scale fluidised-bed coal gasification unit by using neural networks , 2000 .

[39]  D. Kibler,et al.  Instance-based learning algorithms , 2004, Machine Learning.

[40]  J. C. Bruno,et al.  Artificial neural network models for biomass gasification in fluidized bed gasifiers. , 2013 .

[41]  Vladimir Vapnik,et al.  Support-vector networks , 2004, Machine Learning.

[42]  Serol Bulkan,et al.  Prediction of medical waste generation using SVR, GM (1,1) and ARIMA models: a case study for megacity Istanbul , 2020, Journal of Environmental Health Science and Engineering.

[43]  D. Baruah,et al.  Artificial neural network based modeling of biomass gasification in fixed bed downdraft gasifiers , 2017 .

[44]  Özgün Yücel,et al.  An artificial intelligence based approach to predicting syngas composition for downdraft biomass gasification , 2018, Energy.

[45]  Nicola Maffulli,et al.  Artificial intelligence. A tool for sports trauma prediction. , 2020, Injury.

[46]  B. Kulkarni,et al.  Development of data-driven models for fluidized-bed coal gasification process , 2012 .

[47]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[48]  Indranil Pan,et al.  Multi-gene genetic programming based predictive models for municipal solid waste gasification in a fluidized bed gasifier. , 2015, Bioresource technology.

[49]  S. Tambe,et al.  Co-gasification of High Ash Coal–Biomass Blends in a Fluidized Bed Gasifier: Experimental Study and Computational Intelligence-Based Modeling , 2018, Waste and Biomass Valorization.

[50]  Aboul Ella Hassanien,et al.  A random forest classifier for lymph diseases , 2014, Comput. Methods Programs Biomed..

[51]  Stefan Kramer,et al.  Alternating model trees , 2015, SAC.

[52]  Jianhua Yan,et al.  Comparison of ANN (MLP), ANFIS, SVM, and RF models for the online classification of heating value of burning municipal solid waste in circulating fluidized bed incinerators. , 2017, Waste management.

[53]  S. Sathiya Keerthi,et al.  Improvements to the SMO algorithm for SVM regression , 2000, IEEE Trans. Neural Networks Learn. Syst..

[54]  Bilal Alatas,et al.  Fake news detection within online social media using supervised artificial intelligence algorithms , 2020 .