Supervised machine learning techniques in the desulfurization of oil products for environmental protection: A review

Abstract Desulfurization, known as the removal of sulfur from oil, is extremely important in the petroleum processing industry and in the environmental protection. Several oil-upgrading processes such as desulfurization and catalysts such as alumina loaded with molybdenum have been proposed to deal with the problem of removing sulfur-containing compounds from light oil. Thus, several parameters are required to be experimentally optimized which demands a lot of work including reagents. Advanced mathematical tools can be used to optimize the desulfurization process and to study the related factors. The modeling and simulation of the desulfurization process have been proposed in several studies in order to facilitate a better understanding of the process operations. Machine Learning (ML) is regarded as a promising methodological area to perform such optimization and analysis. This review describes the relevant methods for dealing with the applications of ML for desulfurization in oil. Although a good number of research papers have appeared in recent years, the application of ML for desulfurization is still a promising area of research. The review presents an overview of the ML methods and their categories in desulfurization. It discusses and compares the methods that employ ML to optimize the desulfurization process. The review also highlights the findings and possible research directions.

[1]  T. Simpson,et al.  Analysis of support vector regression for approximation of complex engineering analyses , 2005, DAC 2003.

[2]  Ali Mohebbi,et al.  Estimating the initial pressure, permeability and skin factor of oil reservoirs using artificial neural networks , 2006 .

[3]  Suk-Tae Seo,et al.  Support Vector Regression-based Model to Analyze Prognosis of Infants with Congenital Muscular Torticollis , 2010, Healthcare informatics research.

[4]  Mahdi Koolivand Salooki,et al.  Design of neural network for manipulating gas refinery sweetening regenerator column outputs , 2011 .

[5]  D. Basak,et al.  Support Vector Regression , 2008 .

[6]  Tawfik A. Saleh,et al.  Simultaneous adsorptive desulfurization of diesel fuel over bimetallic nanoparticles loaded on activated carbon , 2018 .

[7]  Tatyana Plaksina,et al.  Application of artificial intelligence techniques in the petroleum industry: a review , 2019, Artificial Intelligence Review.

[8]  Amir H. Mohammadi,et al.  Prediction of sour gas compressibility factor using an intelligent approach , 2013 .

[9]  K. Rostamizadeh,et al.  Oxidative Desulfurization of Fuel Oil: Modeling Based on Artificial Neural Network , 2008 .

[10]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.

[11]  Yasuaki Okamoto,et al.  A novel preparation-characterization technique of hydrodesulfurization catalysts for cleaner fuels , 2008 .

[12]  Jane Labadin,et al.  Ensemble machine learning: An untapped modeling paradigm for petroleum reservoir characterization , 2017 .

[13]  Jose M. Campos-Martin,et al.  Oxidative processes of desulfurization of liquid fuels , 2010 .

[14]  Zhang Haifeng,et al.  A novel acoustic emission detection module for leakage recognition in a gas pipeline valve , 2017 .

[15]  Ali Moradzadeh,et al.  Applications of artificial intelligence methods in prediction of permeability in hydrocarbon reservoirs , 2014 .

[16]  Tawfik A. Saleh,et al.  Adsorptive desulfurization of dibenzothiophene from fuels by rubber tyres-derived carbons: Kinetics and isotherms evaluation , 2016 .

[17]  Y. Uemichi,et al.  Catalytic Activities of Noble Metal Phosphides for Hydrogenation and Hydrodesulfurization Reactions , 2018 .

[18]  James Cussens,et al.  Using Inductive Logic Programming for Natural Language Processing , 1997 .

[19]  D. Pham,et al.  Statistical approach to normalization of feature vectors and clustering of mixed datasets , 2012, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[20]  Tawfik A. Saleh,et al.  Response surface methodology optimization of adsorptive desulfurization on nickel/activated carbon , 2017 .

[21]  C. G. Ma,et al.  Application of Artificial Neural Network in the Residual Oil Hydrotreatment Process , 2009 .

[22]  Martin J. Blunt,et al.  Development of artificial neural network models for predicting water saturation and fluid distribution , 2009 .

[23]  J. Fierro,et al.  A review of deep hydrodesulfurization catalysis , 1996 .

[24]  Hamid Taghavifar,et al.  Appraisal of artificial neural networks to the emission analysis and prediction of CO2, soot, and NOx of n-heptane fueled engine , 2016 .

[25]  Vimal Chandra Srivastava,et al.  An evaluation of desulfurization technologies for sulfur removal from liquid fuels , 2012 .

[26]  Saeid Shokri,et al.  Combination of data rectification techniques and soft sensor model for robust prediction of sulfur content in HDS process , 2016 .

[27]  P. Keshavarz,et al.  Development of an artificial neural network model for the prediction of hydrocarbon density at high-pressure, high-temperature conditions , 2013 .

[28]  Gade Pandu Rangaiah,et al.  Multiobjective Optimization in Distillation with Reactor-Side for Hydrodesulfurization Process of Diesel , 2012 .

[29]  Bashir Rahmanian,et al.  Application of experimental design approach and artificial neural network (ANN) for the determination of potential micellar-enhanced ultrafiltration process. , 2011, Journal of hazardous materials.

[30]  Tawfik A. Saleh,et al.  Nanocomposite of ZnO with montmorillonite for removal of lead and copper ions from aqueous solutions , 2017 .

[31]  A Saito,et al.  Introduction of formal debate into a postgraduate specialty track education programme in periodontics in Japan. , 2011, European journal of dental education : official journal of the Association for Dental Education in Europe.

[32]  Joaquín Pérez Ortega,et al.  A Data Preparation Methodology in Data Mining Applied to Mortality Population Databases , 2015, Journal of Medical Systems.

[33]  Yasuaki Okamoto,et al.  Novel Molecular Approaches to the Structure–Activity Relationships and Unique Characterizations of Co–Mo Sulfide Hydrodesulfurization Catalysts for the Production of Ultraclean Fuels , 2014 .

[34]  Enrique Arce-Medina,et al.  Artificial neural network modeling techniques applied to the hydrodesulfurization process , 2009, Math. Comput. Model..

[35]  Z. Boger,et al.  Deep hydrodesulfurization of atmospheric gas oil; Effects of operating conditions and modelling by artificial neural network techniques , 1996 .

[36]  Jeffrey J. P. Tsai,et al.  Machine learning applications in software engineering , 2005 .

[37]  A. Ya. Grigoriev,et al.  Classification of wear debris using a neural network , 1997 .

[38]  Ivana Lukec,et al.  Prediction of sulphur content in the industrial hydrotreatment process , 2008 .

[39]  E A Medina,et al.  ARTIFICIAL NEURAL NETWORK MODELING TECHNIQUES APPLIED TO THE HYDRODESULFURIZATION PROCESS , 2009 .

[40]  Ali Mohebbi,et al.  A new correlation based on artificial neural networks for predicting the natural gas compressibility factor , 2012 .

[41]  Wei Liang,et al.  Acoustic detection technology for gas pipeline leakage. , 2013 .

[42]  Mohan Liu,et al.  Prediction and analysis of the three major industries and residential consumption CO2 emissions based on least squares support vector machine in China , 2016 .

[43]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[44]  Shankar Narasimhan,et al.  Soft sensor design for hydrodesulfurization process using support vector regression based on WT and PCA , 2015 .

[45]  Moataz A. Ahmed,et al.  Machine Learning-Based Software Quality Prediction Models: State of the Art , 2013, 2013 International Conference on Information Science and Applications (ICISA).

[46]  Moataz A. Ahmed,et al.  Machine learning approaches for predicting software maintainability: a fuzzy-based transparent model , 2013, IET Softw..

[47]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[48]  Therese D. Pigott,et al.  A Review of Methods for Missing Data , 2001 .

[49]  Mark Daniel G. de Luna,et al.  Kinetics of sulfur removal in high shear mixing-assisted oxidative-adsorptive desulfurization of diesel , 2018 .

[50]  Ying Zheng,et al.  Simulation of hydrodesulfurization using artificial neural network , 2010 .

[51]  Utomo Utojo,et al.  Unification of neural and statistical modeling methods that combine inputs by linear projection , 1998 .

[52]  Saeid Shokri,et al.  High reliability estimation of product quality using support vector regression and hybrid meta-heuristic algorithms , 2014 .

[53]  Jane Labadin,et al.  Applied Soft Computing , 2014 .

[54]  Sotiris B. Kotsiantis,et al.  Supervised Machine Learning: A Review of Classification Techniques , 2007, Informatica.

[55]  Saeid Shokri,et al.  Improvement of the prediction performance of a soft sensor model based on support vector regression for production of ultra-low sulfur diesel , 2015, Petroleum Science.

[56]  Lei Liu,et al.  Direct synthesis of ordered mesoporous carbons. , 2013, Chemical Society reviews.

[57]  Sunday Olusanya Olatunji,et al.  Estimation of surface energies of hexagonal close packed metals using computational intelligence technique , 2015, Appl. Soft Comput..

[58]  Ashutosh Kumar Singh,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2010 .

[59]  Tawfik A. Saleh,et al.  Alumina-carbon nanofiber composite as a support for MoCo catalysts in hydrodesulfurization reactions , 2018, Chemical Engineering Journal.

[60]  I. Babich.  Science and technology of novel processes for deep desulfurization of oil refinery streams: a review☆ ☆ , 2003 .

[61]  Tawfik A. Saleh,et al.  Influence of acidic and basic treatments of activated carbon derived from waste rubber tires on adsorptive desulfurization of thiophenes , 2016 .

[62]  Saeid Shokri,et al.  INTEGRATING PRINCIPAL COMPONENT ANALYSIS AND VECTOR QUANTIZATION WITH SUPPORT VECTOR REGRESSION FOR SULFUR CONTENT PREDICTION IN HDS PROCESS , 2014 .

[63]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[64]  Sunday Olusanya Olatunji,et al.  Estimation of physical, mechanical and hydrological properties of permeable concrete using computational intelligence approach , 2016, Appl. Soft Comput..

[65]  Thomas R. Kurfess,et al.  Detection of Wheel and Workpiece Contact/Release in Reciprocating Surface Grinding , 2003 .

[66]  Rosemary Norman,et al.  Neural network modelling of high pressure CO2 corrosion in pipeline steels , 2018, Process Safety and Environmental Protection.

[67]  Sam Ameri,et al.  Application of artificial neural networks for reservoir characterization with limited data , 2005 .

[68]  Tarek Helmy,et al.  A Machine Learning-Based Approach to Estimate the CPU-Burst Time for Processes in the Computational Grids , 2015, 2015 3rd International Conference on Artificial Intelligence, Modelling and Simulation (AIMS).

[69]  Tawfik A. Saleh,et al.  Adsorptive desulfurization of thiophene, benzothiophene and dibenzothiophene over activated carbon manganese oxide nanocomposite: with column system evaluation , 2017 .

[70]  Victoria J. Hodge,et al.  A Survey of Outlier Detection Methodologies , 2004, Artificial Intelligence Review.

[71]  Jeffrey J. P. Tsai,et al.  Machine Learning and Software Engineering , 2004, Software Quality Journal.

[72]  Tawfik A. Saleh,et al.  Effects of bimetallic Ce/Fe nanoparticles on the desulfurization of thiophenes using activated carbon , 2017 .

[73]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.