Recent advances and future prospects of thermochemical biofuel conversion processes with machine learning

[1]  Siming You,et al.  Bioenergy generation from thermochemical conversion of lignocellulosic biomass-based integrated renewable energy systems , 2023, Renewable and Sustainable Energy Reviews.

[2]  Jechan Lee,et al.  A sustainable strategy for organic waste upcycling: Concurrent production of energy and Li-ion battery anode from chicken litter , 2023, Energy.

[3]  Bachirou Guene Lougou,et al.  Catalytic hydrothermal carbonization of wet organic solid waste: A review. , 2023, The Science of the total environment.

[4]  Bao-chu Wang,et al.  Co-hydrothermal carbonization of sewage sludge and coal slime for clean solid fuel production: a comprehensive assessment of hydrochar fuel characteristics and combustion behavior , 2022, Biomass conversion and biorefinery.

[5]  E. Kwon,et al.  Machine learning and statistical analysis for biomass torrefaction: A review. , 2022, Bioresource technology.

[6]  Peter C. St. John,et al.  Physics-informed graph neural networks for predicting cetane number with systematic data quality analysis , 2022, Proceedings of the Combustion Institute.

[7]  V. Gupta,et al.  Synthesis of liquid biofuels from biomass by hydrothermal gasification: A critical review , 2022, Renewable and Sustainable Energy Reviews.

[8]  P. R. Yaashikaa,et al.  A review on biodiesel paroduction by algal biomass: Outlook on lifecycle assessment and techno-economic analysis , 2022, Fuel.

[9]  Y. Tsang,et al.  Effectiveness of CO2-mediated pyrolysis for the treatment of biodegradable plastics: A case study of polybutylene adipate terephthalate/polylactic acid mulch film , 2022, Journal of Cleaner Production.

[10]  Jung Yoon Seo,et al.  Production of biochar from crop residues and its application for biofuel production processes - An overview. , 2022, Bioresource technology.

[11]  Pil Rip Jeon,et al.  Review of the adsorption equilibria of CO2, CH4, and their mixture on coals and shales at high pressures for enhanced CH4 recovery and CO2 sequestration , 2022, Fluid Phase Equilibria.

[12]  Siming You,et al.  A comprehensive artificial neural network model for gasification process prediction , 2022, Applied Energy.

[13]  Siming You,et al.  Process optimization of biomass gasification with a Monte Carlo approach and random forest algorithm , 2022, Energy Conversion and Management.

[14]  Benedetto Nastasi,et al.  Techno-economic analysis of biogas production and use scenarios in a small island energy system , 2022, Energy.

[15]  Hyungseok Nam,et al.  Recent progress in the catalytic thermochemical conversion process of biomass for biofuels , 2022, Chemical Engineering Journal.

[16]  K. Mohanty,et al.  Hydrothermal liquefaction of biomass for bio-crude production: A review on feedstocks, chemical compositions, operating parameters, reaction kinetics, techno-economic study, and life cycle assessment , 2022, Fuel.

[17]  P. Jönsson,et al.  A machine learning model to predict the pyrolytic kinetics of different types of feedstocks , 2022, Energy Conversion and Management.

[18]  Y. Tong,et al.  Understanding and optimizing the gasification of biomass waste with machine learning , 2022, Green Chemical Engineering.

[19]  See-Hoon Lee,et al.  Techno-economic assessment of a solar-assisted biomass gasification process , 2022, Renewable Energy.

[20]  M. W. Anjum,et al.  An Integrated Framework of Data-driven, Metaheuristic, and Mechanistic Modeling Approach for Biomass Pyrolysis , 2022, Process Safety and Environmental Protection.

[21]  Xinyu Yang,et al.  Prediction of Product Yields Using Fusion Model from Co-pyrolysis of Biomass and Coal. , 2022, Bioresource technology.

[22]  Peide Liu,et al.  Prediction of Pyrolysis Kinetics of Biomass: New Insights from Artificial Intelligence-Based Modeling , 2022, International Journal of Chemical Engineering.

[23]  S. Phithakkitnukoon,et al.  Interpretable machine-learning model with a collaborative game approach to predict yields and higher heating value of torrefied biomass , 2022, Energy.

[24]  E. Epelle,et al.  Machine learning methods for modeling conventional and hydrothermal gasification of waste biomass: A review , 2022, Bioresource Technology Reports.

[25]  Moo Sun Hong,et al.  Compact neural network modeling of nonlinear dynamical systems via the standard nonlinear operator form , 2022, Comput. Chem. Eng..

[26]  H. Choi,et al.  Biomass Fast Pyrolysis Prediction Model through Data-based Prediction Models Coupling with CPFD Simulation , 2022, Journal of Analytical and Applied Pyrolysis.

[27]  See-Hoon Lee,et al.  Recent advances of hybrid solar - Biomass thermo-chemical conversion systems. , 2021, Chemosphere.

[28]  Yingru Zhao,et al.  A hybrid data-driven and mechanistic modelling approach for hydrothermal gasification , 2021, Applied Energy.

[29]  Wenguang Zhou,et al.  Machine learning prediction and optimization of bio-oil production from hydrothermal liquefaction of algae. , 2021, Bioresource technology.

[30]  Siming You,et al.  Machine learning methods for modelling the gasification and pyrolysis of biomass and waste , 2021, Renewable and Sustainable Energy Reviews.

[31]  Petar Sabev Varbanov,et al.  Sustainability assessment of biomethanol production via hydrothermal gasification supported by artificial neural network , 2021 .

[32]  Hyungseok Nam,et al.  Recent advances of thermochemical conversieon processes for biorefinery. , 2021, Bioresource technology.

[33]  Yun Huang,et al.  The role of machine learning to boost the bioenergy and biofuels conversion. , 2021, Bioresource technology.

[34]  L. Ricardez‐Sandoval,et al.  Machine Learning in Solid Heterogeneous Catalysis: Recent Developments, Challenges and Perspectives , 2021, Chemical Engineering Science.

[35]  J. Duo,et al.  A novel machine learning-based approach for prediction of nitrogen content in hydrochar from hydrothermal carbonization of sewage sludge , 2021 .

[36]  R. Dewil,et al.  The state of art on the prediction of efficiency and modeling of the processes of pollutants removal based on machine learning. , 2021, The Science of the total environment.

[37]  C. Cheng,et al.  Elucidating the effect of process parameters on the production of hydrogen-rich syngas by biomass and coal Co-gasification techniques: A multi-criteria modeling approach. , 2021, Chemosphere.

[38]  G. Rhee,et al.  Waste furniture gasification using rice husk based char catalysts for enhanced hydrogen generation. , 2021, Bioresource technology.

[39]  Hyunjun Lee,et al.  Machine learning based predictive model for methanol steam reforming with technical, environmental, and economic perspectives , 2021 .

[40]  Gopalakrishnan Kumar,et al.  Lignocellulosic biomass-based pyrolysis: A comprehensive review. , 2021, Chemosphere.

[41]  C. Tsekos,et al.  Estimation of lignocellulosic biomass pyrolysis product yields using artificial neural networks , 2021 .

[42]  Chiu-Yue Lin,et al.  Optimization of Hydrolysis-Acidogenesis Phase of Swine Manure for Biogas Production Using Two-Stage Anaerobic Fermentation , 2021, Processes.

[43]  M. Liu,et al.  Machine learning prediction of pyrolytic gas yield and compositions with feature reduction methods: Effects of pyrolysis conditions and biomass characteristics. , 2021, Bioresource technology.

[44]  Mary Biddy,et al.  Techno-economic analysis and life cycle assessment of a biorefinery utilizing reductive catalytic fractionation , 2021, Energy & environmental science.

[45]  Soteris A. Kalogirou,et al.  Machine learning technology in biodiesel research: A review , 2021, Progress in Energy and Combustion Science.

[46]  Alberto Passalacqua,et al.  Machine Learning Reduced Order Model for Cost and Emission Assessment of a Pyrolysis System , 2021 .

[47]  G. Rhee,et al.  Effect of eggshell- and homo-type Ni/Al2O3 catalysts on the pyrolysis of food waste under CO2 atmosphere. , 2021, Journal of environmental management.

[48]  Michael J. Gollner,et al.  Autonomous Kinetic Modeling of Biomass Pyrolysis using Chemical Reaction Neural Networks , 2021, ArXiv.

[49]  Dongda Zhang,et al.  Machine learning for biochemical engineering: A review , 2021 .

[50]  Chang-Ha Lee,et al.  Artificial neural network modelling for solubility of carbon dioxide in various aqueous solutions from pure water to brine , 2021, Journal of CO2 Utilization.

[51]  Kannappan Panchamoorthy Gopinath,et al.  Optimization of hydrothermal gasification process through machine learning approach: Experimental conditions, product yield and pollution , 2021, Journal of Cleaner Production.

[52]  Annan Zhou,et al.  Modelling of municipal solid waste gasification using an optimised ensemble soft computing model , 2021 .

[53]  Patrick K. Herring,et al.  Perspective—Combining Physics and Machine Learning to Predict Battery Lifetime , 2021, Journal of The Electrochemical Society.

[54]  Shribalaji Shenbagaraj,et al.  Gasification of food waste in supercritical water: An innovative synthesis gas composition prediction model based on Artificial Neural Networks , 2021 .

[55]  C. Cheng,et al.  Modeling the prediction of hydrogen production by co‐gasification of plastic and rubber wastes using machine learning algorithms , 2021, International Journal of Energy Research.

[56]  J. Arun,et al.  Optimization of hydrothermal liquefaction process through machine learning approach: process conditions and oil yield , 2021, Biomass Conversion and Biorefinery.

[57]  Timothy E H Allen,et al.  Machine Learning in Predictive Toxicology: Recent Applications and Future Directions for Classification Models. , 2020, Chemical research in toxicology.

[58]  Daniel Serrano,et al.  Tar prediction in bubbling fluidized bed gasification through artificial neural networks , 2020 .

[59]  Latifur Khan,et al.  MultiCon: A Semi-Supervised Approach for Predicting Drug Function from Chemical Structure Analysis , 2020, J. Chem. Inf. Model..

[60]  Jianzhong Lin,et al.  CFD-DEM Simulation of Biomass Pyrolysis in Fluidized-Bed Reactor with a Multistep Kinetic Scheme , 2020, Energies.

[61]  See-Hoon Lee,et al.  Numerical study of oxy-fuel combustion behaviors in a 2MWe CFB boiler , 2020, Korean Journal of Chemical Engineering.

[62]  P. Balasubramanian,et al.  Predicting algal biochar yield using eXtreme Gradient Boosting (XGB) algorithm of machine learning methods , 2020 .

[63]  Ranjit T. Koodali,et al.  Machine learning in experimental materials chemistry , 2020, Catalysis Today.

[64]  G. Baskar,et al.  Optimization and techno-economic analysis of biodiesel production from Calophyllum inophyllum oil using heterogeneous nanocatalyst. , 2020, Bioresource technology.

[65]  Qingang Xiong,et al.  CFD-based reduced-order modeling of fluidized-bed biomass fast pyrolysis using artificial neural network , 2020 .

[66]  A. Dalai,et al.  A review on subcritical and supercritical water gasification of biogenic, polymeric and petroleum wastes to hydrogen-rich synthesis gas , 2020 .

[67]  Hankwon Lim,et al.  Preliminary techno-economic analysis of biodiesel production over solid-biochar. , 2020, Bioresource technology.

[68]  George Em Karniadakis,et al.  Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations , 2020, Science.

[69]  Sin Yong Teng,et al.  Digestate evaporation treatment in biogas plants: A techno-economic assessment by Monte Carlo, neural networks and decision trees , 2019, Journal of Cleaner Production.

[70]  Jay H. Lee,et al.  Comparative Techno-Economic Analysis of Transesterification Technologies for Microalgal Biodiesel Production , 2019, Industrial & Engineering Chemistry Research.

[71]  Xiaonan Wang,et al.  Machine learning prediction of biochar yield and carbon contents in biochar based on biomass characteristics and pyrolysis conditions. , 2019, Bioresource technology.

[72]  Eliseu Monteiro,et al.  Numerical approaches and comprehensive models for gasification process: A review , 2019, Renewable and Sustainable Energy Reviews.

[73]  J. M. Lee,et al.  Economic analysis of a 600 mwe ultra supercritical circulating fluidized bed power plant based on coal tax and biomass co-combustion plans , 2019, Renewable Energy.

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

[75]  G. Murthy,et al.  Techno-economic and life cycle assessments of anaerobic digestion – A review , 2019, Biocatalysis and Agricultural Biotechnology.

[76]  Zhengang Liu,et al.  Combination of hydrothermal carbonization and oxy-fuel combustion process for sewage sludge treatment: Combustion characteristics and kinetics analysis , 2019, Fuel.

[77]  Xianjun Xing,et al.  Synthesis, characterization and machine learning based performance prediction of straw activated carbon , 2019, Journal of Cleaner Production.

[78]  G. Walker,et al.  ANN-Kriging hybrid model for predicting carbon and inorganic phosphorus recovery in hydrothermal carbonization. , 2019, Waste management.

[79]  F. Cucchiella,et al.  A techno-economic assessment of biogas upgrading in a developed market , 2019, Journal of Cleaner Production.

[80]  Y. Chao,et al.  A Study of the Production and Combustion Characteristics of Pyrolytic Oil from Sewage Sludge Using the Taguchi Method , 2018, Energies.

[81]  J. Görgens,et al.  A new insight into sugarcane biorefineries with fossil fuel co-combustion: Techno-economic analysis and life cycle assessment , 2018, Energy Conversion and Management.

[82]  Ilhan Mutlu,et al.  Activation energy prediction of biomass wastes based on different neural network topologies , 2018 .

[83]  J. Rintala,et al.  Techno-economic analysis of a power to biogas system operated based on fluctuating electricity price , 2018 .

[84]  Denis T. Ring,et al.  Techno-economic analysis of biogas upgrading via amine scrubber, carbon capture and ex-situ methanation , 2018 .

[85]  Jin Woo Kook,et al.  A reaction kinetic study of CO2 gasification of petroleum coke, coals and mixture , 2017, Korean Journal of Chemical Engineering.

[86]  Jim Pfaendtner,et al.  Application of machine learning to pyrolysis reaction networks: Reducing model solution time to enable process optimization , 2017, Comput. Chem. Eng..

[87]  Zhong-yang Luo,et al.  Lignocellulosic biomass pyrolysis mechanism: A state-of-the-art review , 2017 .

[88]  Ming Zhao,et al.  Progress in biofuel production from gasification , 2017 .

[89]  B. Chalermsinsuwan,et al.  Artificial neural network model for the prediction of kinetic parameters of biomass pyrolysis from its constituents , 2017 .

[90]  S. Yaman,et al.  Prediction of Calorific Value of Biomass from Proximate Analysis , 2017 .

[91]  Q. Wang,et al.  Pyrolysis products from industrial waste biomass based on a neural network model , 2016 .

[92]  Vijay Singh,et al.  Techno‐economic analysis of biodiesel and ethanol co‐production from lipid‐producing sugarcane , 2016 .

[93]  A. Çaǧlar,et al.  The pyrolysis process verification of hydrogen rich gas (H–rG) production by artificial neural network (ANN) , 2016 .

[94]  Young-Kwon Park,et al.  Rapid pyrolysis behavior of oleaginous microalga, Chlorella sp. KR-1 with different triglyceride contents , 2015 .

[95]  Sebastião Feyo de Azevedo,et al.  Hybrid semi-parametric modeling in process systems engineering: Past, present and future , 2014, Comput. Chem. Eng..

[96]  Ka In Wong,et al.  Modelling of diesel engine performance using advanced machine learning methods under scarce and exponential data set , 2013, Appl. Soft Comput..

[97]  Abdullah Akbulut,et al.  Techno-economic analysis of electricity and heat generation from farm-scale biogas plant: Çiçekdağı case study , 2012 .

[98]  A. Bridgwater Review of fast pyrolysis of biomass and product upgrading , 2012 .

[99]  Ioannis K. Kookos,et al.  Techno-economic analysis of a biodiesel production process from vegetable oils , 2009 .

[100]  See-Hoon Lee,et al.  The yields and composition of bio-oil produced from Quercus Acutissima in a bubbling fluidized bed pyrolyzer , 2008 .

[101]  A. F. Errazu,et al.  Techno-economic study of different alternatives for biodiesel production , 2008 .

[102]  H J van Can,et al.  An efficient model development strategy for bioprocesses based on neural networks in macroscopic balances. , 1997, Biotechnology and bioengineering.

[103]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1989, Math. Control. Signals Syst..

[104]  Zhen Wang,et al.  Prediction and evaluation of fuel properties of hydrochar from waste solid biomass: Machine learning algorithm based on proposed PSO–NN model , 2022, Fuel.

[105]  Jianren Fan,et al.  Predicting co-pyrolysis of coal and biomass using machine learning approaches , 2022, Fuel.

[106]  E. S. Go,et al.  Analysis of combustion characteristics using CPFD in 0.1 MWth oxy-fuel CFB , 2022, International Journal of Nanotechnology.

[107]  Jechan Lee,et al.  Marine waste upcycling—recovery of nylon monomers from fishing net waste using seashell waste-derived catalysts in a CO2-mediated thermocatalytic process , 2022, Journal of Materials Chemistry A.

[108]  Waste-to-Energy Approaches Towards Zero Waste , 2022 .

[109]  OUP accepted manuscript , 2022, Bioinformatics.

[110]  Jie Li,et al.  Multi-task prediction and optimization of hydrochar properties from high-moisture municipal solid waste: Application of machine learning on waste-to-resource , 2021 .

[111]  Michael D. Porter,et al.  Is hydrothermal treatment coupled with carbon capture and storage an energy-producing negative emissions technology? , 2020 .

[112]  Nuttapol Lerkkasemsan,et al.  Fuzzy logic-based predictive model for biomass pyrolysis , 2017 .

[113]  M. Studer,et al.  Biochemical Conversion Processes of Lignocellulosic Biomass to Fuels and Chemicals - A Review. , 2015, Chimia.

[114]  Kwangsu Kim,et al.  Long-term operation of biomass-to-liquid systems coupled to gasification and Fischer-Tropsch processes for biofuel production. , 2013, Bioresource technology.

[115]  M. Borowitzka,et al.  Standard Methods for Measuring Growth of Algae and Their Composition , 2013 .

[116]  Xavier Py,et al.  Contributions of hemicellulose, cellulose and lignin to the mass and the porous properties of chars and steam activated carbons from various lignocellulosic precursors. , 2009, Bioresource technology.