Decision tree model to efficiently optimize the process conditions of carbonaceous mesophase prepared with coal tar

Zhou Chunru; Wu Peng; Xu Xinyuan; Song Weina

doi:10.1007/s42823-022-00430-x

Decision tree model to efficiently optimize the process conditions of carbonaceous mesophase prepared with coal tar

Carbon Letters
Abbr : Carbon Lett.
2023, 33(2), pp.419-429
DOI : 10.1007/s42823-022-00430-x
Publisher : Korean Carbon Society
Research Area : Natural Science > Natural Science General > Other Natural Sciences General
Received : August 29, 2022
Accepted : October 25, 2022
Published : March 1, 2023

Zhou Chunru ¹, Wu Peng ¹, Xu Xinyuan ², Song Weina ²

¹Heilongjiang University of Science and Technology
²College of Environmental and Chemical Engineering, Heilongjiang University of Science and Technology

Accredited

ABSTRACT

It is difficult to optimize the process parameters of directly preparing carbonaceous mesophase (CMs) by solvothermal method using coal tar as raw material. To solve this problem, a Decision Tree model for CMs preparation (DTC) was established based on the relationship between the process parameters and the yields of CMs. Then, the importance of variables in the preparation process for CMs was predicted, the relationship between experimental conditions and yields was revealed, and the preparation process conditions were also optimized by the DTC. The prediction results showed that the importance of the variables was raw material type, solvothermal temperature, solvothermal time, solvent amount, and additive type in order. And the optimized reaction conditions were as follows: coal tar was pretreated by decompress distillation and centrifugation, the solvent amount was 50.0 ml, the solvothermal temperature was 230 °C, and the reaction time was 5 h. These prediction results were consistent with the actual experimental results, and the error between the predicted yields and the actual yields was about − 1.1%. Furthermore, the prediction error of DTC method was within the acceptable range when the data sample sets were reduced to 100 sets. These results proved that the established DTC for chemical process optimization can effectively lessen the experimental workload and has high application value.

KEYWORDS

Decision tree model Process optimization Carbonaceous mesophases Coal tar Machine learning technology

Citation status

This is the result of checking the information with the same ISSN, publication year, volume, and start page between the WoS and the KCI journals. (as of 2024-07-26)

Total Citation Counts(KCI+WOS) (4) This is the number of times that the duplicate count has been removed by comparing the citation list of WoS and KCI.

Scopus Citation Counts (3) This is the result of checking the information with the same ISSN, publication year, volume, and start page between articles in KCI and the SCOPUS journals. (as of 2024-10-01)

* References for papers published after 2023 are currently being built.

[journal] Zhang Y / 2020 / Anode performance of NaOH-etched mesocarbon microbeads for sodium-ion batteries / Mater Sci Eng B 264(114934) : 1~6

[journal] Kwak Cheol Hwan / 2021 / Waste plastic for increasing softening point of pitch and specific surface area of activated carbon based on the petroleum residue / Carbon Letters / 한국탄소학회 31(5) : 991~1000

[journal] 고승현 / 2020 / Preparation of petroleum-based mesophase pitch toward cost-competitive high-performance carbon fibers / Carbon Letters / 한국탄소학회 30(1) : 35~44

[journal] Sultanov F. R. / 2020 / Microwave-enhanced chemical vapor deposition graphene nanoplatelets-derived 3D porous materials for oil/water separation / Carbon Letters / 한국탄소학회 30(1) : 81~92

[journal] Mudi K. Y. / 2019 / Optimization of bi-metallic (Fe–Co) catalyst on kaolin support for carbon nanofiber growth in a CVD reactor / Carbon Letters / 한국탄소학회 29(3) : 233~253

[journal] Wang Y / 2021 / Binary doping of nitrogen and phosphorus into porous carbon : a novel di-functional material for enhancing CO2 capture and supercapacitance / J Mater Sci Technol 99(04) : 73~81

[journal] Jiang J / 2022 / Sulfur-doped g-C3N4/g-C3N4 isotype step-scheme heterojunction for photocatalytic H2evolution / J Mater Sci Technol 118 : 15~24

[journal] Shang J / 2018 / Preparation and characterization of wood-based pre-oxidized precursors and activated carbon microspheres / BioResources 13(4) : 7983~7997

[journal] Wang Y / 2019 / Carbon microspheres prepared from the hemicelluloses-rich pre-hydrolysis liquor for contaminant removal / Carbohyd Polym 213 : 296~303

[journal] Terao T / 2020 / A machine learning approach to analyze the structural formation of soft matter via image recognition / Soft Mater 18(1/4) : 215~227

[journal] Jia X / 2020 / Mapping soil pollution by using drone image recognition and machine learning at an arsenic-contaminated agricultural field / Environ Pollut 270 : 1~10

[journal] Zhang B / 2019 / A lightweight and discriminative model for remote sensing scene classification with multidilation pooling module / IEEE J Select Topics Appl Earth Observ Remote Sens 99 : 1~18

[journal] Michael G / 2019 / Detection of non-gaussian behavior using machine learning techniques : a case study on the Lorenz 63 model / J Geophys Res D Atmos 125(2) : e2019~

[journal] Han C / 2020 / The rise of aqueous rechargeable batteries with organic electrode materials / J Mater Chem A Mater Energy Sustain 8(31) : 15479~15512

[journal] 이동규 / 2021 / Carbon fiber coating with MWCNT in the presence of polyethyleneimine of different molecular weights and the effect on the interfacial shear strength of thermoplastic and thermosetting carbon fiber composites / Carbon Letters / 한국탄소학회 31(3) : 407~417

[journal] Raccuglia P / 2016 / Machine-learning-assisted materials discovery using failed experiments / Nature 533(7601) : 73~76

[journal] Hussain S / 2019 / Prediction of drug target sensitivity in cancer cell lines using apache spark / J Comput Biol 26(8) : 882~889

[journal] Zhang Y / 2021 / Machine learning glass transition temperature of polyacrylamides using quantum chemical descriptors / Polym Chem 12(6) : 843~851

[journal] Chang CK / 2019 / Decision tree rules for insulation condition assessment of pre-molded power cable joints with artificial defects / IEEE Trans Dielectr Electr Insul 26(5) : 1636~1644

[journal] Guo A / 2020 / Preparation of mesocarbon microbeads as anode material for lithium-ion battery by thermal polymerization of a distillate fraction from an FCC slurry oil after hydrofining with suspended catalyst / Fuel 276(15) : 1~11

[journal] Gao XY / 2020 / Magneto-optical/ferromagnetic-material computation : Bcklund transformations, bilinear forms and N solitons for a generalized(3+1)-dimensional variable-coefficient modified Kadomtsev-Petviashvili system / Appl Math Lett 111 : 106627~106627

[journal] Luo H / 2021 / SDTR : soft decision tree regressor for tabular data / IEEE Access 9 : 55999~56011

[journal] Majidifard H / 2020 / Deep machine learning approach to develop a new asphalt pavement condition index / Constr Build Mater 247 : 118513~

[confproc] Katamaneni M / 2018 / Implementing of decision tree algorithm using R-studio and java / 2018 Fourth International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEICB) : 1~5

[journal] Yuan M / 2020 / Preparation of pitch-based carbon microbeads by a simultaneous spheroidization and stabilization process for lithium-ion batteries / Chem Eng J 400 : 125948~

[journal] Azhagapillai Prabhu / 2021 / Surface functionalization methodologies on activated carbons and their benzene adsorption / Carbon Letters / 한국탄소학회 31(3) : 419~426

[journal] 정현택 / 2020 / Electrochemical performances of semi-transparent and stretchable supercapacitor composed of nanocarbon materials / Carbon Letters / 한국탄소학회 30(1) : 55~61

KJCKorea
Journal Central

Carbon Letters 2023 KCI Impact Factor : 1.35 KCI-WoS Impact Factor : 6.04

Decision tree model to efficiently optimize the process conditions of carbonaceous mesophase prepared with coal tar

ABSTRACT

KEYWORDS

Citation status

This is the result of checking the information with the same ISSN, publication year, volume, and start page between the WoS and the KCI journals. (as of 2024-07-26)

Total Citation Counts(KCI+WOS) (4) This is the number of times that the duplicate count has been removed by comparing the citation list of WoS and KCI.

Scopus Citation Counts (3) This is the result of checking the information with the same ISSN, publication year, volume, and start page between articles in KCI and the SCOPUS journals. (as of 2024-10-01)

* References for papers published after 2023 are currently being built.

Carbon Letters 2023 KCI Impact Factor : 1.35 KCI-WoS Impact Factor : 6.04

Decision tree model to efficiently optimize the process conditions of carbonaceous mesophase prepared with coal tar

ABSTRACT

KEYWORDS

Statistics

Tools

Issue List

Citation status

KCI Citation Counts (1)

WoS Citation Counts (3) This is the result of checking the information with the same ISSN, publication year, volume, and start page between the WoS and the KCI journals. (as of 2024-07-26)

Total Citation Counts(KCI+WOS) (4) This is the number of times that the duplicate count has been removed by comparing the citation list of WoS and KCI.

Scopus Citation Counts (3) This is the result of checking the information with the same ISSN, publication year, volume, and start page between articles in KCI and the SCOPUS journals. (as of 2024-10-01)

REFERENCES (27) * References for papers published after 2023 are currently being built.

Search PDF

Citation

This is the result of checking the information with the same ISSN, publication year, volume, and start page between the WoS and the KCI journals. (as of 2024-07-26)

* References for papers published after 2023 are currently being built.