Prediction of Material Surface Area using Multiple Linear Regression Algorithm with Independent Variables Time, T emperature and Quantity

Dinesh Kumar; Poonam Kumari; Man Vir Singh; Sarita Devi; Yogesh Kumar

doi:10.17485/IJST/v16i44.2624

Article

Prediction of Material Surface Area using Multiple Linear Regression Algorithm with Independent Variables Time, T emperature and Quantity

VIEWS 246
PDF 53

Indian Journal of Science and Technology

DOI: 10.17485/IJST/v16i44.2624

Year: 2023, Volume: 16, Issue: 44, Pages: 4048-4053

Original Article

Prediction of Material Surface Area using Multiple Linear Regression Algorithm with Independent Variables Time, T emperature and Quantity

Dinesh Kumar¹, Poonam Kumari¹, Man Vir Singh², Sarita Devi³, Yogesh Kumar^4*

¹Department of Mathematics, Singhania University, Jhunjhunu, 333515, Rajasthan, India
²Department of Chemistry, Dev Bhoomi University, Uttarakhand, 248007, India
³Department of Chemistry, Govt. College Sec-12, Palwal, 121102, Haryana, India
⁴Department of Physics, Govt. College Sec-12, Palwal, 121102, Haryana, India

*Corresponding Author
Email: [email protected]

Received Date:16 October 2023, Accepted Date:28 October 2023, Published Date:26 November 2023

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objective: In this paper, the surface area of MnO2 was predicted by multiple linear regression algorithm (MLRA) in python Jupyter notebook with accuracy and best correlation between parameters. Methods: A data set was collected from different recent research papers. The dataset underwent data processing and factor analysis, involving the removal of unnecessary data. The surface area was predicted by the different experimentally synthesis parameters (Temperature, Material quantity, reaction time and experimental evaluated surface area). For prediction of surface area the total data set of 11 different research pater data was divided in two part as 72% for training dataset and 28% kept for test dataset. Findings: The surface area of the material can be enhanced from 92 to 196.67 m2/g by changing the synthesis parameters during hydrothermal process. For prediction, the best relationship was found between temperature and surface area in heatmap. The predictions yielded accuracy levels was 94%. Novelty: The surface area of MnO2 was first time predicted by MLRA with high accuracy by tuning the hydrothermally synthesis parameters.

Keywords: Multiple Linear Regression Algorithm (MLRA), Machine Learning, Python, Heatmap, Prediction Of Surface Area

References

Ulusoy U. A Review of Particle Shape Effects on Material Properties for Various Engineering Applications: From Macro to Nanoscale. Minerals. 2023;13(1):91. Available from: https://doi.org/10.3390/min13010091
Rai PK. Novel adsorbents in remediation of hazardous environmental pollutants: Progress, selectivity, and sustainability prospects. Cleaner Materials. 2022;3:100054. Available from: https://doi.org/10.1016/j.clema.2022.100054
Gupta MK, Kumar Y, Sonnathi N, Sharma SK. Synthesis of MnO2 nanostructure and its electrochemical studies with ratio optimization of ZnO. Ionics. 2023;29(7):2959–2968. Available from: https://doi.org/10.1007/s11581-023-04998-w
Komal, Kumar A, Kumar Y, Shukla VK. Parthenium hysterophorus derived activated carbon for EDLC device application. Journal of Materials Science: Materials in Electronics. 2023;34(27):1880. Available from: https://doi.org/10.1007/s10854-023-11309-6
Rani S, Bansal L, Tanwar M, Bhatia R, Kumar R, Sameera I. Role of precursor concentration in tuning the electrochemical performance of MoS2 nanoflowers. Materials Science and Engineering. 2023;292:116436. Available from: https://doi.org/10.1016/j.mseb.2023.116436
Morey GW. Hydrothermal Synthesis. Journal of the American Ceramic Society. 1953;36(9):279–285. Available from: https://doi.org/10.1111/j.1151-2916.1953.tb12883.x
Kumar Y, Uke SJ, Kumar A, Merdikar SP, Gupta M, Thakur AK, et al. Triethanolamine–ethoxylate (TEA-EO) assisted hydrothermal synthesis of hierarchical β-MnO<sub>2</sub> nanorods: effect of surface morphology on capacitive performance. Nano Express. 2021;2(4):040008. Available from: https://doi.org/10.1088/2632-959X/abef21
Selvaratnam B, Koodali RT. Machine learning in experimental materials chemistry. Catalysis Today. 2021;371:77–84. Available from: https://doi.org/10.1016/j.cattod.2020.07.074
Hunter-Zinck H, Siqueira AFD, Vásquez VN, Barnes R, Martinez CC. Ten simple rules on writing clean and reliable open-source scientific software. PLOS Computational Biology. 2021;17(11):e1009481. Available from: https://doi.org/10.1371/journal.pcbi.1009481
Raschka S, Patterson J, Nolet C. Machine Learning in Python: Main Developments and Technology Trends in Data Science, Machine Learning, and Artificial Intelligence. Information. 2020;11(4):193. Available from: https://doi.org/10.3390/info11040193
Dhaval B, Deshpande A. Short-term load forecasting with using multiple linear regression. International Journal of Electrical and Computer Engineering (IJECE). 2020;10(4):3911. Available from: https://doi.org/10.3390/info11040193
Rashid M, Bari BS, Yusup Y, Kamaruddin MA, Khan N. A Comprehensive Review of Crop Yield Prediction Using Machine Learning Approaches With Special Emphasis on Palm Oil Yield Prediction. IEEE Access. 2021;9:63406–63439. Available from: https://doi.org/10.1109/ACCESS.2021.3075159
Ibañez SC, Monterola CP. A Global Forecasting Approach to Large-Scale Crop Production Prediction with Time Series Transformers. Agriculture. 2023;13(9):1855. Available from: https://doi.org/10.3390/agriculture13091855
Vishweshwar S, Meti S, Champa BV, Nagaraja M. Climate Based Coconut Yield Analysis in Chanrayapatna Taluk of Hassan District of Karnataka, India. International Journal of Current Microbiology and Applied Sciences. 2019;8(07):2867–2877. Available from: https://doi.org/10.20546/ijcmas.2019.807.357
Prasetyo A, Salahuddin S, Amirullah A. Prediksi Produksi Kelapa Sawit Menggunakan Metode Regresi Linier Berganda. Jurnal Infomedia. 2021;6(2):76. Available from: https://doi.org/10.30811/jim.v6i2.2343
Sahin G, Isik G, Sark WGJHMV. Predictive modeling of PV solar power plant efficiency considering weather conditions: A comparative analysis of artificial neural networks and multiple linear regression. Energy Reports. 2023;10:2837–2849. Available from: https://doi.org/10.1016/j.egyr.2023.09.097
Ahmed HAY, Mohamed SWA. Rainfall Prediction using Multiple Linear Regressions Model. 2020 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE). 2021;p. 1–5. Available from: https://doi.org/10.1109/ICCCEEE49695.2021.9429650
Harun GS. Multiple Linear Regression Based Analysis of Weather Data for Precipitation and Visibility Prediction. In: Communications in Computer and Information Science. (pp. 60-71) Springer Nature Switzerland. 2023.
Bloice MD, Holzinger A. A Tutorial on Machine Learning and Data Science Tools with Python. In: Lecture Notes in Computer Science. (pp. 435-480) Springer International Publishing. 2016.
Uke SJ, Chaudhari GN, Bodade AB, Mardikar SP. Morphology dependant electrochemical performance of hydrothermally synthesized NiCo2O4 nanomorphs. Materials Science for Energy Technologies. 2020;3:289–298. Available from: https://doi.org/10.1016/j.mset.2019.11.004
Zhao JG, Yin JZ, Yang SG. Hydrothermal synthesis and magnetic properties of α-MnO2 nanowires. Materials Research Bulletin. 2012;47(3):896–900. Available from: https://doi.org/10.1016/j.materresbull.2011.11.023
Wang C, Bongard HJ, Weidenthaler C, Wu Y, Schüth F. Design and Application of a High-Surface-Area Mesoporous δ-MnO2 Electrocatalyst for Biomass Oxidative Valorization. Chemistry of Materials. 2022;34(7):3123–3132. Available from: https://doi.org/10.1021/acs.chemmater.1c04223
Li D, Wu X, Chen Y. Synthesis of Hierarchical Hollow MnO2 Microspheres and Potential Application in Abatement of VOCs. The Journal of Physical Chemistry . 2013;117(21):11040–11046. Available from: https://doi.org/10.1021/jp312745n
Subramanian V, Zhu H, Vajtai R, Ajayan PM, Wei B. Hydrothermal Synthesis and Pseudocapacitance Properties of MnO2 Nanostructures. The Journal of Physical Chemistry B. 2005;109(43):20207–20214. Available from: https://doi.org/10.1021/jp0543330
Su D, Ahn HJ, Wang G. Hydrothermal synthesis of α-MnO2 and β-MnO2 nanorods as high capacity cathode materials for sodium ion batteries. Journal of Materials Chemistry A. 2013;1(15):4845. Available from: https://doi.org/10.1039/C3TA00031A
Chen K, Noh YD, Li K, Komarneni S, Xue D. Microwave–Hydrothermal Crystallization of Polymorphic MnO2 for Electrochemical Energy Storage. The Journal of Physical Chemistry C. 2013;117(20):10770–10779. Available from: https://doi.org/10.1021/jp4018025
Kumar Y, Chopra S, Gupta A, Kumar Y, Uke SJ, Mardikar SP. Low temperature synthesis of MnO2 nanostructures for supercapacitor application. Materials Science for Energy Technologies. 2020;3:566–574. Available from: https://doi.org/10.1016/j.mset.2020.06.002
Li L, Nan C, Lu J, Peng Q, Li Y. α-MnO2 nanotubes: high surface area and enhanced lithium battery properties. Chemical Communications. 2012;48(55):6945. Available from: https://doi.org/10.1039/C2CC32306K
Rong S, Bao-Wen Z. The research of regression model in machine learning field. MATEC Web of Conferences. 2018;176:01033. Available from: https://doi.org/10.1051/matecconf/201817601033
Yağcı M. Educational data mining: prediction of students' academic performance using machine learning algorithms. Smart Learning Environments. 2022;9(1):11. Available from: https://doi.org/10.1186/s40561-022-00192-z
Nurazizah S, Winarno S. Prediction of Catfish Yield To Fulfill Community Needs Using Multiple Linear Regression Algorithm Method. Devotion Journal of Community Service. 2022;3(13):2146–2153. Available from: https://doi.org/10.36418/dev.v3i13.270
Dabhade P, Agarwal R, Alameen KP, Fathima AT, Sridharan R, Gopakumar G. Educational data mining for predicting students’ academic performance using machine learning algorithms. Materials Today: Proceedings. 2021;47:5260–5267. Available from: https://doi.org/10.1016/j.matpr.2021.05.646
Srivastava AK, Safaei N, Khaki S, Lopez G, Zeng W, Ewert F, et al. Winter wheat yield prediction using convolutional neural networks from environmental and phenological data. Scientific Reports. 2022;12(1):3215. Available from: https://doi.org/10.1038/s41598-022-06249-w
Musić A, Telalović JH, Đulović D. The Influence of Stringency Measures and Socio-Economic Data on COVID-19 Outcomes. In: HTJ, KM., eds. Communications in Computer and Information Science. (Vol. 2021, pp. 39-54) Springer International Publishing. 2021.

Copyright

© 2023 Kumar et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)