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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi The Journal of Pure and Applied Chemistry Research
Uswatun Hasanah
Brawijaya University
Chemical Engineering, Chemistry & Bioengineering Chemistry Materials Science & Nanotechnology Mathematics Medicine & Pharmacology

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Synthesis and Activation Study of Iron (Fe) Based Fischer Tropsch (FT) Catalyst Using Sol-gel Method Muchammad Zainul Anwar; Rachmat Triandi Tjahjanto; Uswatun Hasanah
The Journal of Pure and Applied Chemistry Research Vol 8, No 3 (2019): Edition September-December 2019
Publisher : Chemistry Department, The University of Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jpacr.2019.008.03.480

Abstract

As oil consumption increases from year to year, efforts need to be made to increase energy reserves by developing new renewable energy. One way to develop energy sources is by the synthesis Fischer Tropsch (FT). FT is a synthetic gas conversion reaction (mixture of CO and H2) into a long chain hydrocarbon mixture. The FT reaction requires a catalyst called the FT catalyst. So far, many studies that examine the effectiveness of catalysts in converting synthesis gas into long chain hydrocarbons, but rarely information about the composition of the phases that exist on the surface of the catalyst. To study about it, we synthesized FT catalysts at various variations of calcination temperature. Fe(NO3)3 as a precursor and Cu(NO3)2 as promoter (20:1) used in this study. The calcination temperature used are 300, 500, and 700°C. Characterization and analysis of catalysts were formed with XRD and SEM-EDX. Calcined catalysts were activated using CO2 and H2 gas and then re-characterized with XRD and SEM-EDX. Calcination results the formation of an iron oxide phase, while activation results the formation of iron carbide and zero Fe phases.
Robust PCA Using MCD and MM Estimators in MARS A Simulation Study Uswatun Hasanah; Solimun Solimun; Atiek Iriany
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 11, No 2 (2026): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v11i2.41547

Abstract

Multivariate Adaptive Regression Splines (MARS) models nonlinear relationships through adaptive basis functions but remain sensitive to outliers in the predictor variables. Existing robust extensions of MARS primarily address response outliers, while the few studies integrating Robust Principal Component Analysis (RPCA) with MARS use RPCA only for dimension reduction without comparing robust estimators. This study evaluates RPCA as a robust predictor transformation and systematically compares two robust covariance estimatorsthe Minimum Covariance Determinant (MCD) and the MM-estimatorwithin the RPCA-MARS framework. A full factorial simulation with 100 replications per condition covered 45 conditions: five sample sizes (n = 50, 100, 200, 500, 1000), three outlier proportions (5%, 10%, 25%), and three MARS interaction levels (1, 2, 3) with eight predictor variables. Outliers were extreme values in a specified proportion of predictor observations. Performance was measured by Root Mean Square Error (RMSE). For analysis, the 45 conditions were collapsed into 15 scenarios by selecting the interaction level with the minimum RMSE for each sample size and outlier proportion. The MM estimator outperformed the MCD estimator in 8 of 15 scenarios, achieving lower RMSE under moderate-to-high outlier contamination (10%25%) with moderate sample sizes (n = 100500). MCD performed better in the remaining 7 scenarios: under low contamination (5%) at n 200 and n 1000, and across all contamination levels at n = 1000. MCD showed higher variability at small samples with moderate-to-high contamination, while MM produced tighter confidence intervals and lower standard deviations. Within the RPCA-MARS framework, MM is recommended for moderately sized, highly contaminated data, while MCD is preferable under low contamination or in large-scale settings.
Co-Authors Atiek Iriany Muchammad Zainul Anwar Rachmat Triandi Tjahjanto Solimun, Solimun