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Enzymatic Hydrolysis of Sorghum Bagasse to Readily Fermentable Sugar for Bioethanol Soeprijanto; Katherin Indriawati; Nurlita Abdulgani
Jurnal Rekayasa Proses Vol 8, No 1 (2014)
Publisher : Departemen Teknik Kimia Fakultas Teknik Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (395.735 KB) | DOI: 10.22146/jrekpros.5019

Abstract

Produksi gula dari bagase sorghum menggunakan enzim selulase dan selobiase dilakukan dalam kultur batch. Tujuan percobaan adalah mempelajari pengaruh beban bagase sorghum dan waktu pretreatment kapur terhadap produksi gula dan yield gula. Pretreatment kapur dilakukan dalam 1000 ml labu leher tiga dengan beban kapur 0,1 g Ca(OH)2 /g sorghum bagasse dan ditambah dengan 500 ml air distilasi. Pengaruh waktu pretreatment (1, 2, 3, dan 4 jam) pada suhu 100°C dan pengaruh beban biomassa (5, 10, 15 % w/v) pada hidrolisis enzim untuk menghasilkan gula. Hasil penelitian menunjukkan bahwa konsentrasi gula maksimum dicapai sebesar 28,04 g/l dalam waktu pretreatment 4 jam; dan yield maksimum gula diperoleh 0,4 g glukose/ g biomassa dengan beban biomassa 5% (w/v). Kata kunci: Bagase sorghum, selulase, selobiase, hidrolisis enzim, kultur batch, pretreatment kapur Production of sugar from sorghum bagasse using enzyme of cellulase and cellobiase in a batch culture was conducted. The purpose of this experiment was to study of the effect of sorghum baggase loadings and lime pretreatment time on production and yield of sugar. Lime pretreatment was carried out in a 1000 ml three-neck flask with a lime loading of 0.1 g Ca(OH)2 /g sorghum bagasse and added with 500 ml distilled water. Effects of pretreatment time course (1, 2, 3, and 4 h) at temperature of 100°C and biomass loading (5, 10, 15 % w/v) were observed to produce sugar. The results showed that maximum concentration of sugar obtained was 28.04 g/l with a pretreatment time of 4 h; and the maximum yield of sugar obtained was 0.4 g glucose/ g biomass with a biomass loading of 5% (w/v). Keywords: Batch culture, cellulose, cellobiase, Enzymatic hydrolysis, ime pretreatment, sorghum bagasse
Design of Fault Tolerant Control on Wind Turbine Speed Control Based on Bias Fault Estimation Method with Optimization l_0 Norm Constraint Putri Yeni Aisyah; Katherin Indriawati
IPTEK The Journal of Engineering Vol 7, No 2 (2021)
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j23378557.v7i2.a9033

Abstract

The availability of onshore wind power plant systems (PLTB) reaches 98%, but the maintenance costs required are still very high for the wind turbine generator system. Meanwhile, the availability of offshore PLTB is decreased by 60% due to the main cause of damage to some components in wind turbine systems. This study proposes the use of fault estimation methods of wind turbine system components in fault tolerant control (FTC) strategy. The error estimation method is build using the ℓ0 norm constraint optimization. The optimization formulation with ℓ0 norm constraint is derived by applying the compressed sensing technique so that the estimation of the bias error can be used to estimate the error of several components by a single observer. This answers the observability issues encountered in single observer use cases. The proposed implementation of observers with the FTC results in better response characteristics when compared to systems without FTC. Response characteristics on actuator errors of 0.3 – 1.3pu, system with observers resulting in a maximum undershoot value of 0.4-1% while systems without observers resulting in a maximum undershoot value of 6.2-26.4%. The characteristics of the response with the observer on sensor errors resulting 0.3-1.3pu resulting in value of 1.6-4%, 0% and 63.9- 70.7s. System without observers, with sensor errors of 0.3-1.3pu resulting in maximum undershoot, steady state error and settling time of 6.2-26.2%, 6.2-26.4%, and 0s
Enzymatic Hydrolysis of Sorghum Bagasse to Readily Fermentable Sugar for Bioethanol Soeprijanto; Katherin Indriawati; Nurlita Abdulgani
Jurnal Rekayasa Proses Vol 8, No 1 (2014)
Publisher : Departemen Teknik Kimia Fakultas Teknik Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jrekpros.5019

Abstract

Produksi gula dari bagase sorghum menggunakan enzim selulase dan selobiase dilakukan dalam kultur batch. Tujuan percobaan adalah mempelajari pengaruh beban bagase sorghum dan waktu pretreatment kapur terhadap produksi gula dan yield gula. Pretreatment kapur dilakukan dalam 1000 ml labu leher tiga dengan beban kapur 0,1 g Ca(OH)2 /g sorghum bagasse dan ditambah dengan 500 ml air distilasi. Pengaruh waktu pretreatment (1, 2, 3, dan 4 jam) pada suhu 100°C dan pengaruh beban biomassa (5, 10, 15 % w/v) pada hidrolisis enzim untuk menghasilkan gula. Hasil penelitian menunjukkan bahwa konsentrasi gula maksimum dicapai sebesar 28,04 g/l dalam waktu pretreatment 4 jam; dan yield maksimum gula diperoleh 0,4 g glukose/ g biomassa dengan beban biomassa 5% (w/v). Kata kunci: Bagase sorghum, selulase, selobiase, hidrolisis enzim, kultur batch, pretreatment kapur Production of sugar from sorghum bagasse using enzyme of cellulase and cellobiase in a batch culture was conducted. The purpose of this experiment was to study of the effect of sorghum baggase loadings and lime pretreatment time on production and yield of sugar. Lime pretreatment was carried out in a 1000 ml three-neck flask with a lime loading of 0.1 g Ca(OH)2 /g sorghum bagasse and added with 500 ml distilled water. Effects of pretreatment time course (1, 2, 3, and 4 h) at temperature of 100°C and biomass loading (5, 10, 15 % w/v) were observed to produce sugar. The results showed that maximum concentration of sugar obtained was 28.04 g/l with a pretreatment time of 4 h; and the maximum yield of sugar obtained was 0.4 g glucose/ g biomass with a biomass loading of 5% (w/v). Keywords: Batch culture, cellulose, cellobiase, Enzymatic hydrolysis, ime pretreatment, sorghum bagasse
SEISMIC SITE QUALITY ASSESSMENT IN NORTH SUMATRA USING SPECTRAL DENSITY ANALYSIS AND MACHINE LEARNING-BASED CLUSTERING Triya Fachriyeni; Katherin Indriawati; Kevin W. Pakpahan; Irfan Rifani; Anne M. M. Sirait; Yusran Asnawi; Hendro Nugroho; Andrean V. H. Simanjuntak
Jurnal Geosaintek Vol. 11 No. 3 (2025)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j25023659.v11i3.8972

Abstract

Seismic noise strongly influences the accuracy and reliability of earthquake monitoring, particularly in tectonically active regions such as North Sumatra. This study investigates the quality of seismic stations by analyzing noise characteristics using Power Spectral Density (PSD), Probability Density Functions (PDFs), and machine learning clustering. PSD was computed through the Fast Fourier Transform (FFT) and compared against the New High Noise Model (NHNM) and New Low Noise Model (NLNM) benchmarks. Noise variability was further quantified using PDFs, while fuzzy c-means (FCM) clustering was applied to classify temporal noise patterns. Results from the MUTSI seismic station demonstrate strong diurnal and weekly variability, with horizontal components (SHE and SHN) exhibiting significantly higher noise levels and fluctuations than the vertical component (SHZ). Noise amplitudes peaked during morning hours (06:00–09:00 UTC), correlating with anthropogenic activity, and decreased substantially at night, indicating that optimal recording conditions occur during late evening to early morning. FCM clustering identified five dominant noise regimes, separating stable low-noise baselines from sporadic high-noise anomalies likely associated with human activity or instrumental disturbances. These findings highlight the importance of integrating spectral analysis with clustering techniques to evaluate seismic station performance, improve real-time monitoring, and guide optimal site selection and operational scheduling for earthquake detection.