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All Journal Research Report - Social Science JURNAL INTEGRASI PROSES
Budi Husodo Bisowarno
Parahyangan Catholic University
Social Sciences Other

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Journal : JURNAL INTEGRASI PROSES

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Pyrolysis Carbonization of Sago Starch Haryadi Wibowo; Arenst Andreas Arie; Budi Husodo Bisowarno
JURNAL INTEGRASI PROSES VOLUME 10 NOMOR 1 JUNI 2021
Publisher : JURNAL INTEGRASI PROSES

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36055/jip.v10i1.11289

Abstract

Battery requirements are increasing over time, the anode for sodium ion batteries (SIB) can use amorphous carbon. Carbon synthesis is carried out via pyrolysis. Research on the synthesis of carbon derived from sago starch is still rare. This study aims to determine the carbon characteristics of sago starch treated with nitrogen doping according to the SIB anode by taking into account the morphology, size distribution, material structure, material composition, and the distance between layers. The carbonization method used in this research is the pyrolysis process at 900 o C for 1 hour. Variations in the experiment were carried out through direct pyrolysis process with variations of urea against starch 3:1, 2:1, and pure starch. The experimental results were analysis using SEM (EDS) and XRD. The results showed that the pyrolysis process doped with nitrogen with a ratio of 3:1 urea had an interlayer distance of 0.353304 nm, 2:1 had an interlayer of 0.368059 nm, and 0.390178 nm of pure sago starch. This value indicates that carbon is a non-graphite material (> 0.3354 nm). The carbon produced from pyrolysis produces carbon that is amorphous and has a similar shape, which is like wood.
NANO CARBON SYNTHESIS FROM MICROALGAE CHLORELLA VULGARIS AS PRECURSOR OF SOLID PHASE CARBON Alexander William Prijadi; Arenst Andreas Arie; Budi Husodo Bisowarno
JURNAL INTEGRASI PROSES Vol 11, No 2 (2022)
Publisher : JURNAL INTEGRASI PROSES

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36055/jip.v11i2.15652

Abstract

Nanocarbon is a nanometer-scale substance made entirely of carbon atoms. Generally, the synthesis of nanocarbon is usually conducted using a gas-phase carbon raw material which is toxic. Therefore, the use of solid-phase carbon raw material to synthesize nanocarbon started to develop. In this research, nanocarbon will be synthesized using variations of solid-phase carbon raw material derived from a novel carbon precursor, microalgae Chlorella vulgaris. Nanocarbons produced were analyzed using SEM, XRD, and TEM. SEM analysis did not show the nanocarbon formed. However, this may be due to the nanocarbon being very small. To ensure whether nanocarbon is formed or not, XRD analysis is conducted. XRD analysis shows the possibility of forming carbon nanotubes from the sample with activated carbon as a raw material. There were peaks at diffraction angles of 24–26° and 42–43,5°, which indicated carbon nanotubes. In addition, the dc value of this sample has a value like carbon nanotubes, which is 0.344 nm. In contrast, the other two samples, which used hydrochar and microalgae as raw materials, only indicated activated carbon after the synthesis. The TEM analysis supported by XRD analysis for a sample with activated carbon as a raw material showed the presence of carbon nanotubes. This is indicated by their rope-like morphology, which is not visible in SEM analysis due to their tiny size. At the same time, the other two samples did not show any morphology indicating the presence of nanocarbon. So, it can be concluded that the synthesis of nano carbon has been successfully carried out using activated carbon as raw material and produces nano carbon with the type of carbon nanotubes.
DEEP LEARNING DENGAN METODE LSTM (LONG SHORT-TERM MEMORY) UNTUK PEMODELAN KELAKUAN DINAMIK SISTEM DISTILASI MENGGUNAKAN REAL OPERATING DATA William Kurniawan; Budi Husodo Bisowarno
JURNAL INTEGRASI PROSES Vol 13, No 1 (2024)
Publisher : JURNAL INTEGRASI PROSES

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62870/jip.v13i1.22856

Abstract

Kemurnian produk tidak dapat diukur secara on-line dan terdapat time delay dalam pengukuran kemurnian secara offline dan tidak kontinu dengan hasil analisis kemurnian dari laboratorium. Kontrol berbasis variabel inferensial merupakan alternatif untuk memprediksi dan mengendalikan kemurnian produk dengan variabel kondisi operasi yang tepat secara real-time. Pabrik memiliki data kondisi operasi (input) serta data kemurnian produk (output) berasal dari kondisi nyata pabrik dapat digunakan untuk pembangunan model deep learning. Penelitian ini dibagi menjadi tiga tahapan besar, yaitu pertama adalah tahapan persiapan data untuk menentukan variabel paling sensitif dalam memprediksi kemurnian produk menggunakan metode korelasi pearson yaitu temperatur upper column dan process knowledge yaitu pressure top column. Tahapan kedua adalah penyusunan algoritma deep learning menggunakan metode LSTM (long short-term memory) menggunakan bantuan program python.  Optimizer yang digunakan untuk melatih data adalah optimizer SGD (stochastic gradient descent). Data yang digunakan untuk melatih dan melakukan validasi model deep learning adalah data dari 2018 hingga semester pertama tahun 2019 dengan rasio training set:test data = 70:30. Tahapan ketiga adalah melatih model dan validasi model, memprediksi hasil, serta melakukan evaluasi model terhadap data pabrik terhadap set data semester kedua tahun 2019. Hasil dari pelatihan adalah model LSTM dengan nilai RMSE (root mean squared error) sebesar 1,4. Nilai validasi untuk model ini adalah RMSE sebesar 0,6389. Model ini dapat memprediksi set data semester kedua 2019 dengan nilai R2 =0,74 dan RMSE = 0,56.
Co-Authors Alexander William Prijadi Arenst Andreas Arie Arenst Andreas Arie Ariestya Arlene Arbita Ceicalia Tesavrita Haryadi Wibowo Jenny Novianti M. Katherine Kho Kristianto, Hans Orpha Jane Soetedjo, Jenny Novianti M William Kurniawan