Judul: Kajian teknologi produksi poligliserol dari hasil samping industri biodiesel
Abstrak: The transesterification process in biodiesel production, produces main product (90% methyl ester) and by product (10% glycerol). The glycerol from the biodiesel process consists of 40-90% glycerol, 8-50% water content, and methanol less than 5-10%. The pure glycerol had been produced by the glycerol purification process when the crude glycerol had been separated from impurities (Idaho, 2006). Glycerol purification process by using H3P04 was carried out to neutralize and separate the fatty acid. The colour and minerals are absorbed by the active carbon. The polymerization of glycerol is one of the alternative process in order to reuse the by product of biodiesel industry. Polyglycerol could be used as emulsifier, emollient, disperant, spreading agent, and solubilizer. The polyglycerol and derivative could be used in many industry such us polymer additive, lubricant, drilling fluid, hydraulic fluid, cutting oil, etc. The objectives of this research is to find out the right process of polyglycerol production froin by-prod~~ocft biodiesel industry (glycerol). The treatments of the process were temperature (200,225, and 245 OC), heating time (6, 8, and 10 hours), and type of catalyst (KOH and NaOH) which has concentration 0,5 and 1 %. The result of this research showed that the higher temperature and the longer heating time can increase the viscosity and density of the product as well as the darken of the colour. Hydroxyl value was used to determine degree of polymerization. The degree of polymerization has value r 5. According to analysis of density, viscosity, boiling point, hydroxyl value and functional group, the best process of polymerization is reached at temperature 200 and 225oC.
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Judul: Kajian Pemurnian Gliserol Hasil Samping Biodiesel Jarak Pagar Menggunakan Asam Nitrat, Sulfat, dan Fosfat
Abstrak: About ±10% glycerol is produced as byproduct in KOH alkali-catalysis transesterification of Jatropha curcas oil and methanol. The glycerol contains some soluble impurities such as catalyst, soap, excess methanol, water, and other substances. The glycerol needs purification treatment so that glycerol can be utilized into other products. The first step in the glycerol purification is mineral acid addition to neutralize the base content and to split the soap content into salt and free fatty acid that are more easily separated from glycerol. The purposes of this research are to select the kind of acid that could separate the glycerol from the base catalyst and soap, to study the effect of the molar amount of added acid, and to identify the quality of salt as byproduct of glycerol purification with the best treatment. The experiments conducted in complete randomized factorial design, consisted of two treatment factors: kinds and molar amount of mineral acids. Kind of acids were HNO3 15.27 M, H2SO4 18.01 M, and H3PO4 14.74 M. Molar amount of acids were 54 mmol, 90 mmol, 126 mmol, 162 mmol, and 198 mmol (added into 200 g of glycerol). The observed response variables were pH of glycerol, glycerol content, and ash content of glycerol. The crude glycerol from KOH alkali-catalysis of Jatropha curcas biodiesel production contained: glycerol (40.48%), KOH (2.39%), soap (7.82%), evaporated compounds at 1050C (46.81%), and ash (5.16%). The kind and molar amount of mineral acid significantly affected the pH-value, glycerol content, and ash content. The treatment with 126 mmol H3PO4 14.74 M, in 200 grams glycerol, resulted highest glycerol content (78.77%), lowest ash content (1.085%), and pH-value 6.63. Precipitated salt produced from the best treatment contained (based on dry base, except evaporated compounds): K2O (13.17%), P2O5 (20.14%), chloride (2.67%), free acid (26.18%), and evaporated compounds at 1300C (33.93%).
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Judul: Quantum Convolutional Neural Network (QCNN) for Classification Brain Tumor MRI Image
Abstrak: Penelitian ini bertujuan untuk mengembangkan model deteksi tumor otak menggunakan citra MRI dengan metode Quantum Convolutional Neural Network. Data yang digunakan adalah citra MRI otak yang terdiri dari citra tumor otak glioma, meningioma, hipofisis, dan negatif tumor yang dibagi kedalam data latih dan uji dengan rasio 80:20. Selanjutnya data latih dilakukan penyeimbangan dengan teknik oversampling. Dilakukan pengembangan model untuk mendeteksi tumor otak (biner), dan juga model untuk mengklasifikasikan jenis tumor otak (multiclass). Parameter yang digunakan dalam pengembangan model adalah jumlah ukuran filer = 2 dan kedalaman = 4, dengan qubit 4, dan dilatih dengan jumlah epoch sebanyak 10. Data akan diproses dengan metode Quantum Convolutional Neural Network yang terdiri dari lapisan quantum convolution, lapisan flatten, dan lapisan dense. Model multiclass mendapatkan akurasi 52%, setelah data latih diseimbangkan akurasi model meningkat menjadi 62%, sedangkan model biner mendapatkan akurasi 88%, setelah data latih diseimbangkan akurasi model meningkat menjadi 89%.
Keyword: Citra MRI, Klasifikasi Citra, QCNN, Tumor Otak