Studi Rantai Pasok LNG: Pemanfaatan Gas Bumi sebagai Bahan Bakar Wahana Transportasi Laut

Biaya kebutuhan bahan bakar di kapal merupakan komponen yang tertinggi dalam pengoperasian kapal. Salah satu upaya untuk mengurangi biaya kebutuhan bahan bakar ini adalah dengan menggunakan alternatif bahan bakar gas. Selain itu dapat menghemat biaya operasional kapal, penggunaan gas sebagai bahan bakar juga dapat mengurangi emisi kapal. Studi ini bertujuan untuk mengkaji pengggunaan gas bumi menjadi bahan bakar di kapal dengan cara modifikasi sistem untuk fuel system kemudian menentukan lokasi bunkering LNG dan skema pengisian bahan bakar (bunkering) LNG. Analisis penentuan lokasi bunkering dilakukan dengan metode Greedy Random Search untuk mencoba semua alternatif dari semua variabel kemungkinan (possible variable). Hasil dari modifikasi adalah dengan menambahkan gas transfer dari replace menuju ke main engine meliputi LNG Replace, LNG Cryogenic Pump, Heat Exchanger, Gas Valve Unit (GVU), dan Conversion System. Pemilihan skema bunkering menggunakan pembobotan dengan metode Analytical Hierarchy Process (AHP). Alternatif lokasi bunkering yang terpilih adalah di pelabuhan Merak, Pelabuhan Ketapang Pelabuhan Lembar, Pelabuhan di Makassar, Pelabuhan di Surabaya, Pelabuhan di Sorong, Pelabuhan di Ambon, Pelabuhan di Semarang, Pelabuhan di Jakarta, dan Pelabuhan di Jayapura. Skema bunkering LNG yang dipakai pada setiap pelabuhan adalah Truck to Ship, sesuai dengan opsi yang terpili

Comparison of Spectral and Image Morphological Analysis for Egg Early Hatching Property Detection Based on Hyperspectral Imaging

The use of non-destructive methods to detect egg hatching properties could increase efficiency in commercial hatcheries by saving space, reducing costs, and ensuring hatching quality. For this purpose, a hyperspectral imaging system was built to detect embryo development and vitality using spectral and morphological information of hatching eggs. A total of 150 green shell eggs were used, and hyperspectral images were collected for every egg on day 0, 1, 2, 3 and 4 of incubation. After imaging, two analysis methods were developed to extract egg hatching characteristic. Firstly, hyperspectral images of samples were evaluated using Principal Component Analysis (PCA) and only one optimal band with 822 nm was selected for extracting spectral characteristics of hatching egg. Secondly, an image segmentation algorithm was applied to isolate the image morphologic characteristics of hatching egg. To investigate the applicability of spectral and image morphological analysis for detecting egg early hatching properties, Learning Vector Quantization neural network (LVQNN) was employed. The experimental results demonstrated that model using image morphological characteristics could achieve better accuracy and generalization than using spectral characteristic parameters, and the discrimination accuracy for eggs with embryo development were 97% at day 3, 100% at day 4. In addition, the recognition results for eggs with weak embryo development reached 81% at day 3, and 92% at day 4. This study suggested that image morphological analysis was a novel application of hyperspectral imaging technology to detect egg early hatching properties

Classification of olives using FT-NIR spectroscopy, neural networks and statistical classifiers

Green olives (Olea europaea L. cv. Ayvalik') were classified based on their surface features such as existence of bruise and fly-defect using two NIR spectrometer readings of reflectance and transmittance, and classifiers such as artificial neural networks (ANN) and statistical (Ident and Cluster). Spectral readings were performed in the ranges of 780-2500 and 800-1725nm for reflectance and transmittance modes, respectively. Original spectral readings were used as input features to the classifiers. Diameter correction was applied on reflectance spectra used in ANN classifier expecting improved classification results. ANN classifier performed better in general compared to statistical classifiers. Classification performance in detecting bruised olives using diameter corrected reflectance features and ANN classifier was 99% while it was 98% for Ident and Cluster classification approaches using regular reflectance features. Classification between solid and fly-defected olives was performed with success rates of 93% using reflectance features and 58% using transmittance features with ANN classifier while statistical classifiers of Ident and Cluster performed between 52 and 78% success rates using the same spectral readings. ANN classifier resulted 92% classification success for the classification application considering three output classes of solid, bruised and fly-defected olives using reflectance features while it performed 57.3% success rate using transmittance features.Scientific and Technological Research Council of Turkey (TUBITAK) [104O555]The authors acknowledge the financial support of the Scientific and Technological Research Council of Turkey (TUBITAK, project 104O555) for this study, and thank Edremit Olive Growing Station for providing olives and Dr. Hanife Genc at the Department of Agricultural Biotechnology for the fly cages

Apple Fruit Quality Identification using Clustering

An apples a day keeps doctor away" this proverb gives us important of apple in our healthy life. Apples fruit is consist of plenty of nutrition's therefore, doctors are always prefer to advice to eat the apple in most of the diseases. Hence, there is a huge demand of apples in market. To fulfill this demands suppliers need to provide the good quality fruit. There is a need of quality fruits in market. In this work studied various types of apples quality by using clustering approach. Comparative analysis is performed and given results are much better as compare to earlier work