Pembuatan Bahan Bakar Diesel dari Limbah Plastik HDPE dengan Proses Pirolisis

Indonesia menduduki posisi ke dua setelah cina penghasil sampah plastik terbesar di dunia. Dimana salah satu limbah plastik tersebut adalah HDPE (High Density Polyethylene). Sedangkan plastik merupakan produk hasil pengolahan minyak bumi yang dapat direcycle ke bentuk semulanya karena bahan baku pembuatan limbah plastik adalah nafta yang merupakan salah satu unsur dari minyak bumi. Salah satu solusi yang diperlukan adalah recycle dengan mengubah limbah plastik menjadi bahan bakar dengan proses pirolisis. Pirolisis merupakan salah satu proses terbaik dari recycle limbah plastik, dengan pertimbangan memahami sifat limbah plastik HDPE. Penelitian ini menggunakan alat pirolisis dengan variable suhu proses yaitu 300⸰C, 325⸰C, dan 350⸰C, waktu proses pirolisis yaitu 2 dan 4 jam. Dari proses pirolisis diperoleh hasil volume bahan bakar diesel yaitu pada suhu 300⸰C sebanyak 95 ml, suhu 325⸰C sebanyak 100 ml, dan suhu 350⸰C sebanyak 145 ml. Dari hasil analisa data optimal untuk suhu dan waktu optimum proses pirolisis limbah plastik HDPE yaitu pada suhu 325⸰C selama 2 jam, bahan bakar diesel yang didapat memiliki kadar abu 0,044 (b/b), dan kadar air 0,031(%vol). ABSTRACTIndonesia is in second place after China, the largest plastic waste producer in the world. Where one of the plastic wastes is HDPE (High-Density Polyethylene). Meanwhile, plastic is a product of petroleum processing that can be recycled to its original form because the raw material for making plastic waste is naphtha, which is an element of petroleum. One solution that is needed to recycle by converting plastic waste into fuel by the pyrolysis process. Pyrolysis is one of the best processes for recycling plastic waste, with consideration of understanding the nature of HDPE plastic waste. This study used a pyrolysis tool with process temperature variables, namely 300⸰C, 325⸰C, and 350⸰C, the pyrolysis process time was 2 and 4 hours. From the pyrolysis process, the results of the volume of diesel fuel are at a temperature of 300 ⸰C as much as 95 ml, a temperature of 325 C as much as 100 ml, and a temperature of 350 ⸰C as much as 145 ml. From the results of the optimal data analysis for the optimum temperature and time of the HDPE plastic waste pyrolysis process, which is at a temperature of 325⸰C for 2 hours, the obtained diesel fuel has an ash content of 0.044 (w / w), and a moisture content of 0.031 (vol%).

A Point Set Analysis Method for Hull Blocks Based on CPD and Engineering Constraints

A point set analysis method considering the practical engineering constraints has been proposed in this article. First, the coherent point drift method was used to obtain the initial values of data analysis. Second, the error distribution in different directions was expressed by weight vectors. Last, the multiobjective optimization model was built and the engineering constrains were introduced into the multioptimization objective function to achieve the optimal data analysis results. The experimental results proved that the method could obtain the reasonable data analysis results, which met the engineering constraints. It provides the important basis for the subsequent assembly.

Mediterranean Sea Hydrographic Atlas: towards optimal data analysis by including time-dependent statistical parameters

The goal of the present work is to provide the scientific community with a high-resolution atlas of temperature and salinity for the Mediterranean Sea based on the most recent datasets available and contribute to the studies of the long-term variability in the region. Data from the pan-European marine data infrastructure SeaDataNet were used, the most complete and, to our best knowledge, best quality dataset for the Mediterranean Sea as of today. The dataset is based on in situ measurements acquired between 1900 and 2015. The atlas consists of horizontal gridded fields produced by the Data-Interpolating Variational Analysis, in which unevenly spatial distributed measurements were interpolated onto a 1∕8° × 1∕8° regular grid on 31 depth levels. Seven different types of climatological fields were prepared with different temporal integration of observations. Monthly, seasonal and annual climatological fields have been calculated for all the available years, seasonal to annual climatologies for overlapping decades and specific periods. The seasonal and decadal time frames have been chosen in accordance with the regional variability and in coherence with atmospheric indices. The decadal and specific-period analysis was not extended to monthly resolution due to the lack of data, especially for the salinity. The Data-Interpolating Variational Analysis software has been used in the Mediterranean region for the SeaDataNet and its predecessor Medar/Medatlas Climatologies. In the present study, a more advanced optimization of the analysis parameters was performed in order to produce more detailed results. The past and present states of the Mediterranean region have been extensively studied and documented in a series of publications. The purpose of this atlas is to contribute to these climatological studies and get a better understanding of the variability on timescales from months to decades and longer. Our gridded fields provide a valuable complementary source of knowledge in regions where measurements are scarce, especially in critical areas of interest such as the Marine Strategy Framework Directive (MSFD) regions and subregions. The dataset used for the preparation of the atlas is available from https://doi.org/10.12770/8c3bd19b-9687-429c-a232-48b10478581c.The climatologies in netCDF are available at the following sources: annual climatology (https://doi.org/10.5281/zenodo.1146976), seasonal climatology for 57 running decades (https://doi.org/10.5281/zenodo.1146938), seasonal climatology (https://doi.org/10.5281/zenodo.1146953), annual climatology for 57 running decades (https://doi.org/10.5281/zenodo.1146957), seasonal climatology for six periods (https://doi.org/10.5281/zenodo.1146966), annual climatology for six periods (https://doi.org/10.5281/zenodo.1146970), monthly climatology (https://doi.org/10.5281/zenodo.1146974).

Mediterranean Sea Hydrographic Atlas: towards optimal data analysis by including time-dependent statistical parameters

The goal of the present work is to provide the scientific community with a high-resolution Atlas of temperature and salinity for the Mediterranean Sea based on the most recent datasets available and contribute to the studies of the long-term variability in the region. Data from the Pan-European Marine Data Infrastructure SeaDataNet were used, the most complete and, to our best knowledge, of best quality dataset for the Mediterranean Sea as of today. The dataset is based on in situ measurements acquired between 1900–2015. The Atlas consists of horizontal gridded fields produced by the Data Interpolating Variational Analysis, where unevenly spatial distributed measurements were interpolated onto a 1/8° x 1/8° regular grid on 31 depth levels. Seven different types of climatological fields were prepared with different temporal integration of observations. Monthly, seasonal and annual climatological fields have been calculated for all the available years, seasonal to annual climatologies for overlapping decades and specific periods. The seasonal and decadal time frames have been chosen in accordance with the regional variability and in coherence with atmospheric indices. The decadal and specific periods analysis was not extended to monthly resolution due to the lack of data, especially for the salinity. The Data Interpolating Variational Analysis software has been used in the Mediterranean Region for the SeaDataNet and its predecessor Medar/Medatlas Climatologies. In the present study, a more advanced optimization of the analysis parameters was performed in order to produce more detailed results. The Mediterranean Region past and present states have been extensively studied and documented in a series of publications. The purpose of this Atlas is to contribute to these climatological studies and get a better understanding of the variability on time scales from month to decades and longer. Our gridded fields provide a valuable complementary source of knowledge in regions where measurements are scarce, especially in critical areas of interest such as the Marine Strategy Framework Directive (MSFD) regions. The dataset used for the preparation of the Atlas is available from https://doi.org/10.12770/8c3bd19b-9687-429c-a232-48b10478581c.

Unmanned Aerial Vehicle–Based Traffic Analysis: Methodological Framework for Automated Multivehicle Trajectory Extraction

Unmanned aerial vehicles (UAVs), commonly referred to as drones, are one of the most dynamic and multidimensional emerging technologies of the modern era. This technology has recently found multiple potential applications within the transportation field, ranging from traffic surveillance applications to traffic network analysis. To conduct a UAV-based traffic study, extremely diligent planning and execution are required followed by an optimal data analysis and interpretation procedure. In this study, however, the main focus was on the processing and analysis of UAV-acquired traffic footage. A detailed methodological framework for automated UAV video processing is proposed to extract the trajectories of multiple vehicles at a particular road segment. Such trajectories can be used either to extract various traffic parameters or to analyze traffic safety situations. The proposed framework, which provides comprehensive guidelines for an efficient processing and analysis of a UAV-based traffic study, comprises five components: preprocessing, stabilization, georegistration, vehicle detection and tracking, and trajectory management. Until recently, most traffic-focused UAV studies have employed either manual or semiautomatic processing techniques. In contrast, this paper presents an in-depth description of the proposed automated framework followed by a description of a field experiment conducted in the city of Sint-Truiden, Belgium. Future research will mainly focus on the extension of the applications of the proposed framework in the context of UAV-based traffic monitoring and analysis.

Chemical Selectivity in Diffraction by Statistical Analysis of in situ XRPD Data

X-ray diffraction methods in general allow only a limited chemical selectivity. Structural information on a subset of atoms can be obtained by a modulation enhanced diffraction (MED) experiment, using a periodic stimulus supplied in situ on a crystal, while diffraction data are collected several times within a stimulus period. The data are then treated by statistical methods such as phase sensitive detection (PSD) and Principal component analysis (PCA) techniques. The application of PSD to diffraction has been proposed as a tool to extract crystallographic information on a subset of atoms [1], thus allowing to introduce selectivity in diffraction. Simulated and experimental PSD-MED powder data were produced by using a TS-1 zeolite as spectator, in which Xe, acting as active species, is adsorbed and desorbed in a periodically modulated mode. By first demodulating these data, MED allowed to obtain the powder diffraction pattern of the active subset, i.e. to obtain selectively the crystallographic information on Xe, by solving the crystal structure of the active species out of the zeolite framework. The "real world" experiments indicated that the PSD-MED approach has some limitations related to its theoretical assumptions. PCA is widely used in spectroscopic analyses and was recently applied to XRPD data by some of us [2]. PCA was exploited to evaluate the in situ XRPD data quality, to speed up the data analysis and data pre-treatment required by PSD and improve the extraction of the substructure information from MED data. It resulted that the first two components obtained by PCA are related to the 1- and 2-omega patterns from PSD. The two approaches (PCA and PSD) are finally compared from the viewpoint of their capacity of gathering information on the Xe substructure inside the zeolite channels and used in a synergic way to obtain the optimal data analysis efficiency.