Study Analysis Fuel from Plastic Waste

The explosion of plastic-based waste (polymer) in the environment, as a result of its excessive use, so that this phenomenon causes damage to environmental ecosystems, water absorption is not optimal causes flooding, and polluting nutrients in the soil. Plastic is a polymer compound composed of the main elements, namely carbon and hydrogen. The best results in this study by using this tool have a physical appearance: yellow like premium fuel type "1.0" (color test results using the ASTM D1500 method), very pungent smelling liquid, thicker when compared to premium fuel types. And has specifications: Density value of 786.4 kg/m3, Sulfur Content 0.003% m/m, water content 282 ppm, CCI 53.4.

USED CAR PRICE PREDICTION

Cars have become a necessity in this modern world. Every middle class family needs a vehicle or a mode of transport in order to move from one place to another. Not everyone is able to afford a new vehicle as they are costly and there’s an added cost of taxes and various other expenses by both the provider/company of the car as well as the government. Moreover, not every customer is sure of spending a sum of their wealth on a certain car. The product might not meet their needs. The solution to this problem of having a car despite not being able to afford one is met by buying and selling second hand cars. It has become its own market now. There are already numerous companies and websites and app based services that serve as a mediator or a platform for the dealing of second hand or used cars and other vehicles. Establishment of such places is easy but there is another problem that still remains- How to price the used car appropriately at a price comfortable for both the seller and the buyer? Luckily, the Used Car Price Prediction systems exist and can be developed. Users might think that it’s easy to determine the price of a used car, and whether there is even a need to have such a system. In truth, there are a lot of factors that are important in determining the price of a second hand vehicle. The quality of a vehicle deteriorates with age1 of course but that is not all. Every single vehicle is different even when it is manufactured and sold as a new product and even more so when the same vehicle is used over time. Different people may use their vehicles more or less depending on their everyday activity, making kilometers driven as one of the important factors for the price prediction. It is obvious that a vehicle which is driven for 2000 kilometers in 1 year would be priced less than a vehicle which has been driven for only 500 kilometers in 2 years. This is just one of the factors that determine the price of a used car. In our Car Price Prediction System, we have used the Year of Manufacturing (used to determine the age of the vehicle by subtracting this from the date of selling), the original maximum retail price of the vehicle (the price at which the vehicle was sold at from the manufacturing company/garage), the fuel type of the vehicle (Petrol, Diesel, CNG, Electric ; This affects the pricing severely as different fuel type engines have different prime performance periods and different rates of deterioration), Seller Type (Individual or Dealership), Transmission (Manual or Automatic), Number of past owners of the vehicle. Using all these factors2, we are going to determine which model is best to determine a price for the used vehicle. For the Car Price Prediction System, Regression models3are used since these models give the results as a continuous curve instead of a categorized value as a result. Due to this, we can use the continuous curve to determine an accurate price for each and every scenario which won’t be possible if the results obtained were in the form of a range. The final model of the system will implement the best suited algorithm and have a UI (User Interface) which make it possible for a user to be able to enter the values of these deciding factors and the system will predict the price for them. Keywords: Car price prediction, machine learning, regression analysis, linear regression, correlation analysis

Retrospection of the Optimization Model for Designing the Power Train of a Formula Student Race Car

This article describes the power train design specifics in Formula student race vehicles used in the famed SAE India championship. To facilitate the physical validation of the design of the power train system of a formula student race car category vehicle engine of 610 cc displacement bike engine (KTM 390 model), a detailed design has been proposed with an approach of easing manufacturing and assembly along with full-scale prototype manufacturing. Many procedures must be followed while selecting a power train, such as engine displacement, fuel type, cooling type, throttle actuation, and creating the gear system to obtain the needed power and torque under various loading situations. Keeping the rules in mind, a well-suited engine was selected for the race track and transmission train was selected which gives the maximum performance. Based on the requirement, a power train was designed with all considerations we need to follow. Aside from torque and power, we designed an air intake with fuel efficiency in mind. Wireless sensors and cloud computing were used to monitor transmission characteristics such as transmission temperature management and vibration. The current study describes the design of an air intake manifold with a maximum restrictor diameter of 20 mm.

Global anthropogenic CO₂ emissions and uncertainties as a prior for Earth system modelling and data assimilation

The growth in anthropogenic carbon dioxide (CO2) emissions acts as a major climate change driver, which has widespread implications across society, influencing the scientific, political, and public sectors. For an increased understanding of the CO2 emission sources, patterns, and trends, a link between the emission inventories and observed CO2 concentrations is best established via Earth system modelling and data assimilation. Bringing together the different pieces of the puzzle of a very different nature (measurements, reported statistics, and models), it is of utmost importance to know their level of confidence and boundaries well. Inversions disaggregate the variation in observed atmospheric CO2 concentration to variability in CO2 emissions by constraining the regional distribution of CO2 fluxes, derived either bottom-up from statistics or top-down from observations. The level of confidence and boundaries for each of these CO2 fluxes is as important as their intensity, though often not available for bottom-up anthropogenic CO2 emissions. This study provides a postprocessing tool CHE_UNC_APP for anthropogenic CO2 emissions to help assess and manage the uncertainty in the different emitting sectors. The postprocessor is available under https://doi.org/10.5281/zenodo.5196190 (Choulga et al., 2021). Recommendations are given for regrouping the sectoral emissions, taking into account their uncertainty instead of their statistical origin; for addressing local hot spots; for the treatment of sectors with small budget but uncertainties larger than 100 %; and for the assumptions around the classification of countries based on the quality of their statistical infrastructure. This tool has been applied to the EDGARv4.3.2_FT2015 dataset, resulting in seven input grid maps with upper- and lower-half ranges of uncertainty for the European Centre for Medium-Range Weather Forecasts Integrated Forecasting System. The dataset is documented and available under https://doi.org/10.5281/zenodo.3967439 (Choulga et al., 2020). While the uncertainty in most emission groups remains relatively small (5 %–20 %), the largest contribution (usually over 40 %) to the total uncertainty is determined by the OTHER group (of fuel exploitation and transformation but also agricultural soils and solvents) at the global scale. The uncertainties have been compared for selected countries to those reported in the inventories submitted to the United Nations Framework Convention on Climate Change and to those assessed for the European emission grid maps of the Netherlands Organisation for Applied Scientific Research. Several sensitivity experiments are performed to check (1) the country dependence (by analysing the impact of assuming either a well- or less well-developed statistical infrastructure), (2) the fuel type dependence (by adding explicit information for each fuel type used per activity from the Intergovernmental Panel on Climate Change), and (3) the spatial source distribution dependence (by aggregating all emission sources and comparing the effect against an even redistribution over the country). The first experiment shows that the SETTLEMENTS group (of energy for buildings) uncertainty changes the most when development level is changed. The second experiment shows that fuel-specific information reduces uncertainty in emissions only when a country uses several different fuels in the same amount; when a country mainly uses the most globally typical fuel for an activity, uncertainty values computed with and without detailed fuel information are the same. The third experiment highlights the importance of spatial mapping.

ANALISIS PENGARUH WAKTU DAN JARAK PERJALANAN KAPAL TERHADAP BIAYA LOW SULPHUR SURCHARGE

The danger of ship emission caused by HFO (Heavy Fuel Oil) fuel type has become a serious matter due to its high containment of sulphur as much as 3.50% m/m. The IMO (International Maritime Organization) took action on this problem by releasing new regulation to limit sulphur in the ship fuel as low as 0.50% m/m. This regulation leads to an additional tariff called the LSS (Low Sulphur Surcharge). As an impact, shipping companies charge this fee to customers and ocean freight forwarders, hence there is an increase of the total shipping charges. Meanwhile, the dominant variable which determines the LSS charge amount is not yet known, so it is still uninformative for the public and the academic field. The purpose of this study is to analyse which variable gives the most influence on the amount of the LSS tariff. By using multiple linear regression method, the study finds that the shipping distance variable is the dominant variable with a contribution value of 86.48% and has positive relationship with the LSS tariff. On the other hand, though the voyage time also has influence on the tariff, the effect is weak and it shows negative relationship with the LSS tariff.

Development and Application of a Supervised Pattern Recognition Algorithm for Identification of Fuel-Specific Emissions Profiles

Wildfires have increased in frequency, duration and size in the western United States (U.S.) over the past decades. These trends are projected to continue, with negative consequences for air quality across the U.S. Wildfires emit large quantities of particles and gases that serve as air pollutants and their precursors, and can lead to severe air quality conditions over large spatial and long temporal scales. Characterization of the chemical constituents in smoke as a function of combustion conditions, fuel type, and fuel component is an important step towards improving the prediction of air quality effects from fires and evaluating mitigation strategies. Building on the comprehensive characterization of gaseous non-methane organic compounds (NMOCs) identified in laboratory and field studies, a supervised pattern recognition algorithm was developed that successfully identified unique chemical speciation profiles among similar fuel types common in western coniferous forests. The algorithm was developed using laboratory data from single fuel species and tested on simplified synthetic fuel mixtures. The fuel types in the synthetic mixtures were differentiated but as the relative mixing proportions became more similar, the differentiation became poorer. Using the results from the pattern recognition algorithm, a classification model based on linear discriminant analysis was trained to differentiate smoke samples based on the contribution(s) of dominant fuel type(s). The classification model was applied to field data and despite the complexity of contributing fuels, and the presence of fuels "unknown" to the classifier, the dominant sources/fuel types were identified correctly. The pattern recognition and classification algorithms are a promising approach for identifying the types of fuels contributing to smoke samples and facilitating selection of appropriate chemical speciation profiles for predictive air quality modeling, using a highly reduced suite of measured NMOCs. Utility and performance of the pattern recognition and classification algorithms can be improved by expanding the training and test sets to include data from a broader range of single and mixed fuel types.