scholarly journals Analysis of Fairness Limit on Fuel Oil Consumption of TwoTail Test Based Excavator and Dump Truck A Case Study in PT XYZ

2020 ◽  
Vol 1 (1) ◽  
pp. 6-12
Author(s):  
Bella Puspa Octaviania ◽  
Supriyadi ◽  
Ambran Hartono
Keyword(s):  
Fuel Consumption ◽  
Statistical Approach ◽  
Fuel Oil ◽  
Dump Truck ◽  
Oil Consumption ◽  
Mining Operations

A lack of method to find out the fairness limit of fuel consumption in mining operations enables statistical approach with two-tail test be applied to observe the fairness limit of actual fuel oil consumption compared to the manual handbook of its equipment. Fuel consumption according to the manual handbook for EXCA LIEBHERR 9350 excavator is 207.23 liters/hour and EXCA HITACHI 2500 is 191.51 liters/hour, while CATERPILLAR 777D Dump Truck is 36-53 liters/hour consider as low, 53-73, 8 liters/hour medium, and 73.8-96.5 liters/hour as high. This statistical approach has been carried out after fulfilling the concept of mechanized earth-moving. As a result, the differences in fuel consumption of LIEBHERR 9350 and HITACHI 2500 are 3.72% and 3.26%, which are still in range of a reasonable fuel consumption limit, while CAT 777D operating on LIEBHERR 9350 and CAT 777D operating on HITACHI 2500, each shows a difference in fuel consumption. The differences are 29.65%, meaning that it has exceeded the reasonable limits of fuel consumption and 7.15%, meaning that it is still in range of a reasonable fuel consumption limit.

Neural Network-Based Engine Propeller Matching (NN-EPM) for Trimaran Patrol Ship

2014 ◽  
Vol 493 ◽  
pp. 388-394 ◽  
Author(s):  
Eddy S. Koenhardono ◽  
Eko Budi Djatmiko ◽  
Adi Soeprijanto ◽  
Mohammad I. Irawan
Keyword(s):  
Neural Network ◽  
Fuel Consumption ◽  
Fuel Oil ◽  
Training Data ◽  
Reduction Gear ◽  
Maximum Relative Error ◽  
Oil Consumption ◽  
Testing Data ◽  
Ship Speed

In recent years efforts on reducing fuel consumption has become the greatest issue related to energy crisis and global warming. The reduction of fuel consumption can be obtained, if the ship propulsion could be operated in its best performance level. Generally this is done by an appropriate analysis of engine propeller matching (EPM). In this study an EPM based on neural-network method, or NN-EPM, is established to predict the best performance of main engines, leading at minimum fuel oil consumption. A trimaran patrol ship is selected as a case study. This patrol ship is equipped with two 2720 kW main engines each connected to a controllable pitch propeller (CPP) through a reduction gear. The input parameters are ship speedVand service margin SM, with the corresponding output parameters comprise of engine speednE, engine break horse powerPB, propeller pitchP/D, and the fuel consumptionFC. An NN-EPM 2-20-15-4 configuration has been constructed out of 100 training data and then validated by 30 testing data. The maximum relative error between results from NN-EPM and EPM analysis is 2.1%, that is in term of the fuel consumption. For other parameters the errors are well below 1.0%. These facts indicate that the use of NN-EPM to predict the main engines's performance for trimaran patrol ship is satisfactory.

Nonlinear Droop Load Sharing to Minimize Gas Emissions and Fuel Consumption

2017 ◽  
Author(s):  
Michel Rejani Miyazaki ◽  
Asgeir Johan Sørensen
Keyword(s):  
Optimum Condition ◽  
Fuel Consumption ◽  
Load Sharing ◽  
Fuel Oil ◽  
Oil Consumption ◽  
Gas Emission ◽  
Nonlinear Load ◽  
Gas Emissions ◽  
Marine Systems ◽  
Fine Tune

In this paper, load sharing curves are generated for marine systems with multiple gensets, where the goal is to reduce both gas emissions and fuel consumption. Initially, the average emissions and fuel consumption for each engine are calculated based on the specific emission and Specific Fuel Oil Consumption (SFOC) curves of each generator set (genset). An optimal nonlinear load sharing subject to gas emission and fuel consumption minimization is found for each engine. One result is that identical gensets should not have the same droop curve on the optimum condition, since it would lead to equal load sharing among them and a suboptimal configuration. Cases with two identical engines, two different engines, and multiple different engines were studied. The results show that by modifying the usually linear droop curve of engines, it is possible to reduce the fuel consumption and the gas emission, and it is also possible to fine tune the solution such that the fuel consumption or gas emissions are reduced.

scholarly journals Pengaruh medan elektromagnetik pada prestasi mesin motor bakar empat langkah dengan bahan bakar gas

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Achmad Rifqi In'Amullah ◽  
Nasrul Ilminnafik
Keyword(s):  
Fuel Consumption ◽  
Alternative Fuels ◽  
Copper Wire ◽  
Research Data ◽  
Fuel Oil ◽  
Oil Consumption ◽  
Gas Fuel ◽  
Hc Emissions ◽  
High Level ◽  
Engine Power

The high level of fuel oil consumption in Indonesia caused by increases number of vehicles. Fuel oil consumption has switched into gas fuel as one of the secure alternative fuels and obtained more little gas emissions if compared with fuel oil. LPG (Liquified Petroleum Gas) is one of the alternative fuel was environmentally friendly. This research is purposed for compared performance of four-step engine with premium fuel and LPG fuel with a variety of additional electromagnetic field 600, 800, and 1000 total of copper wire windings. Using LPG fuel can increase torque generated by engine, but the result of engine power to be lower. Based on research data 800 copper wire windings can increase the number of torque and generated power compared to LPG fuel standard. LPG fuel can save fuel consumption compared to premium fuel. The most optimum decrease in fuel consumption is generated by using 1000 copper wire windings. Using LPG fuel can also reduce CO, CO2, and HC emissions levels. The best CO, CO2, and HC emissions levels are obtained from 1000 copper wire windings.Keywords: torque, power, fuel consumption, emissions, and LPG.

scholarly journals Tuna Fisheries Fuel Consumption Reduction and Safer Operations

2021 ◽  
pp. 377-388
Author(s):  
Jose A. Fernandes ◽  
Zigor Uriondo ◽  
Igor Granado ◽  
Iñaki Quincoces
Keyword(s):  
Fault Detection ◽  
Fuel Consumption ◽  
Fuel Oil ◽  
Oil Consumption ◽  
Probabilistic Forecasting ◽  
Fuel Consumption Reduction ◽  
Fishing Strategies ◽  
Consumption Reduction ◽  
Fishing Fleets ◽  
Tuna Fisheries

AbstractThis chapter demonstrates the potential of tuna fishing fleets to reduce their fuel oil consumption. In the “Oceanic tuna fisheries, immediate operational choices” pilot, the data monitoring system on vessels  periodically upload data to the server for shore analysis. The data analytics employs fuel oil consumption equations and propulsion engine fault detection models. The fuel consumption equations are being used to develop immediate operational decision models. The fault detection models are used to plan  maintenance operations and to prevent unexpected engine malfunctions. The data-driven planning software allows probabilistic forecasting of tuna biomass distribution and analysing changes in fishing strategies leading to fuel consumption reduction. These changes in fishing strategies can be summarized as a transition from hunting to harvesting. Vessels do not  search for fish, but instread  take less risks and fish, where it is more likely that the fish can be found and is easier to capture. Buoy data are  increasingly used to improve stock assessments and have the potential to allow better monitoring and planning of fish quotas fulfilment.

scholarly journals Deviations and errors review on measuring and calculating heavy fuel oil consumption and fuel stock onboard vessels equipped with volumetric fuel consumption flowmeters

Pomorstvo ◽  
2021 ◽  
Vol 35 (2) ◽  
pp. 297-307
Author(s):  
Josip Dujmović ◽  
Dean Bernečić
Keyword(s):  
Fuel Consumption ◽  
Numerical Models ◽  
Fuel Oil ◽  
Water Drainage ◽  
Oil Consumption ◽  
Heavy Fuel Oil ◽  
Fuel Mass ◽  
Fuel Flow ◽  
Mass Losses ◽  
Mass Conversion

A common way of measuring heavy fuel oil consumption on board a vessel is to use volumetric fuel flow meters installed at fuel systems inlets for each of the major fuel consumers. At each stage of the fuel processing cycle, certain mass fuel losses or deviations and calculation errors occur that are not counted accurately into fuel consumption figures. The goal of this paper is to identify those fuel mass losses and measuring/calculating errors and perform their quantitative numerical analysis based on actual data. Fuel mass losses defined as deviations identified during the fuel preparation process are evaporation of volatile organic compounds, water drainage, fuel separation, and leakages while errors identified are flow meter accuracy and volumetric/mass flow conversion accuracy. By utilizing statistical analysis of obtained data from engine logbook extracts from three different ships numerical models were generated for each fuel mass loss point. Measuring errors and volumetric/mass conversion errors are numerically analyzed based on actual equipment and models used onboard example vessels. By computational analysis of the obtained models, approximate percentage losses and errors are presented as a fraction of fuel quantity on board or as a fraction of fuel consumed. Those losses and errors present between 0,001% and 5% of fuel stock or fuel consumption figures for each identified loss/error point. This paper presents a contribution for more accurate heavy fuel oil consumption calculation and consequently accurate declaration of remaining fuel stock onboard. It also presents a base for possible further research on the possible influence of fuel grade, fuel water content on the accuracy of consumption calculation.

scholarly journals MODEL ESTIMASI KONSUMSI BAHAN BAKAR KAPAL IKAN HUHATE DAN RAWAI TUNA

2017 ◽  
Vol 23 (2) ◽  
pp. 99
Author(s):  
Suryanto Suryanto ◽  
Wudianto Wudianto
Keyword(s):  
Fuel Consumption ◽  
Fuel Oil ◽  
Production Factor ◽  
Fishing Gear ◽  
Oil Consumption ◽  
Estimation Model ◽  
Indonesian Seas ◽  
Fishing Vessels ◽  
Long Line ◽  
The Government

Huhate dan rawai tuna merupakan alat tangkap utama untuk menangkap ikan tuna di perairan Indonesia. Hasil tangkapannya harus bersaing dalam perdagangan global dimana biaya bahan bakar merupakan faktor produksi yang dominan. Namun kebijakan Pemerintah terkait subsidi bahan bakar minyak terlalu sering berubah karena keterbatasan kemampuan keuangan Pemerintah. Disisi lain peraturan subsidi bahan bakar kapal perikanan yang berlaku kurang mencerminkan kondisi nyata armada perikanan Nasional. Oleh karena itu, perkiraan konsumsi bahan bakar yang diperlukan harus dilakukan secara cermat. Penelitian ini bertujuan untuk mengembangkan model estimasi konsumsi BBM mesin induk dan mesin bantu, khususnya untuk armada huhate dan rawai tuna. Uji model Kleppesto, Digernes dan Hollenbach digunakan untuk mengestimasi daya mesin induk armada huhate dan rawai tuna berdasarkan data SIPI (Surat Ijin Penangkapan Ikan) dan pengukuran kecepatan kapal dilapangan. Hasil penelitian menunjukan bahwa model Kleppesto mendapatkan hasil lebih akurat. Selanjutnya model ini dipakai untuk memperkirakan faktor konsumsi BBM mesin induk dan mesin bantu (Cbbm) dengan 2 skenario efisiensi quasi propulsive optimis dan pesimis. Hasil penelitian menunjukan, dengan kedua skenario tersebut, Cbbm armada huhate dan rawai tuna didapatkan nilai 0,121-0,160 dan 0,136-0,180 (kg/HP.jam). Hal ini menjelaskan bahwa untuk mendapatkan faktor konsumsi BBM kapal ikan perlu memperhatikan jenis alat tangkap ikan yang digunakan. Pole and line and long line are main fishing gear for catching tuna in Indonesian seas. Their catches must compete in global trade where as fuel cost is a dominant production factor. However the Government’s policy on fuel subsidies has changed too often due to the limited financial capacity of the Government. In addition, the present regulation of fuel subsidy for fishing vessels does not reflect the real condition of the national fishing fleet. Thus, the estimation of the required fuel consumption must be done carefully. This paper aims to develop fuel consumption estimation model for pole and liner and tuna longliner. Based on the data of fishing licences and in situ vessel speeds measurements; Kleppesto, Digernes and Hollenbach models were used to estimate the required engine power of pole and liner and tuna longliner samples. The study indicates that Kleppesto model is more accurate compared to the other two. Using the scenario of optimistic and pessimistic quasi propulsive efficiencies, then the models were used to estimate the fuel oil consumption factor for main and auxiliary engines (Cbbm) of the fleets. The research shows, Cbbm of pole and liner and tuna longliner are 0,121-0,160 dan 0,136-0,180 (kg/HP.jam)  respectively. The result showed that fuel oil consumption factor of fishing vessel depends on fishing gear used.

Traffic Flow Management Impact on Delay and Fuel Consumption: an Atlanta Case Study

2012 ◽  
Vol 20 (3) ◽  
pp. 203-224 ◽  
Author(s):  
Shon R. Grabbe ◽  
Banavar Sridhar ◽  
Avijit Mukherjee ◽  
Alexander Morando
Keyword(s):  
Fuel Consumption ◽  
Traffic Flow ◽  
Flow Management ◽  
Traffic Flow Management

Statistical approach for describing failures and lifetimes of water mains

1998 ◽  
Vol 38 (6) ◽  
pp. 209-217 ◽  
Author(s):  
Jianhua Lei ◽  
Sveinung Sægrov
Keyword(s):  
Data Base ◽  
Ductile Iron ◽  
Counting Process ◽  
Statistical Approach ◽  
Process Models ◽  
Average Lifetime ◽  
Maintenance Practice ◽  
Water Mains ◽  
Near Future

This paper demonstrates the statistical approach for describing failures and lifetimes of water mains. The statistical approach is based on pipe inventory data and the maintenance data registered in the data base. The approach consists of data pre-processing and statistical analysis. Two classes of statistical models are applied, namely counting process models and lifetime models. With lifetime models, one can estimate the probability which a pipe will fail within a time horizon. With counting process models one can see the deteriorating (or improving) trend in time of a group of “identical” pipes and their rates of occurrence of failure (ROCOF). The case study with the data base from Trondheim municipality (Norway) demonstrates the applicability of the statistical approach and leads to the following results: 1). In the past 20 years, Trondheim municipality has experienced approximately 250 to 300 failures per year. However, the number of failures per year will significantly increase in the near future unless better maintenance practice is implemented now. 2). Unprotected ductile iron pipes have a higher probability of failures than other materials. The average lifetime of unprotected ductile iron pipes is approximately 30 to 40 years shorter than the lifetime of a cast iron pipe. 3). Pipes installed 1963 and 1975 are most likely to fail in the future; 4) The age of a pipe does not play a significant role for the remaining lifetime of the pipe; 5). After 2 to 3 failures, a pipe enters a fast-failure stage (i.e., frequent multiple between failures).

Sign in / Sign up

Export Citation Format

Share Document