Introduction of a New Index of Field Operations Efficiency

The evaluation and prediction of the agricultural machinery field efficiency is essential for agricultural operations management. Field efficiency is affected by unpredictable (e.g., machine breakdowns) and stochastic (e.g., yield) factors, and thus, it is generally provided by average norms. However, the average values and ranges of the field efficiency are of limited value when a decision has to be made on the selection of the appropriate machinery system for a specific operational set up. To this end, in this paper, a new index for field operability, the field traversing efficiency (FTE), a distance-based measure, is introduced and a dedicated tool for estimation of this measure is presented. In order to show the degree of the dependence of the FTE index on the operational features, a number of 864 scenarios derived from the consideration of six sample field shapes, three conventional fieldwork patterns, four driving directions, and twelve combinations of machine unit kinematics and implement width were evaluated by the developed tool. The test results showed that variation of FTE was up to 23% in the tested scenarios when using different operational setups.

Comparative Analysis of Linear and Non-Linear Programming Techniques for the Optimization of Ship-Machinery Systems

The selection of a proper machinery system is one of the primary decisions to be taken during the ship-design phase. Nonetheless, this selection is made challenging by the presence of a variety of alternatives, and by the limited data availability at the early stages of the design phase. An optimization framework is presented in this paper, supporting decision making at the earliest stages of the ship-design process. The framework is suitable to perform the screening and the selection of optimal machinery configurations for a predefined ship operational profile, and it includes both linear and non-linear optimization routines. The results of the linear and the non-linear approaches are compared, and indications on what conditions are the most suitable for the application of one or the other approach are provided. Both approaches are tested for two case studies, a bulk carrier and a small cruise ship. The results indicate that both optimization approaches lead to the same layout of the machinery system, but to slightly different unit scheduling. This suggests that the use of the linear approach is suitable for design purposes, but less appropriate for operational optimization. In addition, the findings of the work suggest that the trade-off between fuel consumption and volume of the engines should be considered when selecting the machinery system for a ship.

Pengaruh Penambahan Zat Aditif X terhadap Viskositas, Performa Mesin dan Emisi Gas Buang Sepeda Motor Honda CB150R

<em>Mechanical contact is the process which can not be avoided in machinery system. The way to diminish thrist conditon which caused by the process is to give engine lubricant into that system. The lubricant resistance over the temperature system is influenced by base stock and its additives where contained of. Lubricant oil additives are chemical compounds that will improve or enhance the lubricant performance of base oil. These additives are carefully designed to ensure the functions in machines system. Engine lubricant that used in this research is MPX 1 SAE 10 W-30 and the additives that used is Engine Additive with brand Lupromax. The aim of this research is to analyze the reaction of increasing the additive EA over to viscosity and machine function of Honda All New CB 150R. In this research, it is found that the fusion with EA 5.25% (1200 ml lubricant with 63 ml additives) could: (1) reduce viscosity to 66.56 mm²/s and 9.9 mm²/s at temperatures of 40°C and 100°C, respectively, (2) enhance the torque and energy to 14.5 kW @9000 rpm and 12.23 Nm @7000 rpm with reducing the rate emision of CO to 0.44% vol, HC to 196 ppm vol, CO₂ to 6.5% vol and enhancing the rate of O₂ to 10.71%, and (3) reduce fuel consumption to 18.75 ml/km.</em>