Bus AC, Optimization, Exergy
The use of a roof-top Bus air-conditioning (AC) system has been steadily growing in in the emerging markets. An AC system is the second biggest energy consumer component in a bus. The addition of an air conditioning system in a bus would cause higher fossil fuel consumption and increasing the impact on the environment due to the increasing amounts of exhaust emission from the combustion engines. For this reason, researchers and AC system manufacturers seek to improve Bus AC systems design and technology to reduce the fuel consumption rate without forfeiting passenger thermal comfort. Unlike in Developed Markets,Bus AC in Emerging Markets pose a unique challenge of undersigned Main engine , not well insulated walls and higher emphasis on first cost. Thus, there is a need to develop an affordable and efficient bus AC system featured by low first cost, economical operation in terms of energy saving and stable passenger thermal comfort at all atmospheric conditions. This paper presents a design optimization study that is used in assessing the best configuration of a bus air conditioning system, using a thermo economic approach. A cost function is introduced, defined as the sum of three contributions, the efficiency of system obtained by minimizing the rate of exergy destruction, the investment expense, and the operational expense of a roof top Bus AC system that is usually coupled with the main bus engine. The optimal trade-off between these contributions efficiency, investment and operating cost is investigated. The design optimization is conducted by investigating the effect of geometrical and operational parameters of system configuration which have a significant influence on the objective functions of the design optimization. Based on this sensitivity analysis different practical system enhancement methods like using variable displacement compressors, enhanced and demand based dehumidification, dedicated sub cooling etc are investigated to minimize the cost function. A Pareto trade-off frontier is built to give a guidance on the optimal trade-off on the multi objectives. Finally the design solutions are presented for various trade-offs. An in-house refrigeration simulation tool is used to build the base line system model, this model is calibrated using the actual test data. This calibrated model is used to run the optimization exercises. The objective function and constraints are developed for different cases and optimization solution is obtained using Matlab Optimization Toolbox.