Design and analysis of kinetic energy recovery system for automobiles: a case study for commuters in Edinburgh.

Transport and its energetic and environmental impacts affect our daily lives. The transport sector is the backbone of the United Kingdom’s economy with 2.3 million people being employed in this sector. With a high dependency on transport for passengers and freight and with the knowledge that oil reserves are rapidly decreasing a solution has to be identified for conserving fuel. Passenger vehicles account for 61% of the transport fuel consumed in the U.K. and should be seen as a key area to tackle. Despite the introduction and development of electric powered cars, the widespread infrastructure that is required is not in place and has attributed to their slow uptake, as well as the fact that the electric car’s performance is not yet comparable with the conventional internal combustion engine. The benefits of the introduction of kinetic energy recovery systems to be used in conjunction with internal combustion engines and designed such that the system could easily be fitted into future passenger vehicles are examined. In this article, a review of automobile kinetic energy recovery system is presented. It has been argued that the ultracapacitor technology offers a sustainable solution. An optimum design for the urban driving cycle experienced in the city of Edinburgh has been introduced. The potential for fuel savings is also presented

PCA-ANFIS based prediction for water Injection effectiveness cycle in oil fields.

By proposing a numerical based method on PCA-ANFIS(Adaptive Neuro-Fuzzy Inference System), this paper is focusing on solving the problem of uncertain cycle of water injection in the oilfield. As the dimension of original data is reduced by PCA, ANFIS can be applied for training and testing the new data proposed by this paper. The correctness of PCA-ANFIS models are verified by the injection statistics data collected from 116 wells inside an oilfield, the average absolute error of testing is 1.80 months. With comparison by non-PCA based models which average error is 4.33 months largely ahead of PCA-ANFIS based models, it shows that the testing accuracy has been greatly enhanced by our approach. With the conclusion of the above testing, the PCA-ANFIS method is robust in predicting the effectiveness cycle of water injection which helps oilfield developers to design the water injection scheme.