ANN technique to predict performances of diesel engine runs by butanol-diesel blends

Abstract EN

Performance of a diesel engine running under butanol-diesel blends one of important cases to evaluate the variance in the engine performance due to the fuel type change.

Many efforts exerted in this field.

Artificial neural network (ANN) model one of modern technique is used to predict the engine performance.

ANN using a multi layer feed forward back propagation learning algorithm is developed to evaluate diesel engine performance.

The brake efficiency, fuel consumption and exhaust temperature are predicted.

The data required for training of ANN model are collected from experimental tests carried out on multi cylinder diesel engine.

More than forty different architectures are tested for obtaining best fitting model.

Maximum, minimum as well as average percentage errors are calculated for each architecture and R &  test is carried out to decide upon the best architecture for this model.

The training process is set to stop when all errors are below 0.01 for training and below 3% for the validation.

The results obtained from trained model are compared with experimental data of engine performance.

The numerical investigation demonstrated that the ANN model is the best approach and assessment program for diesel engine performance with only 0.7% absolute average errors.

The precise results of the model indicated an excellent and prompting training of ANN model.