Estimation of aircraft trip fuel consumption: a novel self-organizing machine learning constructive neural network

Abstract

Accurate estimation of aircraft fuel consumption is critical for airlines in terms of safety and profitability. Recently, the growth in the aviation industry worldwide has made accurate fuel estimation an important research topic. It can be revealed that the demand for passengers and cargos has increased by 7.4% and 3.4% respectively in 2018 as compared to the level in 2017. Similarly, in 2006, the airline industry fuel consumption was 69 billion gallons and it was forecasted that it will change to 97 billion gallons in 2019. Despite these pleasing economic conditions for airlines, the increase in jet fuel prices and restrictions to protect environmental degradation comprises a lot of challenges. It was forecasted that jet fuel prices will increase by 31.18% in 2019 as compared to those in 2015. International authorities stress to reduce carbon dioxide (CO2) emissions by 50% in 2050 and reduce fuel consumption by 1.5% per year to avoid ozone depletion. In the future, international authorities are planning to make it mandatory for airlines to certify their aircraft according to CO2 certification standards. These challenges and restrictions force airlines operating organizations to control excess fuel consumption. Furthermore, among various airline operating expenses, fuel consumption cost accounts for the highest contribution of 28.2% of the total operating cost. A slight change in fuel prices can create an enormous impact on airlines operating expenses making it more valuable to study. The increasing awareness for environmental protection by international authorities in conjunction with growing fuel prices and boosting demand from tourism are encouraging airline operating companies to adopt competitive strategies in fuel management to control excess fuel consumption for long-term sustainability. In current practice, estimation of fuel consumption for a flight trip is usually done by an energy balance approaches (EAs). However, according to the existing literature, the information needed to determine the coefficients are not always available in a real scenario and a lot of flight testing need to be performed to generate data which may make the EAs expensive. The unavailability of data may result in the usage of global parameters with default values rather than local parameters, which may lead to inaccurate results. The complex mathematical computation, high testing and consultation cost, expert involvement, and estimation errors, which may become worse in certain conditions limit EA methods applicability. Later, machine learning backpropagation neural networks (BPNNs) was proposed to provide an alternative to EAs. The main limitation of earlier works on the application of a BPNNs for fuel estimation is that they only covered a small number of aircraft types with limited flight data. The reasons for this may be the weak generalization performance and slow convergence drawbacks of a BPNN based on trial-and-error approaches to select the best hyperparameters. Besides, BPNN based fuel estimation models were proposed by considering low-level operational parameters with the future recommendation of incorporating more parameters that may have a significant effect on fuel consumption. Other than EAs and BPNNs, many other models for fuel estimation are proposed in the literature by considering distinct flight phases. Their cumulative effect to estimate fuel for the whole journey may even result in the suboptimal estimation. The application of neural networks (NNs) is gaining much popularity in the airline sector to improve its various operations and enhance services. Limiting our study scope to EA and BPNN based fuel estimation models, the actual performance of aircraft usually deviates from such estimation. The required quantity of fuel needed for a safe journey is dependent on many operational parameters and estimation methods. Loading suboptimal or abundant fuel in aircraft may result in deviation of fuel from estimation. The fuel deviation may be either positive or negative known as overestimation or underestimation, respectively. Therefore, extra fuel is loaded in the discrepancy reservoirs based on experience because of less confidence in the estimation methods and to meet unforeseen conditions and accounts for aircraft deterioration. This increases the weight of the aircraft, requiring more thrust to balance drag and weight. Ultimately more fuel is consumed, and more frequent aircraft maintenance is required than planned. To overcome the above limitations of trip fuel estimation, our objectives are threefold: i) to formulate a model and define an objective function of minimizing fuel deviation, ii) to propose a novel self-organizing constructive neural network (CNN) featuring a cascade topology and capable of analytically calculating connection weight coefficients to achieve better generalization performance and faster learning speed, and iii) to apply the novel CNN for minimizing fuel deviation and make performance comparison with the existing airline energy approach (AEA) and the BPNN. The purpose is to achieve better estimation by adding high-level operational parameters, avoid using global operational parameters, eliminate the need for a trial-and-error approach, reduce the number of hyperparameter adjustments and expert involvement. We consider that insufficient attempts have been reported in the literature concerning estimates of trip fuel using CNNs along with high-dimensional data for the entire trip flight phases collectively. A comparative study of the proposed CNN with the existing AEA and the BPNN gives an important managerial insight. The numerical results demonstrate that the trip fuel estimation by the proposed CNN achieves better results compared to AEA and BPNN while requiring the shortest time compared to the BPNN. The significant improvement in trip fuel estimation creates greater confidence in the proposed CNN given that it may eliminate the need for adding more fuel based solely on experience.