Artificial Neural Network modeling of a hydrogen dual fueled diesel engine characteristics: An experiment approach. (25th May 2017)

• Record Type: Journal Article
• Authors: Syed, Javed; Baig, Rahmath Ulla; Algarni, Salem; Murthy, Y.V.V. Satyanarayana; Masood, Mohammad; Inamurrahman, Mohammed
• Is Part Of: International journal of hydrogen energy. Volume 42:Number 21(2017)
• Page Start: 14750
• Page End: 14774
• Publication Date: 2017-05-25
• Subjects: Hydrogen fuel -- Artificial Neural Network -- Diesel engine -- Performance & emission characteristics -- Emission–performance trade-off -- Uncertainty analysis; Hydrogen as fuel -- Periodicals; Hydrogène (Combustible) -- Périodiques; Hydrogen as fuel; Periodicals; 665.81
• DOI: 10.1016/j.ijhydene.2017.04.096
• Languages: English
• ISSNs: 0360-3199

Abstract

Abstract: The rapid growth of vehicular pollution; mostly running on the diesel engine, emissions emerging are the concerns of the day. Owing to clean burn characteristics features, Hydrogen (H2 ) as a fuel is the paradigm of the researcher. Extensive research presented in the literature on H2 dual fueled diesel engine reveals, the significant role of H2 in reducing emissions and enhancing the performance of a dual fueled diesel engine. With meager qualitative experiment data, the feasibility to develop an efficient Artificial Neural Network (ANN) model is investigated, the developed model can be utilized as a tool to investigate the H2 dual fueled diesel engine further. In the process of developing an ANN model, engine load and H2 flow rate are varied to register performance and emission characteristics. The creditability of the experiment is ascertained with uncertainty analysis of measurable and computed parameters. Leave-out-one method is adopted with 16 data sets; seven training algorithms are explored with eight transfer function combinations to evolve a competent ANN model. The efficacy of the developed model is adjudged with standard benchmark statistic indices. ANN model trained with Broyden, Fletcher, Goldfarb, & Shanno (BFGS) quasi-Newton backpropagation (trainbfg) stand out the best among other algorithms with regression coefficient ranging between 0.9869 and 0.9996. Graphical abstract: Highlights: Performance and emission characterizers were register for H2 dual fueled diesel engine. The experiment show, the H2 flow rate has a significant role on performance and emissions characteristics. ANN models were developed with 16 data set emanated from experiments. Employing Leave-One-Out concept, models were trained with various algorithms and transfer functions. Precise predictions were obtained for model trained with trainbfg algorithm and tansig–tansig transfer function.