Development of Artificial Neural Networks Trained by Heuristic Algorithms for Prediction of Exhaust Emissions and Performance of a Diesel Engine Fuelled with Biodiesel Blends

The main goal of this study is to investigate the capability of several heuristic algorithms, including cuckoo search (CS), gravitational search algorithm (GSA), particle swarm optimization (PSO), and genetic algorithm (GA) in training neural networks for predicting performance and exhaust emissions of the diesel engine fuelled with biodiesel blends. The case application is a Hyundai D4CB 2.5 engine together with B0, B10, and B20 biodiesel blends, which are popularly used in Vietnam. The engine process parameters are used as inputs and the outputs include predicted torque and NOx emission. Different predicting models based on neural network trained by different algorithms are developed and investigated. The performance of each model is evaluated and compared using correlation coefficient (R), mean absolute error (MAE), root mean squared error (RMSE), relative absolute error (RAE), and root relative squared error (RRSE). The expected results indicate that neural networks with parameters optimized by heuristic algorithms can be utilized to develop the model for the prediction of performance and exhaust emissions. The study also provided a better understanding of the effects of engine process parameters on performance and exhaust emissions.

[1]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1992, Math. Control. Signals Syst..

[2]  Bidyut Baran Chaudhuri,et al.  Handwritten Numeral Databases of Indian Scripts and Multistage Recognition of Mixed Numerals , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[4]  J. V. Gerpen,et al.  Biodiesel processing and production , 2005 .

[5]  A. S. Ramadhas,et al.  Biodiesel production from high FFA rubber seed oil , 2005 .

[6]  L. C. Meher,et al.  Technical aspects of biodiesel production by transesterification—a review , 2006 .

[7]  Nilanjan Dey,et al.  Discrete cuckoo search algorithms for two-sided robotic assembly line balancing problem , 2017, Neural Computing and Applications.

[8]  Niels Kjølstad Poulsen,et al.  Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner’s Handbook , 2000 .

[9]  Xin-She Yang,et al.  Cuckoo Search via Lévy flights , 2009, 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC).

[10]  R. A. Mat Noor,et al.  Recent Developments of Neural Networks in Biodiesel Applications , 2014, SEMCCO.

[11]  Serhat Duman,et al.  GRAVITATIONAL SEARCH ALGORITHM FOR ECONOMIC DISPATCH WITH VALVE-POINT EFFECTS , 2010 .

[12]  Abhipsa Sahu,et al.  Performance comparison of 2-DOF PID controller based on Moth-flame optimization technique for load frequency control of diverse energy source interconnected power system , 2018, 2018 Technologies for Smart-City Energy Security and Power (ICSESP).

[13]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[14]  Hossein Nezamabadi-pour,et al.  GSA: A Gravitational Search Algorithm , 2009, Inf. Sci..

[15]  G. Lakshmi Narayana Rao,et al.  Optimal Performance and Emission Analysis of Diesel Engine Fuelled with Palm Oil Methyl Ester with an Artificial Neural Network , 2016 .

[16]  Qing-song Zuo,et al.  An artificial neural network developed for predicting of performance and emissions of a spark ignition engine fueled with butanol–gasoline blends , 2018 .

[17]  Mustafa Canakci,et al.  Prediction of performance and exhaust emissions of a diesel engine fueled with biodiesel produced from waste frying palm oil , 2009, Expert Syst. Appl..

[18]  Ken-ichi Funahashi,et al.  On the approximate realization of continuous mappings by neural networks , 1989, Neural Networks.

[19]  Rich Caruana,et al.  Overfitting in Neural Nets: Backpropagation, Conjugate Gradient, and Early Stopping , 2000, NIPS.

[20]  Avinash Kumar Agarwal,et al.  Performance evaluation of a vegetable oil fuelled compression ignition engine , 2008 .

[21]  Nilanjan Dey.  Advancements in Applied Metaheuristic Computing , 2017 .

[22]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[23]  Randall S. Sexton,et al.  Comparing backpropagation with a genetic algorithm for neural network training , 1999 .

[24]  Meenakshi De,et al.  A reliable energy management and generation scheduling model in microgrids using modified cuckoo search algorithm , 2018, 2018 Emerging Trends in Electronic Devices and Computational Techniques (EDCT).

[25]  Liu Mingguang,et al.  Artificial Neural Network Co-optimization Algorithm Based on Differential Evolution , 2009, 2009 Second International Symposium on Computational Intelligence and Design.

[27]  Kurt Hornik,et al.  Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks , 1990, Neural Networks.

[28]  Nilanjan Dey,et al.  Improved Cuckoo Search and Chaotic Flower Pollination optimization algorithm for maximizing area coverage in Wireless Sensor Networks , 2016, Neural Computing and Applications.