Experimental and artificial neural network (ANN) study of hydrogen enriched compressed natural gas (HCNG) engine under various ignition timings and excess air ratios. (15th October 2018)

• Record Type: Journal Article
• Authors: Mehra, Roopesh Kumar; Duan, Hao; Luo, Sijie; Rao, Anas; Ma, Fanhua
• Is Part Of: Applied energy. Volume 228(2018)
• Page Start: 736
• Page End: 754
• Publication Date: 2018-10-15
• Subjects: Hydrogen enrichment -- HCNG -- ANN -- Lean burn combustion -- Combustion -- Emission; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2018.06.085
• Languages: English
• ISSNs: 0306-2619

Abstract

Graphical abstract: Highlights: Presents, the novel & first comprehensive HCNG engine's ANN model. Experimental study of HCNG engine had been performed under various λ and θ i . The calibration of the engine is carried out to know MBT ignition timing. The prediction accuracy of ANN model checked under various number of neurons. The value of R is found very close to 1.00 in each case for the validation. Abstract: In today's era, the computational capabilities of artificial neural network has endorsed to be a bedrock and inferences in many fields, including internal-combustion engines. The presented research in ANN has been germinated to anticipate the performance and emission characteristics of a turbocharged SI engine fueled with various HCNG mixtures. The experiments were accomplished at various excess air ratios ( λ ), ignition timings ( θ i ) at MAP of 105 kPa and 140 kPa, while engine speed was kept constant at 1600 rpm to obtain data for testing and training ANN model. The test results show that with the increase in values of λ, MAP, and hydrogen addition, the torque output effectively decreases while BSFC first decreases and after attaining minimum value it further increases. The NOx, CO, THC, and CH4 emissions all declined with the hike of ignition advance angle, and inclined with increase of the load. ANN's popular backpropagation algorithm is adopted in multilayered feedforward networks. In order to predict the performance and emission characteristics of HCNG engine, the four-input and one-output network structure are used. HCNG0, HCNG20 and HCNG40 blends has been studied in the presented ANN model in which the excess air ratio ( λ ), engine load, ignition timing, and HCNG blends has been taken as four-input parameters. The bestowed model has been trained using hyperbolic tangent transfer activation function (tansig) and Levenberg-Marquardt learning algorithm (LM) along with numerous neurons. The lowest and highest value of correlation coefficient (R) and mean square errors (MSE) for validation were dig-out for various parameters, which were BSFC, torque, NOx, CO, THC, CH4 . The values of R (min–max) and MSE (min–max) were BSFC; 09519–0.9963 and 5.72–64.29, torque; 0.9948–0.9997 and 8.3935–95.8350, NOx ; 0.9938–0.9992 and 0.5271–3.4340, CO; 0.8595–0.9960 and 0.0188–1.0154, and THC; 0.9644–0.9972 and 0.0714–1.1695, CH4 ; 0.7965–0.9973, 0.0351–1.1789 respectively for various number of neurons. This research work provides the gist of ANN as an attractive choice to researchers for classical modelling techniques. In this regard, the ANN modeling can be utilized to minutely predict the performance and emission characteristics of hydrogen added CNG engines.