Artificial Neural Network Led Optimization of Oxyhydrogen Hybridized Diesel Operated Engine

Abstract

The prevailing massive exploitation of conventional fuels has staked the energy accessibility to future generations. The gloomy peril of inflated demand and depleting fuel reservoirs in the energy sector has supposedly instigated the urgent need for reliable alternative fuels. These very issues have been addressed by introducing oxyhydrogen gas (HHO) in compression ignition (CI) engines in various flow rates with diesel for assessing brake-specific fuel consumption (BSFC) and brake thermal efficiency (BTE). The enrichment of neat diesel fuel with 10 dm3/min of HHO resulted in the most substantial decrease in BSFC and improved BTE at all test speeds in the range of 1000–2200 rpm. Moreover, an Artificial Intelligence (AI) approach was employed for designing an ANN performance-predicting model with an engine operating on HHO. The correlation coefficients (R) of BSFC and BTE given by the ANN predicting model were 0.99764 and 0.99902, respectively. The mean root errors (MRE) of both parameters (BSFC and BTE) were within the range of 1–3% while the root mean square errors (RMSE) were 0.0122 kg/kWh and 0.2768% for BSFC and BTE, respectively. In addition, ANN was coupled with the response surface methodology (RSM) technique for comprehending the individual impact of design parameters and their statistical interactions governing the output parameters. The R2 values of RSM responses (BSFC and BTE) were near to 1 and MRE values were within the designated range. The comparative evaluation of ANN and RSM predicting models revealed that MRE and RMSE of RSM models are also well within the desired range but to be outrightly accurate and precise, the choice of ANN should be potentially endorsed. Thus, the combined use of ANN and RSM could be used effectively for reliable predictions and effective study of statistical interactions.

Energy Demand of the Road Transport Sector of Saudi Arabia—Application of a Causality-Based Machine Learning Model to Ensure Sustainable Environment

“…Every neuron except the neuron in the input layer receives the information from the neurons in the preceding layer. After this, the neuron passes information to the output through a sigmoid function [55][56][57][58]. A training algorithm is adopted to obtain the weights while the algorithm minimizes the cost function, such as mean squared error considering the target and the model output.…”

“…The corresponding activation function modifies the input and transfers the result to the nodes of the subsequent layer or the environment. The output of a node in the hidden layer and the output layer are in the following part as adopted from [55][56][57][58]. The output of the hidden node j:…”

A Recent Study on Remediation of Direct Blue 15 Dye Using Halloysite Nanotubes

“…Response DB15-HNT = 42.1 + 18.5 × A − 6.2 × B + 19.5 × C − 32.0 × D − 13.4 × E − 3.4 × AB + 11.8 × AC − 3.0 × BC − 19.8 × A 2 + 1.6 × B 2 − 27.5 × C 2 + 24.2 × D 2 + 23.4 × E 2 (1) The regression coefficient [51][52][53][54][55][56][57][58][59][60][61][62][63] indicates the effect of various features affecting the adsorption capacity on the adsorption capacity [64][65][66][67]. The surface and contour plots represented in Figure 14 show the combined effect of two features on the process of adsorption.…”

Comparative Analysis of Performance, Emission, and Combustion Characteristics of a Common Rail Direct Injection Diesel Engine Powered with Three Different Biodiesel Blends

“…As compared with diesel activity, the diffusion combustion process shown by the second peak is higher for JAMNSOB (B20). This effect with biodiesel is due to incomplete combustion caused by the higher viscosity of biodiesel blends and reduced air entrainment and air-fuel mixing rates [53][54][55][56][57]. This results in lower fuel and air mixing during a delay period leading to lower rapid combustion phase with biodiesel.…”