In this study, with the aim of reducing the energy consumption in the production of HHO gas for use in the combustion process of diesel fuel, different modes of gas production were investigated using electrolyzers. According to previous studies, the energy consumption rate of the electrolyzer to produce a high volumetric flow of HHO gas is very high. This high rate will restrict the use of equipment such as high-capacity batteries. The effects of HHO gas injection at the idle speed of the engine at a low temperature were evaluated. Because in this situation, the engine makes high air pollution. The results showed that the percentage of CO, CO2, HC, and NOX gases decreased by 66%, 33%, 38%, and 11%, respectively. On the other hand, the amount of O2 gas in the exhaust increased by 18%. These results were reported for HHO gas injection from 10 to 45 ml/s. The performance of Group Method of Data Handling (GMDH) neural network was desirable in modeling diesel engine pollutants. Because the Root-Mean-Square Error (RMSE) criterion for all evaluated gases is less than 0.32. The GMDH neural network was used for modeling the operation of the diesel engine with HHO supplemental fuel. The GMDH results showed that this artificial network can measure all engine exhaust gases. It can be used as a sensor and virtual simulator for this diesel engine with HHO supplemental fuel.

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