Estimation of NOx pollutants in a spark engine fueled by mixed methane and hydrogen using neural networks and genetic algorithm.

Nowadays, due to stricter pollution standards, more attention has been focused on pollutants emitted from cars. As a very dangerous pollutant, NOx has always triggered the sensitivity of the related organizations. In the process of developing and designing the engine, estimating the amount of this pollutant is of great importance to reduce future expenses. Calculating the amount of this pollutant has usually been complicated and prone to error. In the present paper, neural networks have been used to find the coefficients of correcting NOx calculation. The Zeldovich method calculated the value of NOx with 20% error. By applying the progressive neural network and correcting the equation coefficient, this value decreased. The related model has been validated with other fuel equivalence ratios. The neural network model has fitted the experimental points with a convergence ratio of 0.99 and a squared error of 0.0019. Finally, the value of NOx anticipated by the neural network has been calculated and validated according to empirical data by applying maximum genetic algorithm. The maximum point for the fuel composed of 20% hydrogen and 80% methane occurred in the equivalence ratio of 0.9; and the maximum point for the fuel composed of 40% hydrogen occurred in equivalence ratio of 0.92. The consistency of the model findings with the empirical data shows the potential of the neural network in anticipating the amount of NOx.

Reduction of the environmental impacts of the hydropower plant by microalgae cultivation and biodiesel production.

Hydropower plants supply their energy needs for electricity generation from rivers or water canals, so these power plants cannot be used for sustainable electricity generation, and the best time to use these power plants is during peak power consumption. These power plants are less polluting in terms of environment than other power plants, but they also have negative environmental effects, such as freshwater eutrophication and water salinity or microalgae. This study focused its attention on microalgae extraction as an environmentally friendly method to reduce water salinity and how they can be used for biodiesel production as an auxiliary fuel to enhance the energy production by hydropower plants. The information of the sample hydroelectric power plant (Gotvand Dam) that was required for the processing and simulation process is stated. The step-by-step simulation is reviewed and the results and optimizations are described. The highest separation of microalgae for 1 min electrolysis with distance of 1 cm between the two electrodes is 90%, which reduces the salt content of the water in which the microalgae is grown by 13%. The maximum separation of salt from water is 19.5%, which is reduced to 9.5% in the centrifuge method. Water salinity reduction to microalgae extraction ratio is 14.45%. The optimal combination of diesel and biodiesel is 80%-20%. As can be seen from the results it is recommended to use microalgae for reducing negative environmental impacts in addition to increasing the power generation capacities of hydropower plants. Also more specific studies on terms of the culture of the microalgae and its individual cultivation methods for hydropower plants beneficial programs should be taken into account and be used by policy makers in the future.