<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Coal is an important energy resource. How to utilize it efficiently and cleanly is a hot topic nowadays. In the coal gasification process, the process parameter indexes have a significant impact, and the uncertainty of these factors will lead to a decrease in the quality of gas production. Therefore, in this paper, the uncertainty of process parameters is considered, and Aspen plus software with the Monte Carlo method is used to simulate the coal chemical process and measure the effect of uncertainty of process parameters on the yield of the coal gasification process. On this basis, in addition, coal flow rate, pressure, and steam/oxygen are taken as the process parameters and optimized, and three sets of multi-objective optimization models are established with gas calorific value, gasification efficiency, and gas yield, respectively, which are solved by improved multi-objective genetic algorithm based on crossover operator and variational operator to obtain Pareto curves, so as to adjust the parameter values according to the actual needs. The results show that the fluctuation of pressure has a big influence on the carbon conversion rate and gasification efficiency, and the carbon conversion rate and gasification efficiency can be made more stable by controlling the change of pressure. The improved genetic algorithm NSGA-II can reach the actual optimal objective function value in both high and low iteration times, providing the required parameters for the decision maker, and the optimal program results in TEC of 402,758 kW and CO<sub>2</sub> content of 0.12%, which is effective in energy saving and emission reduction.</p>
</abstract>

Coal is an important energy resource. How to utilize it efficiently and cleanly is a hot topic nowadays. In the coal gasification process, the process parameter indexes have a significant impact, and the uncertainty of these factors will lead to a decrease in the quality of gas production. Therefore, in this paper, the uncertainty of process parameters is considered, and Aspen plus software with the Monte Carlo method is used to simulate the coal chemical process and measure the effect of uncertainty of process parameters on the yield of the coal gasification process. On this basis, in addition, coal flow rate, pressure, and steam/oxygen are taken as the process parameters and optimized, and three sets of multi-objective optimization models are established with gas calorific value, gasification efficiency, and gas yield, respectively, which are solved by improved multi-objective genetic algorithm based on crossover operator and variational operator to obtain Pareto curves, so as to adjust the parameter values according to the actual needs. The results show that the fluctuation of pressure has a big influence on the carbon conversion rate and gasification efficiency, and the carbon conversion rate and gasification efficiency can be made more stable by controlling the change of pressure. The improved genetic algorithm NSGA-II can reach the actual optimal objective function value in both high and low iteration times, providing the required parameters for the decision maker, and the optimal program results in TEC of 402,758 kW and CO2 content of 0.12%, which is effective in energy saving and emission reduction.

Optimization of coal chemical process parameters and energy saving and emission reduction based on genetic algorithm

Department of Resources and Chemical Engineering

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Coal is an important energy resource. How to utilize it efficiently and cleanly is a hot topic nowadays. In the coal gasification process, the process...

Optimization of coal chemical process parameters and energy saving and emission reduction based on genetic algorithm

Publicado en línea: 05 ago 2024

Páginas: -

Recibido: 10 abr 2024

Aceptado: 09 jul 2024

DOI: https://doi.org/10.2478/amns-2024-2212

Palabras clave
Aspen Plus software, Monte Carlo method simulation, Multi-objective optimization, Genetic algorithm NSGA-Ⅱ, Coal chemical process parameters

© 2024 Xiang Song, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Optimization of coal chemical process parameters and energy saving and emission reduction based on genetic algorithm

Publicado en línea: 05 ago 2024

Páginas: -

Recibido: 10 abr 2024

Aceptado: 09 jul 2024

DOI: https://doi.org/10.2478/amns-2024-2212

Palabras claveAspen Plus software, Monte Carlo method simulation, Multi-objective optimization, Genetic algorithm NSGA-Ⅱ, Coal chemical process parameters

© 2024 Xiang Song, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Palabras clave
Aspen Plus software, Monte Carlo method simulation, Multi-objective optimization, Genetic algorithm NSGA-Ⅱ, Coal chemical process parameters