Modeling, prediction and multi-objective optimization of the coal gasification system

Ran Li,Zhen Yang,Yuanyuan Duan,H.J Kim

doi:10.1051/e3sconf/202124202001

Abstract

As global energy demand continues to increase, coal as basic energy still accounts for a significant proportion. Under the pressure of environmental protection, clean and efficient coal utilization technologies are in great demand. Coal gasification technology has the potential to realize near-zero-emissions for coal utilization. This paper establishes the coal gasification system model and analyzes the effect of oxygen/coal ratio and water/coal ratio on the system performance index of cold syngas efficiency, effective component ratio, carbon conversion ratio, and production ratio of hydrogen. The results show that when the oxygen/coal ratio increases, the efficiency of cold syngas and effective components ratio increase first and then decrease, carbon conversion ratio first increases and then remains unchanged, hydrogen production ratio gradually decreases; When the steam/coal ratio increases, the cold syngas efficiency, and carbon conversion ratio first increase and then decrease, effective component ratio ingredients gradually decreases, and the hydrogen production ratio increases. Using BP neural network to realize the prediction of the gasification system, and the mean square error reaches the magnitude of 10e-7. Multi-objective optimization results show that the oxygen/coal ratio and steam/coal ratio corresponding to the highest production ratio of hydrogen is 0.52 and 0.05. The highest carbon conversion ratio corresponds to the oxygen/coal ratio of 0.95 and the steam/coal ratio of 0.05.

Highlights

With the increasing pressure on environmental protection, carbon emissions have become the focus of attention in energy consumption
The calculation method adopted in this paper is to firstly build the coal gasification model in Aspen Plus, change different operation parameters, and obtain different output parameters; Secondly, BP neural network is used to establish the functional relationship between inputs and outputs; utilize the genetic algorithm to optimize the system, and the optimal operating parameters of the system are obtained
It can be seen that when the oxygen/coal ratio changes, the peak points of cold syngas efficiency, effective component ratio, and carbon conversion rate are very close

Summary

Introduction

With the increasing pressure on environmental protection, carbon emissions have become the focus of attention in energy consumption. While the gasification method and coal type are selected, the main operating parameters of the gasifier include pressure, temperature, and the proportion of gasification agent. The innovation of this paper mainly include: the research methods, using the neural network to connect the simulation results and genetic algorithm to achieve optimization; In terms of research content, the gasification process is optimized with multiple objectives to obtain the optimal solution. The research content of this paper mainly includes: (1) Establishes a coal gasification calculation model; (2) Uses BP neural network to establish the functional relationship between input and output parameters in the coal gasification process; (3) Multi-objective optimization of the coal gasification process using the genetic algorithm to obtain the optimal solution

Gasification model

BP neural network

Result

Findings

Conclusion