A Complex Evaluation and Optimization Approach for Oxygen-Enriched Combustion Characteristics of Blended Fuels Based on Response Surface Methodology

Abstract

With the energy consumption increasing, the coal supply in China has been becoming tight, which has made it difficult for thermal power generation in Yunnan Province. Making full use of local inferior coal and biomass resources in Yunnan can remedy the lack of fuel in power plants. In this paper, an oxygen-rich atmosphere thermogravimetric experiment was performed for a blended sample of Xiaolongtan lignite, Yiliang tobacco rod, and Fuyuan bituminous coal. The combustion characteristics of the mixed fuel under several key operating parameters (i.e., mass ratios, oxygen concentration, and heating rates) were studied. The response surface methodology was used to determine the optimal blending ratio of the three fuels. The results show that the ignition and burnout temperature of coal decrease and the combustion time diminishes with the enrichment of oxygen. The optimal oxygen concentration in the practical application is around 30%. The activation energy and preexponential factor increase with the enlargement of oxygen concentration. Such complex evaluation and optimization approach ensure the stable operation of thermal power plant production.

Application of Response Surface Methodology in Dance Health and Training Safety

“…Response surface methodology (RSM) is a test design method proposed by Box et al It is an optimization method that integrates test design and mathematical modeling [22][23][24][25][26][27]. By performing tests on representative local points, the functional relationship between factors and results in the global scope is regressed and fitted, and the optimal level value of each element is obtained.…”

“…Initially used for the fitting of physical experiments, in recent years, it has become a newly developed optimization theory method in the world and is widely used in the fields of the chemical industry, agriculture, pharmacy, environment, and mechanical engineering. Many scholars and researchers at home and abroad have carried out lots of research [27]. Compared with the currently widely used orthogonal test design method, orthogonal experiments cannot obtain a clear functional expression between the test factors and the response target over the specified area, so the optimal combination of design variables and the optimal value of the response target cannot be obtained.…”