首页|Wuxi Institute of Technology Reports Findings in Machine Learning (Co-firing cha racteristic prediction of solid waste and coal for supercritical CO2 power cycle based on CFD simulation and machine learning algorithm)
Wuxi Institute of Technology Reports Findings in Machine Learning (Co-firing cha racteristic prediction of solid waste and coal for supercritical CO2 power cycle based on CFD simulation and machine learning algorithm)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New research on Machine Learning is the subject o f a report. According to news originating from Wuxi, People's Republic of China, by NewsRx correspondents, research stated, "The co-firing technology of combust ible solid waste (CSW) and coal in the supercritical CO (S-CO) circulating fluid ized bed (CFB) can effectively deal with domestic waste, promote social and envi ronmental benefits, improve the coal conversion rate, and reduce pollutant emiss ion. This study focuses on the co-firing characteristics of CSW and coal under S -CO power cycle, and simulations are conducted by employing Multiphase Particle- in-cell (MP-PIC) method integrated with the comprehensive chemical reaction mode ls in a 300 MW S-CO CFB boiler." Our news journalists obtained a quote from the research from the Wuxi Institute of Technology, "Effects of operating parameters including fuel mixture proportio n and first stage stoichiometry on the gas emission characteristics are further analyzed. Based on training and testing database based on the simulation results , a novel Improved Whale Optimization Algorithm and Bi-dictionary Long Short-Ter m Memory (IWOABiLSTM) algorithm model is established to predict CFB temperature , NOx emission concentration, and SO emission concentration, respectively. CO and SO decrease with the coal mass ratio of the fuel mixture increasing, while NOx increases. With the increase of first stage stoichiometry, CO increases, NOx de clines, and the change of SO is not obvious."
WuxiPeople's Republic of ChinaAsiaAlgorithmsCyborgsEmerging TechnologiesMachine Learning
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