软测量技术在火电机组中的典型应用研究进展OA

[Objective]With the increasing complexity of energy structures in China,coal-fired power units operating under variable conditions face safety and economic challenges such as combustion instability,rising energy consumption,and inaccurate pollutant regulation.Fluctuations in unit load cause dynamic variations in critical parameters,including fuel feed rate,calorific value,flue gas composition,and main steam flow rate.However,traditional measurement methods suffer from limitations such as long cycle times,high costs,or reliance on empirical values.[Methods]To address these issues,this study establishes a hybrid mechanism-data-driven soft sensor framework.A correlation network of auxiliary variables is constructed based on thermodynamic equilibrium equations and combustion reaction mechanisms.Data quality is enhanced through outlier removal,time-series alignment,and principal component analysis.By integrating the nonlinear mapping capabilities of algorithms such as support vector machines(SVM)and random forests,along with embedded dynamic compensation mechanisms like filtering,an adaptive prediction model for variable operating conditions is developed.[Results]Case studies demonstrate that the application of soft sensor technology in coal-fired power units achieves a coal calorific value prediction errors below 0.5 MJ/kg,NOx concentration measurement delays under 30 s,and main steam flow measurement accuracy exceeding 98.5%,supporting a 1.1 g/(kW·h)reduction in coal consumption for a 660 MW unit and a 35%decrease in SCR system ammonia slip.Multi-scenario modeling criteria are proposed.SVM is prioritized for small-sample/linear problems,while long short-term memory(LSTM)networks are recommended for parameters with strong temporal characteristics,offering engineers cross-condition algorithm selection guidelines.[Conclusion]Propose multi-scenario modeling criteria,prefer the SVM model for small-sample/linear problems,adopt the LSTM network for parameters with strong time-series characteristics,and provide algorithm selection criteria for engineers across loading conditions.Through precise sensing and dynamic optimization of core parameters via soft sensor technology,this study aims to significantly enhance the control quality and operational efficiency of coal-fired power units under wide-load conditions.