A Review on Energy-Saving and Consumption-Reducing Technologies for Thermal Power Units Based on Economic Benefit Evaluation
Thermal power units have long dominated China's energy structure due to the low cost of coal and their role in ensuring grid stability. However, under the dual pressures of climate change and national carbon peaking/neutrality goals, the environmental impact of their 'three wastes' has become critical, necessitating energy-saving retrofits. This review systematically examines mainstream energy-saving technologies for thermal power units, including boiler combustion optimization, heating surface cleaning, turbine flow path upgrades, waste heat recovery and cascade utilization, and cold-end system optimization. Using coal consumption rate as the core economic index, the study integrates case studies and operational data from typical domestic and international units to evaluate the latest progress, practical effects, advantages, and limitations of each technology. Results indicate that these technologies significantly improve energy efficiency and reduce pollution. For instance, boiler combustion optimization based on support vector machines and neural networks enhances thermal efficiency and reduces NOx emissions. Turbine flow path modifications, from full three-dimensional CFD optimization to advanced blades and combined steam seals, yield notable gains in cylinder efficiency and heat rate reduction. Low-temperature economizers reduce coal consumption and auxiliary power/water use in dust removal and desulfurization systems. Heat pump applications include absorption, compression, and hybrid types. In cold-end optimization, data-driven predictive maintenance and real-time performance tuning of condensers achieve nearly 50% energy savings in circulating water pumps and an average coal consumption reduction of 2-3 g/(kW·h). Despite these advances, gaps remain in multi-objective optimization robustness, intelligent diagnosis, and advanced materials. Future research should focus on deep reinforcement learning for adaptive control, sensor networks for real-time diagnostics and predictive maintenance, and high-temperature corrosion-resistant materials for heat exchangers, while balancing initial investment and maintenance costs.