基于改进NSGA-II算法的单塔双循环脱硫系统运行策略优化OA

[Objective]Driven by stringent environmental regulations,coal-fired power plants urgently require highly efficient and intelligent emission control technologies.The widely utilized single-tower dual-loop wet flue gas desulfurization(WFGD)system often faces significant challenges during practical operation,notably severe fluctuations in sulfur dioxide(SO2)emissions at the system outlet and high desulfurization costs.These issues primarily stem from the system's complex nonlinear dynamics,large inertia,and time-delay characteristics.To address these operational bottlenecks,this study proposes a novel hybrid optimization strategy integrating an advanced deep-learning prediction model with a multi-objective optimization algorithm.The primary goal is to achieve a win-win operational paradigm,ensuring strict environmental compliance while simultaneously minimizing economic expenditures.[Methods]The proposed strategy is implemented through a systematic two-stage framework.First,a predictive model is constructed by fusing the Transformer architecture with a bidirectional long short-term memory(BiLSTM)network.The Transformer effectively captures global long-range dependencies of operational data,while the BiLSTM extracts complex bidirectional temporal features.To overcome the limitations of empirical hyperparameter tuning,an improved black-winged kite algorithm(BKA)is introduced.This heuristic algorithm systematically optimizes the model's key hyperparameters,significantly enhancing the forecasting accuracy of the outlet SO2 concentration.Second,a comprehensive multi-objective optimization model is established,taking into account both the predicted outlet SO2 concentration and the system's overall desulfurization costs as conflicting objectives.To solve this complex trade-off problem,an improved non-dominated sorting genetic algorithm II(NSGA-II)is applied to optimize operational parameters and derive precise setpoints for key controllable variables.[Results]Extensive experimental results demonstrate that the improved BKA-optimized Transformer-BiLSTM prediction model achieves superior performance.It significantly outperforms baseline models,including standard BiLSTM,CNN-BiLSTM,and the unoptimized Transformer-BiLSTM,across critical evaluation metrics such as root mean square error(RMSE),mean absolute error(MAE),and mean absolute percentage error(MAPE).Furthermore,by utilizing the improved NSGA-II algorithm,the optimization system successfully outputs a set of well-distributed Pareto optimal solutions.These solutions effectively balance environmental protection and economic efficiency,providing power plant operators with a diverse range of flexible control strategies tailored to varying load conditions.[Conclusion]The proposed hybrid optimization strategy proves highly effective in the intelligent management of the single-tower dual-loop WFGD system.By integrating accurate deep learning predictions with robust multi-objective optimization,the strategy successfully mitigates outlet SO2 fluctuations and substantially reduces desulfurization costs.This research rigorously validates the framework's ability to achieve a balance between environmental protection and economic efficiency,offering a practical technical reference for upgrading industrial emission control systems.