[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(SO

2

) 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 g

oal 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 twostage 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 SO

2

concentration. Second

a comprehensive multi-objective optimization model is established

taking into account both the predicted outlet SO

2

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 BKAoptimized 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. [Con

clusion

]

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 SO

2

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.