Optimization of a Wet Flue Gas Desulfurization Scrubber through Mathematical Modeling of Limestone Dissolution Experiments

Thermodynamic insights into selenium oxyanion removal from synthetic flue gas desulfurization wastewater with temperature-swing solvent extraction

Temperature-swing solvent extraction (TSSE) is a cost-effective, simple, versatile, and industry-ready technology platform capable of desalinating hypersaline brines toward zero liquid discharge. In this work, we demonstrate the potential of TSSE in the effective removal of selenium oxyanions and traces of mercury with the coexistence of high contents of chloride and sulfate often encountered in flue gas desulfurization wastewater streams. We compare the rejection performance of the two common solvents broadly used for TSSE, decanoic acid (DA) and diisopropylamine (DPA), and correlate those with the solvent physicochemical properties (e.g., dielectric constant, polarity, molecular bulkiness, and hydrophobicity) and ionic properties (e.g., hydrated radii and H-bonding). The results show that TSSE can remove >99.5% of selenium oxyanions and 96%-99.6% of mercury traces coexisting with sulfate (at a sixfold Se concentration) and chloride (at a 400-fold Se concentration) in a synthetic wastewater stream. Compared to diisopropylamine, decanoic acid is more effective in rejecting ions for all cases, ranging from a simple binary system to more complex multicomponent systems with highly varied ionic concentrations. Furthermore, the H-bonding interaction with water and the hydrated radii of the oxyanions (i.e., selenate vs. selenite) along with the hindrance effects caused by the molecular bulkiness and hydrophobicity (or lipophilicity) of the solvents play important roles in the favorable rejection of TSSE. This study shows that TSSE might provide a technological solution with a high deionization potential for the industry in complying with the Environmental Protection Agency regulations for discharge streams from coal-fired power facilities.

Prediction of inlet SO2 concentration of wet flue gas desulfurization (WFGD) by operation parameters of coal-fired boiler

Circulating fluidized bed (CFB) boilers with wet flue gas desulfurization (WFGD) system is a popular technology for SO₂ removal in the coal-fired thermal power plant. However, the long response time of continues emission monitoring system (CEMS) and the hardness of continuously monitoring the coal properties leads to the difficulties for controlling WFGD. It is important to build a model that is adaptable to the fluctuation of load and coal properties, which can obtain the SO₂ concentration ahead CEMS, without relying on coal properties. In this paper, a prediction model of inlet SO₂ concentration of WFGD considering the delay between the features and target based on long-short term memory (LSTM) network with auto regression feature is established. The SO₂ concentration can be obtained 90 s earlier than CEMS. The model shows good adaptability to the fluctuation of SO₂ concentration and coal properties. The root-mean-squared error (RMSE) and R squared (R²) of the model are 30.11 mg/m³ and 0.986, respectively. Meanwhile, a real-time prediction system is built on the 220 t/h unit. A field test for long-term operation has been conducted. The prediction system is able to continuously and accurately predict the inlet SO₂ concentration of the WFGD, which can provide the operators with an accurate reference for the control of WFGD.

Authentication of emission monitoring data and optimization of desulfurization in the molybdenum roasting process based on BAT-OOPN and the response surface method

This paper presents a quantitative pollutant discharge model for a typical molybdenum roasting plant, which combines the best available technology and object-oriented Petri net concepts. The proposed model was used to verify whether the best available technology in a molybdenum roasting process meeting the current pollutant emission limits by comparing the results of multiple simulations with online monitoring data records. Theoretical SO₂ emission values were obtained via multiple simulations and compared with the online monitoring data of a typical molybdenum roasting plant to verify the authenticity of the online monitoring data. The relationship between the different operating parameters and desulfurization efficiency is established through analyzing the historical operation parameters of the enterprise and response surface method. It was found that the optimal operating parameters for the flue gas desulfurization system of this plant could be characterized by a flue gas temperature of 90-93 °C, a pH range of 6.20-6.30, and a liquid-gas ratio of 23-25 L/m³.

Modeling of Limestone Dissolution for Flue Gas Desulfurization with Novel Implications

Solid-liquid dissolution is a central step in many industrial applications such as pharmaceutical, process engineering, and pollution control. Accurate mathematical models are proposed to improve reactor design and process operations. Analytical methods are significantly beneficial in the case of iterative methods used within experimental investigations. In the present study, a detailed analytical solution for the general case of solid particles dissolving in multiphase chemical reaction systems is presented. In this model, the authors consider a formulation that considers the particles’ shape factor. The general case presented could be utilized within different problems of multiphase flows. These methods could be extended to different cases within the chemical engineering area. Examples are illustrated here in relation to limestone dissolution taking place within the Wet Flue Gas Desulfurization process, where calcium carbonate is dissolving in an acidic environment. The method is the most common used technology to abate SO2 released by fuel combustion. Limestone dissolution plays a major role in the process. Nevertheless, there is a need for improvements in the optimization of the WFGD process for scale-up purposes. The mathematical model has been tested by comparison with experimental data from several mild acidic dissolution assays of sedimentary and metamorphic limestone. We have found that R2 ⊂ 0.92 ± 0.06 from dozens of experiments. This fact verifies the model qualifications in capturing the main drivers of the system.

Modeling and optimization of wet flue gas desulfurization system based on a hybrid modeling method

Sulfur dioxide (SO₂) is one of the main air pollutants from many industries. Most coal-fired power plants in China use wet flue gas desulfurization (WFGD) as the main method for SO₂ removal. Presently, the operating of WFGD lacks accurate modeling method to predict outlet concentration, let alone optimization method. As a result, operating parameters and running status of WFGD are adjusted based on the experience of the experts, which brings about the possibility of material waste and excessive emissions. In this paper, a novel WFGD model combining a mathematical model and an artificial neural network (ANN) was developed to forecast SO₂ emissions. Operation data from a 1000-MW coal-fired unit was collected and divided into two separated sets for model training and validation. The hybrid model consisting a mechanism model and a 9-input ANN had the best performance on both training and validation sets in terms of RMSE (root mean square error) and MRE (mean relative error) and was chosen as the model used in optimization. A comprehensive cost model of WFGD was also constructed to estimate real-time operation cost. Based on the hybrid WFGD model and cost model, a particle swarm optimization (PSO)-based solver was designed to derive the cost-effective set points under different operation conditions. The optimization results demonstrated that the optimized operating parameters could effectively keep the SO₂ emissions within the standard, whereas the SO₂ emissions was decreased by 30.79% with less than 2% increase of total operating cost. Implications: Sulfur dioxide (SO₂) is one of the main pollutants generated during coal combustion in power plants, and wet flue gas desulfurization (WFGD) is the main facility for SO₂ removal. A hybrid model combining SO₂ removal mathematical model with data-driven model achieves more accurate prediction of outlet concentration. Particle swarm optimization with a penalty function efficiently solves the optimization problem of WFGD subject to operation cost under multiple operation conditions. The proposed model and optimization method is able to direct the optimized operation of WFGD with enhanced emission and economic performance.

Ultrasonic Power to Enhance Limestone Dissolution in the Wet Flue Gas Desulfurization Process. Modeling and Results from Stepwise Titration Experiments

The goal of this work is to assess the application of ultrasonic power to the reactive dissolution of limestone particles in an acidic environment; this would represent a novel method for improving wet Flue Gas Desulfurization industrial systems. In this study a stepwise titration method is utilized; experiments were done by using different particle size distributions with and without the application of ultrasound. The use of ultrasonic power sensibly affected the reaction rate of limestone and its dissolution; a major difference could be observed when samples from the Wolica region in Poland were studied. In this case, the overall dissolution rate was found to increase by more than 70%. The reactive dissolution of limestone does not follow the same mathematical model when sonication is in effect; in this case, an extra Ultrasonic Enhancement Constant was introduced. It was demonstrated that the dissolution is proportional to an Effective Reaction Surface and, therefore, surface interactions should also be taken into consideration. For this purpose, a study is presented here on the Z-potential and electrophoretic mobility of limestone samples measured in aqueous dispersions by means of Laser Doppler Micro-Electrophoresis.