Mathematical Modelling And Multi-variable Optimization Design Of Carbon Dioxide Capture And Utiliaztion Technologies

Due to the rapid growth of industrialization on a global scale,the CO₂ emissions has been increasing each year,which is a major contributor to global warming.To achieve the goal of carbon neutrality,the focus is shifting towards the removal of CO₂,and the carbon capture utilization and storage（CCUS）technology is seen as a promising solution that can both reduce CO₂ emissions and recycle resources.Although some CCUS technologies like the Enhanced Weathering（EW）process and the gas diffusion electrode（GDL）-based electrochemical CO₂ reduction process have demonstrated commercial application potential,there is still a need to understand the mechanisms behind these systems to improve their performance and reduce their costs.This paper investigates two CCUS technologies for CO₂ capture and utilization by constructing multi-physics mechanism models to analyze and simulate the systems.Data-driven surrogate models were then built based on the mechanism models to further analyze the system variables.The systems were optimized by combining the surrogate model and optimization algorithm,and an economic and technical analysis was performed based on the multi-objective optimization results to guide their industrial application.The ultimate goal is to enhance the competitiveness of CCUS technology with other technologies by improving their performance and reducing their costs.The research content of this paper is summarized as follows:1.Based on the EW effect,a mechanism model and surrogate model were constructed for the CO₂capture system in two traditional chemical reactors（trickle bed and bubble column）under freshwater conditions.The reliability and accuracy of the two models were verified and compared,and the impact of different variables on CO₂ capture rate,energy consumption and water consumption in the reactors was analyzed based on the models.The results showed that both the response surface methodology（RSM）and the extended adaptive hybrid functions（E-AHF）surrogate models could predict the system’s performance well,with the E-AHF model having higher predictive accuracy than the RSM model,but the RSM model could obtain specific polynomial expressions and require fewer data sets.Sensitivity analysis showed that the particle diameter and surface gas velocity were the most significant variables affecting the performance of the trickling bed and bubble column reactors,and the performance of the reactors would also improve with their increase.2.Due to some problems in CO₂ capture in a single freshwater/seawater bubble column reactor（such as requiring a large amount of freshwater resources and low CO₂ removal efficiency）,we designed a seawater-freshwater series bubble column reactor as a reaction container for the EW effect and constructed a mechanism model and E-AHF surrogate model under seawater conditions.After sensitivity analysis of eight design variables,we conducted multi-variable and multi-objective optimization design and techno-economic analysis of the system based on the fast non-dominated sorting genetic algorithm II（NSGA-II）.The results showed that the relative errors of the E-AHF surrogate model predictions were all less than 5%,and it could accurately predict the system’s CO₂capture and energy consumption.The Sobol global sensitivity analysis results showed that the surface gas velocity and initial bed height in the series bubble column reactor had the most significant impact on the performance.The optimal trade-off solution for CO₂ capture rate and energy consumption obtained through multi-objective optimization was 0.10135 kg h^-1 and 6.18546 MJ kg^-1CO₂.Under these conditions,the net cost of the reactor was about$400 t^-1 CO₂,and the loss of freshwater was greatly reduced(the freshwater cost decreased by$80.49 t^-1 CO₂)while ensuring a high enough CO₂removal rate（99.99%）.3.Based on the electrochemical CO₂ reduction,a mechanism model and an E-AHF surrogate model were developed for the GDE-based system.The effects of eight design variables on the yield（CO and formate）,CO₂ conversion rate and specific energy consumption of the system were analyzed.A multi-variable three-objective optimization design was carried out based on NSGA-II,and finally,an economic and technical analysis of the system was conducted.The results showed that the R₂ of the E-AHF model for the three objective functions was greater than 0.96,indicating that the E-AHF model could accurately predict the performance of the GDE system within the range of study.The eight variables collectively determined the system performance,with the initial electrolyte concentration and cathode potential having the most significant impact on performance.The optimal values of yield,CO₂ conversion rate and specific energy consumption in the multi-objective optimization three objectives’final optimal trade-off solution were 3.25×10-9 kg s^-1,0.6626%,and9.9498 k Wh kg^-1,respectively.Compared with the results before optimization,the system performance was significantly improved.The production cost of the GDE-based electrochemical CO₂reduction system was approximately$378 t^-1product（CO and formate）,much lower than that of traditional CO₂ utilization factories($835 t^-1product).The electricity cost accounted for more than80%of the total cost,amounting to$318.39 t^-1,indicating that cheaper and cleaner electricity sources would further reduce production cost of the system,which is the key to the economics of this technology.