Determining alkalinity, carbon loading, and degradation of aqueous amine in CO2 capture processes using heuristic methods

Abstract

In-line process measurements of pH, temperature, density, and viscosity can be used as inputs for heuristic models to provide an accurate calculation of alkalinity, carbon loading, and degradation of amine solvents used in CO₂ capture processes. The accuracy of calculated alkalinity and carbon loading is comparable to repeated off-line sample analyses with similar measurement variations. By incorporating these models into process control software, real-time monitoring of critical physical parameters can be achieved. This can allow for fast process optimization, neural network integration, and instantaneous feedback without the need for regular, costly, and arduous off-line analysis. The aim of this paper is to lay the foundational groundwork for advanced process control schemes for CO₂ capture processes with a focus on dynamic operation to match production demands. The models follow amine chemistry, as an amine solvent captures CO_2, its carbon loading increases, pH decreases, and density increases. Adjusting for temperature, a correlation can be made between carbon loading and pH. Alkalinity can be calculated by correlating density, temperature, and the carbon loading model output. Total degradation can be calculated with viscosity measurements and alkalinity model outputs, with comparison to known results. Using least-squared methods, the carbon to nitrogen ratio (C/N), alkalinity, and degradation calculations were found to be within 7.27 %, 4.02 %, and ± 2000 ppm of the measured values, respectively. These models have been implemented at the UK 0.7 MWe small pilot CO₂ capture plant and were successful in delivering accurate real-time calculations.