Affiliation:
1. School of Petrochemical Engineering, Lanzhou University of Technology, Lanzhou 730050, China
2. Key Laboratory of Low Carbon Energy and Chemical Engineering of Gansu Province, Lanzhou 730050, China
3. No. 1 Department of Ethylene Operation, Lanzhou Petrochemical Company of PetroChina, Lanzhou 730060, China
Abstract
The pursuit of low-energy-consumption CO2 capture technology has promoted the renewal and iteration of absorbents for chemical absorption. In order to evaluate the regeneration energy consumption of absorbents and obtain the distribution of energy consumption, a coupling method combining rigorous energy balance and simple estimation is proposed in this study. The data regarding energy balance and material balance from process simulation are transformed into the model parameters required in the simple estimation model. Regenerative energy consumption and distribution are determined by the empirical estimation formula. Two CO2 capture processes of an MEA aqueous solution and MEA–n-propanol aqueous solution (phase-change absorbent) were used to verify the feasibility and applicability of the coupling method. The effects of n-propanol concentration, CO2 loading in the lean solution, and temperature on energy consumption were discussed. The results show that the energy consumption of 30 wt% MEA aqueous solution is the lowest at 3.92 GJ·t−1-CO2 when CO2 load in the lean solution is 0.2 mol CO2·mol−1-MEA, and the reaction heat Qrec, sensible heat Qsen, and latent heat Qlatent were 1.97 GJ·t−1-CO2, 1.09 GJ·t−1-CO2, and 0.86 GJ·t−1-CO2, respectively. The lowest energy consumption of the phase-change absorbent with CO2 loading of 0.35 mol CO2·mol−1-MEA in the lean solution is 2.32 GJ·t−1-CO2. Qrec, Qsen, and Qlatent were 1.9 GJ·t−1-CO2, 0.29 GJ·t−1-CO2, and 0.14 GJ·t−1-CO2, respectively. This study provides a simple and meaningful method for accurately assessing absorber performance and process improvement, which can accelerate the development of economically viable, absorption-based CO2 capture processes.
Funder
National Natural Science Foundation of China
Basic Research Innovation Group Project of Gansu Province
Key Research and Development Program of Gansu Province
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