Energy analysis and surrogate modeling for the green methanol production under dynamic operating conditions

被引:50
作者
Cui, Xiaoti [1 ]
Kaer, Soren Knudsen [1 ,2 ]
Nielsen, Mads Pagh [1 ]
机构
[1] Aalborg Univ, Dept Energy, Pontoppidanstr 111, DK-9220 Aalborg, Denmark
[2] REintegrate ApS, Langerak 15, DK-9220 Aalborg, Denmark
关键词
Dynamic simulation; CO2; hydrogenation; Power-to-methanol; Methanol synthesis; Methanol distillation; Nonlinear autoregressive exogenous model; CARBON-DIOXIDE; POWER; GAS; HYDROGEN; FUEL; TECHNOLOGIES; REACTORS; FUTURE; HEAT; CO2;
D O I
10.1016/j.fuel.2021.121924
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Green methanol production, based on intermittent renewable energy sources, requires a more flexible operation mode and close integration with other sections, such as, the electrical grid and electrolysis processes. In this study, methanol synthesis and distillation processes (MSD) for pilot-scale green methanol production (corresponding to 22,236 tons/year) were investigated by dynamic modeling, focusing on energy analysis and dynamic characteristics during load change (LC) operations. The dynamic simulation results with a ramp rate of 50% load / h indicated energy efficiencies of 87.7% (at full-load) and 90.2% (at half-load) for the methanol synthesis process, 86.8% (full-load) and 82.4% (half-load) for the methanol distillation process, and 77.1% (full-load) and 75.4% (half-load) for the MSD process. Relatively small fluctuations were achieved with a ramp time of 1 h for the LC operations. Based on the constructed dynamic model, a surrogate modeling for the MSD process was conducted using the nonlinear autoregressive exogenous model (NARX) model, which exhibited good accuracy with the evaluated performance for the testing data of the root-mean-square error (RMSE) = 3.09 x 10(-5), mean absolute error (MAE) = 2.30 x 10(-4), and R-2 = 1.0. The constructed NARX model can be further integrated with models for other sections of the power-to-methanol process.
引用
收藏
页数:13
相关论文
共 45 条
[1]   Modeling, simulation and advanced control of methanol production from variable synthesis gas feed [J].
Abrol, Sidharth ;
Hilton, Courtland M. .
COMPUTERS & CHEMICAL ENGINEERING, 2012, 40 :117-131
[2]  
[Anonymous], 2015, POWER GEN EUROPE
[3]  
[Anonymous], 2020, REINTEGRATE
[4]   A Review of The Methanol Economy: The Fuel Cell Route [J].
Araya, Samuel Simon ;
Liso, Vincenzo ;
Cui, Xiaoti ;
Li, Na ;
Zhu, Jimin ;
Sahlin, Simon Lennart ;
Jensen, Soren Hojgaard ;
Nielsen, Mads Pagh ;
Kaer, Soren Knudsen .
ENERGIES, 2020, 13 (03)
[5]   Flexible production of green hydrogen and ammonia from variable solar and wind energy: Case study of Chile and Argentina [J].
Armijo, Julien ;
Philibert, Cedric .
INTERNATIONAL JOURNAL OF HYDROGEN ENERGY, 2020, 45 (03) :1541-1558
[6]  
ASTM, 5797 ASTM
[7]   Feasibility study of methanol production plant from hydrogen and captured carbon dioxide [J].
Bellotti, D. ;
Rivarolo, M. ;
Magistri, L. ;
Massardo, A. F. .
JOURNAL OF CO2 UTILIZATION, 2017, 21 :132-138
[8]   Power-to-heat for renewable energy integration: A review of technologies, modeling approaches, and flexibility potentials [J].
Bloess, Andreas ;
Schill, Wolf-Peter ;
Zerrahn, Alexander .
APPLIED ENERGY, 2018, 212 :1611-1626
[9]   A Nonlinear Autoregressive Exogenous (NARX) Neural Network Model for the Prediction of the Daily Direct Solar Radiation [J].
Boussaada, Zina ;
Curea, Octavian ;
Remaci, Ahmed ;
Camblong, Haritza ;
Bellaaj, Najiba Mrabet .
ENERGIES, 2018, 11 (03)
[10]   Wind Speed Prediction Using a Univariate ARIMA Model and a Multivariate NARX Model [J].
Cadenas, Erasmo ;
Rivera, Wilfrido ;
Campos-Amezcua, Rafael ;
Heard, Christopher .
ENERGIES, 2016, 9 (02)