Performance assessment of a combined coal gasification and methanation system with particle swarm optimization method

Performance assessment of a combined coal gasification and methanation system with particle swarm optimization method

Carbon dioxide hydrogenation is a promising method of producing alternative fuels in an environmentally friendly way. Researchers in the current literature have mainly investigated the performance of carbon dioxide hydrogenation systems that use carbon dioxide from various sources and hydrogen from water electrolysis units. In the present study, the performance of a combined coal gasification and methanation unit is investigated to produce methane and power. The carbon dioxide and hydrogen for the methanation unit are provided from the coal gasification system. A Particle swarm optimization (PSO) method is applied to optimize the carbon dioxide and hydrogen values here. Therefore, the water electrolysis unit, which needs high amounts of energy is removed from the system, effectively. The results from the studied system showed that it is possible to produce ~225 kilotons of methane annually by using ~946 kilotons of coal per year. In addition, the results revealed that annual carbon dioxide utilization of ~624.3 kilotons is possible. The system efficiency is estimated at around 49%.

___

  • [1] C. Yılmaz and M. Kanoglu, “Investigation of hydrogen production cost by geothermal energy”, International Advanced Researches and Engineering Journal, Vol. 1, No. 1, pp. 5-10, 2017.
  • [2] O. Sen and C. Yılmaz, “Thermodynamic performance analysis of geothermal and solar energy assisted power generation and residential cooling system”, International Advanced Researches and Engineering Journal, Vol. 4, No. 1, pp. 2020.
  • [3] S. Schemme et al., “H2-based synthetic fuels: A techno-economic comparison of alcohol, ether and hydrocarbon production”, International journal of hydrogen energy, Vol. 45, No.8, pp. 5395-5414, 2020.
  • [4] V. Dieterich et al., “Power-to-liquid via synthesis of methanol, DME or Fischer–Tropsch-fuels: a review”, Energy & Environmental Science, Vol. 13, No. 10, pp. 3207-3252, 2020.
  • [5] S. Schemme et al., “Promising catalytic synthesis pathways towards higher alcohols as suitable transport fuels based on H2 and CO2”, Journal of CO2 utilization, Vol. 27, pp. 223-237, 2018.
  • [6] M.S. Herdem et al., “Simulation and modeling of a combined biomass gasification-solar photovoltaic hydrogen production system for methanol synthesis via carbon dioxide hydrogenation”, Energy Conversion and Management, Vol. 219, 113045, 2020.
  • [7] H. Zhang et al., “Techno-economic optimization of CO2-to-methanol with solid-oxide electrolyzer”, Energies, Vol. 12, No. 19, 3742, 2019.
  • [8] J. Kotowicz, M. Brzeczek and D. Wecel, “Analysis of the work of a" renewable" methanol production installation based on h2 from electrolysis and co2 from power plants”, Energy, 119538, 2020.
  • [9] V. Eveloy, “Hybridization of solid oxide electrolysis-based power-to-methane with oxyfuel combustion and carbon dioxide utilization for energy storage”, Renewable and Sustainable Energy Reviews, Vol. 108, pp. 550-571, 2019.
  • [10] M. Momeni et al., “A comprehensive analysis of a power-to-gas energy storage unit utilizing captured carbon dioxide as a raw material in a large-scale power plant”, Energy Conversion and Management, Vol. 227, 113613, 2021.
  • [11] S. Hanggi et al., “A review of synthetic fuels for passenger vehicles”, Energy Reports, Vol. 5, pp. 555- 569, 2019.
  • [12] S. Brynolf et al., “Electrofuels for the transport sector: A review of production costs”, Renewable and Sustainable Energy Reviews, Vol. 81, pp. 1887-1905, 2018.
  • [13] S. Buyrukoğlu, “Promising Cryptocurrency Analysis using Deep Learning”, 5th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), 21-23 Oct., Ankara, Turkey, 2021.
  • [14] S. Buyrukoğlu, F. Batmaz and R. Lock, “Increasing the similarity of programming code structures to accelerate the marking process in a new semi-automated assessment approach”, 11th International Conference on Computer Science & Education (ICCSE), 23-25 Aug., Nagoya, Japan, 2016.
  • [15] M. J. Azarhoosh, H. Ale Ebrahim and S. H. Pourtarah, “Simulating and optimizing auto-thermal reforming of methane to synthesis gas using a non-dominated sorting genetic algorithm II method”, Chem. Eng. Commun., Vol. 203, No. 53, doi: https://doi.org/10.1080/00986445.2014.942732, 2016.
  • [16] M. Shamsi, H. Ale Ebrahim and S. H. Pourtarah, “Simulation and Optimization of Coal Gasification in a Moving-bed Reactor to Produce Synthesis Gas Suitable for Methanol Production Unit”, Chem. Biochem. Eng. Q., Vol. 33, No. 4, pp. 427–435, 2019.
  • [17] S. Buyrukoğlu and A. Akbaş, “Machine Learning based Early Prediction of Type 2 Diabetes: A New Hybrid Feature Selection Approach using Correlation Matrix with Heatmap and SFS”, Balkan Journal of Electrical and Computer Engineering, Vol. 10, No. 2, pp. 110 – 117, 2022.
  • [18] S. Buyrukoğlu, Y. Yılmaz and Z. Topalcengiz, “Correlation value determined to increase Salmonella prediction success of deep neural network for agricultural waters”, Environ. Monit. Assess., Vol. 194, 373, 2022.
  • [19] S. Buyrukoğlu and S. Savaş, “Stacked-Based Ensemble Machine Learning Model for Positioning Footballer”, Arab J Sci Eng., https://doi.org/10.1007/s13369-022-06857-8, 2022.
  • [20] M.S. Herdem et al., “Thermodynamic modeling and assessment of a combined coal gasification and alkaline water electrolysis system for hydrogen production”, International Journal of Hydrogen Energy, Vol. 39, No. 7, pp. 3061-3071, 2014.
  • [21] Z. Qin et al., “Methanation of coke oven gas over Ni-Ce/γ-Al2O3 catalyst using a tubular heat exchange reactor: Pilot-scale test and process optimization”, Energy Conversion and Management, Vol. 204, 112302, 2020.
  • [22] R.T. Zimmermann, J. Bremer and K. Sundmacher, “Optimal catalyst particle design for flexible fixed- bed CO2 methanation reactors”, Chemical Engineering Journal, Vol. 387, 123704, 2020.
  • [23] T. Chwola et al., “Pilot plant initial results for the methanation process using CO2 from amine scrubbing at the Łaziska power plant in Poland”, Fuel, Vol. 263, 116804, 2020.
  • [24] W. Liu, Z. Wang, N. Zeng, F.E. Alsaadi and X. Liu, “A PSO-based deep learning approach to classifying patients from emergency departments”, International Journal of Machine Learning and Cybernetics, Vol. 12, pp. 1939–1948, 2021
  • [25] N. Christofides, A. Mingozzi and P. Toth, “The Vehicle Routing Problem”, Chichester, UK: Wiley, pp. 315–338, 1979.