A Novel Coal-Based Calcium Carbide-Acetylene System Process Based on Multistage Carbon Capture and Waste Slag Recycling Design: A Multiobjective Optimization Evaluation Perspective

Large amounts of raw material consumption, high energy consumption, and severe pollution have always been the key factors restricting the stable development of the coal-to-calcium carbide industry. Therefore, this paper presents a novel combined carbon capture and utilization and solid waste recycling system of the coal-based calcium carbide-acetylene production (CCAP) process to improve these deficiencies. This improved CCAP system consists of a gasifier, carbide furnace, acetylene furnace, the newly designed coupling module of a recarbonization furnace and calciner, and mineralization units. The thermodynamic model of the CCAP system is developed to calculate the effective atomic yield, carbon consumption, CO2emission, etc. to evaluate the performance. The improved system performed better with the effective atomic yield (98.50%), CO2capture rate (69.57%), CO2emission (1.1064 t CO2·(t–1C2H2)), CO2e emission (7.2092 t CO2e·(t–1C2H2)), and carbon consumption (3.2101 t Coal·(t–1C2H2)) than the referenced system with 64.57%, 0, 2.25 t CO2·(t–1C2H2), 20.4122 t CO2e·(t–1C2H2), and 5.43 t Coal·(t–1C2H2), respectively. The exergy analysis shows that the improved system performed better for efficient energy utilization with an exergy efficiency of 48.97% for a large-scale chemical process. Besides, the trade-off between the maximum effective atomic yield, minimum carbon consumption, CO2equivalent emission, and total annual cost (TAC) is conducted through multiobjective optimization. By three-objective optimization, the improved system could achieve a lower TAC of 8.51 × 106$/year, CO2e emission of 4.1573 t CO2e·(t–1C2H2), and carbon consumption of 1.9874 t Coal·(t–1C2H2) simultaneously, which decreased by 38.78%, 79.63%, and 63.40%, respectively, than that of the referenced system. The all-around performance of the improved CCAP system after multiobjective optimization has been dramatically improved, making it more competitive in energy conservation, emission reduction, and resource recovery.