Perbandingan Kinerja Reactor Rquil, Rstoic, Dan Rgibs Pada Pembentukan Hydrochloric Acid (HCl) Dengan Kapasitas 178.000 Ton/Tahun Dari Proses Reaksi Asam Sulfat (H2SO4) Dan Natrium Klorida (NaCl)

Abstract

The production of hydrochloric acid (HCl) is an important process in the chemical industry due to its wide range of uses, including in metal processing, catalyst regeneration, and the manufacture of various basic chemical compounds. One commonly used production process is the reaction between sulfuric acid (H₂SO₄) and sodium chloride (NaCl), which produces HCl as the main product and sodium sulfate (Na₂SO₄) as a byproduct. This simulation aims to compare the performance of three types of reactors in Aspen Plus software, namely RStoic, RGibbs, and RQuil, in the HCl production process with a capacity of 178,000 tons per year. The simulation was conducted to analyze the effect of reactor type on key parameters such as reactant conversion, mole fraction, mass flow rate, density, and system enthalpy and entropy. The simulation results show that the three reactors have different characteristics. The RStoic reactor yielded stoichiometric results with incomplete reaction conversion, while the RGibbs reactor was able to represent the Gibbs free energy equilibrium with the highest absolute enthalpy value (-97,299.7 cal/mol), which indicates that the reaction is exothermic. However, the RQuil reactor provided the most optimal performance with an HCl mole fraction of 0.986206, an average molecular weight of 36.4634, and a gas density of 0.000991571 g/cc, indicating the formation of HCl with a fairly high purity. Based on these results, the RQuil reactor is more efficient for use in chemical industrial processes because it is able to produce HCl products with high purity, good energy stability, and a fairly high conversion efficiency. This simulation provides an important contribution to the development of reactor design and optimization in HCl production on an industrial scale with a more realistic and efficient thermodynamic simulation approach.