Energetic, exergetic analysis and machine learning of methane chlorination process for methyl chloride production

Abstract

Nowadays, with the growing demand for energy and effective utilization of various available sources with the exorable techniques and approaches to maximize the efficiency of energy systems. This work has developed the synthesis of Methyl chloride (MC) from the methane chlorination process using the ASPEN HYSYS simulation tool. A Searchable analysis has been done on thermodynamic derivatives (likely Energy, Exergy) to probation on the entire process. This analysis calculates all process components’ energy loss, destruction and energy, and exergy efficiencies. A heavier energy loss has been found at Reactor (ERV) with 1785.5 kW and exergy destruction of 18.8% share. Heat Exchanger Network (HEN) has energy loss (960.32kW) & exergy destruction (791.29kW). The proposed new retrofit sustainable model recovered the waste heat from the HEN and achieved energy efficiency of 87.6% and exergy efficiency of 87.3% of the total MC process. Four Machine learning models were developed for the reactor (ERV) process to predict exergy destruction. The artificial Neural network (ANN) gave good testing predictions, followed by the Random Forest (RF) with a determination coefficient (R2) of 0.999957 and 0.999981.