Optimization of an industrial methanol reactor using Aspen Plus simulator and Design Expert / Mohd Azahar Mohd Ariff ... [et al.]

The chemical conversion of carbon dioxide into methanol has the potential to address two major sustainability issues: the economically viable replacement of fossil energy resources and the avoidance of greenhouse gas emissions. However, the chemical stability of carbon dioxide poses a difficult barrier to its conversion, necessitating extreme reaction conditions, resulting in increased energy input and, subsequently, elevated equipment, operation, and environmental costs. This, in turn, could potentially undermine its promising sustainability as a raw material for the chemical and energy industries. This research aims to optimise methanol production from a methanol reactor by using design of experiment (DOE) in Design Expert and Aspen Plus as a reactor simulator. The optimisation process involved eight parameters: five inlet molar flowrates (CO, CO2, H2O, H2, CH3OH) and three reactor conditions (inlet temperature, pressure, and temperature profile). The methanol production in Design Expert was optimised using the Box-Behnken method and a quadratic model. This study used the maximum range for each parameter as the actual industrial data and the minimum range to be 50% below it. The validated Aspen-Plus model was used for methanol production simulation. Response surface methodology (RSM) was used to determine the optimal parameters. This simulation required 120 samples. The optimal parameter values from the RSM were 142.2 kmol/hr of inlet CH3OH, 2250.91 kmol/hr of CO, 1398.3 kmol/hr of CO2, 15.6973 kmol/hr of H20, 19053.7 kmol/hr of H2, 497.883K of reactor inlet temperature, 81.9999 bar of reactor pressure, and 30K for reactor temperature profile with the actual methanol production of 2814.23 kmol/hr. The optimal values were simulated to test the accuracy of the predictive model, and the error was less than 5.2 percent. Overall, the inlet H2 molar flowrate was reduced in optimised conditions, reducing raw material usage and increasing output.