In this research, the chemical reaction behavior of the methanol-to-olefin (MTO) process was formulated by kinetic models to predict the yield of product and selectivity. Two types of kinetic models with different complexity depending on the applicati...
In this research, the chemical reaction behavior of the methanol-to-olefin (MTO) process was formulated by kinetic models to predict the yield of product and selectivity. Two types of kinetic models with different complexity depending on the application were presented for the MTO process under the SAPO-34 catalyst.
In Chapter 2, a mechanistic kinetic model that takes into account the elementary steps including ions of MTO reaction was developed. Referring to the preceding studies, an overall reaction mechanism was established that reflected the autocatalysis nature of the MTO reaction and the interplay between the hydrocarbon pools. For kinetic modeling based on the complex chemical reaction mechanism, the approximate approach based on transition state theory, Evans-Polanyi relation, and thermodynamic constraints are applied to reduce the kinetic parameters to be estimated. The kinetic parameters are determined using experimental data obtained from literature by the genetic algorithm. This model provides satisfactory information on product distribution in various operating conditions based on highly theoretical approaches.
In Chapter 3, a lumped kinetic model was developed to predict the seven lumps in which the main products of MTO are grouped. The deactivation kinetics of the MTO reaction is studied based on the proposed 7-lump kinetic model. The model is based on assumptions that methanol conversion is a first-order reaction and the active catalyst reduction is proportional to the conversion. The kinetic parameters were determined using experimental data measured in a fixed bed reactor by the genetic algorithm. By defining the deactivation of SAPO-34 as the loss of the active catalyst, the deactivation constant is the only intrinsic parameter required to describe the effect of catalyst deactivation on the conversion and product yields with time on stream. This theoretical approach has been demonstrated to be effective in modeling the complex deactivation kinetics of MTO.
Both proposed models have been theorized in many parts to reduce the computational loads compared to the previous studies, but they also guarantee reasonable results. The validity of the two models has been verified by experimental data and statistical methods. These models are expected to be used in mechanism research, catalytic design, reactor design, and operating condition optimization.