(246d) Superstructure Formulation and Optimization of a Methane-Based Chemical Refinery for Co-Producing Olefins and Aromatics | AIChE

(246d) Superstructure Formulation and Optimization of a Methane-Based Chemical Refinery for Co-Producing Olefins and Aromatics

Authors 

Yuan, Z. - Presenter, Tsinghua University
Depletion of petroleum source along with the increased accessibility to cheap, abundant natural gas and shale gas reserves forms the ideal conditions to synthesize and design processes that upgrade these resources to petrochemicals. Methane as the primary component of natural gas and shale gas is becoming increasingly attractive as a feedstock. Up to date, various indirect and direct routes have been proposed for methane conversion. The most conventional route for converting methane sets syngas as intermediate. Direct conversion of natural gas without going through syngas route has been under investigation for several decades, but no commercial processes are practiced to date. As an effective complementary to experimental activity for methane conversion, superstructure formulation and optimization enable the identification of optimal technologies for potential methane conversion routes allowing more effective screening of catalysts and concepts, and identifying the types of devices and processes that should be considered for more detailed modeling and analysis.

In order to achieve a cost-effective and environmentally-benign methane-based chemical refinery producing C2~C4 olefins and C6~C8 aromatics simultaneously, we propose a process superstructure with alternatives regarding (i) methane reforming, (ii) syngas upgrading, (iii) methanol upgrading, (iv) direct methane to olefins/aromatics, (v) direct methane to methanol, and (vii) direct methanol to aromatics, among others. Simultaneous heat, power, and water integration is included to ensure minimum utility requirements and wastewater discharge. The objective function of the proposed mixed integer nonlinear programming model is to maximize the profit of the refinery. In addition to the input-output representations of complex reaction units via symbolic regression, the bilinear, trilinear, quadrilinear, and concave terms are reformulated to simplify the complexity associated with solving the MINLP model. In general, the proposed optimal methane-based chemical refinery effectively integrates the methane bi-reforming (steam reforming coupled with dry reforming), liquid-phase methanol synthesis, Dalian methanol to olefins, olefins to aromatics, and direct methanol to aromatics for maximum aromatics production to contribute highest net present value while achieving the lowest emission.