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Optimization Modeling for Advanced Syngas to Olefins Reactive Systems
Advanced reactor designs with multiple catalysts are game-changers for process intensification. These reactors transform large, complex processes with multiple reactors to one-shot reactors, where complex reaction mechanisms can be exploited within a single unit. Such designs lead to layered and mixed catalyst beds that overcome equilibrium limitations, manage heat effects and improve product selectivity. These graded bed reactors have been considered for a number of reactive systems, ranging from Fischer-Tropsch synthesis, benzene hydrogenation, oxidative coupling of methane and steam reforming. This project develops and applies a new approach for the optimization of graded bed systems, based on EO-based optimization of fully discretized DAE (differential-algebraic equations) and PDAE (partial differential-algebraic equations) models. Known as direct transcription, this approach has been widely applied to challenging dynamic optimization problems, adapted to large-scale optimization software and is generally much faster and more reliable than with standard commercial tools. In particular, for graded beds, this approach stabilizes exponential forward modes and applies specialized regularization strategies in order to handle singular problem characteristics. As the target application, this project is especially devoted to improving the design and optimization methodologies for syngas to olefin (STO) processes, with emphasis on producing light (<C5=) olefins. Here, the optimization process significantly advances the synergies between methanol synthesis catalysts for syngas reactions and zeolite catalysts that produce light olefins.
Developing efficient and rigorous optimization strategies for graded bed reactive systems
Managing tradeoffs in optimization speed, accuracy, and complexity for evaluation of new PI applications
The simultaneous dynamic optimization approach, coupled with a bi-level formulation for singular control, forms the core technology to determine optimal catalyst and temperature distributions for graded beds. A study for partial oxidation reactors has shown that the graded bed optimization increases the yield up to 34%, merely through the design of optimal catalyst and temperature distributions profiles within the reactor, without capital expense. The impact is also significant for new plant designs, where traditionally multi-reactor beds can be simplified to a single vessel, offering potential capital and energy benefits, in addition to chemistry yield improvements.
The process Systems Engineering (PSE) group at the CMU is internationally renowned and is the largest in the United States. The group focuses on systematic modeling and optimization strategies for multi-scale process systems engineering, covering the full spectrum from the molecular to the enterprise level. The Dow chemical is providing support for pilot plant data studies, validation of reactor models, and comparison with detailed industrial reactor models. The Dow chemical also provides technical guidance, industrial experience and internal modeling support.