(98g) State-Task Network Representation of Early-Stage Process for Identification of Best Processing Route – in Application of Reactive Carbon Capture

Superstructure has been in limelight for conceptualization method of a large-scale chemical system due to its excellence of capturing characteristics from multiple processes. A proper representation of superstructure allows identification of all the mathematically feasible pathways by mixed integer programming (MIP). Combination State-task network (STN) representation with superstructure optimization can find the best pathways.¹ Here, state is information of a stream (compound flowrate, temperature, and pressure) and task is a process which relates the inlet streams and the outlet streams, obeying mass and energy balance. Hence, a superstructure as STN representation commonly represents tasks as nodes and states as edges. There is one branch of STN representation, a generic model, where four tasks (mixing, reaction, waste separation, and product separation) are incorporated into one node allows is proposed.² This generic model is adopted to develop a framework to construct, analyze, and optimize different superstructures.^3,4

Herein, we introduce our previous work, where a versatile superstructure framework which can represent various types of chemical processes is proposed.⁵ STN representation in this framework does not limit any number of states, tasks, inlets, and outlets of a process. Logic-based outer approximation (LOA)⁶ which reformulates a strategy-finding problem from a complicated system into MIP is adopted, allowing quick optimization of a given superstructure. Any nonlinear equations can be incorporated into each task, thus flexibility to represent a chemical process in a superstructure is guaranteed. Compared with conventional superstructure representation as mixed-integer nonlinear programming (MINLP), this framework outperforms the conventional one in terms of both optimization time and solvability.

Application of this framework can stretch identification of promising pathways from a system in its technological infancy. An illustrating example is reactive carbon capture (RCC), a strategy that captures CO₂ from flue gas or air by amine scrubbing and utilizes it into chemicals (e. g. methanol, CO, and formic acid) without CO₂ release (Figure 1). As energy to produce pure CO₂ can be saved, RCC can be a true solution for carbon neutrality;⁷ however, all the RCC processes are in early stage. This study construct RCC superstructure using the framework, by representing the process as STN representations, keeping the first-principles of the processes. As it is known that the price of renewable energy varies by climate, this study identifies which route is best among various RCC technologies for each country. Compared to other carbon capture and utilization (CCU) pathways, the competitiveness of RCC routes in terms of both profitability and carbon reduction will be measured.

Figure 1. Reactive carbon capture (RCC). a) RCC by using amine scrubbing and electrochemical CO₂ conversion to syngas⁷ and b) superstructure representation of CCU system with RCC.

References

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