The aim of “traditional” separation network synthesis (SNS) is to identify the most “efficient” network, often in terms of cost or energy requirements, to recover and purify certain products from a given feed. Generally, the following problem statement has been considered for a distillation-based SNS problem: we are given (1) a (fixed) number of inlet streams with specified component flow rates; (2) a (fixed) number of outlet streams with specified component flow rates; (3) a set of candidate distillation tasks. Our goal is to find the cost-optimal configuration of distillation columns, mixers, and splitters.
The typical approaches to the above problem statement are based on limiting assumptions such as the existence of a single inlet stream with specified component flow rates and/or pure product outlet streams. However, in reality, any separation system includes rich interactions with reactors. Therefore, our group studies the general problem consisting of multiple input stream as well as non-pure outlets (to be recycled to reactors). Accordingly, we propose the following generalized problem statement (Ryu et al., 2020): we are given (1) a number of candidate inlet streams with variable component flow rates (possibly zero); (2) a number of candidate outlet streams, some with and some without strict specifications. Our goal is to determine the cost-optimal configuration of distillation columns, mixers, and splitters.
Recently, we proposed a superstructure-based approach for distillation sequence synthesis using a ”matrix” representation (Kong and Maravelias, 2020). This approach handles zero component flow rates and allows rich connectivity between distillation units, thereby addressing the above generalized SNS problem. We have also developed versatile shortcut distillation network models which can be used to estimate an energy requirement target for a separation task without finding detailed configurations (Ryu and Maravelias, submitted).
Kong L, Maravelias CT. Expanding the scope of distillation network synthesis using superstructure-based methods. Computers and Chemical Engineering, 133, 2020.
Ryu J, Kong L, Pastore de Lima AE, Maravelias,CT. A generalized superstructure-based framework for process synthesis. Computers and Chemical Engineering, 133, 2020.
Ryu J, Maravelias CT. Versatile and Computationally Efficient Models for Separation Energy Targeting Tailored for Superstructure-based Process Synthesis: Distillation Column and Network Models. Computers and Chemical Engineering, Submitted.