Dynamic process intensification of binary distillation based on output multiplicity

Abstract

Process intensification focuses largely on process and equipment design. Much less emphasis has been placed on operational changes to achieve cost savings and increased efficiency. This article introduces the concept of dynamic intensification, defined as changes to the dynamics, operating strategy and/or control of a process that lead to a substantially more efficient processing path. This idea is illustrated in the context of binary distillation. Output multiplicity properties are exploited to establish a new periodic operating mode based on switching between two auxiliary products, which, on a time‐average basis, is more energy efficient than steady‐state operation. An extensive case study is presented concerning the distillation of a propanol–acetic acid mixture, confirming the theoretical developments. In contrast to previous research (e.g., on cyclic distillation), the present concept has significant advantages as it relies on existing hardware and exploiting system nonlinearity, rather than using specialized equipment operated in a discontinuous fashion. © 2018 American Institute of Chemical Engineers AIChE J, 65: 1162–1172, 2019

Maximizing energy savings attainable by dynamic intensification of binary distillation

“…First, the energy savings are quite significant, and achieving them only requires changes in operating strategy with minimal hardware modifications. Second, we note that the implementation of results similar to the ones presented above in the form of a transient, periodic operation strategy was successfully demonstrated via dynamic simulation in our previous papers [10,11].…”

“…In several recent publications [10,11], we introduced dynamic process intensification (DPI) as a novel operational approach for lowering energy use in distillation columns. DPI exploits the nonlinearities inherently present in the static behavior of distillation columns to create periodic, dynamic operating patterns that produce the same output (in terms of time-averaged flow rates and purities of the products) as an equivalent conventional column, but with lower energy consumption.…”

Operability and control in process intensification and modular design: Challenges and opportunities

“…A representative example is the periodic operation of conventional chemical reactors by oscillating pressures, concentrations, or temperatures to shift process over reaction equilibrium 66,67 . A more recent work by Yan et al 42 reduced the energy consumption of a binary distillation system by periodically altering column operation among multiple steady states while maintaining the time‐average product concentration at purity specification. Dynamic PI is achieved based on the observation that the target product can be obtained as a blend of two auxiliary products (one of higher purity than desired, the other of lower purity), both of which have lower specific energy consumption.…”

“…Current efforts toward process intensification and modularization through PSE approaches can be generally classified as the following: Design and optimization Process design, synthesis, and intensification — especially highlighting the phenomena‐based approaches to generate innovative process solutions 35‐37 Optimization of modular production — to compare the efficiency, economics, and/or reliability of numbering up vs. scaling up 12,38,39 Optimal operation and control Operability analysis — including steady state and dynamic feasibility, flexibility, controllability analysis, and so on 31,40 Advanced model‐based control — especially highlighting the need for distributed and decentralized model‐based control in intensified and modular systems, as well as integrated considerations of design, control (and scheduling) 22,41 Dynamic/periodic operation — to purposefully perturb operation along an optimal trajectory by exploiting process nonlinearity and output multiplicity 42,43 Supply chain optimization and operational planning of modular production — especially highlighting the flexibility and mobility of modular processing 44,45 …”

“…Optimal operation and control Operability analysis — including steady state and dynamic feasibility, flexibility, controllability analysis, and so on 31,40 Advanced model‐based control — especially highlighting the need for distributed and decentralized model‐based control in intensified and modular systems, as well as integrated considerations of design, control (and scheduling) 22,41 Dynamic/periodic operation — to purposefully perturb operation along an optimal trajectory by exploiting process nonlinearity and output multiplicity 42,43 Supply chain optimization and operational planning of modular production — especially highlighting the flexibility and mobility of modular processing 44,45 …”