Systematic Development of Adsorption Processes for the Separation of Close-boiling Gas Mixtures

Abstract

The drive to reduce costs and maximise profits, as well as increased awareness of the environmental impacts of greenhouse gas emissions, has led the chemicals industry to seek to improve energy efficiency and source cheaper or more sustainable feedstocks. Traditionally, efforts to improve efficiency focused on improving or optimising existing technologies and processes, leading to highly heat-integrated and complex flowsheets. This approach, although leading to improvements in process performance, is inherently limited in its potential to bring about energy savings, as the core technology underpinning unit operations is the same. Novel approaches and technologies are required to truly revolutionise the performance of chemical processes. This work presents a systematic, multidisciplinary approach to explore the potential of adsorptive separation processes as alternatives to cryogenic distillation-based processes for the separation of challenging gas mixtures. The proposed approach is intended to be used at the early, conceptual stage of process development. As such, it focuses on assessing the technical capability of adsorbents and adsorption processes to achieve a desired separation, without explicit consideration of cost implications. Process energy demand and working capacity are used as indicators of potential cost. The project focuses on the separation of fluid mixtures where the constituents have similar volatilities. Separation by distillation of such mixtures is inherently difficult and energy intensive, as distillation relies on exploiting volatility differences to achieve separation. A simplified adsorption process model is proposed for rapid screening of adsorbents for the separation of binary gas mixtures in temperature swing adsorption (TSA) processes. The proposed model is validated by comparison with a rigorous adsorption column model and found to be in good agreement, with a maximum deviation of 14% observed in performance predictions over a range of operating conditions. The simplified model is also circa four orders of magnitude faster to compute than the rigorous model, making it ideal for large-scale adsorbent screening exercises. The model is applied to screen adsorbents for post-combustion CO2 capture, and identifies potential adsorbents which could meet industrial CO2 capture targets. This project is an industrial-academic collaboration and is sponsored and technically supported by BP through its International Centre for Advanced Materials (ICAM). As such, in developing and demonstrating the proposed approach, separation of CO from N2-containing gas mixtures – a challenging separation of particular interest to BP – is primarily considered as a case study. The impact of N2 contamination on the performance of the conventional cryogenic distillation process for the production of high purity (~99 mol%) CO from syngas is also investigated. Cryogenic distillation process flowsheets for CO production from syngas feeds containing up to 4 mol% N2 are developed and evaluated using a commercial process simulation software. Results show that N2-contamination to the degree where a CO/N2 separation unit becomes necessary has significant capital and operating cost implications for the conventional cryogenic distillation technology. This work also proposes a comprehensive multiscale methodology for integrated adsorbent and process selection. The proposed methodology combines molecular simulations, experimental adsorbent synthesis and characterisation, and shortcut and detailed process simulation and optimisation to identify the most suitable adsorbent(s) and process scheme for a given gas separation problem. The methodology is applied to investigate the potential to produce CO from blast furnace gas, a cheaper and more sustainable potential alternative to syngas. Blast furnace gas is the flue gas produced during the iron oxide reduction step of steel production. Due to its low heating value, blast furnace gas is not

Date of Award	31 Dec 2020
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Megan Jobson (Supervisor) & Flor Siperstein (Supervisor)