Fluidized Bed Scale-Up for Sustainability Challenges. 2. New Pathway

被引:3
作者
Chew, Jia Wei [1 ]
Cocco, Ray A. [2 ]
机构
[1] Chalmers Univ Technol, Chem Engn, S-41296 Gothenburg, Sweden
[2] Particles Mot LLC, Elmhurst, IL 60126 USA
关键词
RESIDENCE TIME DISTRIBUTION; NEURAL-NETWORK; ARTIFICIAL-INTELLIGENCE; TRANSFER COEFFICIENT; CONSTITUTIVE MODELS; CATALYST ATTRITION; PARTICLE; HYDRODYNAMICS; SIMULATION; RESTITUTION;
D O I
10.1021/acs.iecr.4c00421
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
Despite more than 100 years of commercialization of wide-ranging fluidized bed reactors, scale-up tools and methods have remained quite similar. To exploit the benefits of fluidized beds for the time-critical sustainability challenges, scale-up has to be implemented quicker and better. Correspondingly, a companion Part 1 ( Ind. Eng. Chem. Res. 2024, 63, 2519-2533) reviewed the evolution of the tools used in scaling up fluidized beds. Leveraging that, the current Part 2 aims to first overview the traditional pathway for scale-up and then propose a new pathway. Notably, instead of the traditional way of focusing on a linear sequence of progressively larger units, the emphasis is on the Phases of Discovery, Research, and Development, which apply the new tools consistently, as well as address risk mitigation and economics throughout. Based on an acrylonitrile case study, a Monte Carlo analysis indicates the new proposed pathway offers more promising economic feasibility, with net present value over 20 years (NPV20) of $310MM higher, along with 35% and 42% reductions in start-up and break-even times, respectively. The increase in costs for incorporating modeling efforts is insignificant compared to the overall benefits with respect to time and economics.
引用
收藏
页码:8025 / 8043
页数:19
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