(37b) Grams - a Dynamic Intensification and Optimization Platform for Modular Chemical Process Systems
- Conference: AIChE Spring Meeting and Global Congress on Process Safety
- Year: 2019
- Proceeding: 2019 Spring Meeting and 15th Global Congress on Process Safety
- Group: Process Intensification
- Time: Monday, April 1, 2019 - 4:00pm-4:30pm
We have developed GRAMS (Generalized Reaction-Adsorption modeling, optimization and Simulation), which is a first-of-its-kind computational framework for the optimal design and operation of dynamically intensified packed-bed systems. For given feeds and products specifications, GRAMS is used to optimally design the cycle configurations, column design specifications and process operating conditions of a periodically operated MCPS [1-3]. The GRAMS platform combines an in-house dynamic process simulator with a cost estimator and a data-driven constrained grey-box optimizer . The high-fidelity process simulator is based on a first-principles-based model of a generalized reaction-adsorption system to predict the performances of processes incorporating periodic gas adsorption-desorption (e.g., pressure/temperature/vacuum swing adsorption: PSA, TSA, VSA, SMB), reaction (e.g., PFR, SMBR), or a combination of both (e.g., SERP). The model predictions are extensively validated with experimental data for industrially relevant pressure swing adsorption (PSA), steam methane reforming (SMR), methanol synthesis, sorption-enhanced SMR (SE-SMR), and sorption-enhanced water gas shift reaction (SE-WGSR) processes. The framework has been used for the optimal synthesis of three multi-mode, multi-step and periodic SERP systems, namely SE-SMR, SE-WGSR and sorption-enhanced methanol (SE-MeOH). The optimized SE-SMR produces hydrogen from natural gas with 35% higher productivity and more than 10% lower cost in comparison to existing small-scale systems . Furthermore, the novel SE-MeOH process, designed using GRAMS, could lead to more than 7% improvement in methanol yield with only 2% decrease in production capacity .
 A. Arora, S. S. Iyer, and M. M. F. Hasan, âGRAMS: A General Framework Describing Adsorption, Reaction and Sorption-Enhanced Reaction Processes,â Chem. Eng. Sci., vol. 192, pp. 335â358, 2018.
 A. Arora, I. Bajaj, S. S. Iyer, and M. M. F. Hasan, âOptimal Synthesis of Periodic Sorption Enhanced Reaction Processes with Application to Hydrogen Production,â Comput. Chem. Eng., vol. 115, pp. 89â111, 2018.
 A. Arora, S. S. Iyer, I. Bajaj, and M. M. F. Hasan, âOptimal Methanol Production via Sorption Enhanced Reaction Process,â Ind. Eng. Chem. Res., 2018.
 I. Bajaj, S. S. Iyer, and M. M. F. Hasan, âA Trust Region-based Two Phase Algorithm for Constrained Black-box and Grey-box Optimization with Infeasible Initial Point,â Comput. Chem. Eng., 2017.