Journal
CHEMICAL ENGINEERING SCIENCE
Volume 248, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ces.2021.117134
Keywords
Forced periodic operations; Methanol synthesis; Nonlinear frequency response; Process improvement; Single input modulations
Categories
Funding
- Priority Programme 2080 of the German Research Foundation DFG 'Catalysts and Reactors under Dynamic Operating Conditions for Energy Storage and Conversion' within the project 'Analysis of forced periodic operation of chemical reactors
- Ministry of Education, Science and Technological Development of the Republic of Serbia [451-03-9/2021-14/200135]
- [PE 2915/1-1]
- [Kl 417/6-1]
- [SE 586/24-1]
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This two-part paper presents a comprehensive study on improving performance criteria of a methanol synthesis reactor through forced periodical operations. The study uses the Nonlinear Frequency Response method to analyze single input modulations and will be extended to simultaneous modulations of two inputs for significant performance enhancements.
In this two-part paper a comprehensive study of the potential to improve performance criteria of a methanol synthesis reactor through forced periodical operations is presented. The study uses the Nonlinear Frequency Response method, a powerful analytical and approximate tool which gives an answer whether and under which conditions certain periodic operation would lead to improvement of process performance. To demonstrate the method, isothermal and isobaric methanol synthesis in a lab-scale CSTR is considered. In Part I, the analysis is performed for single input modulations. Partial pressures of each reactant in the feed stream and the total inlet volumetric flow-rate are considered as possible modulated inputs. The results show that modulations of single inputs essentially do not provide potential for significant improvements. In Part II, the study will be extended to analysis of periodic operations with simultaneous modulations of two inputs and conditions offering significant performance enhancements will be identified. (c) 2021 Elsevier Ltd. All rights reserved.
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