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Systems engineering approach for chemical process energy optimization
Author(s) -
Koehler Jürgen,
Schadler Norbert
Publication year - 1996
Publication title -
chemical engineering and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.403
H-Index - 81
eISSN - 1521-4125
pISSN - 0930-7516
DOI - 10.1002/ceat.270190209
Subject(s) - process (computing) , process systems , process integration , profitability index , process engineering , resource (disambiguation) , chemical process , biochemical engineering , resource efficiency , work in process , product (mathematics) , risk analysis (engineering) , process design , computer science , energy (signal processing) , industrial engineering , engineering , operations management , medicine , computer network , ecology , statistics , geometry , mathematics , finance , chemical engineering , economics , biology , operating system
Process energy integration and continuous improvement of process technology are everlasting issues to ensure profitability of chemical productions. And both objectives become increasingly important due to long‐term environmental effects of energy degradation, such as resource depletion, emissions and the release of “waste” heat. The key success factor for process improvement lies in combining up‐to‐date expertise from different areas in an overall approach. One such approach is the systems engineering concept. It helps to structure and to organize problem‐solving process. We strongly believe that the increasing complexity of large and interlinked chemical production system and the tightening of global economic pressure force us to use more than ever systematic analysis and design methods to guarantee optimality throughout the entire product life. First we present a brief introduction to systems engineering in general. Then, in the main part of the paper, we give examples for optimizing the use of energy in chemical plants in order to illustrate advantages of the systems engineering concept. The examples range from improving the performance of individual pieces of equipment over changes in the process structure up to optimizing process clusters.