4.6 Article

A Modular Approach to Sustainability Assessment and Decision Support in Chemical Process Design

Journal

INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
Volume 49, Issue 17, Pages 7870-7881

Publisher

AMER CHEMICAL SOC
DOI: 10.1021/ie901943d

Keywords

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Funding

  1. Universiti Malaysia Pahang
  2. Berlin Institute of Technology
  3. U.S. National Science Foundation [0730383]
  4. Direct For Education and Human Resources
  5. Division Of Undergraduate Education [0736739] Funding Source: National Science Foundation
  6. Directorate For Engineering
  7. Div Of Chem, Bioeng, Env, & Transp Sys [0730383] Funding Source: National Science Foundation

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In chemical and allied industries, process design sustainability has gained public concern in academia, industry, government agencies, and social groups. Over the past decade, a variety of sustainability indicators have been introduced, but with various challenges in application. It becomes clear that the industries need urgently practical tools for conducting systematic sustainability assessment on existing processes and/or new designs and, further, for helping derive the most desirable design decisions. This paper presents a systematic, general approach for sustainability assessment and design selection through integrating hard (quantitative) economic and environmental indicators along with soft (qualitative) indicators for social criteria into design activities. The approach contains four modules: a process simulator module, an equipment and inventory acquisition module, a sustainability assessment module, and a decision support module. The modules fully utilize and extend the capabilities of the process simulator Aspen Plus, Aspen Simulation Workbook, and a spreadsheet, where case model development, data acquisition and analysis, team contribution assessment, and decision support are effectively integrated. The efficacy of the introduced approach is illustrated by the example of biodiesel process design, where insightful sustainability analysis and persuasive decision support show its superiority over commonly practiced technoeconomy evaluation approaches.

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