## Abstract The purpose of this contribution is to present a case study illustrating how environmental impact assessment using the U.S. EPA Waste Reduction Algorithm has been incorporated into conceptual process design. This will be achieved via the introduction of a simple methodology for integrat
Incorporating uncertainty in chemical process design for environmental risk assessment
β Scribed by Vasquez, Victor R. ;Whiting, Wallace B.
- Publisher
- American Institute of Chemical Engineers
- Year
- 2004
- Tongue
- English
- Weight
- 562 KB
- Volume
- 23
- Category
- Article
- ISSN
- 0278-4491
- DOI
- 10.1002/ep.10050
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β¦ Synopsis
Abstract
The effects of uncertainty in thermophysical properties on the evaluation of the environmental performance is demonstrated with a chemical process to recover toluene and ethyl acetate by absorption from a gaseous waste stream of a cellophane production plant. In this case study, the environmental performance is defined as the estimation of the volatile organic compounds (VOCs) and total emissions of the plant and of several environmental risk indexes. We found that estimations of VOCs are very sensitive to uncertainty in thermophysical properties such as infiniteβdilution activity coefficients, and vapor pressures (through uncertain temperature variations). Additionally, we concluded that calculation of the total emissions can be very sensitive to fuel content factors such as those used to estimate greenhouse gases. This can have such an impact on the emission calculations that a detailed model of the given chemical process might not be required for the estimation of the total emissions. In other words, a simpler process flowsheet model can perform the same task just as well, with the results within the variations caused by uncertainty in the thermophysical properties. We demonstrate a Monte Carlo approach that allows the detection of such uncertainty characteristics in a design, providing a rational basis for prediction of the associated environmental risks. This procedure also enables the deconvolution of various sources of uncertainty, and the estimation of physical property uncertainty through a similarity approach. We concluded that our framework can be used to enhance decision making by uncovering uncertainties and sensitivities in chemical process simulation. Β© 2004 American Institute of Chemical Engineers Environ Prog, 2004
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