ASSESSMENT OF WASTE GASIFICATION TECHNOLOGY UNDER DATA UNCERTAINTY AND VARIABILITY

Process design and life-cycle analysis (LCA) methods do not usually account data variability and uncertainty which may contribute to misleading estimates of pollution prevention, performance, and cost of potentially promising new environmentally conscious technologies. To account this weakness, this research attempts to develop a novel assessment methodology for evaluation of the risks and potential pay-offs of new technologies that avoid pollutant production; and demonstrates the methodology via a case study of gasification of municipal solid wastes. Based upon the previous works in emissions’ estimation, exposure assessment, and risk assessment, a methodology for simultaneous characterization of both variability and uncertainty is developed here. Probabilistic modeling is explored here to represent uncertainties in any process technology. Thus, assessments of advanced process technologies that are in early developmental stages should be based on proper understanding and representation of uncertainties.