Development of an assessment methodology for waste gasification technology under stochastic data

The inability of existing process design and life-cycle analysis (LCA) methods to account for variability and uncertainty may contribute to misleading estimates of pollution prevention, performance, and cost of potentially promising new environmentally conscious technologies. It is hypothesized that the quantification of variability and uncertainty, in combination with detailed process simulation, LCA and integrated assessment yield new insights regarding how to minimize the risks and maximize the pay-offs of such technologies. The objectives of this work are to develop a novel assessment methodology for evaluation of the risks and potential pay-offs of new technologies that avoid pollutant production; and demonstrate the methodology via a case study of high-temperature waste gasification technology. In this study, a methodology for simultaneous characterization of both variability and uncertainty based upon previous work in emissions estimation, exposure assessment, and risk assessment is employed. To represent uncertainties in any process technology, a probabilistic modeling has been applied. Probabilistic simulation is performed using Latin hypercube sampling, a variance reduction technique based on a stratified sampling approach. The key features of the method include (1) the development of a consistent set of engineering performance and cost models for each technology to be evaluated, (2) the characterization of uncertainties in specific parameters of the engineering models, (3) application of the models in a probabilistic modeling environment to characterize uncertainties in model outputs, and (4) analysis of model results for the purposes of technology evaluation and research planning. Waste gasification is investigated as a case study which is a promising approach for clean and efficient power generation as well as for polygeneration of a variety of products, such as steam, sulfur, hydrogen, methanol, ammonia, and others. Hence, assessments of advanced process technologies that are in early stages of development should be based on a proper understanding and representation of uncertainties. Probabilistic analysis has implications for the development of more realistic cost estimates that capture the notion of cost growth often experienced with innovative process technologies