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

Impacts of Thai bio-ethanol policy target on land use and greenhouse gas emissions

The growing demand for biofuels has led to an increased demand for feedstocks which in turn is anticipated to induce changes in the cropping systems or land requirement for agriculture use. This study used consequential life cycle assessment (LCA) to evaluate the environmental consequences of possible (future) changes in agricultural production systems and determine their effects on land use change (LUC) and greenhouse gas (GHG) implications when cassava demand in Thailand increases. Six different cropping systems to increase cassava production including converting unoccupied land to cropland, yield improvement, displacement of area currently under sugarcane cultivation and the other potential changes in cropping systems in Viet Nam and Australia are modeled and assessed. The comparative results show that LUC is an important factor in overall GHG emissions of the first generation biofuels especially change in soil carbon stock contributing about 58-60% of the net GHG emissions. Increased cassava production by expanding cultivation area has a significantly larger effect on GHG emissions than increased productivity. The analysis shows that increasing productivity of both sugarcane and cassava are important ways to maximize benefits in using of certain area of Thailand to serve both the food and fuel industries.Consequential LCA Bio-ethanol Cassava Land use Greenhouse gas

Progress towards Sustainable Production: Environmental, Economic, and Social Assessments of the Cellulose Nanofiber Production Process

We assessed the environmental, economic, and social impacts of the process for producing cellulose nanofibers (CNFs), which are considered to be a valuable sustainable woody biomass feedstock. The greenhouse gas (GHG) emissions associated with CNF production are greater than the emissions associated with producing most plastic materials used in vehicle components because the grinding process during CNF production generates significant GHG emissions. The cost of CNF production is also higher than the cost of producing comparable plastics for automotive use because of the high cost of the pulverization process. The sensitivity analysis in this study suggested that GHG emissions and manufacturing costs could be reduced by 19.1–76.4% and 3.6–12.2%, respectively, by improving the energy efficiency of CNF production by two to five times. We compared the potential social risks associated with CNF production between Japan and Vietnam using a product social impact life cycle assessment database. It is desirable to reduce the social risk on the fair salary and child labor, and to improve the safe and healthy living conditions in the local communities that import wood chips harvested in Vietnam