Nikhil Krishnan

A screening life cycle metric to benchmark the environmental sustainability of waste management systems.

The disposal of municipal solid waste (MSW) can lead to significant environmental burdens. The implementation of effective waste management practices, however, requires the ability to benchmark alternative systems from an environmental sustainability perspective. Existing metrics--such as recycling and generation rates, or the emissions of individual pollutants--often are not goal-oriented, are not readily comparable, and may not provide insight into the most effective options for improvement. Life cycle assessment (LCA) is an effective approach to quantify and compare systems, but full LCA comparisons typically involve significant expenditure of resources and time. In this work we develop a metric called the Resource Conservation Efficiency (RCE) that is based on a screening-LCA approach, and that can be used to rapidly and effectively benchmark (on a screening level) the ecological sustainability of waste management practices across multiple locations. We first demonstrate that this measure is an effective proxy by comparing RCE results with existing LCA inventory and impact assessment methods. We then demonstrate the use of the RCE metric by benchmarking the sustainability of waste management practices in two U.S. cities: San Francisco and Honolulu. The results show that while San Francisco does an excellent job recovering recyclable materials, adding a waste to energy (WTE) facility to their infrastructure would most beneficially impact the environmental performance of their waste management system. Honolulu would achieve the greatest gains by increasing the capture of easily recycled materials not currently being recovered. Overall results also highlight how the RCE metric may be used to provide insight into effective actions cities can take to boost the environmental performance of their waste management practices.

Life Cycle Inventory of a CMOS Chip

A life cycle inventory for comparative assessment of assorted semiconductor device types is assembled using a library of process step-related information. In this paper, we present the structure of this library of energy use, material inputs and emissions data at the process equipment-level and facilities-scale, normalized per wafer. Selected results from a case study of a 130nm node CMOS device are presented and compared with a previous study of a comparable chip. Comparative production impacts of 6-layer and 8-layer CMOS devices are shown

Case studies in energy use to realize ultra-high purities in semiconductor manufacturing

With increasing sophistication of products, there is a general trend towards higher purity (lower tolerances) in materials and parts. The purification of input materials and the need to create low-entropy environments in manufacturing lead to significant energy and materials use - referred to as secondary materialization. In this article we explore secondary materialization in semiconductor manufacturing by characterizing energy use trends for three cases: cleanrooms, producing ultrapure water (UPW), and purifying elemental gases. For purification of water and elemental gases, increasing purity standards are correlated with dramatic increases in energy use. For cleanrooms, while electricity use per square foot tends to increase with increasing air purity, this growth is cancelled by the evolution towards larger wafers and mini-environments. The net result is reductions in energy use per area of wafer processed when moving from 200 mm to 300 mm wafer processing. Given the continuing trend towards higher purity standards and growth in high-tech manufacturing, the high growth in secondary energy use suggests that the characterization and management of energy and materials use for purification deserves increased attention.

Use of Statistical Entropy and Life Cycle Analysis to Evaluate Global Warming Potential of Waste Management Systems

The statistical entropy (SE) function has been applied to waste treatment systems to account for dilution or concentration effects on metals. We later extended it to account for carbon flows, especially in waste management systems involving thermal treatment. Now, a simple lifecycle “net energy” metric ‐ encompassing the “lost energy” that would have been gained when high-calorific materials are landfilled rather than combusted with energy recovery ‐ is introduced to account for additional influxes of carbon when using landfilling as the primary disposal method. When combining net energy calculations and long terms effects of landfilling, waste to energy (WTE) becomes a more attractive option for dealing with non-recycled municipal solid waste (MSW). A greenhouse gasforcing factor is also introduced to account for the entropy generating effects of methane. When incorporating forcing and lost energy, WTE performs notably better than landfills with respect to entropy generation and carbon.

Life Cycle Comparison of Two Options for MSW Management in Puerto Rico: Thermal Treatment vs. Modern Landfilling

The management of municipal solid wastes (MSW) in Puerto Rico is becoming increasingly challenging. In recent years, several of the older landfills have closed due to lack of compliance with federal landfill requirements. Puerto Rico is an island community and there is limited space for construction of new landfills. Furthermore, Puerto Rico residents generate more waste per capita than people living on the continental US. Thermal treatment, or waste to energy (WTE) technologies are therefore a promising option for MSW management. It is critical to consider environmental impacts when making decisions related to MSW management. In this paper we quantify and compare the environmental implications of thermal treatment of MSW with modern landfilling for Puerto Rico from a life cycle perspective. The Caguas municipality is currently considering developing a thermal treatment plant. We compare this to an expansion of a landfill site in the Humacao municipality, which currently receives waste from Caguas. The scope of our analysis includes a broad suite of activities associated with management of MSW. We include: (i) the transportation of MSW; (ii) the impacts of managing waste (e.g., landfill gas emissions and potential aqueous run-off with landfills; air emissions of metals, dioxins and greenhouse gases) and (iii) the implications of energy and materials offsets from the waste management process (e.g., conversion of landfill gas to electricity, electricity produced in thermal treatment, and materials recovered from thermal treatment ash). We developed life cycle inventory models for different waste management processes, incorporating information from a wide range of sources — including peer reviewed life cycle inventory databases, the body of literature on environmental impact of waste management, and site-specific factors for Puerto Rico (e.g. waste composition, rainfall patterns, electricity mix). We managed uncertainty in data and models by constructing different scenarios for both technologies based on realistic ranges of emission factors. The results show that thermal treatment of the unrecyclable part of the waste stream is the preferred option for waste management when compared to modern landfilling. Furthermore, Eco-indicator 99 method is used to investigate the human health, ecosystem quality and resource use impact categories.Copyright

Trends in the environmental impacts of CMOS manufacturing

A life cycle inventory model is presented in this study which describes resource demands and chemical emissions for the semiconductor manufacturing process flow of CMOS logic devices. The model is used to analyze several technology generations of chips, from the 1995-era 350 nm node to the 45 nm node, the most advanced CMOS design in current production. Detailed equipment-level data allows an understanding of the relative magnitude of resource demands and impacts by process step or chemical. The analysis illustrates trends in energy use and emissions, including hazardous, volatile organic and global warming gas emissions, over time and thus provides a means to forecast energy and water demands associated with near-future logic device manufacturing.