Integrated Techno-Economic and Life Cycle Analyses of Biomass Utilization for Value-Added Bioproducts in the Northeastern United States

A multi-stage spatial analysis was first conducted to select locations for lignocellulosic biomass-based bioproduct facility, using Geographical Information System (GIS) spatial analysis, multi-criteria analysis ranking algorithm, and social-economic assessment. A case study was developed to determine locations for lignocellulosic biorefineries using feedstocks including forest residue biomass and three energy crops for 13 states in the northeastern United States. In the entire study area, 11.1% of the counties are high-suitable, 48.8% are medium-suitable for biorefinery siting locations. A non-parametric analysis of cross-group surveys showed that preferences on biorefinery siting are homogeneous for experts in academia and industry groups, but people in government agencies presented different opinions. With the Maximum Likelihood test, parameters of distributions and mean values were estimated for nine weighted criteria. Social asset evaluation focusing on degree of rurality and social capital index further sorted counties with higher community acceptance and economic viability. A total of 15 counties were selected with the highest potential for biorefinery sites in the region. A mixed-integer linear programming model was then developed to optimize the multiple biomass feedstock supply chains, including feedstock establishment, harvest, storage, transportation, and preprocessing. The model was applied for analyses of multiple biomass feedstocks at county level for 13 states in the northeastern United States. In the base case with a demand of 180,000 dry Mg/year of biomass, the delivered costs ranged from $67.90 to$86.97 per dry Mg with an average of $79.58 /dry Mg. The biomass delivered costs by county were from$67.90 to 150.81 per dry Mg across the northeastern U.S. Considered the entire study area, the delivered cost averaged $85.30 /dry Mg for forest residues,$84.47 /dry Mg for hybrid willow, $99.68 for switchgrass and$97.87 per dry Mg for Miscanthus. Seventy seven out of 387 counties could be able to deliver biomass at $84 per dry Mg or less a target set by US DOE by 2022. A sensitivity analysis was also conducted to evaluate the effects of feedstock availability, feedstock price, moisture content, procurement radius, and facility demand on the delivered cost. Our results showed that procurement radius, facility capacity, and forest residue availability are the most sensitive factors affecting the biomass delivered costs. An integrated life cycle and techno-economic assessment was carried out for three bioenergy products derived from multiple lignocellulosic biomass. Three cases were studied for production of pellets, biomass-based electricity, and pyrolysis bio-oil. The LCA was conducted for estimating environmental impacts on cradle-to-gate basis with functional unit of 1000 MJ for bioenergy production. Pellet production had the lowest GHG emissions, water and fossil fuels consumption, for 8.29 kg CO2 eq, 0.46 kg, and 105.42 MJ, respectively. Conversion process presented a greater environmental impact for all three bioenergy products. With producing 46,926 tons of pellets, 260,000 MWh of electricity, and 78,000 barrels of pyrolysis oil, the net present values (NPV) for all three cases indicated only pellet and biopower production cases were profitable with NPVs$1.20 million for pellet, and $81.60 million for biopower. The pellet plant and biopower plant were profitable only when discount rates are less than or equal to 10A study evaluated the environmental and economic impacts of activated carbon (AC) produced from lignocellulosic biomass was evaluated for energy storage purpose. Results indicate that overall “in-plant production” process presented the highest environmental impacts. Normalized results of life cycle impact assessment showed that the AC production had environmental impacts mainly on carcinogenics, ecotoxicity, and non-carcinogenics categories. We then further focused on life cycle analysis from raw biomass delivery to plant gate, the results showed “feedstock establishment” has the most significant environmental impact, ranging from 50.3$6.66 million, and annual operation cost was $15.46 million. The AC required selling price (RSP) was$16.79 per kg, with the discounted payback period (DPB) of 9.98 years. Alternative cases of KOH-reuse and steam processes had GHG emission of 15.4 kg CO2 eq, and 10.2 kg CO2 eq for every 1 kg activated carbon, respectively. Monte Carlo simulation showed 49.96% of the probability for an investment to be profitable in activated carbon production for supercapacitor electrodes

Essays on Health-related Disparities

As the most critical conditions of human life and a significant contributor to human capability, health is the fundamental unit for a functioning society. As a construct, health is also inherently multi-dimensional, and to understand and to evaluate whether the infrastructure of a society endows fair "health opportunities" to its people can be an enduring task for both researchers and policy-makers. In this dissertation, I explore this complex and ever more relevant issue of health disparity from different angles using administrative data and extensive exploration of the literature. In particular, I analyse the geographic disparity in quality of care and the potential drivers - differential provider behaviour. Looking at health status, I investigate the disparity of health outcomes due to external economic shocks and found that individuals from economically disadvantaged areas exhibit significantly worse mental health conditions. Given the geographic disparity, I further examine how different sources of information on provider quality affect patient choice and decision to travel for care. Moreover, I survey on how the internet has facilitated the disparity in information and diverging opinions on health. Finally, from a systems perspective, I scrutinise structural characteristics in health care system design that create disparities in benefits and access. My inquiry into the complex phenomenon of health disparity presents a humble contribution to the exiting literature at the intersection of health economics, medical sociology and social epidemiology

Mozi: Universal Love and Human Agency

Mozi (c. 480-390 B.C.E.) was a Chinese philosopher from the Warring States period in early Chinese history whose utilitarianism contrasted with Confucian and Daoist thought. Through reading the Mohist text in translation, I examined the relationship between two central principles in Mozi’s philosophy: Heaven’s standard universal love and human action. Mozi builds his arguments in the framework of a sociopolitical hierarchy that incorporates utilitarian analyses of customs, morality, and beliefs in ghosts and spirits. By comparing secondary sources in English and Chinese, reading different translations, and learning from graphical analysis of the text’s key terms’ original Chinese ideographs, I concluded that in Mozi’s philosophy, human action is the necessary agent to carry out Heaven’s will of universal love for the ultimate goal of benefitting all people

Robust heavy-tailed versions of generalized linear models with applications in actuarial science

Generalized linear models (GLMs) form one of the most popular classes of models in statistics. The gamma variant is used, for instance, in actuarial science for the modelling of claim amount in insurance. A flaw of GLMs is that they are not robust against outliers (i.e., against erroneous or extreme data points). A difference in trends in the bulk of the data and the outliers thus yields skewed inference and prediction. Cantoni and Ronchetti (2001) proposed a robust frequentist approach which is now the most commonly applied. It consists in an estimator which is derived from a modification of the derivative of the log-likelihood. We propose an approach which is modelling-based and thus fundamentally different. It allows for an understanding and interpretation of the modelling, and it can be applied for both frequentist and Bayesian statistical analyses. We show that the approach possesses appealing theoretical and empirical properties. In particular, we show through a simulation study that it offers an advantage in terms of estimation performance

How Chinese social media sentiment about COVID changed during 2020

Chinese social media sentiment about the pandemic fluctuated during 2020. Yan Wang and Yuxi Zhang (LSE-Fudan Global Public Policy Hub) use platform data to analyse how these waves of sentiment emerged and shifted, and look at the case of Chengdu Girl, a woman who caught COVID in December of that year

Asymptotic stability of rarefaction waves for compressible Navier-Stokes equations with relaxation

The asymptotic stability of rarefaction wave for 1-d relaxed compressible isentropic Navier-Stokes equations is established. For initial data with different far-field values, we show that there exists a unique global in time solution. Moreover, as time goes to infinity, the obtained solutions are shown to converge uniformly to rarefaction wave solution of $p$-system with corresponding Riemann initial data. The proof is based on $L^2$ energy methods

Get Out of the Valley: Power-Efficient Address Mapping for GPUs

GPU memory systems adopt a multi-dimensional hardware structure to provide the bandwidth necessary to support 100s to 1000s of concurrent threads. On the software side, GPU-compute workloads also use multi-dimensional structures to organize the threads. We observe that these structures can combine unfavorably and create significant resource imbalance in the memory subsystem causing low performance and poor power-efficiency. The key issue is that it is highly application-dependent which memory address bits exhibit high variability. To solve this problem, we first provide an entropy analysis approach tailored for the highly concurrent memory request behavior in GPU-compute workloads. Our window-based entropy metric captures the information content of each address bit of the memory requests that are likely to co-exist in the memory system at runtime. Using this metric, we find that GPU-compute workloads exhibit entropy valleys distributed throughout the lower order address bits. This indicates that efficient GPU-address mapping schemes need to harvest entropy from broad address-bit ranges and concentrate the entropy into the bits used for channel and bank selection in the memory subsystem. This insight leads us to propose the Page Address Entropy (PAE) mapping scheme which concentrates the entropy of the row, channel and bank bits of the input address into the bank and channel bits of the output address. PAE maps straightforwardly to hardware and can be implemented with a tree of XOR-gates. PAE improves performance by 1.31 x and power-efficiency by 1.25 x compared to state-of-the-art permutation-based address mapping