Techno-economic, uncertainty, and optimization analysis of commodity product production from biomass fast pyrolysis and bio-oil upgrading

Advanced biofuel is a promising replacement to fossil fuels for the purpose of protecting the environment and securing national energy supply, but the high cost of producing advanced biofuels makes it not as competitive as petroleum-based fuels. Recent technology developments in biomass fast pyrolysis and bio-oil upgrading introduced several innovative pathways to convert bio-oil into other commodity products, such as bio-asphalt, bio-cement, dextrose and benzene, toluene, xylene (BTX). Before commercializing these products, a comprehensive techno-economic analysis should be employed to examine the economic feasibility of producing them. This thesis compared the economic performance of biofuels, biochemicals, and hydrocarbon chemicals portfolios and optimized the product selection of an integrated bio-refinery. Based on a fast pyrolysis and bio-oil fractionation system, three product portfolios were proposed: biofuels (gasoline and diesel), biochemicals (bio-asphalt, cement and dextrose) and hydrocarbon chemicals (BTX and olefins). The production process, operating costs and capital costs were simulated based on the model data, experimental data, and literature data. Minimum product selling price (MPSP), maximum investment cost (MIC) and net present value (NPV) were used to evaluate and compare the economic performance of three portfolios with a 10% internal rate of return (IRR). A bio-refinery concept integrating all products was proposed to improve the flexibility to respond to changes in the market prices of the proposed products. The ratio of bio-oil upgrading to different product groups was manipulated to maximize the NPV under different price situations. Several major conclusions were drawn from this study. Due to high capital costs and operating costs associated with biofuels production, hydrocarbon chemical and biochemical products can be attractive bio-refinery products. However, there has been limited development of the hydrocarbon chemical and biochemical product technologies. This study attempts to address this risk by evaluating the uncertainty in the NPV and MIC. In particular, the biochemicals scenario has the highest MIC, which indicates that it has the greatest potential for remaining profitable with increased capital investment. The hydrocarbon chemicals production yields relatively high revenues and is more robust to fluctuations in market prices based on historical data. Biofuels production is economically attractive only when the price of transportation fuels is at historically high values

Fast Incremental SVDD Learning Algorithm with the Gaussian Kernel

Support vector data description (SVDD) is a machine learning technique that is used for single-class classification and outlier detection. The idea of SVDD is to find a set of support vectors that defines a boundary around data. When dealing with online or large data, existing batch SVDD methods have to be rerun in each iteration. We propose an incremental learning algorithm for SVDD that uses the Gaussian kernel. This algorithm builds on the observation that all support vectors on the boundary have the same distance to the center of sphere in a higher-dimensional feature space as mapped by the Gaussian kernel function. Each iteration involves only the existing support vectors and the new data point. Moreover, the algorithm is based solely on matrix manipulations; the support vectors and their corresponding Lagrange multiplier $\alpha_i$'s are automatically selected and determined in each iteration. It can be seen that the complexity of our algorithm in each iteration is only $O(k^2)$, where $k$ is the number of support vectors. Experimental results on some real data sets indicate that FISVDD demonstrates significant gains in efficiency with almost no loss in either outlier detection accuracy or objective function value.Comment: 18 pages, 1 table, 4 figure

Algorithmic Decision-Making Safeguarded by Human Knowledge

Commercial AI solutions provide analysts and managers with data-driven business intelligence for a wide range of decisions, such as demand forecasting and pricing. However, human analysts may have their own insights and experiences about the decision-making that is at odds with the algorithmic recommendation. In view of such a conflict, we provide a general analytical framework to study the augmentation of algorithmic decisions with human knowledge: the analyst uses the knowledge to set a guardrail by which the algorithmic decision is clipped if the algorithmic output is out of bound, and seems unreasonable. We study the conditions under which the augmentation is beneficial relative to the raw algorithmic decision. We show that when the algorithmic decision is asymptotically optimal with large data, the non-data-driven human guardrail usually provides no benefit. However, we point out three common pitfalls of the algorithmic decision: (1) lack of domain knowledge, such as the market competition, (2) model misspecification, and (3) data contamination. In these cases, even with sufficient data, the augmentation from human knowledge can still improve the performance of the algorithmic decision

Blind Inpainting with Object-aware Discrimination for Artificial Marker Removal

Medical images often contain artificial markers added by doctors, which can negatively affect the accuracy of AI-based diagnosis. To address this issue and recover the missing visual contents, inpainting techniques are highly needed. However, existing inpainting methods require manual mask input, limiting their application scenarios. In this paper, we introduce a novel blind inpainting method that automatically completes visual contents without specifying masks for target areas in an image. Our proposed model includes a mask-free reconstruction network and an object-aware discriminator. The reconstruction network consists of two branches that predict the corrupted regions with artificial markers and simultaneously recover the missing visual contents. The object-aware discriminator relies on the powerful recognition capabilities of the dense object detector to ensure that the markers of reconstructed images cannot be detected in any local regions. As a result, the reconstructed image can be close to the clean one as much as possible. Our proposed method is evaluated on different medical image datasets, covering multiple imaging modalities such as ultrasound (US), magnetic resonance imaging (MRI), and electron microscopy (EM), demonstrating that our method is effective and robust against various unknown missing region patterns