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The earth abundant and environmentally friendly element iron (Fe) forms various functional materials of metallic iron, iron oxides, iron carbides, natural iron ore, and iron-based metallic-organic frameworks. The Fe-based materials have been intensively studied as oxygen carriers, catalysts, adsorbents, and additives in bioenergy production. This review was to provide a fundamental understanding of the syntheses and characteristics of various Fe-based materials for further enhancing their functionalities and facilitating their applications in various bioenergy conversion processes. The syntheses, characteristics, and applications of various iron-based materials for bioenergy conversion published in peer-reviewed articles were first reviewed. The challenges and perspectives of the wide applications of those functional materials in bioenergy conversion were then discussed. The functionalities, stability, and reactivity of Fe-based materials depend on their structures and redox phases. Furthermore, the phase and composition of iron compounds change in a process. More research is needed to analyze the complex phase and composition changes during their applications, and study the type of iron precursors, synthesizing conditions, and the use of promoters and supports to improve their performance in bioenergy conversion. More studies are also needed to develop multifunctional Fe-based materials to be used for multi-duties in a biorefinery and develop green processes to biologically, economically, and sustainably produce those functional materials at a large scale.

 

Abstract Chemical Looping Combustion (CLC) is a technology that efficiently combines power generation and CO 2 capture. In CLC, the fuel is oxidized by a metal oxide called an oxygen carrier (OC). CLC uses two reactors: a fuel reactor and an air reactor. The fuel reactor oxidizes the fuel and reduces the OC. The air reactor oxidizes the OC using air and then the OC is cycled back to the fuel reactor. It is typical for both the fuel and the air reactors to be fluidized beds (FBs). In this research, an Aspen Plus model was developed to simulate a CLC system. Aspen Plus has recently included a built-in FB unit operation module. To our knowledge, no literature has been reported using this FB module for simulating fluidized bed combustion or gasification. This FB unit process was investigated in Aspen Plus and a kinetic based model was used and compared the simulation results to experimental data and the commonly used Gibbs equilibrium model. The FB unit and the kinetic model well fit the experimental data for syngas and methane combustion within 2% of the molar composition of syngas combustion and within 4% for the methane combustion. An advantage of this model over other kinetic models in literature is that the core shrinking model kinetic rate equations have been converted into a power law form. This allows Aspen Plus to use a calculator instead of an external Fortran compiler. This greatly simplifies the modeling process. The reaction rate equations are given for all reactions. A sensitivity analysis of the reaction kinetics was conducted. All data, code, and simulation files are given. 

Anaerobic digestion (AD), microalgae cultivation, and microbial fuel cells (MFCs) are the major biological processes to convert organic solid wastes and wastewater in the agricultural industry into biofuels, biopower, various biochemical and fertilizer products, and meanwhile, recycle water. Various nanomaterials including nano zero valent irons (nZVIs), metal oxide nanoparticles (NPs), carbon-based and multicompound nanomaterials have been studied to improve the economics and environmental sustainability of those biological processes by increasing their conversion efficiency and the quality of products, and minimizing the negative impacts of hazardous materials in the wastes. This review article presented the structures, functionalities and applications of various nanomaterials that have been studied to improve the performance of AD, microalgae cultivation, and MFCs for recycling and valorizing agricultural solid wastes and wastewater. The review also discussed the methods that have been studied to improve the performance of those nanomaterials for their applications in those biological processes. 

Statistical network models have been used to study the competition among different products and how product attributes influence customer decisions. However, in existing research using network-based approaches, product competition has been viewed as binary (i.e., whether a relationship exists or not), while in reality, the competition strength may vary among products. In this paper, we model the strength of the product competition by employing a statistical network model, with an emphasis on how product attributes affect which products are considered together and which products are ultimately purchased by customers. We first demonstrate how customers’ considerations and choices can be aggregated as weighted networks. Then, we propose a weighted network modeling approach by extending the valued exponential random graph model to investigate the effects of product features and network structures on product competition relations. The approach that consists of model construction, interpretation, and validation is presented in a step-by-step procedure. Our findings suggest that the weighted network model outperforms commonly used binary network baselines in predicting product competition as well as market share. Also, traditionally when using binary network models to study product competitions and depending on the cutoff values chosen to binarize a network, the resulting estimated customer preferences can be inconsistent. Such inconsistency in interpreting customer preferences is a downside of binary network models but can be well addressed by the proposed weighted network model. Lastly, this paper is the first attempt to study customers’ purchase preferences (i.e., aggregated choice decisions) and car competition (i.e., customers’ co-consideration decisions) together using weighted directed networks. 

Statistical network models allow us to study the co-evolution between the products and the social aspects of a market system, by modeling these components and their interactions as graphs. In this paper, we study competition between different car models using network theory, with a focus on how product attributes (like fuel economy and price) affect which cars are considered together and which cars are finally bought by customers. Unlike past work, where most systems have been studied with the assumption that relationships between competitors are binary (i.e., whether a relationship exists or not), we allow relationships to take strengths (i.e., how strong a relationship is). Specifically, we use valued Exponential Random Graph Models and show that our approach provides a significant improvement over the baselines in predicting product co-considerations as well as in the validation of market share. This is also the first attempt to study aggregated purchase preference and car competition using valued directed networks.