Heterotrophic Culture of Microalgae Using Biodiesel Derived Glycerol

Algae-based technologies are fast growing and the growing demand for sustainable technologies is evident from the growing energy demand and global warming. Microalgae culturing for higher lipid contents have been a hot topic of intense discussion in the past years. Crude glycerol, a byproduct from biodiesel production seems to be an attractive feedstock for microbial cultivation and crude glycerol has been proven as a good alternative feedstock for cultivation of Chlorella protothecoides. The effect of impurities present in the crude glycerol is important to develop a method for high-density cultivation of microalgae to increase the commercialization potential of algae systems. Through this study, a method to partially refine crude glycerol was developed to increase the suitability of biodiesel-derived glycerol for high-density cultivations. C. protothecoides grew best at an initial glycerol concentration of 90 g/L and a maximum biomass and lipid productivity of 4.45 and 2.28 g/L-day was achieved at an initial glycerol concentration of 120 g/L. Fed-batch studies increased the biomass and lipid concentrations and productivities. A maximum biomass and lipid concentration of 95.3 and 49.5 g/L-day was achieved while using PRG as a carbon source with a maximum productivity of 10.6 g/L-day. Yield biomass per substrate in the fed batch mode was observed to be 0.53. Comparing the data to published literature, these are the best results. Fatty acid profiles were observed to be very comparable to data published by other researches on C.protothecoides. Further studies on the effect of salinity on the growth of C.protothecoides, yielded no statistical significance in the biomass concentration and lipid content at a KCl concentration of 10 and 20 g/L, and a NaCl concentration of 10 g/L. Further increase in NaCl concentration to 20 g/L decreased the maximum biomass concentration. No growth was observed at salt concentrations of 40 g/L. Increasing salt concentrations had no impact on the relative fatty acid percentage of oleic acid (most abundant fatty acid produced by Chlorella protothecoides). Biomass productivities were significantly lower in the presence of salts, indicating that the present of salts decreases the biomass productivity. Increasing methanol concentrations were evaluated, and the results proved that methanol was not significantly consumed but evaporated by this species of algae. A methanol concentration 1 % (v/v) yielded similar biomass and lipid concentrations, Yx/sand Yp/s. The biomass productivity, however, was significantly lower with increase in methanol concentrations. Xylose proved to be detrimental to the growth of C. protothecoides as increasing xylose concentrations decreased biomass concentration. No growth was observed at a xylose concentration of 30 g/L. In summary, the effect of some impurities present in crude glycerol was evaluated and a method to refine crude glycerol proved successful. A method for high-density cultivation of C. protothecoides for increased productivities while using a waste stream is presented

A comparative analysis of models for predicting delays in air traffic networks

In this paper, we compare the performance of different approaches to predicting delays in air traffic networks. We consider three classes of models: A recently-developed aggregate model of the delay network dynamics, which we will refer to as the Markov Jump Linear System (MJLS), classical machine learning techniques like Classification and Regression Trees (CART), and three candidate Artificial Neural Network (ANN) architectures. We show that prediction performance can vary significantly depending on the choice of model/algorithm, and the type of prediction (for example, classification vs. regression). We also discuss the importance of selecting the right predictor variables, or features, in order to improve the performance of these algorithms. The models are evaluated using operational data from the National Airspace System (NAS) of the United States. The ANN is shown to be a good algorithm for the classification problem, where it attains an average accuracy of nearly 94% in predicting whether or not delays on the 100 most-delayed links will exceed 60 min, looking two hours into the future. The MJLS model, however, is better at predicting the actual delay levels on different links, and has a mean prediction error of 4.7 min for the regression problem, for a 2 hr horizon. MJLS is also better at predicting outbound delays at the 30 major airports, with a mean error of 6.8 min, for a 2 hr prediction horizon. The effect of temporal factors, and the spatial distribution of current delays, in predicting future delays are also compared. The MJLS model, which is specifically designed to capture aggregate air traffic dynamics, leverages on these factors and outperforms the ANN in predicting the future spatial distribution of delays. In this manner, a tradeoff between model simplicity and prediction accuracy is revealed. Keywords- delay prediction; network delays; machine learning; artificial neural networks; data miningNational Science Foundation (U.S.) ( Award 1239054

A Methodology for Deriving System Requirements Using Agent Based System Modeling

In this paper, we duscuss a method to derive the requirements for developing an Industrial Automation and Constrol System (IACS). An IACS has software components and associated hardware, which together implement the required monitoring, supervision and control of operations an a production plant. The requirements of such a system are multi-dimensional and may require multiple layers of abstraction. For this domain, we propose an agent-based modeling adopting an agent-based modeling approach is the implicit flexibility afforded by agents and the negotiation techniques that can be implemented to streamline the change management process associated with requirements modeling and analysis. This paper utilizes modeling constructs from UML/SysML to model and visualize the interactions among the agents. The types of agents and their roles are discussed in detail

Clusters and communities in air traffic delay networks

The air transportation system is a network of many interacting, capacity-constrained elements. When the demand for airport and airspace resources exceed the available capacities of these resources, delays occur. The state of the air transportation system at any time can be represented as a weighted directed graph in which the nodes correspond to airports, and the weight on each arc is the delay experienced by departures on that origin-destination pair. Over the course of any day, the state of the system progresses through a time-series, where the state at any time-step is the weighted directed graph described above. This paper presents algorithms for the clustering of air traffic delay network data from the US National Airspace System, in order to identify characteristic delay states (i.e., weighted directed graphs) as well as characteristic types-of-days (i.e., sequences of such weighted directed graphs) that are experienced by the air transportation system. The similarity of delay states during clustering are evaluated on the basis of not only the in- and out-degrees of the nodes (the total inbound and outbound delays), but also network-theoretic properties such as the eigenvector centralities, and the hub and authority scores of different nodes. Finally, the paper looks at community detection, that is, the grouping of nodes (airports) based on their similarities within a system delay state. The type of day is found to have an impact on the observed community structures.United States. National Aeronautics and Space Administration (FA8721-05-C-0002)National Science Foundation (U.S.) (1239054

Updates on the pretreatment of lignocellulosic feedstocks for bioenergy production–a review

Lignocellulosic biomass is the most abundant renewable energy bioresources available today. Due to its recalcitrant structure, lignocellulosic feedstocks cannot be directly converted into fermentable sugars. Thus, an additional step known as the pretreatment is needed for efficient enzyme hydrolysis for the release of sugars. Various pretreatment technologies have been developed and examined for different biomass feedstocks. One of the major concerns of pretreatments is the degradation of sugars and formation of inhibitors during pretreatment. The inhibitor formation affects in the following steps after pretreatments such as enzymatic hydrolysis and fermentation for the release of different bioenergy products. The sugar degradation and formation of inhibitors depend on the types and conditions of pretreatment and types of biomass. This review covers the structure of lignocellulose, followed by the factors affecting pretreatment and challenges of pretreatment. This review further discusses diverse types of pretreatment technologies and different applications of pretreatment for producing biogas, biohydrogen, ethanol, and butanol

Rethinking the Role of Scale for In-Context Learning: An Interpretability-based Case Study at 66 Billion Scale

Language models have been shown to perform better with an increase in scale on a wide variety of tasks via the in-context learning paradigm. In this paper, we investigate the hypothesis that the ability of a large language model to in-context learn-perform a task is not uniformly spread across all of its underlying components. Using a 66 billion parameter language model (OPT-66B) across a diverse set of 14 downstream tasks, we find this is indeed the case: $\sim$70% of attention heads and $\sim$20% of feed forward networks can be removed with minimal decline in task performance. We find substantial overlap in the set of attention heads (un)important for in-context learning across tasks and number of in-context examples. We also address our hypothesis through a task-agnostic lens, finding that a small set of attention heads in OPT-66B score highly on their ability to perform primitive induction operations associated with in-context learning, namely, prefix matching and copying. These induction heads overlap with task-specific important heads, reinforcing arguments by Olsson et al. (arXiv:2209.11895) regarding induction head generality to more sophisticated behaviors associated with in-context learning. Overall, our study provides several insights that indicate large language models may be under-trained for in-context learning and opens up questions on how to pre-train language models to more effectively perform in-context learning.Comment: Accepted at Annual Meeting of the Association for Computational Linguistics (ACL) 2023, Main Proceeding