Life Cycle Costs and Life Cycle Assessment for the Harvesting, Conversion, and the Use of Switchgrass to Produce Electricity

This paper considers both LCA and LCC of the pyrolysis of switchgrass to use as an energy source in a conventional power plant. The process consists of cultivation, harvesting, transportation, storage, pyrolysis, transportation, and power generation. Here pyrolysis oil is converted to electric power through cocombustion in conventional fossil fuel power plants. Several scenarios are conducted to determine the effect of selected design variables on the production of pyrolysis oil and type of conventional power plants. The set of design variables consist of land fraction, land shape, the distance needed to transport switchgrass to the pyrolysis plant, the distance needed to transport pyrolysis oil to electric generation plant, and the pyrolysis plant capacity. Using an average agriculture land fraction of the United States at 0.4, the estimated cost of electricity from pyrolysis of 5000 tons of switchgrass is the lowest at $0.12 per kwh. Using natural gas turbine power plant for electricity generation, the price of electricity can go as low as 7.70 cent/kwh. The main advantage in using a pyrolysis plant is the negative GHG emission from the process which can define that the process is environmentally friendly

Microchannel Reactor System for Catalytic Hydrogenation

We successfully demonstrated a novel process intensification concept enabled by the development of microchannel reactors, for energy efficient catalytic hydrogenation reactions at moderate temperature, and pressure, and low solvent levels. We designed, fabricated, evaluated, and optimized a laboratory-scale microchannel reactor system for hydrogenation of onitroanisole and a proprietary BMS molecule. In the second phase of the program, as a prelude to full-scale commercialization, we designed and developed a fully-automated skid-mounted multichannel microreactor pilot plant system for multiphase reactions. The system is capable of processing 1 – 10 kg/h of liquid substrate, and an industrially relevant immiscible liquid-liquid was successfully demonstrated on the system. Our microreactor-based pilot plant is one-of-akind. We anticipate that this process intensification concept, if successfully demonstrated, will provide a paradigm-changing basis for replacing existing energy inefficient, cost ineffective, environmentally detrimental slurry semi-batch reactor-based manufacturing practiced in the pharmaceutical and fine chemicals industries

Large Scale Benchmark of Materials Design Methods

Lack of rigorous reproducibility and validation are major hurdles for scientific development across many fields. Materials science in particular encompasses a variety of experimental and theoretical approaches that require careful benchmarking. Leaderboard efforts have been developed previously to mitigate these issues. However, a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with both perfect and defect materials data is still lacking. This work introduces JARVIS-Leaderboard, an open-source and community-driven platform that facilitates benchmarking and enhances reproducibility. The platform allows users to set up benchmarks with custom tasks and enables contributions in the form of dataset, code, and meta-data submissions. We cover the following materials design categories: Artificial Intelligence (AI), Electronic Structure (ES), Force-fields (FF), Quantum Computation (QC) and Experiments (EXP). For AI, we cover several types of input data, including atomic structures, atomistic images, spectra, and text. For ES, we consider multiple ES approaches, software packages, pseudopotentials, materials, and properties, comparing results to experiment. For FF, we compare multiple approaches for material property predictions. For QC, we benchmark Hamiltonian simulations using various quantum algorithms and circuits. Finally, for experiments, we use the inter-laboratory approach to establish benchmarks. There are 1281 contributions to 274 benchmarks using 152 methods with more than 8 million data-points, and the leaderboard is continuously expanding. The JARVIS-Leaderboard is available at the website: https://pages.nist.gov/jarvis_leaderboar

Computational models in plant-pathogen interactions: the case of Phytophthora infestans

<p>Abstract</p> <p>Background</p> <p><it>Phytophthora infestans </it>is a devastating oomycete pathogen of potato production worldwide. This review explores the use of computational models for studying the molecular interactions between <it>P. infestans </it>and one of its hosts, <it>Solanum tuberosum</it>.</p> <p>Modeling and conclusion</p> <p>Deterministic logistics models have been widely used to study pathogenicity mechanisms since the early 1950s, and have focused on processes at higher biological resolution levels. In recent years, owing to the availability of high throughput biological data and computational resources, interest in stochastic modeling of plant-pathogen interactions has grown. Stochastic models better reflect the behavior of biological systems. Most modern approaches to plant pathology modeling require molecular kinetics information. Unfortunately, this information is not available for many plant pathogens, including <it>P. infestans</it>. Boolean formalism has compensated for the lack of kinetics; this is especially the case where comparative genomics, protein-protein interactions and differential gene expression are the most common data resources.</p

On The Use Of Quasi-Newton Based Training Of A Feed Forward Neural Network For Time Series Forecasting

This paper examines the effectiveness of using a quasi-Newton based training of a feedforward neural network for forecasting. We have developed a novel quasi-Newton based training algorithm using a generalized logistic function. We have shown that a well designed feed forward structure can lead to a good forecast without the use of the more complicated feedback /feedforward structure of the recurrent network. keywords: Feed forward neural network, quasi-Newton, forecasting 1 Introduction Many time series have significant chaotic components like short time fluctuations (seasonal variations and cyclical fluctuations), random fluctuations, and long time fluctuations (trend). Stochastic model building and forecasting is one of the techniques for the analysis of discrete time series in the time-domain. Autoregressive Integrated Moving Average (ARIMA) models are examples of these statistical models. These models have largely been linear and as such are not able to capture trends accurately..