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Software for analysis of the efficiency of high-tech production is proposed. The software is based on a multidimensional model of product-information transformation during the manufacturing process, with a complex relation between the product parameters. Technological inheritance is taken into account.
Filamentous fungi are intensively used for producing industrial enzymes, including lignocellulases. Employing insoluble cellulose to induce the production of lignocellulases causes some drawbacks, e.g., a complex fermentation operation, which can be overcome by using soluble inducers such as cellobiose. Here, a triple β-glucosidase mutant of Neurospora crassa, which prevents rapid turnover of cellobiose and thus allows the disaccharide to induce lignocellulases, was applied to profile the proteome responses to cellobiose and cellulose (Avicel). Our results revealed a shared proteomic response to cellobiose and Avicel, whose elements included lignocellulases and cellulolytic product transporters. While the cellulolytic proteins showed a correlated increase in protein and mRNA levels, only a moderate correlation was observed on a proteomic scale between protein and mRNA levels (R 2 = 0.31). Ribosome biogenesis and rRNA processing were significantly overrepresented in the protein set with increased protein but unchanged mRNA abundances in response to Avicel. Ribosome biogenesis, as well as protein processing and protein export, was also enriched in the protein set that showed increased abundance in response to cellobiose. NCU05895, a homolog of yeast CWH43, is potentially involved in transferring a glycosylphosphatidylinositol (GPI) anchor to nascent proteins. This protein showed increased abundance but no significant change in mRNA levels. Disruption of CWH43 resulted in a significant decrease in cellulase activities and secreted protein levels in cultures grown on Avicel, suggesting a positive regulatory role for CWH43 in cellulase production. The findings should have an impact on a systems engineering approach for strain improvement for the production of lignocellulases.
Kalion, Inc. is an industrial biotechnology manufacturer of high-purity chemicals from biomass including glucaric acid to be used in the industrial, material, and pharmaceutical markets. The project goal is application of machine learning in conjunction with high-throughput cultivations and metabolomics to understand and overcome two different challenges: (i) improve glucaric acid productivity (the rate of production slows considerably after 48 hours, which limits the overall productivity that can be achieved in the process), and (ii) decrease its production costs (components from complex media are necessary to achieve robust production). Through a series of experiments, a set of metabolites contained in rich media that improved performance were identified using metabolomics, high throughput cultivations in bioreactors, and machine learning-friendly experimental designs. It was found that the substrate feeding strategy was the potential major driver to enhance bacterial performance of glucaric acid production. The major industrial impacts by this project are the improvement of the production process technology, as well as advancing infrastructures and workflows (where machine learning approaches in conjunction with high-throughput cultivation and metabolomics are used) to improve the analyses by making them more rapid and comprehensive.
Many of the properties of Silicon carbide (SiC) are advantageous for optical applications, such as telescope mirrors and industrial laser systems. However, the base shapes of complex components are costly and difficult to manufacture. Leveraging additive manufacturing, near net complex components are readily processed. In this work, we investigate the post processing of additively manufactured SiC (AM SiC) compared to chemical vapor deposited (CVD) SiC. The specific grinding energy for the AM SiC was lower than CVD, however the trends were the same. A specular finish was observed on both materials but the AM SiC finish was limited due to residual porosity.
Now a days, corrosion studies are important for reducing the wastage of metals. The importance of corrosion studies is two folds i.e. first is economic, including the reduction of material losses resulting from the wasting away or sudden failure of materials and second is conservation Electroless process is an autocatalytic reduction method in which metallic ions are reduced in the solution. Nanocomposite coatings of Ni-P-TiO{sub 2} on mild steel are deposited by varying volume of TiO{sub 2} nano-powder by electroless method from Ni-P plating bath containing Nickel Sulphate as a source of nickel ions, sodium hypophosphite as the reducing agent, lactic acid as a complexing agents and TiO{sub 2} nano powder. Electroless Ni-P-TiO{sub 2} coating have been widely used in the chemical process industries, mechanical industries, electronic industries and chloroalkali industries due to their excellent corrosion with mechanical properties. In the present work, deposition of Ni-P alloy coating and Ni-P-TiO{sub 2} nanocomposited coatings were done on the mild steel and corrosion properties were studied with Potentio-dynamic polarization measurements method in 3.5 wt% sodium chloride solution. It showed in the experiments that Ni-P-TiO{sub 2} nanocomposited coating has better corrosion resistance as comparedthan Ni-P alloy coating. Morphological studies were done by field emission scanning electron microscopy (FESEM), energy–dispersive analysis of X-ray (EDAX) and X-ray diffraction (XRD). These studies confirmed the deposition of Ni-P alloy coating and Ni-P-TiO{sub 2} nanocomposited coating.
Technique substantially reduces machine complexity, improves injector performance and is useful for products mixed or homogenized by injector process. Method is applicable to pharmaceutical, food, dairy and petrochemical industries.
Graphite/polyimide composite materials are attractive candidates for a wide range of aerospace applications. They have many of the virtues of graphite/epoxies, i.e., high specific strengths and stiffness, and also outstanding thermal/oxidative stability. Yet they are not widely used in the aerospace industry due to problems of procesability. By their nature, modern addition polyimide (PI) resins and prepregs are more complex than epoxies; the key to processing lies in characterizing and understanding the materials. Chemical and rheological characterizations are carried out on several addition polyimide resins and graphite reinforced prepregs, including those based on PMR-15, LARC 160 (AP 22), LARC 160 (Curithane 103) and V378A. The use of a high range torque transducer with a Rheometrics mechanical spectrometer allows rheological data to be generated on prepreg materials as well as neat resins. The use of prepreg samples instead of neat resins eliminates the need for preimidization of the samples and the data correlates well with processing behavior found in the shop. Rheological characterization of the resins and prepregs finds significant differences not readily detected by conventional chemical characterization techniques.
Climate change, driven by anthropogenic carbon emissions, demands urgent action to prevent a 2050 tipping point. With CO 2 levels at 427 ppm (50% above pre-industrial levels), deploying energy-efficient carbon capture technologies is crucial. Electrochemical carbon capture processes that have been touted to have the potential to meet these needs rely on the applied cell voltage, and electron utilization (CO 2 molecules separated per electron), which has generally been asserted to have a theoretical limit of one. Here, we introduce an electron-leveraging strategy to enhance electron utilization beyond this limit to 1.43 by employing Fe-EDDHA, a redox-active coordination complex having a ligand with multiple hemi-labile coordination sites. The reversibility and robustness of the system were enabled by the efficient prevention of CO 2 reduction upon the introduction of nicotinamide as a guardian of the iron(2+) center. The proof-of-concept cyclic system exhibits a minimum operational energy of 22.6 kJ e mol −1 and an average of 63.7 kJ e mol −1 over 29 cycles, using a simulated flue gas (15% CO 2 ). Our electron-leveraging strategy holds promise for advancing energy-efficient electrochemical carbon capture technologies, and offers an alternative to prevalent redox potential shifting methods proposed to mitigate undesired electron transfer reactions in redox-active materials across diverse operational conditions.
Transient pulse response experiments are used to construct rate/concentration (RC) kinetic dependencies—RC petals—and provide a new method to distinguish the timing and interplay of adsorption, surface reaction, and product formation on complex (industrial) materials. A petal shape arises as the dynamic “reaction-diffusion” experiment forces the concentration and reaction rate to return to zero. In contrast to the typical steady-state “Langmuir-type” RC dependence, RC petals have two branches, which arise as a result of decoupled gas and surface concentrations in the non-steady-state regime. To demonstrate this approach, the characteristics of petal shapes using ammonia decomposition as a probe reaction are presented. Ammonia, hydrogen, and nitrogen transformation rates are compared on three simple materials: Fe, Co, and a bimetallic CoFe preparation when ammonia is pulsed at 550°C in a low-pressure diffusion reactor. All materials demonstrate a two-branch kinetic RC dependence for ammonia adsorption, and rate constants are quantified in the low-coverage regime. We found that H 2 and N 2 product formation was dependent on the concentration of surface intermediates for all materials with one exception: for Co, an additional fast hydrogen generation process was observed; the rate of which coincided with ammonia adsorption. Nitrogen generation was only significant for CoFe and Co and on the CoFe catalyst, a self-inhibition property was observed. A method for estimating the number of active sites based on the RC petals is presented and was applied to the Fe and CoFe samples. The surface coverage and rate of formation/conversion of surface intermediates are interpreted from the examination of shape characteristics of the RC petals for each material.
In this pilot project, the feasibility around the creation, management, and deployment of derivative datasets as a potential new class of intellectual property was explored. The objective of these derivative datasets was to address environmental, industrial, and commercial challenges in a novel way that could provide additional impact based on the research performed at PNNL. As a result of efforts in FY2021, two new derivative datasets were created using newly created code that would enable the facile creation of additional derivative datasets of varying complexity and integration from pre-existing and published originating datasets. In addition, the process workflows were documented, detailing how the creation, management, and deployment of the derivative datasets could integrate with existing processes within PNNL. Finally, exploratory interviews with industry allowed for the scoping of future collaboration and commercialization opportunities around derivative datasets.
A circular plastics economy can leverage the lightweight, strong and durable characteristics of macromolecular materials, while simultaneously reducing the negative environmental impacts associated with polymer waste. Advanced recycling technologies provide an opportunity to valorize plastics waste and extend the lifespan of these materials by converting waste into new monomers, polymers or specialty chemicals. Although many advanced technologies appear promising, assessments of economic and environmental sustainability are often not conducted in a standardized fashion and neglect factors such as plastics waste transportation, sorting and pretreatment. These shortcomings can lead to inaccurate or misleading predictions, reduce opportunities for optimization and limit industrial relevance. In this Review, we highlight select industrial case studies to underscore the notable consequences of underestimating the complexity of real-life consumer plastics waste. In addition, the current challenges associated with the assessment of the industrial viability of laboratory-scale processes are explored. Here, by discussing relevant analysis frameworks and system boundaries, along with potential analytical pitfalls, future research will be guided beyond chemical considerations and toward impactful circular solutions.
Enhancing the throughput of the laboratory will minimize the immediate impacts seen with a reduction in force and will help the laboratory and greater industrial complex achieve its mission
This capstone offers the introduction of Lean concepts to an office activity to demonstrate the versatility of Lean. Traditionally Lean has been associated with process improvements as applied to an industrial atmosphere. However, this paper will demonstrate that implementing Lean concepts within an office activity can result in significant process improvements. Lean first emerged with the conception of the Toyota Production System. This innovative concept was designed to improve productivity in the automotive industry by eliminating waste and variation. Lean has also been applied to office environments, however the limited literature reveals most Lean techniques within an office are restricted to one or two techniques. Our capstone confronts these restrictions by introducing a systematic approach that utilizes multiple Lean concepts. The approach incorporates: system analysis, system reliability, system requirements, and system feasibility. The methodical Lean outline provides tools for a successful outcome, which ensures the process is thoroughly dissected and can be achieved for any process in any work environment.
Hybrid unitary air conditioning systems offer a pathway to substantially reduce energy use and peak electrical demand for cooling, heating, and ventilation in commercial buildings. Hybrid air conditioners incorporate multiple subsystems that are carefully orchestrated to provide climate- and application-specific efficiency advantages. There are a multitude of hybrid system architectures, but common subsystems include: heat recovery ventilation, indirect evaporative cooling, desiccant dehumidification, variable speed fans, modulating dampers, and multi-stage or variable-speed vapor compression cooling. Categorically, hybrid systems can operate in numerous discrete modes. For example: indirect evaporative cooling may operate for periods when the subsystem provides adequate sensible cooling, then vapor compression cooling will be included when more cooling or dehumidification is necessary. Laboratory assessments, field studies, and simulations have demonstrated that hybrid unitary air conditioners could reduce energy use for cooling and ventilation by 30-90% depending on climate and application. Heretofore, it has been challenging - if not impossible - for practitioners to model hybrid air conditioners as part of building energy simulations; and the limitation has severely obstructed broader adoption of technologies in this class. In this project, we developed a new feature for EnergyPlus that enables modeling hybrid unitary air conditioning equipment for building energy simulations. This is a significant advancement for both theory and practice, and confers public benefit by enabling practitioners to evaluate this compelling efficiency technology as a part of building energy simulations. The feature is a black-box model that requires extensive performance data for each hybrid unitary product. In parallel, we also developed new features for the Technology Performance Exchange to enable manufacturers to submit performance data in a standard format that can be used with the hybrid unitary model in EnergyPlus. Additionally, through this project we expanded university educational resources, and university- manufacturing industry collaborations in the field of energy efficiency technology. Over two years, we involved 20 undergraduate students in ambitious research projects focused on modeling complex multi-mode mechanical systems, supported three mechanical engineering bachelor theses, established undergraduate apprenticeships with multiple industry partners, and involved those partners in the process of design, validation, and debugging for the new EnergyPlus feature. The EnergyPlus feature is described and discussed in an academic article, as well as in an engineering reference, and input/output reference documentation for EnergyPlus. The Technology Performance Exchange features are live and publicly accessible, our manufacturer partners are primed to submit initial product information and performance data to the exchange, and the EnergyPlus feature is scheduled for public release in Spring 2018 as a part of EnergyPlus v8.9.
This application provides a web-based graphical user interface to generating Campbell Diagrams and visualizing mode shapes for OpenFAST turbine models. Determining the aeroelastic stability and dynamic characteristics of wind turbines is a critical step in turbine design and analysis. Historically, extracting natural frequencies and mode shapes from OpenFAST—the industry-standard whole-turbine simulation code—has been a fragmented and tedious process. It required manual model configuration, command-line linearization execution, and complex post-processing via proprietary scripts to handle rotating-frame dynamics. To address these workflow bottlenecks, we present the Automated Campbell Diagram Code (ACDC), an open-source graphical software tool developed by the National Laboratory of the Rockies (NLR) under the DOE-funded Distributed Wind Aeroelastic Modeling (dWAM) project. ACDC streamlines the end-to-end linearization and stability analysis workflow into a single, intuitive cross-platform application. The software guides users through OpenFAST model configuration, definition of operating points, and the automated execution of steady-state trim and linearization simulations. Under the hood, ACDC automates the complex mathematical post-processing steps required for rotating systems, including Multi-Blade Coordinate (MBC) transformations, eigenanalysis, and advanced modal tracking utilizing the Modal Assurance Criterion (MAC) and spectral clustering. Finally, ACDC processes these results to automatically generate Campbell diagrams and features a robust 3D visualization engine to animate full-system mode shapes. By eliminating the reliance on external post-processing environments and manual data manipulation, ACDC significantly accelerates dynamic analysis and lowers the barrier to entry for wind energy researchers and engineers.
The technological inception and challenges, as well as current applications of the electron beam freeform fabrication (EBF3) process are outlined. The process was motivated by the need for a new metals technology that would be cost-effective, enable the production of new alloys and that would could be used for efficient, lightweight structures. EBF3 is a rapid metal fabrication, layer-additive process that uses no molds or tools and which yields properties equivalent to wrought. The benefits of EBF3 include it near-net shape which minimizes scrap and reduces part count; efficiency in design which allows for lighter weight and enhanced performance; and, its "green" manufacturing process which yields minimal waste products. EBF3 also has a high tensile strength, while a structural test comparison found that EBF3 panels performed 5% lower than machined panels. Technical challenges in the EBF3 process include a need for process control monitoring and an improvement in localized heat response. Currently, the EBF3 process can be used to add details onto forgings and to construct and form complex shapes. However, it has potential uses in a variety of industries including aerospace, automotive, sporting goods and medical implant devices. The novel structural design capabilities of EBF3 have the ability to yield curved stiffeners which may be optimized for performance, low weight, low noise and damage tolerance applications. EBF3 has also demonstrated its usefulness in 0-gravity environments for supportability in space applications.
This presentation highlights the role of advanced visualization techniques at the National Renewable Energy Laboratory (NREL) in supporting cutting-edge research across diverse energy domains. From immersive analytics and uncertainty visualization to high-resolution and real-time data analysis, NREL's visualization capabilities enable scientists to explore complex datasets more effectively. These tools are critical for advancing research in materials science, renewable energy technologies, biofuels, electric vehicle infrastructure, energy efficiency - from industrial processes to entire communities - and then bringing these innovations to practice through energy systems integration. NREL's visualization tools drive innovation across renewable energy and grid modernization efforts by providing deeper insights and improving decision-making.
Large immersive display systems play a critical role in the nuclear industry by enabling advanced training, design reviews, scientific visualization, and safety simulations. These systems, such as CAVE and powerwalls, allow engineers and operators to interact with virtual nuclear environments in real-time, providing a deeper understanding of complex systems. Their ability to simulate real-world scenarios for testing and optimization in a safe, controlled environment ensures that operators and engineers can refine process, enhance safety protocols, and troubleshoot complex challenges. This article outlines key lessons learned from deploying these systems, practical insights into their applications, and considerations for future improvements, aiming to guide their broader adoption and effective use in the nuclear industry.