Modular Process Research Articles

A Bimetallic Electrocatalyst Platform for Understanding the Roles of Surface Chemistry and Functionalization on Nitrogen Reduction to Ammonia

The Haber-Bosch industrial process for ammonia production is the cornerstone of modern commercial-fertilizer-based agriculture. Haber-Bosch ammonia fueled the global population growth of the 20th century, and approximately half of the nitrogen in human bodies today originates from ammonia-based fertilizer produced by the Haber-Bosch process. However, the Haber-Bosch process operates at high temperature and high pressure to achieve high conversion efficiencies, and the hydrogen input comes from steam reforming of coal or natural gas. In addition to the energy costs, the large production of carbon dioxide as a greenhouse gas and the large required economies of scale motivate research efforts to explore other possible options for ammonia production. One potential option is low temperature electrochemical synthesis of ammonia from nitrogen and water. An electrochemical process that directly synthesizes ammonia molecules from nitrogen gas and the hydrogen atoms of water molecules would eliminate the need for fossil-fuel-based hydrogen as a reactant and decrease CO2 emissions. Further, an electrochemical system based on already-developed technology in the fuel cell and electrolysis arenas would enable a modular, scalable, and energy efficient process that could be connected to renewables (i.e., wind or solar) as the energy input. While electrochemical ammonia synthesis is an enticing option in theory, in practice the process is severely limited by the lack of available catalysts that can produce ammonia at high Faradaic efficiencies. All heterogeneous catalyst surfaces that are active for the reduction of di-nitrogen to ammonia in the presence of water are also highly active for the reduction of water molecules to hydrogen gas. Both reactions occur within a similar potential range, and thus electrochemical systems will inherently battle against this reaction selectivity issue. Today, all reported heterogeneous catalysts remain limited to low efficiencies of less than 1% for ammonia production at low temperature because the majority of the applied electrical potential goes towards water splitting and the evolution of hydrogen. In our research, we are interested in understanding how catalyst surface chemistry and ligand functionalization can be used to direct and control reaction selectivity of the electrocatalyst. We synthesize non-precious metal-based catalysts comprised of iron and nickel, which, as a bimetallic material, are theoretically-predicted to potentially result in optimal surfaces for nitrogen reduction. However, these mixed oxide/hydroxide surfaces are also expected to still be dominated by hydrogen adsorption and evolution. Thus, to further address reaction selectivity and create a catalyst surface that is more selective for ammonia synthesis, we also design our catalysts such that the local surface environment of the catalyst is controlled through ligand functionalization. In this talk, our ongoing work to design, synthesize and characterize iron-nickel hydroxide bimetallic catalysts for efficient electrochemical ammonia synthesis will be discussed.

High-resolution, lightweight, and low-cost x-ray optics for the Lynx observatory

We describe an approach to build an x-ray mirror assembly that can meet Lynx’s requirements of high-angular resolution, large effective area, light weight, short production schedule, and low-production cost. Adopting a modular hierarchy, the assembly is composed of 37,492 mirror segments, each of which measures ∼100 mm × 100 mm × 0.5 mm. These segments are integrated into 611 modules, which are individually tested and qualified to meet both science performance and spaceflight environment requirements before they in turn are integrated into 12 metashells. The 12 metashells are then integrated to form the mirror assembly. This approach combines the latest precision polishing technology and the monocrystalline silicon material to fabricate the thin and lightweight mirror segments. Because of the use of commercially available equipment and material and because of its highly modular and hierarchical building-up process, this approach is highly amenable to automation and mass production to maximize production throughput and to minimize production schedule and cost. As of fall 2018, the basic elements of this approach, including substrate fabrication, coating, alignment, and bonding, have been validated by the successful building and testing of single-pair mirror modules. In the next few years, the many steps of the approach will be refined and perfected by repeatedly building and testing mirror modules containing progressively more mirror segments to fully meet science performance, spaceflight environments, as well as programmatic requirements of the Lynx mission and other proposed missions, such as AXIS.

Enabling Collaborative Numerical Modeling in Earth Sciences using Knowledge Infrastructure

Knowledge infrastructure is an intellectual framework for creating, sharing, and distributing knowledge. In this paper, we use knowledge infrastructure to address common barriers to entry into numerical modeling in Earth sciences as demonstrated in three computational narratives: physical process modeling education, replicating published model results, and reusing published models to extend research. We outline six critical functional requirements: 1) workflows designed for new users; 2) community-supported collaborative web platform; 3) distributed data storage; 4) software environment; 5) personalized cloud-based high-performance computing platform; and 6) a standardized open source modeling framework. Our methods meet these functional requirements by providing three interactive computational narratives for hands-on, problem-based research using Landlab on HydroShare. Landlab is an open-source toolkit for building, coupling, and exploring two-dimensional numerical models. HydroShare is an online collaborative environment for the sharing of data and models. We describe the methods we are using to accelerate knowledge development by providing a suite of modular and interoperable process components that allows students, domain experts, collaborators, researchers, and sponsors to learn by exploring shared data and modeling resources. The system is designed to support uses on the continuum from fully-developed modeling applications to prototyping research software tools. Landlab notebooks are available for interactive computing on HydroShare at https://doi.org/10.4211/hs.fdc3a06e6ad842abacfa5b896df73a76 and for further development on Github at https://zenodo.org/badge/latestdoi/187289993.

MEEGIPS-A Modular EEG Investigation and Processing System for Visual and Automated Detection of High Frequency Oscillations.

High frequency oscillations (HFOs) are electroencephalographic correlates of brain activity detectable in a frequency range above 80 Hz. They co-occur with physiological processes such as saccades, movement execution, and memory formation, but are also related to pathological processes in patients with epilepsy. Localization of the seizure onset zone, and, more specifically, of the to-be resected area in patients with refractory epilepsy seems to be supported by the detection of HFOs. The visual identification of HFOs is very time consuming with approximately 8 h for 10 min and 20 channels. Therefore, automated detection of HFOs is highly warranted. So far, no software for visual marking or automated detection of HFOs meets the needs of everyday clinical practice and research. In the context of the currently available tools and for the purpose of related local HFO study activities we aimed at converging the advantages of clinical and experimental systems by designing and developing a comprehensive and extensible software framework for HFO analysis that, on the one hand, focuses on the requirements of clinical application and, on the other hand, facilitates the integration of experimental code and algorithms. The development project included the definition of use cases, specification of requirements, software design, implementation, and integration. The work comprised the engineering of component-specific requirements, component design, as well as component- and integration-tests. A functional and tested software package is the deliverable of this activity. The project MEEGIPS, a Modular EEG Investigation and Processing System for visual and automated detection of HFOs, introduces a highly user friendly software that includes five of the most prominent automated detection algorithms. Future evaluation of these, as well as implementation of further algorithms is facilitated by the modular software architecture.

A Semantic-Based Method for Teaching Industrial Robots New Tasks

This paper presents the results of the Artificial Intelligence (AI) method developed during the European project “Factory-in-a-day”. Advanced AI solutions, as the one proposed, allow a natural Human–Robot-collaboration, which is an important capability of robots in industrial warehouses. This new generation of robots is expected to work in heterogeneous production lines by efficiently interacting and collaborating with human co-workers in open and unstructured dynamic environments. For this, robots need to understand and recognize the demonstrations from different operators. Therefore, a flexible and modular process to program industrial robots has been developed based on semantic representations. This novel learning by demonstration method enables non-expert operators to program new tasks on industrial robots.

A Resource-Efficient and Side-Channel Secure Hardware Implementation of Ring-LWE Cryptographic Processor

Lattice-based cryptography has shown great potential due to its resistance against quantum attacks. With the security requirements for high-precision Gaussian sampling and complex polynomial multiplication over rings, as well as storage of large public-keys, it is extremely challengeable but important to implement lattice-based schemes on resources constrained devices. In this paper, a resource-efficient and side-channel secure Ring-LWE cryptographic processor is presented. A discrete Gaussian sampler with constant response time, high precision, and large distribution tails is designed. The proposed Gaussian sampler is proven to be secure against side-channel timing attack according to the timing analysis attack results on a FPGA-based testing platform. A universal module MPE (Modular Processing Element) is designed to carry out all basic modular operations for Ring-LWE cryptography with high speed. The prototype verification is performed on the Xilinx Spartan-6 FPGA platform. The processor can execute an encryption/decryption operation on a 256-bit message in 4.5/0.9 ms whilst it consumes only 1307 LUTs, 889 FFs, 4 BRAMs, and none DSP module. Compared with other related hardware implementations, the Ring-LWE processor is advantageous not only in hardware efficiency but also in secure protection against side-channel attacks.

Anarchive as technique in the Media Archaeology Lab | building a one Laptop Per Child mesh network

The Media Archaeology Lab (MAL) at the University of Colorado at Boulder (U.S.A.) acts as both an archive and a site for what the authors describe as ‘anarchival’ practice-based research and research creation. ‘Anarchival’ indicates research and creative activity enacted as a complement to an existing, stable archive. In researching the One Laptop Per Child Initiative, by way of a donation of XO laptops, the MAL has devised a modular process which could be used by other research groups to investigate the gap between the intended use and the affordances of any given piece of technology.

Effective Generalized Disjunctive Programming Models for Modular Process Synthesis

We propose an optimization-based strategy to systematically evaluate trade-offs associated with modular alternatives for the multiperiod design of a chemical processing network. We give a general f...

Mine Water as a Resource: Selective Removal and Recovery of Trace Antimony from Mine-Impacted Water

The objective of this study was to evaluate the feasibility of combined modular processes to selectively remove Sb from mine-impacted waters in an Arctic environment in order to fulfil local environmental criteria for discharged waters. Novel ion exchange, selective extraction and ultrafiltration, electrocoagulation, and dissolved air flotation technologies were investigated, individually or in combination, from the laboratory- to pilot-demonstration scale. Laboratory-scale testing using Fe2(SO4)3 precipitation, ion exchange resin, selective ion extraction and ultrafiltration, and electrocoagulation with or without subsequent dissolved air flotation indicated that any of the methods are potentially applicable to Sb removal from mine water. The observed differences between Sb and As removal efficiency by ion exchange resin illustrated the need for Sb-specific removal and recovery technologies. Techno-economic analyses showed that treatment of mine water using electrocoagulation-dissolved air flotation yields the lowest comparative life-cycle cost of examined technologies. Results demonstrated increased Sb attenuation efficiency using either electrocoagulation-dissolved air flotation or selective extraction and ultrafiltration, even when treating only 50% of the mine-impacted water, compared with conventional Fe2(SO4)3 precipitation from mine water. Additional investigation is necessary to characterize the long-term stability of the mineral phases in Sb-containing solid residues and to inform selection of Sb recovery methods and utilisation or final disposal options for the residual materials.

Process network modularity, commonality, and greenhouse gas emissions

AbstractA process network is a complex system of linked unit processes that constitute the life cycle of a product. In this article, we consider how the structural and functional characteristics of a product's process network impact the network's collective greenhouse gas (GHG) emissions. At a unit process level, GHG emissions are primarily related to process efficiency. We hypothesize that a process network's GHG emissions will be less when the process network has a modular structure and when its constituent unit processes are more functionally similar. A modular process network architecture promotes autonomous innovation and improvements in knowledge management and problem‐solving capabilities, leading to more efficient processes. Functional commonality in a process network enables economies of scale and knowledge spillover and also leads to process efficiencies, thus reducing GHG emissions. We test these two hypotheses using a sample of 4,189 process networks extracted from an environmental life cycle inventory database. Empirical results support our hypotheses, and we discuss the implications of our findings for product development and supply network design.

Lean production theory-based simulation of modular construction processes

Abstract One way to encourage adoption of prefabrication and off-site manufacturing (OSM) techniques, such as modular construction, is to improve the efficiency of site operations, which makes the technology more attractive to non-adopters. Lean principles have been widely applied to improve the productivity and efficiency of construction operations, while simulation augments Lean theory by allowing its benefits and issues to be analyzed quantitatively before actual implementation. Thus, this study aims to conduct a detailed simulation study of modular construction operations, otherwise known as Prefabricated Prefinished Volumetric Construction (PPVC) in Singapore. In contrast with existing research, which are frequently focused on the barriers and drivers to the adoption of prefabrication, this study will provide and evaluate recommendations to improve modular construction efficiency through application of Lean concepts. A detailed baseline (As-Is) simulation model of an ongoing PPVC project case study was first developed. Lean Construction principles were then applied to the baseline simulation model. Key Lean Construction principles and concepts implemented includes Total Quality Management, E-Kanban based Just-In-Time deliveries, cross training and the use of construction robotics. Lean (To-Be) simulation models were developed based on the Lean Construction principles. The outputs from the baseline and Lean models were compared to assess the impact of the proposed improvements. The findings demonstrated that through the application of Lean concepts, reductions in cycle time and process time, and increases in process efficiency and labor productivity can be achieved. The case study also provides a detailed description of the simulation approach, which is a useful reference for future application of simulation in offsite construction research.

Two routes of service modularization: advancing standardization and customization

PurposeThis study aims to investigate service modularization in a manufacturing firm, identifies service modularization processes and examines how these processes change the service module characteristics.Design/methodology/approachThe study is based on a longitudinal case study (2008-2017) of a manufacturing firm. The development of six service modules was analyzed using data from interviews with key informants, informal meetings and internal documentation.FindingsThis study suggests five service modularization processes, and that service module characteristics, such as standardization and interconnectedness, change in different ways depending on the service modularization processes used. It further identifies two service modularization routes that each combine the service modularization processes in unique ways with replication as a key process to improve both standardization and customization.Practical implicationsThis study elaborates a framework for service modularization, which can serve as a guideline for developing service modules. It also highlights the differences between product and service modularization, suggesting that the role of service module characteristics such as standardization and customization is specific for services.Originality/valueThis longitudinal case study (2008-2017) provides empirical evidence on service modularization and extends existing knowledge on service modularization processes and how they influence service module characteristics.

Lipid Nanomaterials for Paclitaxel Delivery in Cancer Therapeutics: Effect of Pegylation and Charge on the Morphology and Efficacy

Liposomes have advantages as drug delivery nanomaterials including low toxicity, compatibility with a variety of therapeutics, ease of modular processing, and a diverse set of lipid building blocks to tune their physical and chemical properties. Polyethylene glycol (PEG) is a polymer widely used to create “stealth” nanoparticles (NPs). This steric coating increases the circulation time of NPs in vivo, delaying immune clearance; additionally, short peptide sequences and other moieties can be installed on the distal end of PEG to target specific tissues. When PEG-lipids are inserted into charge-neutral vesicles at very high PEG-lipid mol fraction (> 0.25), they are known to induce a structural transition to micelles. Here we examine shape changes induced by the incorporation of PEG-lipid, from low to high mol fraction, to a series of liposomes ranging from neutral to high cationic charge. Significantly, we report on how these morphological changes correlate to drug delivery efficacy of paclitaxel (PTXL)—a hydrophobic cancer drug that acts as a mitotic inhibitor, inducing apoptosis and halting the proliferation of tumor cells. Due to its hydrophobic nature, PTXL must be well solubilized for administration. In lipid bilayers and micelles, PTXL loads into the hydrophobic core (Steffes et al. Biomaterials 2017, 145, 242 https://doi.org/10.1016/j.biomaterials.2017.08.026). Here we show how liposome composition correlates to shape changes and to interactions of the particles with cells, ultimately dictating PTXL delivery. Next-generation nanomaterial delivery systems will build upon the “stealth” properties of PEGylated NPs and incorporate components such as receptor-specific peptide sequences for tumor targeting and penetration.

Modular sensory hardware and data processing solution for implementation of the precision beekeeping

Modular sensory hardware and data processing solution for implementation of the precision beekeeping

Comparing the Economics of Metal Additive Manufacturing Processes for Micro-scale Plate Reactors in the Chemical Process Industry

Abstract Modular chemical process intensification involves the deployment of process intensification technology within the modular construction of chemical processing plants. Process intensification involves improvements in chemical process plant efficiency that drive size and weight reductions in chemical equipment. The use of smaller, lighter weight equipment reduces costs in modular construction leading to lower capital thresholds for pursuing new distributed and specialty chemical markets. Micro-scale plate reactors, with high surface-area-to-volume ratios, provide a platform for reducing the size and cost of chemical reactors due to high rates of heat and mass transfer. However, widespread adoption of this technology is hampered by economics and the lack of existing supply chains. Additive manufacturing processes have the potential to overcome this roadblock. This paper compares the economics of using two prominent metal additive manufacturing processes to produce micro-scale plate reactors.

Implementation of MATLAB Modular Processing System for Biological Signals

A biological signal is a weak signal, so it is necessary to find new or improve methods of its processing. The paper proposes the implementation of the modular signal processing system for processing and further analysis of the most commonly used biological signals in diagnostics, such as electrocardiogram, electromyogram, electroencephalogram, phonocardio-gram or pulse wave but it is possible to employ it also in other signal processing areas. Based on contemporary literature, a wide range of the processing and filtering methods are embedded (digital filters, adaptive filters, wavelet transform, independent and principal component analysis etc.) into user-friendly interface, serving for the testing of the accuracy and suitability of the individual techniques for the signal processing. The software is designed to ensure as greatest as possible area of the biological signal processing so that basic modifications or analysis of any one-dimensional biological signal can be made.

Capacity Planning for Modular and Transportable Infrastructure for Shale Gas Production and Processing

Shale gas wells typically have steep production decline curves in the first few years of operation. Therefore, if such reduction in production is not accounted for, much of the supporting infrastructure within the shale gas field owned by the exploration and production (E&P) company will be grossly oversized after only a few years of production. Instead of the conventional approach of utilizing spatially fixed processing facilities, this work proposes the use of modular and transportable processing plants. This in turn allows the processing facilities to be composed of multiple modular plants operating in parallel. These modular plants can be reallocated within the field to other processing facilities by the E&P company to combat the uncertainty in production that comes with developing a shale gas field. A superstructure is developed to aid in formulating the capacity planning and allocation problem as a multi-stage stochastic program with uncertain production forecasts. We incorporate a novel recourse fu...

Exploring the Benefits of Modular Renewable-Powered Ammonia Production: A Supply Chain Optimization Study

Small-scale renewable-powered ammonia production is more sustainable than the current fossil fuel- and energy-intensive method. The development of lower capital cost absorbent-enhanced ammonia synthesis as well as the concept of modular chemical processes may improve the economic feasibility of such a paradigm. This possibility is investigated through an ammonia supply chain optimization study wherein modular, wind-powered ammonia production based on this new technology can be added to the existing infrastructure. The benefit of modularity is captured via a mass production exponent which reduces per-module capital cost as more are constructed. Case studies for Minnesota and Iowa show first adoption of 8760 t/y modules at conventional ammonia prices of $610/t and $574/t, respectively, which are considerably lower than those required for incorporation of scaled-down Haber–Bosch. For mass production exponents of 0.9 and less, modular production results in lower supply chain cost and more renewable incorporat...

Dry Reforming in a Milli-Scale Reactor Driven by Simulated Sunlight

In this study, a directly irradiated, milli-scale chemical reactor with a simple nickel catalyst was designed for dry reforming of methane for syngas. A milli-scale reactor was used to facilitate rapid heating, which is conducive to combating thermal transience caused by intermittent solar energy, as well as reducing startup times. Milli-scale reactors also allow for a distributed and modular process to produce chemicals on a more local scale. In this setup, the catalyst involved in the reaction is located directly in the focal area of the solar simulator, resulting in rapid heating. The effects of mean residence time and temperature on conversion and energy efficiency were tested. The process, which is intended to store thermal energy as chemical enthalpy, achieved 10% thermal-to-chemical energy conversion efficiency at a mean residence time of 0.028 s, temperature of 1000 °C, and molar feed ratio of 1:1 CO2:CH4. A significant portion of the thermal energy input into the reactor was directed toward sensible heating of the feed gas. Thus, this technology has potential to achieve solar-to-chemical efficiency with the integration of recuperative heat exchange.

Blocking Oligomeric Insulin Amyloid Fibrillation via Perylenebisimides Containing Dipeptide Tentacles.

Molecular motifs that could interfere with amyloid fibrillation via non-covalent interactions are vital for aberrant protein aggregation and related human diseases. Mutual aggregation ensues in the presence of these structural motifs, and nucleation on the particle surface leads to inhibition of the fibrillization process. This modular process generates a new generation of inhibitory reagents. Oligomers are the primary toxic species that initiate pathogenic aggregation, leading to toxic β-sheet-rich structures. To inhibit these toxic oligomers, two dipeptide-linked perylenebisimide isomers (PAPAP and APPPA) are developed as selective modulators for insulin fibrillization. Early insulin aggregates are adsorbed onto the modulator surface and stabilize soluble oligomeric aggregates. Fibrillation and inhibition are examined by thioflavin T (ThT) assay in the presence and the absence of both inhibitors PAPAP and APPPA. Conformational modulation using far-UV circular dichroism studies also highlights their role as aggregation inhibitors via reduction of α-helix into β-sheet along with increased random coil contents. Moreover, the inhibitory effects were more pronounced due to the ability of these isomers to undergo varying multiple non-covalent interactions, a performance well beyond all known modulators with respect to selectivity and efficiency, with the more-aggregation-prone derivative due to higher probability of a hydrophobic encounter between the protein and the molecular modulator. These results also lead to the elucidation of an insulin fibril regulating mechanism via selective non-covalent binding and provide fundamental insights into the chemistry of peptide-based probes as tools for developing next-generation therapeutics.