An official website of the United States government
Abstract High taxonomic diversity in non-industrial human gut microbiomes is often interpreted as beneficial; however, it is unclear if taxonomic diversity engenders ecological resilience (i.e. community stability and metabolic continuity). We estimate resilience through genus and species-level richness, phylogenetic diversity, and evenness in short-chain fatty acid (SCFA) production among a global gut metagenome panel of 12 populations (n = 451) representing industrial and non-industrial lifestyles, including novel metagenomic data from Burkina Faso (n = 90). We observe significantly higher genus-level resilience in non-industrial populations, while SCFA production in industrial populations is driven by a few phylogenetically closely related species (belonging toBacteroidesandClostridium), meaning industrial microbiomes have low resilience potential. Additionally, database bias obfuscates resilience estimates, as we were 2–5 times more likely to identify SCFA-encoding species in industrial microbiomes compared to non-industrial. Overall, we find high phylogenetic diversity, richness, and evenness of bacteria encoding SCFAs in non-industrial gut microbiomes, signaling high potential for resilience in SCFA production, despite database biases that limit metagenomic analysis of non-industrial populations.
more »
« less
A hybrid mechanistic machine learning approach to model industrial network dynamics for sustainable design of emerging carbon capture and utilization technologies
Industrial networks consist of multiple industrial nodes interacting with each other through material exchanges that support the overall production goal of the network.
more »
« less
- PAR ID:
- 10474299
- Publisher / Repository:
- Royal Society of Chemistry
- Date Published:
- Journal Name:
- Sustainable Energy & Fuels
- Volume:
- 7
- Issue:
- 20
- ISSN:
- 2398-4902
- Page Range / eLocation ID:
- 5129 to 5146
- Subject(s) / Keyword(s):
- Sustainable Networks, Machine Learning
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
With the introduction of Cyber-Physical Systems (CPS) and Internet of Things (IoT) technologies, the automation industry is undergoing significant changes, particularly in improving production efficiency and reducing maintenance costs. Industrial automation applications often need to transmit time- and safety-critical data to closely monitor and control industrial processes. Several Ethernet-based fieldbus solutions, such as PROFINET IRT, EtherNet/IP, and EtherCAT, are widely used to ensure real-time communications in industrial automation systems. These solutions, however, commonly incorporate additional mechanisms to provide latency guarantees, making their interoperability a grand challenge. The IEEE 802.1 Time-Sensitive Networking (TSN) task group was formed to enhance and optimize IEEE 802.1 network standards, particularly for Ethernet-based networks. These solutions can be evolved and adapted for cross-industry scenarios, such as large-scale distributed industrial plants requiring multiple industrial entities to work collaboratively. This paper provides a comprehensive review of current advances in TSN standards for industrial automation. It presents the state-of-the-art IEEE TSN standards and discusses the opportunities and challenges of integrating TSN into the automation industry. Some promising research directions are also highlighted for applying TSN technologies to industrial automation applications.more » « less
-
Abstract One of the characteristic features of the next-generation of Industry 4.0 is human-centricity, which in turn includes two technological advancements: Artificial Intelligence and the Industrial Metaverse. In this work, we assess the impact that AI played on the advancement of three technologies that emerged to be cornerstones in the fourth generation of industry: intelligent industrial robotics, unmanned aerial vehicles, and additive manufacturing. Despite the significant improvement that AI and the industrial metaverse can offer, the incorporation of many AI-enabled and Metaverse-based technologies remains under the expectations. Safety continues to be a strong factor that limits the expansion of intelligent industrial robotics and drones, whilst Cybersecurity is effectively a major limiting factor for the advance of the industrial metaverse and the integration of blockchains. However, most research works agree that the lack of the skilled workforce will no-arguably be the decisive factor that limits the incorporation of these technologies in industry. Therefore, long-term planning and training programs are needed to counter the upcoming shortage in the skilled workforce.more » « less
-
Abstract If the material intensive enterprises in an urban area of several million people shared physical resources that might otherwise be wasted, what environmental and public benefits would result? This study develops an algorithm based on lifecycle assessment tools for determining a city’sindustrial symbiosis potential—that is, the sum of the wastes and byproducts from a city’s industrial enterprises that could reasonably serve as resource inputs to other local industrial processes. Rather than report, as do many previous papers, on private benefits to firms, this investigation focuses on public benefits to cities by converting the maximum quantity of resources recoverable by local enterprises into an estimate of the capacity of municipal infrastructure conserved in terms of landfill space and water demand. The results here test this novel approach for the district of Mysuru (Mysore), India. We find that the industrial symbiosis potential calculated based on analysis of the inputs and outputs of ∼1000 urban enterprises, translates into 84 000 tons of industrial waste, greater than 74 000 tons of CO2e, and 22 million liters per day of wastewater. The method introduced here demonstrates how industrial symbiosis links private production and public infrastructure to improve the resource efficiency of a city by creating an opportunity to extend the capacity of public infrastructure and generate public health co-benefits.more » « less
-
Various technologies and strategies have been proposed to decarbonize the chemical industry. Assessing the decarbonization, environmental, and economic implications of these technologies and strategies is critical to identifying pathways to a more sustainable industrial future. This study reviews recent advancements and integration of systems analysis models, including process analysis, material flow analysis, life cycle assessment, techno-economic analysis, and machine learning. These models are categorized based on analytical methods and application scales (i.e., micro-, meso-, and macroscale) for promising decarbonization technologies (e.g., carbon capture, storage, and utilization, biomass feedstock, and electrification) and circular economy strategies. Incorporating forward-looking, data-driven approaches into existing models allows for optimizing complex industrial systems and assessing future impacts. Although advances in industrial ecology–, economic-, and planetary boundary–based modeling support a more holistic systems-level assessment, more effects are needed to consider impacts on ecosystems. Effective applications of these advanced, integrated models require cross-disciplinary collaborations across chemical engineering, industrial ecology, and economics. Expected final online publication date for the Annual Review of Chemical and Biomolecular Engineering , Volume 15 is June 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1039/D3SE01032E
