Southwire – Southwire Announces Investment in Ndustrial to Scale Industrial Energy Efficiency

EMR Analysis

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More information on Southwire Technology Ventures: https://www.southwire.com/technology-ventures + SWTV collaborates with pioneering startups to build the future of smart power. We invest in and partner with early-stage ventures exploring digital power, smart buildings, electric mobility and the grid of the future.

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More information on Burt Fealing (Executive Vice President of General Counsel and Chief Sustainability Officer, Southwire): See the full profile on EMR Executive Services

More information on the Southwire’s Sustainability Strategy, Five Core Tenets and Growing Green Approach: See the full profile on EMR Executive Services

 

 

More information on Ndustrial.Io, Inc. (Southwire Strategic Investment): https://ndustrial.io/ + Ndustrial provides software and services to help the $60B industrial market combat inflation and climate change by optimizing energy intensity — the crucial metric of energy, emissions and cost required for one unit of production. By uniting production data with energy usage for some of the most complex, energy-demanding operations in the world, Ndustrial’s AI-powered solutions enable smarter energy decisions in real-time.

We like to roll up our sleeves and get in the trenches with our customers. Only then can we truly understand their challenges and build software for optimal energy efficiency and throughput.

Our journey started in 2011 when our founders merged their experiences across Silicon Valley innovation, industrial engineering, and computer science to build a real-time industrial intelligence platform. We started as a team of three and have grown to more than 50 employees based in Raleigh and around the country.

Despite our experience in the venture capital world, we’ve never tried to be a VC darling. We’ve focused instead on building a business for customers. Still, we have attracted investments from ABB, Clean Energy Ventures, ENGIE New Ventures, GS Energy, and more.

We exist to make the world a better place. We work every day to root out waste in the industrial sector. We are also dedicated to being a great place for people to grow and thrive, to have fun and feel part of a great community, and to give back.

As a member of the Pledge 1% community, we donate 1% of employee time to charitable causes like building energy-efficient homes together with Habitat for Humanity. As a Green Places partner, we also track and offset our carbon emissions using high-quality, third-party verified offsets.

More information on Jason Massey (Founder and Chief Executive Officer, Ndustrial.Io, Inc.): https://ndustrial.io/about/ + https://www.linkedin.com/in/jasonmnc/ 

 

 

 

 

 

 

 

EMR Additional Notes: 

• Carbon Dioxide (CO2):
  • Primary greenhouse gas emitted through human activities. Carbon dioxide enters the atmosphere through burning fossil fuels (coal, natural gas, and oil), solid waste, trees and other biological materials, and also as a result of certain chemical reactions (e.g., manufacture of cement). Carbon dioxide is removed from the atmosphere (or “sequestered”) when it is absorbed by plants as part of the biological carbon cycle.
• Biogenic Carbon Dioxide (CO2):
  • Biogenic Carbon Dioxide (CO2) and Carbon Dioxide (CO2) are the same. Scientists differentiate between biogenic carbon (that which is absorbed, stored and emitted by organic matter like soil, trees, plants and grasses) and non-biogenic carbon (that found in all other sources, most notably in fossil fuels like oil, coal and gas).
• Decarbonization:
  • Reduction of carbon dioxide emissions through the use of low carbon power sources, achieving a lower output of greenhouse gasses into the atmosphere.
• Carbon Footprint:
  • There is no universally agreed definition of what a carbon footprint is.
  • A carbon footprint is generally understood to be the total amount of greenhouse gas (GHG) emissions that are directly or indirectly caused by an individual, organization, product, or service. These emissions are typically measured in tonnes of carbon dioxide equivalent (CO2e).
  • In 2009, the Greenhouse Gas Protocol (GHG Protocol) published a standard for calculating and reporting corporate carbon footprints. This standard is widely accepted by businesses and other organizations around the world. The GHG Protocol defines a carbon footprint as “the total set of greenhouse gas emissions caused by an organization, directly and indirectly, through its own operations and the value chain.”
• CO2e (Carbon Dioxide Equivalent):
  • CO2e means “carbon dioxide equivalent”. In layman’s terms, CO2e is a measurement of the total greenhouse gases emitted, expressed in terms of the equivalent measurement of carbon dioxide. On the other hand, CO2 only measures carbon emissions and does not account for any other greenhouse gases.
  • A carbon dioxide equivalent or CO2 equivalent, abbreviated as CO2-eq is a metric measure used to compare the emissions from various greenhouse gases on the basis of their global-warming potential (GWP), by converting amounts of other gases to the equivalent amount of carbon dioxide with the same global warming potential.
    • Carbon dioxide equivalents are commonly expressed as million metric tonnes of carbon dioxide equivalents, abbreviated as MMTCDE.
    • The carbon dioxide equivalent for a gas is derived by multiplying the tonnes of the gas by the associated GWP: MMTCDE = (million metric tonnes of a gas) * (GWP of the gas).
    • For example, the GWP for methane is 25 and for nitrous oxide 298. This means that emissions of 1 million metric tonnes of methane and nitrous oxide respectively is equivalent to emissions of 25 and 298 million metric tonnes of carbon dioxide.
• Carbon Capture and Storage (CCS) – Carbon Capture, Utilisation and Storage (CCUS):
  • CCS involves the capture of carbon dioxide (CO2) emissions from industrial processes. This carbon is then transported from where it was produced, via ship or in a pipeline, and stored deep underground in geological formations.
  • CCS projects typically target 90 percent efficiency, meaning that 90 percent of the carbon dioxide from the power plant will be captured and stored.
• Carbon Dioxide Removal (CDR): 
  • Carbon Dioxide Removal encompasses approaches and methods for removing CO2 from the atmosphere and then storing it permanently in underground geological formations, in biomass, oceanic reservoirs or long-lived products in order to achieve negative emissions.
• Direct Air Capture (DAC): 
  • Technologies extracting CO2 directly from the atmosphere at any location, unlike carbon capture which is generally carried out at the point of emissions, such as a steel plant.
  • Constraints like costs and energy requirements as well as the potential for pollution make DAC a less desirable option for CO2 reduction. Its larger land footprint when compared to other mitigation strategies like carbon capture and storage systems (CCS) also put it at a disadvantage.
• Carbon Credits or Carbon Offsets:
  • Permits that allow the owner to emit a certain amount of carbon dioxide or other greenhouse gases. One credit permits the emission of one ton of carbon dioxide or the equivalent in other greenhouse gases.
  • The carbon credit is half of a so-called cap-and-trade program. Companies that pollute are awarded credits that allow them to continue to pollute up to a certain limit, which is reduced periodically. Meanwhile, the company may sell any unneeded credits to another company that needs them. Private companies are thus doubly incentivized to reduce greenhouse emissions. First, they must spend money on extra credits if their emissions exceed the cap. Second, they can make money by reducing their emissions and selling their excess allowances.

 

 

• AI – Artificial Intelligence:
  • https://searchenterpriseai.techtarget.com/definition/AI-Artificial-Intelligence  +
    • Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems.
    • As the hype around AI has accelerated, vendors have been scrambling to promote how their products and services use AI. Often what they refer to as AI is simply one component of AI, such as machine learning. AI requires a foundation of specialized hardware and software for writing and training machine learning algorithms. No one programming language is synonymous with AI, but well a few, including Python, R and Java, are popular.
    • In general, AI systems work by ingesting large amounts of labeled training data, analyzing the data for correlations and patterns, and using these patterns to make predictions about future states. In this way, a chatbot that is fed examples of text chats can learn to produce lifelike exchanges with people, or an image recognition tool can learn to identify and describe objects in images by reviewing millions of examples.
    • AI programming focuses on three cognitive skills: learning, reasoning and self-correction.
    • What are the 4 types of artificial intelligence?
      • Type 1: Reactive machines. These AI systems have no memory and are task specific. An example is Deep Blue, the IBM chess program that beat Garry Kasparov in the 1990s. Deep Blue can identify pieces on the chessboard and make predictions, but because it has no memory, it cannot use past experiences to inform future ones.
      • Type 2: Limited memory. These AI systems have memory, so they can use past experiences to inform future decisions. Some of the decision-making functions in self-driving cars are designed this way.
      • Type 3: Theory of mind. Theory of mind is a psychology term. When applied to AI, it means that the system would have the social intelligence to understand emotions. This type of AI will be able to infer human intentions and predict behavior, a necessary skill for AI systems to become integral members of human teams.
      • Type 4: Self-awareness. In this category, AI systems have a sense of self, which gives them consciousness. Machines with self-awareness understand their own current state. This type of AI does not yet exist.
    • Machine Learning (ML):
      • Developed to mimic human intelligence, it lets the machines learn independently by ingesting vast amounts of data, statistics formulas and detecting patterns.
      • ML allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so.
      • ML algorithms use historical data as input to predict new output values.
      • Recommendation engines are a common use case for ML. Other uses include fraud detection, spam filtering, business process automation (BPA) and predictive maintenance.
      • Classical ML is often categorized by how an algorithm learns to become more accurate in its predictions. There are four basic approaches: supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning.
    • Deep Learning (DL):
      • Subset of machine learning, Deep Learning enabled much smarter results than were originally possible with ML. Face recognition is a good example.
      • DL makes use of layers of information processing, each gradually learning more and more complex representations of data. The early layers may learn about colors, the next ones about shapes, the following about combinations of those shapes, and finally actual objects. DL demonstrated a breakthrough in object recognition.
      • DL is currently the most sophisticated AI architecture we have developed.
    • Computer Vision (CV):
      • Computer vision is a field of artificial intelligence that enables computers and systems to derive meaningful information from digital images, videos and other visual inputs — and take actions or make recommendations based on that information.
      • The most well-known case of this today is Google’s Translate, which can take an image of anything — from menus to signboards — and convert it into text that the program then translates into the user’s native language.
    • Machine Vision (MV):
      • Machine Vision is the ability of a computer to see; it employs one or more video cameras, analog-to-digital conversion and digital signal processing. The resulting data goes to a computer or robot controller. Machine Vision is similar in complexity to Voice Recognition.
      • MV uses the latest AI technologies to give industrial equipment the ability to see and analyze tasks in smart manufacturing, quality control, and worker safety.
      • Computer Vision systems can gain valuable information from images, videos, and other visuals, whereas Machine Vision systems rely on the image captured by the system’s camera. Another difference is that Computer Vision systems are commonly used to extract and use as much data as possible about an object.
    • Generative AI (GenAI):
      • Generative AI technology generates outputs based on some kind of input – often a prompt supplied by a person. Some GenAI tools work in one medium, such as turning text inputs into text outputs, for example. With the public release of ChatGPT in late November 2022, the world at large was introduced to an AI app capable of creating text that sounded more authentic and less artificial than any previous generation of computer-crafted text.

 

• Edge AI Technology:
  • Edge artificial intelligence refers to the deployment of AI algorithms and AI models directly on local edge devices such as sensors or Internet of Things (IoT) devices, which enables real-time data processing and analysis without constant reliance on cloud infrastructure.
  • Simply stated, edge AI, or “AI on the edge“, refers to the combination of edge computing and artificial intelligence to execute machine learning tasks directly on interconnected edge devices. Edge computing allows for data to be stored close to the device location, and AI algorithms enable the data to be processed right on the network edge, with or without an internet connection. This facilitates the processing of data within milliseconds, providing real-time feedback.
  • Self-driving cars, wearable devices, security cameras, and smart home appliances are among the technologies that leverage edge AI capabilities to promptly deliver users with real-time information when it is most essential.
• Multimodal Intelligence and Agents:
  • Subset of artificial intelligence that integrates information from various modalities, such as text, images, audio, and video, to build more accurate and comprehensive AI models.
  • Multimodal capabilities allows to interact with users in a more natural and intuitive way. It can see, hear and speak, which means that users can provide input and receive responses in a variety of ways.
  • An AI agent is a computational entity designed to act independently. It performs specific tasks autonomously by making decisions based on its environment, inputs, and a predefined goal. What separates an AI agent from an AI model is the ability to act. There are many different kinds of agents such as reactive agents and proactive agents. Agents can also act in fixed and dynamic environments. Additionally, more sophisticated applications of agents involve utilizing agents to handle data in various formats, known as multimodal agents and deploying multiple agents to tackle complex problems.
• Small Language Models (SLM) and Large Language Models (LLM):
  • Small language models (SLMs) are artificial intelligence (AI) models capable of processing, understanding and generating natural language content. As their name implies, SLMs are smaller in scale and scope than large language models (LLMs).
  • LLM means large language model—a type of machine learning/deep learning model that can perform a variety of natural language processing (NLP) and analysis tasks, including translating, classifying, and generating text; answering questions in a conversational manner; and identifying data patterns.
  • For example, virtual assistants like Siri, Alexa, or Google Assistant use LLMs to process natural language queries and provide useful information or execute tasks such as setting reminders or controlling smart home devices.

 

 

• IoT (Internet of Things): 
  • The Internet of Things (IoT) refers to a system of interrelated, internet-connected objects that are able to collect and transfer data over a wireless network without human intervention.
  • Describes the network of physical objects—“things”—that are embedded with sensors, software, and other technologies for the purpose of connecting and exchanging data with other devices and systems over the internet.
  • The Most Popular IoT Devices are:
    • Smart watches are the most popular IoT devices. …
    • Gaming consoles. …
    • Smart TV sets and content streaming devices. …
    • Voice control devices. …
    • Printers. …
    • Cameras. …
    • Lighting appliances. …
    • Smart thermostats.

• IIoT (Industrial IoT):
  • Industrial IoT (IIoT) involves collecting and analyzing sensor-generated data to support equipment monitoring and maintenance, production process analytics and control, and more. In manufacturing IT since 1989, ScienceSoft offers IIoT consulting and development to create secure IIoT solutions.

• AIoT (Artificial Intelligence of Things):
  • Relatively new term and has recently become a hot topic which combines two of the hottest acronyms, AI (Artificial Intelligence) and IoT (Internet of Things).
  • AIoT is transformational and reciprocally beneficial for both types of technology, as AI adds value to IoT through machine learning capabilities and improved decision-making processes, while IoT adds value to AI through connectivity, signalling, and data exchange.
  • Aim: achieve more efficient IoT operations, improve human-machine interactions and enhance data management and analytics.
• xIoT (xTended Internet of Things):
  • xIoT refers to the “xTended” Internet of Things. This xTended IoT category spans Enterprise IoT devices (cameras, printers, and door controllers), OT devices (like PLCs, HMI’s, and robotics) and Network devices (like switches, WiFi routers, and NAS).