April 2021

Innovations in Comfort, Efficiency, and Safety Solutions.

(Click Message to Learn More)

Quantum Digital Twins — from system topologies to AI

This is part 2 of a 3 part series on the Quantum Digital Twin standard. Check out part 1 in the February edition of AutomatedBuildings.com.
Troy Harvey

Troy Harvey,

New Products
Securing Buildings News
Site Search
Past Issues
Secured by Cimetrics
Control Solutions, Inc
The evolution of building and IoT automation is placing a strain on the demands of installers, engineers, and manufacturers of equipment. Growing customer requirements, and the fast moving technology interplays between buildings, occupants, energy, and the business processes within these structures, are adding to already overburdened requirements on automation systems. 

We as an industry need to come to grips with the fact that building systems are the world’s most complex automated systems. Once we do that, we can then address our systemic problems. Even the smallest buildings easily have thousands of I/O points — or what we’d call degrees of freedom in robotic analysis. In large buildings the I/O points can exceed hundreds of thousands, and with the growth of the IoT industry, the complexity is only growing. Only once we give buildings their due respect against comparative cyberphysical systems like autonomous vehicles, Mars rovers, or industrial robotics, can we start the conversation on what we do to address the complexity.

In addition to managing this rising system complexity and evolving customer demand, there is exponential growth in the diversity of applications and use cases We are exhausting our tools with workarounds  to solve this exploding complexity. We are asked to model not only the HVAC systems, but the architectural and engineering workflow. We need more than tags, more that labels, more than interconnections. We need not only to describe hydronic and air flows between mechanical equipment, but the data flow within and between IT and IoT systems. We need to connect not only the building systems to the structural elements, but also the interconnected business systems within — whether that is the processes of occupants, logistics, manufacturing, energy, or any of the myriad services we are currently being asked to integrate with the building.

What we need, and why we need it now
The Quantum Digital Twin standard introduces the market’s first digital twin approach that can address not only the straightforward use cases of describing equipment, their interconnections, and their control interfaces — but also provides the pathway to fully autonomous buildings. Here at PassiveLogic, we are primarily focused on full autonomy, and we required Quantum to usher in this next generation of technology. Quantum’s investors at the Department of Energy needed a unified building description model for interchange and smart grid systems, while many of our industrial partners simply needed to wrangle existing IoT networks or analytics systems with a complete model. None of this is doable using any of today’s semantic standards, nor other digital twin approaches that largely resemble “BIM… but in the cloud”.

Quantum addresses these market use cases (and more): 
Most importantly, Quantum balances the needs of complexity with the requirements of implementation simplicity and democratizing automation to a broader set of users. We’ve built graphical tools for building Digital Twins, commissioning systems, site discovery of existing topologies, design comparison, and building custom autonomous systems to support the workflow. These tools are currently in private beta, but will become available for use later in 2021 — for free.

Sematics vs Ontology
In the first part of our series, we set apart the approach of semantic systems from true ontologies. This is worth decomposing because the history of the terminology is messy. Tim Beners-Lee had a vision of the “semantic web” way back in the 1990s, built on concepts of formal logic and linguistic theory. The concepts were strongly related to machine learning technology of the era. Technologies like knowledge graphs, Prolog, and Lisp — things that in a post deep learning era we now call “traditional machine learning.” While valuable in defining formal semantic systems, this early approach mixed up the usage of the terms “semantics” and “ontology”, in part due to the reference technologies of the time. After all what was state of the art ML in the 1990s were expert systems largely built on semantic concepts. 

While this history is fruitful for endless nerdy discussions, its semantic orientation has affected the course of technology development by affecting the framework and goals of many standards. The underlying framework of existing and currently proposed building standards are largely semantic standards. They ask the question “what is my name?” In contrast, a true ontology asks “who am I?” One is a linguistic question, the other an existential one. 

So what? Simply put, if I know you have a “pump” in English, I can label it. If we both agree that pumps are labeled “pump” and have a format, I can tag it (e.g. Haystack). If we agree on an interconnect scheme I can define a system topology (e.g. Brick). Yet for all this effort, my system still doesn’t know what a “pump” actually is or what it does. And without this you can’t autonomously control it, optimize it, or learn on it.

Physical Fundamentals 
In our previous article we introduced the concept of the “Gravi-Keister-limitor”. The idea that a chair is not a “chair”, but a device to keep your butt from colliding with the ground due to the force of gravity. The concept of Gravi-Keitser-limitor describes an object in existential terms. It doesn’t matter if its semantics are “chair”, “throne”, “chaise”, or “bench” — they all play the same role in the universe. The Quantum standard defines a component’s existential purpose by coupling its actor roles and substance quanta, supporting this coupling with the physics governing them. If you want to know anything’s purpose or how to control it, physics is the meta language from which most other questions can also be derived.

The importance of Actors
So while Quantum includes familiar concepts of assemblies, equipment, components, and properties, it adds the important concept of Actors. Actors are the role a piece of equipment takes in any system. It turns out, there are only 9 roles in any describable system. Take a buffer tank. It’s a store. So is a battery, a sand bed, and a flash drive. A transport on the other hand, moves substance from one place to another. Pumps move water, fans move air, and conveyers move boxes — yet they all do the same role within their respective systems.

If a system understands what Actors are, it can discern the purpose of any equipment, and how to orchestrate a system. More importantly, the system can do this regardless of application or system complexity. Using an underlying systems theory one can define how any set of actors in any configuration can be operated and be controlled. But perhaps more importantly, it answers the key question for equipment (or its proxy): “who am I?” If equipment can answer this existential “machine-2-self” question, it can also answer the simpler ones of machine-2-machine and machine-2-human. Those other interactions become just semantic downcasting.

The role of Quanta
The counterpart to the actors are quanta. Quanta are the packets of substance exchanged between actors. They are quantized so they can be operated on. Quanta can be thought of as packets of continuous flows, or discrete packages. So to expand our previous example of transports, what makes a pump unique is it deals in the flow of liquid quanta, a fan transports air quanta, and a conveyer belt deals with box quanta. Actors are the processors, and quanta are their currency.


 Quantum Create is the tool for building custom Digital Twins in the Quantum format

The evolution of AI
Today semantics only provide labeling. You can collect labeled data in data lakes. But then the long slow process begins to mine that lake of data for information. Machine learning is an afterthought for someone to figure out in the future. After all, the data is labeled… right? The problem is you can never reconstruct what was not there is the first place.  Today’s data from BMS and control platforms is so sparse that there is little to be learned or gained no matter how good the model. Even leaving aside the question of how you are going to label the data in the first place (a subject for the next article in this series), the utility will be limited.

Unlike the traditional ML of the semantic web days, we are in a post deep learning world. This post deep learning world is a radically altered technological landscape from the early semantic web. However, deep learning has been of limited utility in the generalized control world, beyond simple schemes to optimize algorithmic control. 

At PassiveLogic we’ve built a new AI technology called Deep Physics that reimagines neural nets as a heterogeneous network of Actors interconnected by Quanta. It this new world, Quantum literally is the AI, directly computable by a Quantum AI Engine. We just proved a new milestone in March with Quantum, benchmarking 10 Million times faster optimization than EnergyPlus with GenOpt.

Autonomous systems, topology definition, and interchange — with one format

Quantum was designed to scale across the diverse set of use cases we encounter in buildings, the business processes within, and the energy networks that interconnect them. It was designed to scale, from simple use cases, yet maintaining the descriptive power to enable the next generation of applications in the largest of interconnected IT and IoT control applications. 

In the simplest deployments, projects may only need to structure data for an IoT network. In retrofit installs, you may need to capture and control an existing topology — whether a 30 year old analog systems, or a pure IP network. In new construction you might need to automate a whole workflow including engineering, install, commissioning, and analytics. And in innovative edge AI systems, you need the complete descriptive power to achieve full autonomy. 

But the world is more than automation and IoT networks. The digital building is evolving beyond building systems. We must also model business processes, manufacturing, process control, and the like. Today, we are being asked to co-manage and monitor the digital and physical goods and services flowing in and around the built environment — as a system. And beyond the walls of the building, Quantum was also developed to be a portable format for smart grid systems, district systems, demand-response, and real-time peer-to-peer energy grids.

But given all these requirements, how do you enable a larger audience in building systems? In a word: tools. Bridging the divide between simplicity and fully featured AI solutions can only happen with high quality tools. 

We built Quantum Create as a visual application suite that integrators, engineers, manufacturers, and service providers can use to build custom Digital Twins in the Quantum format. Currently in early private beta, Quantum Create will be freely available later this year.

With Quantum as the backbone, complex building topologies are simple to design, build, operate, maintain, and manage. Stay on the lookout for part 3 of this series, where we will dive into the different applications of Autonomy Studio in greater detail.

Troy Harvey is the CEO of PassiveLogic, a company developing the future of automation, built on Quantum digital twins from the ground up


[Click Banner To Learn More]

[Home Page]  [The Automator]  [About]  [Subscribe ]  [Contact Us]


Want Ads

Our Sponsors