The word “ecosystem” is often used in the context of software development and system design– tech lingo for an integrated network of hardware, software, and operators working together to maintain a stable, flexible, scalable automation system. It’s a salient analogy for a real ecosystem: a community of species maintaining a stable, adaptive biological system in a semi-closed environment. Sounds pretty similar, right?
But there’s much more automation engineers and industrial IT/OT can learn from natural ecosystems than parallel architecture: the dynamic and interconnected components of a biotic community hold aeons of wisdom control system integrators can leverage in their industries, too. So let’s get into the weeds about the dual meaning of network architecture – structure, stability, efficiency, adaptability, and resilience.
Process Automation: Control System Design as Forest Succession
Ecologist refer to the development of a plant community as succession. From bare ground, the pioneers move in – light-hungry, fast-growing, weedy species are the first to arrive. As the landscape develops, wildflowers, shade-tolerant herbs, and then shrubs move in. By mid-succession, you’ve got an open forest full of small trees and woody understory. Each new layer creates new niches – opportunities for new species to add connections in the community network.
A fully developed forest with a closed canopy is called a climax community: dominated by a few species that control environmental parameters, supported by diverse and redundant under-species that monitor, alert, adapt to, and help the community recover from changes in the environment. Every species has a niche and plays an important role in the ecosystem’s ability to thrive.
If you think about it, this process is the digital transformation into Industry 4.0. The initial conversion of some components of a manufacturing process to automation is those early pioneers – isolated PLCs and basic PIDs. Upgrading and beginning to integrate system components as the operation grows and technology develops is that journey into a more diverse forest – data historians and HMIs stabilize communications and monitor conditions.
The progression to AI-driven manufacturing intelligence – that’s the climax forest. Industrial analytics, MES implementation, IT/OT security – those are the dominant canopy trees controlling everything within the system. That arrival to Industry 4.0 is the stable, diverse ecosystem running on a fully-integrated network that is able to adapt to environmental changes and recover from major disturbance without major disaster.
System Resilience Through Feedback & Redundancy
Just like integrated industrial automation systems, plant communities operate on feedback loops under an architecture of redundant components. In industry and nature, layered architecture is key to maximizing stability in variable environments:
- Plant communities use ecological feedback to maintain stability and adapt to changes in the environment through nutrient cycling, water balance, and resource partitioning. In a healthy forest, multiple species fill similar niches so that if one is damaged or destroyed via drought or blight, the community doesn’t collapse; other species are ready to fill the niche without interruption to the larger system.
- An intelligent control system also maintains stability through feedback that monitors outputs, compares to parameters, and adjusts to stay within them. In automation system design, redundant PLCs, adaptive controllers, dual network pathways, and hybrid architecture that combines on-premise and edge control ensure that where one piece falters or fails, the whole system doesn’t come crashing down, leaving operations stalled and networks vulnerable.
System Security Through Integrated Networks & Recovery Plans
Integration and failsafes are integral to a secure automation system; you want to be able to stave off invasion and recover from disaster with minimal interruption to operations. A forest wants the same thing – resist small and moderate threats without trouble and recover from extreme disturbance when it eventually comes knocking:
- Plants communicate with each other via chemical signals using rhizome networks, root exudates, and mycorrhizal networks to alert of stress or competition. Trees and clonal plants can redirect resources to a damaged individual to support it or redirect resources away from an infected individual to protect the rest of the system. Mycorrhizal networks can signal warnings of herbivory, and some herbaceous plants can release chemicals to block competitors or invaders. And there are backup plans when disaster does strike; soil seed banks and dormant buds allow a forest to regenerate after major disturbance like a wildfire or logging.
- Sounds a lot like an intelligent automation system, doesn’t it? IT/OT integration and MIS strategies like network segmentation and multi-vendor architecture increase resistance to invaders. SIEM systems detect and alert of anomalous events. MFA and firewalls block unauthorized access. Black start capabilities for building automation and energy management systems ensure efficient, safe startup after major disruption like power surge, outage, or breach that takes the system down.
Systems Intelligence Through Learning & Adapting
Nature’s greatest strength isn’t stability, it’s adaptation. Species evolve when the current community structure can’t thrive in changing conditions. Community architecture changes to accommodate new challenges. The ability to redirect resources, pivot survival strategies, and select advantageous traits through evolution, are biological forms of learning and adapting. Trees can send chemical signals to bend towards free light when a neighboring tree falls; herbaceous species growing near mines can learn to sequester toxic compounds into their cell walls.
The same principles are true for intelligent automation; a control system built for a specific purpose needs new features and components to accommodate variability in materials, process demand, line operations, and energy availability. MES, BMS, batch control systems, and SCADA – the inherent intelligence of system components is an important foundation, but add in machine learning and AI-driven system design and you’ve got the tech version of evolution happening in real time:
- AI-driven process automation doesn’t just hold a set point, it learns the best control strategy over time and tweaks parameters accordingly. Eventually, they’re able to detect and adapt to disturbances more quickly and efficiently than human operators.
- Self-tuning controllers behave similarly to living systems. They start with basic logic, observe outcomes, and evolve their parameters. It’s a continuous process of optimization and proliferation, but it happens on a moment-by-moment scale; we won’t have to wait millennia for Industry 5.0 – it’s just on the horizon.
The Future of Automation is Bio-Inspired Control System Architecture
The whole point of AI is to be able to mimic biological intelligence in its network architecture; even advanced technology knows nature is the most intelligent control system integrator of them all. A building automation system is a forest – it needs decentralized control, integrated communication, and adaptability to thrive.
As we move farther into intelligence-driven control system design, integrators will benefit from taking an organic approach:
- Decentralized Intelligence: Multiple controllers act like individual members of a forest community, each learning and making decisions about local conditions while communicating with the rest of the network.
- Example: An edge PLC in a packaging line can autonomously reroute products when a sensor detects a blockage, without waiting for commands from central control.
- Integrated Communication: Like a diverse, multi-level forest community uses multiple communication pathways to anticipate, pivot, and redirect in order to maintain stability, networks of intelligent machines, analytics platforms, and edge devices communicate and collaborate in real time.
- Example: System analytics aggregate information from the rest of the network to coordinate resource distribution, stave off threats, and guide long-term strategy.
- Adaptive Response: AI software acts like natural selection. Instead of simply following set parameters, adaptive automation systems use industrial analytics and machine learning to continuously recalibrate, correct, and improve system efficacy.
- Example: HVAC controls detect humidity variations in a climate-controlled room of a pharmaceutical plant and adjust settings to correct it.
By combining these natural talents of the ecosystems around us, intelligent control systems will be able to better defend against cyber threats, recover from disturbance, and optimize throughput, material use, and energy use dynamically. High-end manufacturing automation solutions that incorporate these principles will be more resilient, flexible, scalable, and sustainable so operations don’t just struggle to compete in their sector – they thrive.