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    Digital water: discovering the ghost in the machine

    The concept of ‘digital water’ is still more of a philosophy than a science, and can appear ethereal and nebulous at first glance.

    Recognizing that many sciences begin as philosophies, I looked to concepts such as the ‘ghost in the machine’ as a frame of reference to understand the opportunities presented by digital water.

    The term ‘ghost in the machine’ first appeared in the book ‘The Concept of Mind’ by Gilbert Ryle, in which he strongly opposed the theory of dualism that suggested the separation of mind and body. As we know now, our nervous and endocrine systems connect mind and body and are responsible for making us (sometimes) smart integrated systems rather than a collection of individual cells.

    Nowadays, dualism exists between technology and control systems. Digital technology has a potential to create a bridge between the two and recognize the opportunities behind the ‘ghost in the machine’.

    In the water technology-system continuum, digital starts with local automation and control of a technology or unit process, and can be expanded to a plant-wide use of data to integrate unit processes into a smart system.

    At the local level, you have external inputs like weather data, sewer modeling, and fluctuating energy tariffs. At the next level, you can take in the data from hundreds of plants globally and implement the acquired knowledge back at the local level. This is analogous to how we use epidemiological surveys of the human population in longitudinal studies that ultimately tell us how we can alter our diet and exercise habits to reduce the risk of heart disease.

    For example, a membrane control system like Intelliflux optimizes at the technology level. At the process level, software like ROTEC optimizes an RO process. At the level of a treatment plant, a solution such as that offered by Emagin takes in upstream, downstream and external data, like weather, sewer modeling or energy tariffs, and supports decision making at the plant-wide level.

    Who is going to monetize the digital data? 

    Chemical companies have been on the frontline in that regard. This includes Ecolab with 3D Trasar and Solenis with On-Guard. Both focus on leveraging sensors and software to help control chemical addition to optimize processes.

    In the meantime, technology and solutions companies such as GE with Predix, or Sembcorp with VirtualBrain™ focus more on offering predictive analytics services and remote service support.

    While the chemical companies do not necessarily have an advantage in data-driven services over a technology provider such as a DOW, Pentair or Suez, they benefit from the proximity to the end-user in the value chain.

    When it comes to creating a bridge between a technology provider and a client without necessarily threatening the traditional role of OEM or systems integrator, there is an opportunity for solutions that build in analytics and cloud-based monitoring into the technology. The key question here is how quickly it will become important to optimize the entire treatment plant and not just a unit technology or process in isolation.

    We are seeing examples from the pulp and paper industry, where ProcessMiner has developed an end-to-end software product aimed at optimizing the production process, as well as practical cases, including L’Oreal’s Dry Factory concept in Burgos in Spain. There, moving to ‘closed loop’ water re-use resulted in moving to zero liquid discharge (ZLD) at a plant-wide level that allowed L’Oreal to reap unanticipated benefits of reductions in lost raw material and improved performance of the boiler water make-up systems.

    Avoiding Arthur Koestler’s progress trap 

    Arthur Koestler brought Ryle’s concept of the ‘ghost in the machine’ to wider attention in his 1967 book of the same name. [7] One of the book’s central concepts is that of the human brain being built upon an earlier, more primitive brain structure. According to Koestler, we are still tied to the primitive thinking that can overpower logical functions and will lead to our destruction.

    In water, Koestler’s ‘ghost in the machine’ is represented by the outdated centralized water system we have inherited from our Victorian forefathers. It is far from being an intelligent design, and while we can improve the outdated system through digital innovation, without some fundamental changes it won’t become smart.

    Transporting water across increasing distances in buried and often ageing infrastructure, mixing all of our waste together, taking it to one centralized facility and trying to take everything back out again, the solids in faecal matter, the nutrients in urine, detergents, oils and grease, food waste, using energy to do this and then discharging the product into a surface water body. None of this makes sense if you think about it at a systems level in today’s increasingly resource-poor, but technology-abundant world.

    We have an increasing number of wastewater treatment plants that are net producers of energy, nutrients and water. That is a paradigm shift at a systems level. Digital and IoT are enablers for a remote decentralized systems revolution, and the democratization of real-time water quality data and cloud based monitoring of point of use treatment systems are drivers that may change the role of the consumer in the potable water supply value chain.

    Conclusion

    The digital revolution is happening on every level, from built-in intelligence in single units to solutions like VirtualBrain, 3D Trasar, ProcessMiner, and Emagin that operate at a systems level. We see opportunities for monetization of digital data and enabling of new business models and services, and we can identify key players that are best placed to take advantage of the emerging opportunities. The process is set in motion and it is just a matter of time before the ghost inside the machine is released.