AI-driven process simulation | Using AI to control systems
04 Apr 2018
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Machine learning offers the opportunity to build control systems which can adapt to changing conditions and optimise themselves to meet business requirements – for example reducing energy use, cost, or improving output quality.

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​Industrial processes from waste water treatment to factory packing lines, are controlled by manual or semi-automated rule-based approaches. Machine learning offers the opportunity to build control systems which can adapt to changing conditions and optimise themselves to meet business requirements – for example reducing energy use, cost, or improving output quality.


By building simulation models, or by learning how a process behaves from operational data, we can provide recommendations as to how process operations can be improved. Contact us to discuss collaborative R&D projects​ to model and improve your business processes.

To see our AI controller in action and explore how it could be applied in your industry – contact us

​​Applications

Optimisation and control of industrial processes including: 
  • Production and packing lines
  • Water treatment and distribution

​Benefits

  • Knowledge capture by training AI using expert input
  • Improved efficiency of existing capital infrastructure​

​​​​Conferences

​​​The following poster was presented at the European Geosciences Union General Assembly, April ​2018:

Deep Reinforcement Learning for wastewater treatment control: An application
Lan Ngoc Hoang, Edward Pyzer-Knapp, and Christopher Thompson IBM Research, STFC Daresbury Laboratory, Warrington, UK

Advances in modelling and control have helped improved resilience and performance of environmental systems. Recently, Deep Reinforcement Learning has e​merged as a promising technique in providing flexible and evolving strategies in various domains. We present an application of Deep Reinforcement Learning implemented on two controllers of a wastewater treatment model. In each of these controllers, a deep neural network was trained to predict the value function Q which represents the objective function of energy operation cost, effluent quality and environmental fines. The controllers learn to operate and adapt the treatment plant’s controller setting under different weather and inflow conditions. The application demonstrates a potential for applying Deep Reinforcement Learning on environmental systems to improve management and control.



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