Frequently Asked Questions

These are the most frequently asked questions from our customers.

For more information and questions from users of the IntelliSense.io products please see our Intercom Support

Processes with high-variability inputs, such as is found in the mining sector, are constantly battling to compensate for changing conditions and produce a consistent product. IntelliSense.io Brains.app and its Optimization as a Service (OAAS) applications shed light on the unknown through data modelling and AI, so operators can make better decisions and processes can proactively adjust to changing conditions.

 

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Brains.app supports the following standards: 

  • Data Acquisition: we support a portfolio of data acquisition standards that range from: 
    • IOT Devices: MQTT is the ubiquitous messaging standard generally supported by all IOT devices. brains.app platform supports XMLRPC which is the underlying technology for MQTT. To date no customers have asked for MQTT support.
    • Industrial Data: OPC DA and UA protocol is the ubiquitous data standard followed by all industrial systems that brains.app platform supports. All client situations to date has asked for this requirement. This is typically from OSI Soft Pi systems.
    • Web services: REST is the ubiquitous web data standard that is supported
    • Batch data: we support all types of batch data like XML, CSV, XL and manual data uploads. 
  • Data Modelling: we support various data modelling libraries like Tensorflow: An Open Source Library used for numerical computation & large-scale machine learning. This can be used to create dataflow graphs and run on any target (cloud or local) 
  • Deployment Architecture: we use Docker containers and Kubernetes as the container orchestration layer to deploy our platform + application in any environment: cloud, client data center or local on site deployment.

Yes, the brains.app platform is a modular platform. We are working with customers who have their own data platforms, and we have separate layers between algorithms, data lake and end user interface.

All of these layers are modular, connected by micro services and standardised enabling us to be cloud (host) agnostic, data lake agnostic and deployment platform agnostic. brains.app can be deployed as an individual docker container on a different data platform or cloud host.

We have implemented the above as part of our Kazakhstan Joint Venture with their government where localization of the platform was essential. In addition, we are working with various customers who have internal data lakes built on MS Azure or other cloud hosts. 

By creating a secure, multi-access interface to view, analyze, interpret and change functions across a range of industrial activities, the brains.app platform de-silos data and gives users all the power they need to improve productivity and efficiency.

There are three primary challenges that the Internet of Things faces:

  • Data tends to be stored in silos, which makes broad-based access and overview challenging
  • Security: by its nature, the Internet of Things requires connecting multiple data points and IT functions together to produce an inclusive and non-silo capability or functionality. This challenges numerous standard security protocols and requires a complex permissions-orientated security system that ringfences the correct data points and IT functionalities without compromising efficiencies and capabilities.
  • Accessibility via numerous devices, multiple users requiring varying types of functionality, data, reporting etc. creates another challenge for IoT. The system needs to be able to adapt to a constantly fluctuating user landscape.

IntelliSense.io solves these challenges by harnessing the power of Cloud-based technologies and Machine Learning to provide a robust real-time decision making AI Applications platform.

Our focus is on the mine to market value chain with applications segmented across 

  • Digital Mine
  • Digital Plant
  • Digital Markets 

Our Applications include:

  • Thickener Circuit
  • Stockpile
  • Floatation Circuit
  • Pipeline Pumping
  • Grinding Circuit
  • Heap Leach
  • Custom Applications

IntelliSense.io solutions including software, pre-build AI models and support sold as a service through an annual subscription.This negates the need of buying any hardware, software licenses or services to develop custom models therein reducing upfront (CAPEX cost) and eliminates the need of expensive services costs for developing and deploying AI projects. The applications are delivered out of the box, kept up to date on a continuous basis (including the retraining of the models) to ensure the application delivers value to users and scale with them. The standard name for this business model ‘is software as a service’

Benefits of Business Model ‘OaaS’

  • Quick to deploy and scale without requiring upfront CAPEX
  • Faster benefit realisation
  • Guaranteed levels of service
  • Long term relationship with = models adapted and software updated to site needs and changes.
  • Accessible anywhere and No Need for additional fees for upgrades. 

A typical payback period of the technology is achieved within 8 to 12 months (of the total contract value of 5 years).

We are not equipment experts but we have process expertise in the team in both mine and plant. We also use data and models to learn about the system and by using algorithms to evaluate performance so that we can predict future performance and make recommendations.

We have expertise on the specific mining process and use data to derive non-linear correlations across the end to end process. This allows us to get a better understanding of the entire system, this system knowledge is used to optimise the process. Leveraging this system knowledge we develop prediction models that predict process behaviour, we then use this process prediction and apply financial as well as technical constraints to deduce the optimum control set point for the equipments. These control set points are delivered on a continuous basis.

We see the fundamental breakthrough is in providing a system based optimisation by combining process expertise, insights generated from data and deploying them through a real-time decision support system to support both manual and automated decision making.

We combine Physics (first principle) models with machine learning / AI models that draw patterns from the data using Neural Networks (combine of generic black box and first principle/equipment models) and our models are learning and adapt in real time to evolving situations. Models are updated/upgraded regularly (quarterly at least) to adapt to a specific system e.g. site modifications.

We are one of the only companies in the mining industry to combine leading-edge machine learning / artificial intelligence based models with physical models to create digital equipment and process models. These models are used for process predictions that learn in real time and adapts to changing conditions as such is relevant for dynamic mining environments. We use simulation based optimization enabling our continuous optimisation algorithms to be tested in a process simulator which also doubles up as a training simulator.

We use the predictive control philosophy to predict process performance and then apply both financial and technical optimisation constraints to recommend optimum set points on a continuous basis. By incorporating upstream and downstream process information, our optimiser is constantly looking for the optimum set point ranges for specific processes and equipments factoring in geometallurgical properties of materials and residence time of the materials.

We are not a controls company, our focus is completely on delivering software driven continuous optimisation for processes by incorporating upstream and downstream process information. We undertake this by modelling the entire system across the pit to port process and then using our prediction models to predict future process behaviour across the system.

Traditional process control models degrade over time, aren’t flexible to add new variables and equipments and aren’t dynamic requiring plant intervention to be calibrated while IntelliSense.io models are self learning, flexible to add new equipments and variables without requiring any process intervention.

Honeywell systems require you to upgrade to new versions of the software to get new features, also requiring hardware while IntelliSense.io follows a NO CAPEX policy requiring no upfront hardware cost and also provides new features free of charge as part of the annual subscription.

We are also control system agnostic and can work with any existing controls platform (Honeywell, ABB etc). This allows us to ensure your existing control system can be used to run the process at an optimum efficiency. We add new functionality to our software every quarter that is included as a part of our annual subscription whereas Honeywell would want to sell you new upgrades for new functionality.