Coal Washery Digitization

Cost Optimization and Improved Productivity


One of the largest mining company in India wanted to implement Industrial IoT across one of their sites. The use case was focused on digitizing coal washery so as to measure the real-time performance of assets. With existing layers of automation, the stakeholders were unable to perform the granular analytics of the assets in quick manner and below were the challenges faced:
1) Earlier data is being pushed to local historian with the limited space, data is manually downloaded to excel and reports are generated.
2) No centralized reporting platform. Existing reports lacked analytical capabilities.
3) Asset stoppage reason codes aren’t captured
4) Proactive critical alerts to respective stakeholders , especially that of Thickener Torque, which avoids the plant downtime


Carbynetech conducted assessment of use-case and could quickly draw out the features, user stories and wire frames with the manufacturing experience of similar domain companies. Our engineering services and technology team studied the automation level at coal washery and configured OPC software for data acquisition. The data was acquired from existing PLC and sensor system and tags are mapped in the OPC. The Big Data Platform chosen was Splunk Enterprise because of the lower operating cost. Technically, the same solution can be deployed on Microsoft Azure, AWS as well. The solution covered below modules.

  • Integrated Plant Performance Dashboards
  • Loss Classification Analytics
  • Digital MIS Reports

Asset availability

increased by

6 – 8%

which resulted in

soft and hard


Data insights helped

to identify the hidden

opportunities -Idle

running of assets

without load

Mobile On the

Go – Analytics

- Ease of

data access

An immediate improvement in asset performance, real-time asset visibility and feature rich analytics which provided the washery operations team to take proactive measures at the right time. It also resulted in significant cost saving in terms of idle energy consumption and machine stoppages.



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