Condition monitoring & Predictive maintenance in Iron production process

Challenge Summary

To perform predictive analysis and provide suitable maintenance measures for critical assets such as Boiler, Oven Refractory, BLT Blast Furnace, ID Fan, Conveyor Belt , hydraulic compacting system, Tilting Table and Hydraulics in order to reduce downtime and increase production.

Challenge Scenario

The iron making process involves an array of critical assets such as Oven refractory used for making coke, bell less top blast furnace used for melting Iron , boilers used in steam production which is used for heating and cooling purposes induced draft fan, conveyor belts for transportation of raw materials, finished products to respective destinations, hydraulic compacting system to make iron with different grades, tilt table for precasting of parts, induced draft fans for filtering and releasing flue gas in to atmosphere. All these equipments used for the process are critical to each steps and breakdown in any of the equipment severely affects the production of Iron and its related products. The current process involved in monitoring such equipment is condition based monitoring and time based maintenance of the equipment, during this process the operators translate OEM guidelines into a check-list. The data and anomalies gathered during the inspection and regular maintenance are utilised for historical analysis and correlations, the decisions made are based on individual judgement who have limited knowledge on site subject matter expertise leading to productivity loss and higher Operating and maintenance costs.

Profile of the End-User

Iron Manufacturing Process Manager

Existing method : Condition based monitoring and online data of equipment parameters are available viaMES systems. but the long term analytics of outcomes and conditions is very manual and need based.

Gaps No continuous monitoring of equipment, no holistic problem solving approach, limited knowledge and expertise on site subject matter.

Functional Requirements of the End-User

  • Analytics to show what conditions and what time the equipment is tending to failure.
  • prescriptive maintenance measures from analysed data.
  • holistic approach to maintenance scheduling.

Functional & Operational Capabilities

  • Equipment failure prediction.
  • Analysis of all equipment in the process of iron production.
  • Effective maintenance measure suggestion of each equipment and maintenance scheduling.
  • Constant alerts to the users on condition of the equipments / process.
  • Dashboard to visualise the gathered data on equipments and process.

Operational constraints

  • Importing all data from legacy systems
  • Integration of the system with MES system for data accessibility
  • Sensor and data transmission infrastructure to collect data from each equipment and related parameters.

Expected Tangible Benefits and Measurable Gains

  • Real time Data driven decision making.
  • Increased process efficiency and equipment service life.
  • Zero downtime of machinery and equipment failure.

Performance Metrics or Outcomes

  • Reduction of Boiler Downtime - Existing 200 Hrs ( Total Loss ~ INR 1-2 Cr)
  • Additional production of coke by 3000 to 4000 tonnes (Increase availability from 93% - 98% )
  • Increase in production by at least 12000 tons
  • Reduction of sinter by at least 6000 tons
  • Productivity improvement by at least 1 %.