Shivaan Asset Management

Manufacturing

Turning Frontline Expertise into Stable Ball-Mill Performance

Shivaan Asset Management combined operating data, field observations and frontline expertise to improve grinding-circuit stability and retain critical mill knowledge.

In brief

  • The client wanted better use of installed grinding capacity without compromising product quality, equipment integrity or operating safety.
  • Shivaan Asset Management connected operating data, field observations and frontline expertise, then tested the relationships against physical internal inspection.
  • A planned outage and fresh grinding-media charge created a known baseline for regular trend review and decision-rule development.
  • Within the following three months, operators reported fewer rejects and steadier mill operation, while recharge-planning confidence improved and key-person dependency reduced.

Project overview

The client wanted to make better use of installed grinding capacity without compromising product quality, equipment integrity or operating safety. Shivaan Asset Management approached the engagement as a whole-system performance review rather than a narrow equipment study.

The review examined where scheduled production time and grinding rate were being lost, compared stable and unstable operating periods, walked the circuit with operators and trades, and tested whether formal procedures reflected how the equipment actually behaved.

One experienced fitter demonstrated a highly developed ability to recognise changes in the mill through its acoustic behaviour. He did not rely on sound alone. He listened for movement away from a known baseline, checked operating context with the control room, and observed how the circuit responded to controlled changes.

The client challenge

Installed capacity was available, but stable and repeatable use of it was not. The mill could hold a strong rate under some conditions, yet require intervention, produce higher rejects or become less predictable under others.

  • Where was productive grinding time being lost?
  • Which losses were mechanical, process-related or caused by upstream and downstream constraints?
  • Which combinations of signals provided the earliest dependable warning of drift or instability?
  • Were operating responses consistent across shifts?
  • Could the plant improve utilisation using existing assets before considering capital expenditure?
  • What critical knowledge would leave when experienced personnel retired?

A closed-circuit cement ball mill is not controlled by one variable. Feed rate, material characteristics, grinding-media condition, motor power, ventilation, outlet temperature, grinding-aid dose, separator performance, reject return and downstream storage conditions interact.

Utilisation loss path from scheduled production time through available, running and effective-rate time to productive time.
Utilisation was defined through time, sustainable rate and saleable output, not running status alone. On smaller screens, swipe horizontally to view the full diagram.

How we investigated the circuit

The investigation used a simple evidence path that operating, maintenance, process and management teams could all follow.

  1. Defined the system boundary from incoming clinker and additives through the mill, separator, reject return, finished-product transport and storage.
  2. Built a time-and-rate loss baseline from available production records, trends, operator logs, trip records and maintenance history.
  3. Compared stable and unstable operating windows and treated process parameters as interacting relationships rather than isolated tags.
  4. Engaged operators, mechanical fitters, electricians, process personnel, planners and controls support as part of the analysis.
  5. Tested working hypotheses against operating trends, controlled responses and physical condition.
  6. Converted supported relationships into visible decision aids, operating guidance and governed controls work.

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The field discovery

Two industrial professionals in full PPE safely reviewing a horizontal ball mill from a guarded access platform.
Illustrative image. Field engagement revealed a disciplined decision process that was effective but largely undocumented.

During the interviews, one experienced fitter indicated that the mill was already communicating much of what the team wanted to understand. At a safe observation point beside the operating equipment, he asked a Shivaan Asset Management consultant to listen.

The fitter recognised a change from an internal baseline developed through years of exposure. He then asked the control room for the operating information that gave the sound context, including outlet temperature, motor power and the current circuit condition.

What appeared at first to be intuition was a disciplined, experience-built decision process. Its weakness was not the quality of the judgement. It was that the process existed almost entirely in one person's memory.

From tacit knowledge to testable engineering knowledge

Shivaan Asset Management treated the fitter's judgement as a high-value engineering hypothesis rather than a conclusion. The next planned outage and media recharge created a useful calibration point.

  1. Recorded the condition of liners, diaphragm, media and other relevant internal components during the planned outage.
  2. Established the acoustic and operating baseline associated with the fresh media charge.
  3. Captured relevant startup and steady-state parameters, including feed, power, temperature, grinding-aid rate, separator condition and rejects where available.
  4. Compared what the fitter heard, what operators observed, what historian trends showed and what had changed in product or operating conditions.
  5. Translated repeatable relationships into simple graphs, operating bands and decision rules.
  6. Continued monthly internal inspections and compared physical measurements with the operating trends.

The multi-signal decision model

The project established a practical distinction between a signal and a diagnosis. The expertise was in understanding how several signals moved together, what had changed beforehand and what physical condition was later confirmed.

Multi-signal model combining acoustic change, operating context, recent process change and physical inspection before a governed decision.
No input was given automatic or equal weight. Physical inspection remained the verification point. On smaller screens, swipe horizontally to view the full diagram.

How the main signals were interpreted

Acoustic response
A change could indicate movement away from the known baseline, but required operating context to interpret what had changed.
Power and temperature
These signals provided mechanical and process context and were compared with feed, material condition and recent operating changes.
Separator rejects and feed response
Reject behaviour and the response to controlled feed or dosing changes helped distinguish temporary conditions from developing limitations.
Physical inspection
Internal inspection remained the verification point for media, liner, diaphragm and other relevant condition.

A planning window and a whole-plant response

P-F-style deterioration and planning map

The trends were used as a P-F-style decision map. Detectable drift meant a repeatable move away from the known-good relationship. The functional limit was the site-defined point at which throughput, product quality, process stability or wear would no longer remain acceptable.

The mill was a system constraint, not an isolated asset

A local mill decision could shift a bottleneck, increase risk or create idle time elsewhere. The response therefore considered the bucket elevator and reject-return path, fresh feed, storage buffers, silo filling, dispatch and relevant upstream conditions.

  • Protect the mill circuit and associated handling equipment.
  • Use available buffers before shifting disruption upstream.
  • Coordinate grinding, storage, loading and dispatch constraints.
  • Keep product quality and approved operating limits visible.

From governed action to retained capability

Only observed and tested relationships moved into shared guidance or controls work. Site implementation still required locally engineered setpoints, controls review, risk assessment, management of change, operator override provisions and commissioning.

  1. Capture the pattern the experienced person recognised.
  2. Define the signals, operating context, persistence, limits, safeguards and ownership.
  3. Test the response under supervision across suitable operating conditions.
  4. Implement approved guidance or control changes through the site's governance and commissioning process.

Outcomes reported within the following three months

  • Operators reported fewer rejects and steadier mill operation.
  • Maintenance and process teams gained greater confidence in media recharge and wear planning.
  • Dependence on ad hoc individual intervention reduced.
  • Operating trends and internal wear observations were more clearly connected.
  • Operators, maintenance and process personnel shared one decision model.
  • Monthly internal inspections continued as the physical verification point.

Value delivered

  • A stronger basis to pursue better use of installed capacity before new capital.
  • Lower operational and decision variability.
  • Improved maintenance and recharge planning.
  • Reduced key-person risk.
  • Stronger cross-functional operating capability.
  • A repeatable method for other constrained asset systems.

The outcomes are presented qualitatively to protect client confidentiality and avoid overstating precision where historical records were not structured for formal before-and-after attribution.

Frequently asked questions

What problem was the cement manufacturer trying to solve?

The client wanted to improve effective utilisation of an operating closed-circuit ball mill by understanding downtime, low-rate operation, reject behaviour and decision variability, without compromising product quality, equipment integrity or safe operating limits.

Was mill sound used as the complete diagnosis?

No. Acoustic change was an initial cue that the mill had moved away from a known baseline. It was interpreted with motor power, outlet temperature, feed condition, separator response, recent operating changes and continued physical inspection.

How was frontline knowledge converted into a repeatable method?

Shivaan Asset Management captured the experienced fitter's decision logic, established a post-recharge baseline, compared observations with operating trends, built visible decision aids and continued checking the relationships against internal inspections.

What did the P-F-style planning map do?

It provided a governed early-warning and planning window between detectable drift from the known-good relationship and a site-defined functional limit. It was not presented as a precise remaining-life prediction.

What outcomes were reported?

Within the following three months, operators reported fewer rejects and steadier mill operation. Maintenance and process teams gained greater confidence in media recharge and wear planning, and the plant reduced dependence on one person's undocumented knowledge.

Why are the outcomes presented qualitatively?

The outcomes are presented qualitatively to protect client confidentiality and avoid overstating precision where historical records were not structured for formal before-and-after attribution.

Related projects

Your plant may already know more than its systems show

Experienced operators and trades often recognise changes before they appear in formal reporting. Shivaan Asset Management helps capture, test and embed that knowledge so decisions become repeatable, governable and less dependent on one person.