The best cement-mill optimization system is the one that fits your installed grinding circuit, available instrumentation, operating modes and quality constraints—not the one with the longest feature list. Compare proposals in that order: configuration coverage, control method, response to disturbances, integration effort, lifecycle support and a site-specific acceptance test.
Define what “optimization” must deliver at your plant
A cement mill process optimization system is an advanced control layer that adjusts operating targets and manipulated variables so the circuit can produce the required cement at the lowest practical energy and stability cost. It should coordinate the mill, separator, fans, feed system, water or grinding-aid additions and quality constraints rather than display trends on a dashboard.
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Write the business objectives in measurable terms before inviting bids. Typical objectives include higher tonnes per hour, lower specific power consumption, tighter product fineness, fewer mill trips, faster product changes, steadier outlet temperature and less operator intervention. Set priorities explicitly: a plant that must protect cement quality during frequent product changes may value constraint handling and stability more than maximum instantaneous throughput.
- Production: tonnes per hour by cement grade, with the permitted feed and quality ranges.
- Energy: kWh per tonne, and which meters are accepted for the calculation.
- Quality: fineness, residue, Blaine or other laboratory and online limits, including sampling lag.
- Reliability: trips, vibration events, blocked chutes, high-temperature alarms and recovery time.
- Operations: startup, shutdown, grade changes, reduced-rate running and manual override requirements.
Do not accept “more efficient” as an outcome. Require each bidder to state the baseline, operating envelope, measurement period, control-use rate and acceptance calculation that will support its claim.
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Start with the exact grinding configuration
Configuration fit is the first screening test. A controller designed around one mill and separator arrangement may need different models, measurements and tuning for another. Ask the supplier to draw the control boundary on your process-flow diagram and identify every equipment item and product grade included.
Ball mills
For a ball mill, the relevant interactions can include feed rate, mill filling or load indication, separator speed, ventilation, outlet temperature, grinding-aid flow and the circulating load. The proposal should explain how it deals with the time delay between a feed change and a laboratory fineness result, and how it prevents a throughput push from causing high mill differential pressure, vibration or off-specification cement.
Vertical roller mills
VRMs have different material and pressure dynamics. FLS describes VRM operating conditions as capable of changing faster than those of a ball mill and presents predictive setpoint updates for that environment. Ask for a model or operating philosophy developed for your VRM, not an assurance that a ball-mill application transfers unchanged.
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Roller-press and hybrid circuits
Combined roller-press/ball-mill circuits have additional interactions between press loading, separator operation, recirculation and finish-mill feed. KIMA describes MILLPILOT coverage for combined roller-press/ball-mill systems. Require the bidder to state whether the controller optimizes the complete circuit or only the finish mill.
Raw, coal and finished-cement grinding
ABB describes Expert Optimizer applications for coal, raw-material and finished-cement grinding, as well as ball-mill, vertical-roller-mill and hybrid roller-press/ball-mill configurations. Confirm which of those applications are included in your project, because a supplier’s broad portfolio does not prove that every module is configured, licensed or supported at your site.
| Configuration question | Evidence to request |
|---|---|
| Mill and separator arrangement | A marked-up process-flow diagram, equipment list and control boundary for the installed circuit. |
| Products in scope | A grade-by-grade matrix showing recipes, fineness limits, additives and permitted operating ranges. |
| Operating modes | Sequences for startup, normal operation, grade change, reduced-rate operation, shutdown and recovery after a trip. |
| Constraints | Explicit limits for vibration, pressure, temperature, motor power, feed rate, emissions and quality. |
| Excluded equipment | A list of upstream or downstream assets that operators must continue to control manually. |
Compare the control approach, not just the acronym
Suppliers use different terms for their control technology. The useful question is what the method does with your process signals, delays and constraints.
Model-predictive and multi-input control
FLS describes a multi-input, multi-output approach for ball mills, using self-adaptive control and model-predictive control (MPC) to address delays and interactions. ABB describes a combination of linear and nonlinear MPC, fuzzy logic, neural networks and analytical AI in Expert Optimizer. Ask the bidder which elements will actually be deployed in your application, which model parameters are adapted online and what happens when a key measurement is unavailable.
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A credible MPC proposal should show the controlled variables, manipulated variables, prediction horizon, constraint priorities and handling of delayed laboratory results. It should also explain how operators can view predicted values, active constraints and the reason for a setpoint move.
Rule-based expert control
KIMA describes MILLPILOT as a rule-based expert system for ball mills, VRMs and combined roller-press/ball-mill systems. Rule-based control can be transparent to operators when rules are well documented. Ask how rules are maintained when raw materials, recipes or equipment change, how conflicting rules are resolved and how the system behaves outside the conditions covered by its rules.
Variables that must appear in the control narrative
ABB lists load and throughput optimization, fineness and temperature control, mill-startup handling and automatic product-type switching among its grinding functions. Use that list as a prompt, not as proof that the same functions will be enabled in your project. Require a signal-to-function map for every proposed feature.
| Variable category | Examples to map | Bidder must explain |
|---|---|---|
| Controlled outcomes | Throughput, fineness, outlet temperature, vibration, differential pressure, product quality | Target, allowable band, measurement source and priority when objectives conflict |
| Manipulated variables | Mill feed, separator speed, fan damper or speed, water, grinding aid, press load | Range, rate limit, interlock and operator ownership |
| Disturbances | Clinker grindability, moisture, additive ratio, feed interruptions, ambient conditions | Detection method, adaptation time and safe response |
| Quality feedback | Laboratory fineness, Blaine, residue or online analyzer values | Sample timing, validation, bad-data treatment and action during analyzer failure |
Test the system against real mill dynamics
Ask bidders to describe responses to the events that cause production losses in your plant. A normal steady-state demonstration is not enough.
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- Product switch: How are new targets, constraints and recipes loaded, and can the sequence be aborted safely?
- Startup: Which steps are automated, which permissives remain in the DCS and how is unstable operation prevented?
- Instrument fault: Does the system freeze the last valid target, fall back to a local loop or hand control to the operator?
- Trip and recovery: What is retained after a mill trip, and how are controller states reinitialized?
- Manual intervention: Can an operator temporarily bias a setpoint, and is the reason and duration recorded?
Require trend examples using your own historical disturbances where possible. If a supplier cannot run a plant-data assessment, make that limitation explicit in the proposal and reduce the weight assigned to unverified performance claims.
Audit DCS, PLC and instrumentation integration
Product descriptions do not establish compatibility with a particular plant. Obtain a project-specific interface design covering the DCS, PLCs, historians, laboratory systems, analyzers, cybersecurity zones and remote-access policy.
Build a tag and data-quality inventory
- Export the candidate process tags from the DCS and historian, including engineering units, scan rates, alarms and quality flags.
- Mark each tag as measured, calculated, manually entered or unavailable. Record sensor range, calibration status and known bias.
- Identify the minimum signals required for automatic control and the signals needed only for monitoring or reporting.
- Check timestamp alignment between fast process tags and slow laboratory results. Document sample collection, transport and result-entry delays.
- List permissives, trips and hard interlocks that the optimization layer may not bypass.
Ask who supplies any new transmitters, analyzers, load measurements or KIMA SMARTFILL measurement products proposed for the project. Evaluate each addition against existing instrumentation accuracy, maintenance capability, hazardous-area requirements and the plant’s calibration program.
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Clarify cybersecurity and ownership
The contract should identify network zones, approved protocols, patch responsibilities, account management, remote-support approval and data retention. It should also state who owns the control application, model files, rule sets, historian data and engineering documentation at handover.
Compare implementation and lifecycle support
Advanced control performance depends heavily on commissioning quality and ongoing maintenance. ABB describes a plant fingerprint or audit and roadmap before APC implementation, and its product material refers to lifecycle support. Ask every bidder to provide an equivalent work breakdown rather than comparing software licenses alone.
| Phase | Questions to put in the proposal |
|---|---|
| Audit and design | Who surveys the circuit, validates data, defines the control objectives and produces the functional design? |
| Model or rule development | How many operating conditions and cement grades are covered? What plant data and operator knowledge are required? |
| Installation | What downtime, test equipment, network changes and instrumentation work are required? |
| Commissioning | Who tunes the controller, verifies interlocks, trains operators and records open issues? |
| Stabilization | How long is assisted operation provided, and what response time applies to controller alarms? |
| Lifecycle | What software updates, retuning, remote monitoring, cybersecurity maintenance and on-site visits are included? |
Include staffing assumptions. A system may require a process engineer to review constraints, an instrumentation technician to maintain measurements and an automation engineer to manage DCS changes. If those resources are not included, price and schedule them separately rather than treating them as free plant capacity.
Demand a site-specific baseline and acceptance test
Vendor case studies are useful for identifying what to investigate, but they are not guarantees. ABB reports that Tokuyama first tested Expert Optimizer on a finish mill in 2024 and then extended deployment to six more mills—four cement ball mills and two vertical mills. ABB reports 3% higher grinding throughput and 3% lower specific power consumption while operations remained stable. That is a vendor-reported result from one customer deployment, not an independently normalized cross-vendor benchmark.
ABB also markets approximately 6% less energy for mill optimization. The retrieved product material does not establish a publication year, methodology or comparable baseline for that figure, so do not use it as a guaranteed saving in a business case.
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- Freeze the baseline definition. Specify the mill, cement grades, clinker and additive ranges, production constraints, energy meters, quality tests and excluded periods.
- Collect enough operating history. Include representative steady operation, product changes, disturbances and maintenance exclusions. State the date range and data completeness.
- Define like-for-like comparisons. Compare the same products and quality limits. Separate gains caused by recipe changes, equipment maintenance or raw-material changes from gains attributable to the controller.
- Set control-utilization rules. Record the percentage of eligible hours when the controller is in automatic operation, in manual mode or inhibited by bad data.
- Specify energy accounting. Define whether auxiliaries, fans, separators, presses and material-handling equipment are included in kWh per tonne.
- Write acceptance criteria before commissioning. Include throughput, specific power, quality compliance, stability, availability and data-validity thresholds.
- Agree the test and recovery plan. State test duration, exclusions, statistical treatment, operator sign-off and what happens if the test is interrupted.
Require guaranteed outcomes and illustrative case-study outcomes to appear in separate sections of the proposal. A supplier should not be allowed to substitute a favorable historical example for a contractually defined acceptance test.
Use a transparent proposal scorecard
A weighted score prevents a polished demonstration from outweighing a poor configuration fit. Adjust the weights to your risk profile, but publish the scoring rules to every bidder.
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| Suggested category | What earns a high score |
|---|---|
| Configuration fit | Direct evidence for the exact mill, separator, hybrid arrangement, recipes and operating modes. |
| Control capability | Clear variable map, constraint strategy, disturbance response and explainable operator workflow. |
| Integration readiness | Validated tag list, interface design, cybersecurity plan and realistic instrumentation scope. |
| Measured evidence | Comparable baseline, transparent measurement method, control-use rate and acceptance proposal. |
| Implementation risk | Detailed schedule, outage requirements, commissioning staffing and fallback plan. |
| Lifecycle support | Defined training, response times, retuning, software maintenance and performance monitoring. |
| Commercial clarity | Separated license, engineering, hardware, travel, support, renewal and optional-cost lines. |
Score evidence, not adjectives. “AI-powered,” “self-learning” or “proven” should receive no credit until the bidder explains the mechanism, required data, operating limits and plant references relevant to your circuit.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the named supplier descriptions establish—and what they do not
ABB Ability Expert Optimizer
ABB describes Expert Optimizer as an advanced process-control platform using linear and nonlinear MPC, fuzzy logic, neural networks and analytical AI. For grinding, its material lists load and throughput optimization, fineness and temperature control, startup handling and automatic product-type switching, with strategies tailored to different circuit configurations.
For procurement, ask ABB to identify which control technologies are in the proposed application, which functions are licensed, what measurements are required and how performance will be accepted. The Tokuyama figures above should be treated as a site-specific case result.
FLS ECS/ProcessExpert
FLS describes ECS/ProcessExpert for ball mills as a multi-input, multi-output system using self-adaptive control and MPC to address coupled variables and process delays. Its VRM application material describes predictive setpoint updates based on differences between predicted and measured values.
Ask for separate evidence for your mill type, because the ball-mill and VRM descriptions address different dynamics. The product descriptions are supplier statements, not a comparative test against other systems.
KIMA MILLPILOT
KIMA describes MILLPILOT as a rule-based expert controller for ball mills, VRMs and combined roller-press/ball-mill systems. KIMA also lists SMARTFILL measurement products. If additional measurement hardware is proposed, compare its accuracy, installation requirements, maintenance burden and diagnostic coverage with the instruments already installed.
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Account for cost without inventing a price comparison
Public material reviewed for these systems does not establish a normalized price comparison. Request a five-year total-cost schedule with the same assumptions from every bidder.
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- Application license and initial configuration.
- Engineering, process audit, model or rule development and DCS integration.
- New sensors, analyzers, networking, cabinets and installation.
- Commissioning travel, outage time, operator training and documentation.
- Annual support, software updates, remote monitoring and retuning.
- Optional modules, extra cement grades, additional mills and future expansion.
- Plant labor, calibration, cybersecurity work and replacement hardware.
Separate one-time capital costs from recurring costs and state the currency, tax treatment, escalation and renewal assumptions. A lower license price can be more expensive if it requires extensive instrumentation or leaves the plant responsible for specialist tuning.
Common comparison mistakes and how to correct them
Choosing by feature count
Problem: A long list of algorithms is treated as proof of suitability.
Correction: Require a signal-to-function map, constraint strategy and demonstration using your operating data.
Comparing different products or operating conditions
Problem: One bidder reports a high-rate cement while another uses a different recipe or quality limit.
Correction: Freeze the product mix, quality limits, energy boundary and exclusion rules before the test.
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Ignoring measurement quality
Problem: The controller is blamed when a biased pressure transmitter or late laboratory result drives poor decisions.
Correction: Complete calibration, timestamp and bad-data checks before tuning, and include sensor maintenance in the lifecycle plan.
Automating around interlocks
Problem: The optimization layer is expected to override trips or permissives.
Correction: Keep safety and equipment-protection logic in the appropriate PLC or DCS layer; document every handoff and fallback state.
Stopping after commissioning
Problem: The project is declared successful when the controller first reaches automatic mode.
Correction: Schedule a stabilization period, review controller utilization and constraints, and perform the agreed performance test after operators are trained.
Procurement checklist for the final request for proposal
- Attach the current process-flow diagram, equipment list and cement-grade matrix.
- Provide historical trends for feed, power, separator speed, fan operation, temperature, vibration, pressure, quality and trips.
- List every required and optional measurement, with accuracy and calibration expectations.
- Require a written description of controlled, manipulated, disturbance and fallback variables.
- Ask for mill-specific references and identify whether each result is vendor-reported or independently verified.
- Define startup, product switching, manual override, bad-data and trip-recovery behavior.
- Specify DCS/PLC interfaces, network zones, cybersecurity responsibilities and remote-access rules.
- Require an implementation schedule, outage plan, training plan and named support roles.
- Separate guaranteed performance from illustrative case-study results.
- Write the baseline, test duration, data-validity rules, energy boundary and acceptance criteria into the contract.
- Obtain a five-year total-cost schedule with renewal and optional-scope assumptions.
- Make final payment or performance retention depend on the agreed acceptance test, not on software installation alone.
Make the final decision
Shortlist systems that demonstrably cover your mill and circuit, then compare how each one handles your actual variables, delays, disturbances and operator practices. Give the greatest weight to a credible plant-data baseline, integration design and lifecycle support. The available supplier descriptions identify relevant capabilities from ABB, FLS and KIMA, but they do not establish an independent, normalized head-to-head winner. Your acceptance test and operating data must decide which proposal creates value at your plant.
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