Advanced process control is becoming an increasingly important technology for energy-intensive industries as manufacturers look for ways to improve productivity while reducing energy consumption and operator workload. A recent deployment at Tokuyama Corporation's Nanyo cement plant in Japan provides a practical example of how industrial automation software can improve process stability and increase the level of automatic operation.

Tokuyama has reported significant operational improvements after expanding the use of ABB Ability Expert Optimizer across multiple cement mills. The advanced process control platform has now been deployed across seven finish mills, while the plant has also used the technology for kiln optimization.

According to the latest results, the finish mills have achieved an automatic operation rate of more than 90 percent. The deployment has also helped improve grinding throughput and reduce specific power consumption while maintaining stable production conditions.

The project demonstrates how advanced automation can be applied to an established industrial process without requiring a complete replacement of the plant's existing automation infrastructure.

Cement Manufacturing Requires Continuous Process Optimization

Cement production is one of the most energy-intensive industrial processes.

Large quantities of electrical and thermal energy are required throughout the production cycle. Raw materials must be crushed and prepared, heated in kilns, cooled, and finally ground into finished cement.

Grinding is particularly important because cement mills consume significant amounts of electrical power.

The performance of a grinding mill depends on multiple variables, including feed rate, mill load, material characteristics, separator performance, power consumption, and operating conditions.

These variables interact with one another.

Changing one parameter can affect several other process conditions.

This makes cement grinding a good application for advanced process control.

Traditional control systems can maintain individual parameters within defined ranges, but advanced control technologies can analyze relationships between multiple variables and continuously optimize the overall process.

ABB Ability Expert Optimizer at the Nanyo Plant

Tokuyama's Nanyo plant is one of the largest cement manufacturing facilities in Japan.

The plant initially tested ABB Ability Expert Optimizer on one finish mill in 2024.

Following the initial results, the solution was expanded to six additional mills.

The current deployment covers four cement ball mills and two vertical mills used for pre-grinding and slag, in addition to the original installation.

This gradual expansion is important because it illustrates a practical approach to industrial digital transformation.

Instead of attempting to automate the entire production facility simultaneously, the manufacturer started with a specific process and expanded the technology after demonstrating value.

For industrial customers, this can reduce implementation risk.

More Than 90 Percent Automatic Operation

One of the most notable results from the Nanyo plant is an automatic operation rate exceeding 90 percent across the finish mills.

This does not mean that human operators have been removed from the process.

Instead, automation is taking over a greater proportion of routine process adjustments.

Operators can therefore spend more time supervising overall production conditions, handling exceptions, analyzing performance, and dealing with tasks that require human judgment.

This is an important distinction in modern industrial automation.

The goal of advanced process control is not necessarily to eliminate operators.

Instead, the objective is to allow operators to work at a higher level.

Model Predictive Control for Cement Grinding

ABB Ability Expert Optimizer uses advanced process control technologies including model predictive control.

Model predictive control works by estimating how a process is likely to behave under different operating conditions.

The system can then determine suitable control actions while considering process constraints.

In a cement mill, this can involve variables such as feed rate, mill power, material load, and throughput.

Rather than responding only after a process variable moves outside a desired range, the control system can anticipate changes and adjust operating parameters proactively.

This can help maintain a more stable process.

For cement producers, stable operation is valuable because instability can lead to lower throughput, higher energy consumption, quality variation, and additional operator intervention.

Improving Mill Throughput

The Nanyo deployment has reportedly improved grinding throughput by approximately 3 percent while maintaining stable operation.

A 3 percent improvement may appear relatively small when viewed in isolation.

However, for a large industrial facility operating continuously, even a few percentage points can translate into substantial additional production capacity.

The benefit becomes even more important when it is achieved without a proportional increase in energy consumption.

This is one of the main reasons advanced process control has become attractive to energy-intensive manufacturers.

The objective is not simply to make equipment run faster.

The objective is to find a more efficient operating point.

Reducing Specific Power Consumption

The project has also delivered an approximately 3 percent reduction in specific power consumption.

Specific power consumption measures the amount of electrical energy required to produce a defined quantity of product.

This is generally more useful than simply measuring total electricity consumption because production volume can change.

A plant could reduce total energy consumption simply by producing less material.

That would not necessarily represent an efficiency improvement.

Specific energy consumption provides a better indication of how efficiently the process operates.

The combination of higher throughput and lower specific power consumption therefore provides a stronger indication of process improvement.

Lessons for PLC and DCS Engineers

The Tokuyama project also demonstrates how advanced process control fits into a conventional automation architecture.

The PLC or DCS remains responsible for core control functions.

Field instruments continue to measure process conditions.

Drives continue to control motors.

Valves and actuators continue to manipulate the physical process.

Industrial networks continue to transport data.

The advanced process control layer operates on top of this infrastructure.

This architecture is important because it allows manufacturers to add optimization capabilities without necessarily replacing every controller in the facility.

For PLC and DCS engineers, this means the future automation system may contain several layers of intelligence.

The basic control layer handles deterministic operation.

The supervisory layer handles monitoring and coordination.

The advanced process control layer optimizes production.

Higher-level digital systems can then use the resulting data for reporting, maintenance, and business analysis.

AI and Advanced Automation in Process Industries

The cement industry is also becoming an important application area for industrial AI.

The process generates large quantities of operational data.

Temperature, pressure, vibration, motor current, material flow, mill power, feed rate, and other measurements can be analyzed to identify relationships and operating patterns.

However, industrial AI is most valuable when it is connected to actual process control.

A model that identifies a theoretical improvement has limited value if it cannot influence the production process.

Advanced process control bridges this gap.

It combines process models, industrial data, control logic, and optimization algorithms.

This makes it possible to move from data analysis toward operational action.

Reducing Manual Operator Intervention

Another benefit reported by Tokuyama is reduced operator workload.

When production processes are unstable, operators may need to make frequent manual adjustments.

This can create several problems.

Different operators may respond differently to the same situation.

Manual interventions can also increase workload during periods of high production demand.

Advanced automation can standardize many routine responses.

The control system can continuously monitor process conditions and apply predefined optimization strategies.

This creates greater consistency.

At the same time, operators remain available to respond to unusual conditions that fall outside the normal operating envelope.

Supporting More Autonomous Manufacturing

The Nanyo project illustrates a broader movement toward autonomous industrial operations.

Autonomous manufacturing does not necessarily mean a completely human-free factory.

Instead, it can mean that routine operational decisions are increasingly handled automatically while humans supervise the system and intervene when necessary.

This approach is particularly attractive for continuous-process industries.

Cement plants, chemical plants, refineries, power plants, and mineral-processing facilities often operate continuously.

Even a modest improvement in automation can produce benefits across thousands of operating hours.

Why Brownfield Automation Matters

One of the most important lessons from the project is that digital transformation can happen in an existing facility.

Many industrial plants around the world are not new.

They contain decades of investment in motors, mills, conveyors, process equipment, instrumentation, PLCs, DCS systems, and electrical infrastructure.

Replacing everything is rarely practical.

Advanced process control provides an alternative.

The manufacturer can preserve the physical plant while adding a new layer of intelligence.

This approach can make digital transformation more financially attractive.

Implications for Industrial Automation Suppliers

The project also highlights the changing role of automation suppliers.

Customers increasingly want measurable outcomes rather than simply new hardware.

Instead of asking only how fast a controller can execute a program, manufacturers may ask:

• Can the system reduce energy consumption?
• Can it increase throughput?
• Can it reduce manual intervention?
• Can it improve process stability?
• Can it support predictive maintenance?
• Can it provide reliable production data?

This changes how automation projects are evaluated.

Hardware remains essential, but software and process expertise are becoming increasingly important.

The Future of Cement Plant Automation

Cement manufacturers face continuing pressure to reduce operating costs and improve environmental performance.

Energy efficiency will remain an important part of that challenge.

Advanced process control provides one practical method for improving efficiency without requiring major changes to the physical production process.

The Tokuyama Nanyo project shows how an industrial manufacturer can start with a single application, validate the results, and then expand the solution across additional equipment.

That gradual model may become increasingly common in other energy-intensive industries.