by George Obeng Akrofi, N.A.M. Kusi Fordjour and Dirk Maier

KANSAS CITY, MISSOUR, US — Hammermilling is one of the most energy-intensive operations in feed manufacturing, yet it remains one of the least systematically evaluated. In many feed mills, performance is judged by using simple indicators such as power consumption or average particle size. While useful, these metrics alone often fail to capture how effectively and efficiently the grinding system is operating.

At the Iowa State University (ISU) Kent Feed Mill and Grain Science Complex (FMGSC), efforts are underway to move beyond isolated metrics toward a structured, criteria-based approach for evaluating hammermill performance. This article presents a practical framework grounded in both controlled research and commercial-scale validation.

Current evaluation methods fall short

In commercial feed mills, hammermills rarely operate under constant conditions. Equipment is shared across different products/formulations, throughput fluctuates, and process interruptions are common. As a result, power and energy data often are interpreted without a clear definition of steady-state operation.

This leads to two major issues:

Energy consumption is frequently misrepresented due to the inclusion of transient conditions. 

Particle size data varies depending on sampling (frequency and timing) and analytical methods. 

Without a consistent evaluation approach, benchmarking hammermill performance across time, batches, or facilities becomes unreliable. 

Figure 1 illustrates this challenge in a commercial feed mill where a single hammermill is used to grind multiple ingredients, including corn, soybean meal and roasted full-fat soybeans. The power signal reflects continuous operation across these products, but without clear identification of when each ingredient is being processed or when steady-state conditions are achieved, it becomes difficult to isolate meaningful performance data.

In this scenario, one of the primary limitations in evaluating hammermill performance is the inability to distinguish the specific time intervals and product streams of interest during operation. As a result, energy use and grinding performance may be misinterpreted if data are analyzed without proper segmentation.

Credit: ©IOWA STATE UNIVERSITY

What are we proposing?

To move beyond inconsistent and often misleading metrics, a standardized methodology that reflects actual operating conditions and produces repeatable, comparable results is needed.

At the ISU Kent FMGSC, a criteria-based approach has been developed to systematically assess hammermill performance under commercial conditions. This approach focuses on isolating stable operating periods, aligning energy use with production rate, and ensuring consistently accurate measurement of particle size.

The following steps outline the practical framework that can be applied across feed mills to improve the accuracy and consistency of grinding performance evaluation.

Step 1: Define and isolate steady-state operations

At FMGSC, hammermill performance evaluation begins with clearly defining steady-state operation using measurable criteria. A steady-state interval is identified based on:

• A minimum continuous runtime ( ≥30 minutes).
• Stable feeder operation at the intended setpoint. 
• Continuous operation above no-load conditions. 
• Low variability in power demand, which is mill dependent (e.g., a coefficient of variation ≤5% has been set for our milling operation). 

Data collected during startup, feeder ramp-up, shutdown, or process disturbances as illustrated in Figure 2 are excluded. A key takeaway from this step is that without isolating steady-state conditions, energy metrics can be distorted by transient effects, leading to incorrect conclusions about mill efficiency.

Step 2: Align energy use with throughput

Once steady-state operation is established, energy use must be evaluated relative to production rate. At the ISU FMGSC and in commercial validation trials, hammermills operating under different throughput conditions showed similar absolute power demand (approximately 137-176 kW). However, when normalized:

• At 41.5 tons/hour → 3.30 kWh/ton. 
• At 19.9 tons/hour → 8.97 kWh/ton.

This nearly threefold increase in energy consumption (and associated costs per ton) was not due to equipment inefficiency but reduced system utilization by the operator. 

A key takeaway from this step is that hammermill efficiency is strongly governed by throughput, not just power draw.

Step 3: Collect representative samples and standardize particle size measurement

Once steady-state operation has been clearly identified and confirmed, it is appropriate to collect ground feed samples for particle-size analysis. Sampling outside of steady-state conditions, such as during startup, shutdown, or process disturbances, can introduce variability that does not reflect true grinding performance. At the ISU Kent FMGSC, samples are collected exclusively during steady-state intervals to ensure they are representative of normal operating conditions. This approach aligns sampling with stable system performance, improving the reliability of particle size data.

Particle size average and distribution are key performance indicators of the grinding process, influencing pelleting performance, nutrient availability and feed quality. However, the method used to measure particle size can introduce significant variability.

To address this, we follow the standardized procedure outlined in ANSI/ASAE S319.4, including:

• Sieve agitation using balls and brushes.
• Use of a flow agent to prevent small particles from agglomerating on larger sieves.

In comparative testing with another commercial mill that did not follow the official method, identical samples produced substantially different and misleading particle size average and distribution results:

• Standardized method: 319 µm average and 2.66 µm distribution.
• Non-standard method: 728 µm average and 1.56 µm distribution.

This discrepancy is not due to grinding performance but due to analytical bias caused by particle agglomeration and incomplete separation of finer particles. In this case, the specific energy of the 250-hp hammermill measured during steady-state conditions was 3.3 kWh/ton, achieving an average particle size reduction to 319 µm, and not 728 µm as shown in Figure 3. If the grinding goal is indeed 700 to 750 µm, the hammermill could be slowed to 70% to 80%, screen sizes could be increased, hammer pattern could be changed, or hammers could be moved from the fine to the coarse position, which will result in more control over finished product and reduced specific energy consumption.

A key takeaway from this step is that accurate particle size evaluation requires both sampling during steady-state operation and use of the standard method.

The work at the ISU Kent FMGSC demonstrates that evaluating hammermill performance is not simply about collecting more data, but it is about collecting the right data under the right conditions.

By isolating steady-state operation, normalizing energy use to throughput, and standardizing particle-size measurement, feed mills can establish a reliable, repeatable baseline for performance.

Credit: ©IOWA STATE UNIVERSITY

From measurement to optimization

Accurately evaluating hammermill performance is an essential first step. However, grinding does not operate independently within a feed mill. Thus, optimizing it in isolation can lead to unintended consequences across the production system.

At our facility, we are moving toward a system that assesses grinding performance within the context of the full manufacturing process, particularly its impact on pelleting and overall plant efficiency. Key considerations include:

• Conditioning and moisture control.
• Pellet durability and fines generation.
• Pellet mill throughput and operational stability.
• Total energy use across grinding, conditioning, pelleting and cooling.

For example, producing a finer particle size may improve certain aspects of feed uniformity, but it often comes at the cost of higher grinding energy, reduced pellet mill throughput, and increased fines. Conversely, a slightly coarser grind may reduce grinding energy while maintaining acceptable pellet quality and improving overall system efficiency.

By linking grinding data with downstream outcomes, feed mills can move beyond measurement toward process optimization. This approach allows operators to make informed decisions that balance energy use, product quality and throughput, rather than over-optimizing a single unit operation. In practice, it changes grinding from a standalone unit operation to an integrated component of a data-driven feed manufacturing system.

Looking ahead

Establishing a systematic evaluation framework is only the first step. True optimization requires understanding how key operating variables influence grinding performance.

Among these variables, motor speed remains one of the most underutilized and least understood control levers in feed manufacturing. 

Part two of this series will examine how motor rotational speed affects particle size, energy efficiency, and overall hammer mill performance.

George Obeng-Akrofi is research manager at Iowa State University’s Kent Feed Mill and Grain Science Complex. He may be reached at georgeo@iastate.edu.