The pet food industry is highly competitive and regulated, and consumer trust is paramount. Delivering a consistently high-quality, nutritious and safe product is the primary objective of any manufacturer.
This goal is complicated, however, by inherent variability in both raw materials and process conditions, which can lead to inconsistencies in final product attributes such as texture, density, nutritional content, and shelf life. Traditional product control methods often rely on manual sampling and post-production analysis, which are reactive rather than proactive, resulting in off-specification products, increased waste and reduced operational efficiency.
Using a digital twin to simulate the conditions of the production process provides a powerful solution to these challenges, enabling a more robust, predictive and efficient manufacturing process.
The Challenge of Variability
Pet food manufacturing, particularly the production of extruded kibble, is a complex process. Key unit operations, including mixing, extrusion and drying, are highly sensitive to the properties of incoming ingredients. Raw materials like grains, meals and fats are biological products, and their characteristics can fluctuate based on factors, including:
- Moisture Content: Variations affect the energy required for extrusion and the duration needed for drying. Incorrect settings can lead to under-dried product (risk of mold) or over-dried product (nutrient degradation and brittleness).
- Protein and Fat Content: These variations affect the physical properties of the mixture during extrusion, influencing expansion, shape and density.
- Particle Size Distribution: Inconsistent particle size in ground ingredients can lead to non-uniform mixing and difficult starch gelatinization, which affects the extrusion process by causing product characteristics variability.
- Process Settings: Fluctuations in process delivery inputs like water, steam, oils and meat slurries affect product quality and nutrition and make it difficult to achieve the lethality requirements.
Without a mechanism to dynamically compensate for these fluctuations, a plant is forced to operate with a static “one-size-fits-all” approach, often leading to a wider range of final product quality or the need for costly manual interventions. That mechanism is a digital twin.
The Digital Twin: A Process Engineering Solution
A digital twin is a dynamic, virtual replica of a physical asset, process, or system. From a process engineering standpoint, a pet food manufacturing digital twin is built on a detailed process simulation model that reflects the thermodynamics, kinetics and fluid dynamics of each unit operation.
The key to unlocking this model’s full potential is its connection to the physical world. Real-time data from a network of sensors on the factory floor, measuring parameters such as the moisture content of incoming ingredients, extruder barrel temperature, screw speed, pressure, and dryer airflow, is continuously fed into the digital twin. Based on data, the virtual model synchronizes with the real-world process, creating a living, breathing simulation of the plant’s current state.
The digital twin’s true value lies in its ability to enable integrated, closed-loop control. This is a paradigm shift from traditional reactive control to a proactive, predictive system.
- Incoming Ingredient Analysis: As a new batch of raw material is introduced to the process, smart sensors (e.g., Near-Infrared Spectrometers) instantly measure and report its critical properties, such as protein, moisture, fat and fiber content.
- Predictive Modeling: This real-time data is sent to the digital twin. The simulation model then runs a predictive analysis to determine how these new ingredient properties will affect the final product’s quality if the current process parameters remain unchanged.
- Automated Parameter Adjustment: Based on this prediction, the digital twin’s control system automatically calculates the optimal equipment settings required to compensate for the ingredient variation. For instance, if a high-moisture ingredient is detected, the system may automatically decrease the extruder water and/or steam level, increase the dryer temperature or reduce the conveyor speed to ensure the final product hits the target moisture specification. This adjustment happens in real time, often before the ingredient even reaches the extruder.
- Continuous Optimization: The system continuously monitors and adjusts, learning from each batch to further refine its predictive models and optimize for energy consumption, throughput, and product quality.
This closed-loop system effectively decouples the final product quality from the variability of the raw materials, creating a self-correcting and highly resilient manufacturing process.
Key Benefits and Strategic Impact
Implementing a digital twin for process control delivers significant benefits for a pet food manufacturer:
- Enhanced Product Consistency: The primary benefit is the ability to produce a product with minimal batch-to-batch variation, which is crucial for brand reputation and consumer satisfaction.
- Reduced Waste: Proactive adjustments prevent the production of off-spec products, leading to a substantial reduction in waste and scrap material.
- Increased Operational Efficiency: The system optimizes equipment settings for maximum throughput and energy efficiency, lowering operational costs.
- Accelerated Product Development: New formulations and recipes can be tested virtually in the digital twin, allowing for rapid iteration and de-risking before physical trials.
- Predictive Maintenance: The digital twin can also monitor equipment health and predict potential failures, allowing for scheduled maintenance and minimizing costly unplanned downtime.
Conclusion
The integration of process simulation and digital twin technology represents the next stage of evolution in process control within the pet food industry. By leveraging real-time data to create a predictive, self-correcting manufacturing process, companies can overcome the inherent challenges of ingredient variability. This not only leads to superior product quality and efficiency but also provides a strategic advantage in a competitive market by fostering innovation and ensuring consumer trust through every bag, can, tray or pouch of pet food.
About the author: Gregory Daniel is a seasoned expert in pet food R&D and manufacturing, with over two decades of experience optimizing production processes and both driving and deploying innovation. With a background in mechanical engineering, he spent 22 years at The Iams Company/Procter & Gamble and Mars Petcare, leading advancements in pet food and treat development into production through statistical process optimization and developing novel products that were designed to be successful in production. His expertise spans product and process development, specialized design concepts, and fit-for-purpose equipment selection. Prior to joining Haskell, Gregory served as a trusted advisor to major pet food companies, helping them streamline operations, enhance product flexibility and implement LEAN Six Sigma methodologies.
