The manufacturing process, especially in the food and beverage industry, presents a variety of uncertainties. It can be difficult to identify the factors that lead to failures and to determine what is a good product or when to discard it. However, processing equipment may contain the answers to these questions.
Artificial intelligence (AI) and machine learning provide a means for processing tools to handle complexity and causality that are too complex or numerous for the human mind. AI is already being implemented in some food processing applications and has the potential to revolutionize other areas including handling, sanitation and logistics.
According to Richard Phillips, director of smart manufacturing at Polytron, a member of the Control System Integrators Association, “AI and machine learning will impact most areas of manufacturing and supply chain in one way or another. We are already using predictive maintenance and analytics as well as schedule optimization in the food and beverage industry.
Improving Product Quality with Artificial Intelligence
Artificial Intelligence (AI) has become a popular tool for sorting, grading and inspection both material and finished product. AI can help in two ways: by establishing standards and specifications, and by helping to determine the cause if a product deviates significantly from its specifications.
According to Marco Azzaretti, Director of Marketing, the technology supplier of the sorting systems, most applications begin with a “sort recipe” of the expected appearance of the product. However, the final product is always a customized sorter, programmed for the unique processing line on which it will be installed, as each installation requires acceptable in-spec products as well as precision defects, foreign materials and contaminants. have their own unique definitions. which should be removed.
The key system uses visual inspection in regular light. However, other types of inspections can also benefit from AI. ImagoAI, for example, is employed Hyperspectral Imaging with AI To inspect components such as fats and proteins, detect toxins, and more, replacing traditional methods of detection such as near-infrared.
“Imaging with AI provides higher accuracy and lower detection threshold,” says Abhishek Goyal, CEO and co-founder of ImagoAI. “Companies are increasingly focusing on applications that use imaging AI to achieve lower detection thresholds or address more complex anatomical interactions.”
Like Near Infrared, ImagoAI requires 50 to 100 samples to establish inspection standards. However, ImagoAI can process images remotely and communicate the results to the tool, allowing its customers to take advantage of AI modeling techniques without being experts in the field.
Prediction vs Prevention
AI has the potential to revolutionize maintenance strategies by shifting from preventive to predictive maintenance. The former relies on fixed schedules for equipment service or replacement, while the latter continuously monitors the equipment and analyzes its performance to predict when it needs attention.
Through continuous monitoring of equipment parameters such as temperature and vibration, the AI develops a performance profile that indicates when equipment needs to be replaced. it is done by Attaching Sensors to Equipment Such as motors and gearboxes that constantly feed data back to cloud-based AI applications, which analyze it to identify patterns and develop maintenance schedules.
However, implementing a preventive maintenance program is not an easy task, especially for parameters such as vibration, which is subject to many external variables, David McKenna, director of smart manufacturing solutions at Grantech Systems Integration Ltd, is skeptical about its potential effectiveness due to the unpredictable nature of equipment failures.
According to McKenna, even with the use of sensors, it is difficult to find patterns in random and inconsistent equipment failures. He believes that preventive maintenance will not eliminate a significant amount of downtime.
when it comes to solving issues like downtime or out-of-spec productArtificial intelligence (AI) has the ability to investigate a much wider range of factors and possible causes than the human mind. By detecting variations in inputs and other factors, AI can identify correlations that may go unnoticed by human observers.
Liran Akavia, co-founder of AI software provider Seebo, points out that any production line can have varying losses and inefficiencies, even various products produced on the same line. These losses may result from various factors such as wastage due to overweight, quality rejection, size and shape variation, color inconsistency, unstable yield, etc. The root causes of these losses also differ from one production line to another.
Recently, a bakery in Europe faced the issue of consistently underweight product, which Cibo’s software helped solve. The software successfully identified two parameters that consistently correlated with underweight products: baking temperatures above 400°F and conveyor belt speeds of less than 5 meters per second. After adjusting for these factors, products weighing less decreased 7.4% to 2.2% of productionThis results in annual savings of approximately $1 million.
According to Akavia, to get to the root of the problem, Cibo had to consider several other factors beyond temperature and bake time. Seebo solutions continuously perform multivariate analysis on all data related to the production process, including raw material data, quality data, external data such as weather, temperature or humidity. Literally every relevant piece of data is analyzed 24/7including the complex and dynamic interrelationships between different data tags on the production line.
Revolutionizing Sanitation Equipment with Artificial Intelligence
As technology advances, we keep looking for new ways to improve our lives. One such area is the use of artificial intelligence (AI) in appliance cleaning. Clean-in-place (CIP) is a widely used method for cleaning equipment, but it consumes a large amount of water. To address this issue, researchers at the University of Nottingham in England are developing a system that incorporates AI technology to reduce water usage.
The system uses ultrasound and ultraviolet sensors to provide feedback on equipment and pipework, and to monitor fouling. ultrasonic Sensors are attached to the outside of the device, while the UV system is housed inside the top of a tank, which houses UV lights and a camera. With the use of machine learning models, the sensor detects and determines when all contamination has been removed.
Nicholas Watson, an associate professor of chemical engineering at the University of Nottingham, explains, “The model, as it stands now, aims to reduce water use. Other models may be developed Optimize parameters like flow rateTemperature, and chemical concentration are linked to environmental stability to minimize time and resource use, but this is the next step in our work.
The integration of AI and machine learning models into sanitation processes could revolutionize the efficiency and sustainability of food production. The technology can be judiciously applied to address some of the most challenging sanitation applications.