U.S. food producers waste approximately 40 percent of the end-product before it reaches the final consumer. In total, this number comes out to approximately 36 billion pounds of food.
A primary driver of product loss is poor environmental conditions that cause food to spoil or lose quality. Regularly monitoring the temperature of food and other perishable products is necessary to avoid food safety incidents and needless product loss. Yet many industrial processors conduct only manual temperature checks, which are labor intensive, can be inaccurate and typically are unactionable.
Networked sensors offer manufacturers a simple way to reduce and prevent product loss, increase profits, eliminate manual temperature checks and help ensure food quality.
What is Remote Monitoring?
The main components of remote-monitoring systems are Industrial Internet of Things (IIoT) sensors, hubs and analytics software. They work together:
- The sensors detect temperatures in the surroundings and transmit the data to a centrally located hub.
- The hub receives the data and sends it to the cloud, which connects to traditional IT systems. This enables temperature data to instantly appear on a user’s computer, phone or tablet.
- Data analytics software monitors the temperature data and alerts teams when problems are detected.
There are several ways in which automatically tracking and recording temperature data can optimize a facility. Consider the areas in a warehouse or food processing facility that require cooling. These include freezer rooms, cold rooms, temperature-controlled storage areas, quarantine areas and receiving and loading bays.
The acceptable temperature ranges in these areas often will vary. For example, a freezer area may be held at -13 to 14°F (-25 to 10°C) while refrigerated areas are held at 35 to 45°F (1.6 to 7.2°C). A wireless temperature-monitoring system enables users to detect the ambient temperature in these areas and track any changes over time.
If the refrigeration cooling equipment malfunctions, there can be a delay between the malfunction and the realization that the temperature is out of the acceptable range. With an automated system, when temperatures fall outside the acceptable range for the area, the software is programmed to notify the users immediately. Remote-temperature monitoring enables teams to manage malfunction events continuously.
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Another critical component of such systems is analytics. In manufacturing, analytics typically create impact in two ways:
- Detection of temperature cyclicality.
- Prediction of equipment downtime.
There have been many cases where the temperature in a cold room or freezer room exceeds a particular temperature range at a particular point in the day repeatedly. This usually occurs when a particular room is opened to load or unload items. Because deliveries occur at set times in a week, these cyclical patterns become clear to modern analytics systems over time. Ensuring that these rooms are at a lower temperature during these times, for example, can reduce the probability of a spoilage event.
Another way modern analytics systems can optimize efficiency is by detecting equipment health. Advances in supervised learning techniques allow a system to use historical temperature readings and equipment malfunction data to predict future equipment failures. Training a neural network on a labeled dataset of such failures enables it to detect future instances of equipment failure. A machine-learning system would identify significant variations in temperature readings and gradually or quickly increasing temperatures over time, both of which are often signs of future equipment failure.
Remote Monitoring in Food Manufacturing
Remote-temperature monitoring plays a critical role in food manufacturing because it automates environmental standards that are important for product quality.
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Case In Point: Chocolate Manufacturing. For example, in high-end chocolate production, it is essential that temperature and humidity are continuously monitored to provide high quality output. During the production process, the chocolate is cooled from 113 to 82.4°F (45 to 28°C) before being heated to 86°F (30°C). After manufacturing, the chocolate must be held between 55 and 68°F (13 and 20°C), at 55 percent humidity, and without natural light exposure.
To gain better control over environmental conditions, Moonstruck Chocolates in Portland, Ore., implemented remote monitoring throughout its manufacturing process. Beginning with the storage of the raw materials, Moonstruck employs wireless sensors in locations close to the entryways wherever they store chocolates. Entryways tend to be where warm air permeates due to an event like inventory loading or unloading. Alerts allow the operators to keep an eye on conditions and ensure the products do not deteriorate in substandard conditions.
During production, Moonstruck uses remote monitoring to manage ambient conditions down the line. Sensors at the four entryways of the production line room continuously report ambient conditions. Such tracking is important because the processing of precursors from their original chemical state as a solid to a liquid, then back to a solid must be exact.
In their retail sites, Moonstruck monitors the conditions of their display cases and reach-in refrigerators. This is important because the appearance of the chocolate is almost as important as quality assurance in the production process.
Remote Monitoring in Industrial Warehousing
There are many ways sensors can be placed in an industrial warehouse to be effective. In a technical report, the World Health Organization (WHO) stated operators should arrange sensors in a grid fashioned along the width and length of the area. Such an arrangement helps ensure that the area is covered with sensors located approximately every 15'.
Another strategy is to place sensors in a grid to map air temperature profiles around the warehouse. Doing so can help identify cooling discrepancies and unsafe storage areas for temperature-sensitive products. This technique allows for differentiation between pallet-racking storage areas and walk-in cold rooms. The hub should be centrally located within the data-transmitting range of the sensors so the temperature data can be sent to an online dashboard and remote users.
Energy Savings Potential. The UN Environment Programme (UNEP) estimates 30 to 70 percent energy savings are possible in refrigeration and control systems. One cause of energy inefficiency in these systems is the overcooling of facilities to prevent food spoilage. By using temperature-monitoring technologies, operators can detect hot spots and overcooled areas in a warehouse and implement a capacity-limiting strategy through the factory control systems.
One example capacity-limiting strategy would be to limit the evaporator coil fan speed for all evaporator coils fitted with variable-speed control. Following an excursion event detected by a sensor placed in an over-cooled area, an alert is sent to the building controls.
Another strategy is to implement defrost termination. Similar to capacity-limiting strategies, such a strategy would defer the defrost cycle of an overcooled warehouse area to a later time to save energy usage. Because this zone is already cool, defrosting the evaporator coils is not an essential issue for the given region.
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Higher quality temperature data and analytics creates an upside for warehouse operators and manufacturers. Temperature monitoring is no longer a manual task. Networked sensors and software enable operators to reduce energy use, identify preventive maintenance tasks and help ensure even cooling distribution within warehouses.
When evaluating technology for a warehouse, consider the range that Bluetooth, WiFi and LoRaWAN sensors provide and their speed and efficacy. Select hardware systems that will offer the best usage for your space. Place sensors in the warehouse according to the level of data needed to make informed optimizations.
In conclusion, a plethora of choices exist for optimizing warehouse storage facilities and manufacturing processes. Remote temperature monitoring is becoming an obvious choice, but the question remains: what kind is the best for the application? The ease of integrating LoRaWAN technology into an intelligent, adaptable software suite allows operators to optimize remote monitoring.