In order to know the stock level of a storage plant, it is essential to quantify the filling level of each silo. Depending on the type of raw material (cereals such as oat, sunflower, rice, wheat, soybeans, etc.), quality and level of grain, you will be able to find out the capital available in the form of inventory.
In many cases, the filling level of the stored grain is estimated by visual inspection. Some technicians measure the volume of grain by climbing to the top of the silos and inspecting their interior. However, this method poses serious health and safety concerns, as well as being imprecise.
For this reason, there are much more precise – and safer – options such as automatic grain monitoring.
In this article we cover how our CTC+ software can help you know the filling level, keeping your money safe.
How do we quantify the filling level at Gescaser?
Gescaser’s monitoring system, in addition to controlling the temperature and humidity of the grain, also offers – at no additional cost – a very precise estimation of the filling level. This allows you to maintain inventory control, account for costs or to schedule production, among many other needs.
To quantify the filling level, Gescaser’s CTC+ software applies an algorithm capable of determining which sensors are covered by grain and which sensors are not. Furthermore, since the CTC+ knows the exact location of each sensor, the software is capable of indicating the filingl level of each silo.
With this information in mind, if a grain silo has a capacity of 5,000 m3 and it is currently at 40% of its capacity, you know exactly how much stock you have and, with this, how much money is being stored inside.
So how do we reduce the filling level calculation error?
The calculation error of our algorithm is:
With 11 sensors in the probe, we get an error of ± 5% of the total storage capacity of the silo.
So as a result of adding more sensors to the probe, the distance between sensors is reduced and so is the calculation error. An improvement that gives you very precise fill level information, without the need of large investments. And it results indeed much cheaper to add additional sensors to a probe than purchasing external devices to measure the fill level.
If you still decide to acquire an external system to measure the filling level with maximum precision, there are the following methods:
Single point measurement (1D):
Laser technology is used to automatically measure the volume level in silos and other storage tanks without any contact. It obtains the information by measuring the transit time of a light pulse between its emittance by the transmitter and its reflection and arrival.
In agriculture, this system works well on many types of grains, such as corn or wheat. Also, since it is very narrow, the laser beam can be used in tight spaces and difficult applications.
Still, this type of single point metering systems present different errors depending on the location of the metering system and the shape of the grain during filling or emptying.
Measurement with radar sensor (2D and 3D):
In a nutshell, the measuring with a radar sensor consists in mapping the entire surface of the silo. The distance travelled by the radar wave is determined by the difference in frequencies between the emitted wave and the one that bounces off the material to be measured.
It is a very precise and safe technology, with an error between ±1% and ±5% depending on the number of devices installed. However, the cost is much higher when comparing it with the other systems. In addition, for silos with large diameters, more devices will need to be added, since the beam angle will not cover the entire surface of the material with a single device.
Comparative table of the 3 filling measurement systems
So, if high precision is not necessary, you can obtain the desired information only with the temperature control system without any additional cost.
For this, you must assess whether the investment when purchasing an external device (especially when there are multiple measurement points) is compensated by the possible operational gains.