A Case Study by Live Track & Akenza
For years, Switzerland has been one of the world leaders in recycling. Recycling conserves resources, saves energy, reduces CO2 pollution and creates secondary raw materials. With over 340,000 t and a recycling rate of over 90%, glass is one of the most important recycled materials. Glass is collected together with metal in a dense network of collection points.
The problem today is that containers are emptied without knowing the fill levels. Either the containers are overfilled or not yet full, or the supply vehicles are not optimally loaded. This is a huge potential for reducing logistic costs and CO2 emissions.
The Internet of Things enables an innovation leap. By attaching sensors with low energy consumption and long service life to the waste containers, it is possible to centrally monitor and predict the container's filling level. This enables improved planning of disposal tours and thus increases the efficiency of waste management.
The analysis of real-time data also improves the quality of service, prevents overfilling and reduces littering. The insights gained will also improve long-term site planning.
Together with AVAG, Swisscom and Akenza, Live Track carried out a concept study in which about 100 containers at 20 locations in Emmental were equipped with IoT level sensors.
The aim of this study:
- calculate the savings potential
- to check the suitability of the sensors for glass containers, whether mobile communication works reliably
- how long-term location planning can be improved.
A robust, battery-powered sensor is installed in each container to be monitored. The device is mounted on the inside of the upper part of the container and measures the level by evaluating the reflections from several ultrasonic sources.
Image description: In this specific case netBin sensors have been used.
Installation using a special mounting plate is quick and easy. The mounting proves itself to be reliable and does not impact the normal use of a container. In the case of above-ground containers the installation can be done by one person. Underfloor containers must be lifted, as when emptying. The actual installation effort is small, as with the above-ground container.
«The installation of the sensors takes only five minutes!»
Once a sensor is installed on the field, it gets registered in the Akenza Core via a simple scan of a QR code on the device. The communication is then established via the Narrowband IoT (NB-IoT) network of Swisscom CMP (Ericsson DCP). This network is a special extension of the 4G mobile network and was especially developed for the Internet of Things (IoT). A NB-IoT network achieves a high coverage and is especially suitable for battery powered applications.
By using the Akenza Software Infrastructure, Live Track is free in their choice of sensors, connectivity and network providers avoiding any lock-in effect. Live Track may choose the optimal setup and sensor depending on the situation and also take its solution into other markets, while always receiving the same normalized data via a dedicated REST API interface from the Akenza Core.
Live Track - Platform
The sensor data is then processed and analysed to calculate filling level forecasts. The processed information is presented to the owner, the logistician and the residents via an individual user interface (UI). For further analysis, the fill level data per collection point and material is displayed in total.
Over a period of 16 weeks, fill level data was collected at 23 locations. As before, the waste disposal vehicles drove their usual fixed routes at fixed times. The visualized fill levels over several months, clearly show when a container has been emptied. At the time of the draining, the majority of the containers were only about 50% full.
The better the knowledge of the filling level of an individual container and corresponding adjustments of emptying times, the larger the savings can be realized without changing the disposal route of the garbage truck. The evaluation of the data has shown that containers are often emptied already at very low filling levels. Since the filling level is unknown, the uncertainty is eliminated by the probability of overfilling.
By monitoring the fill level of the containers a forecast algorithm calculates the optimum time for emptying based on historical and current data. To estimate the optimization potential, the first step was to determine the emptying times in such a way that the containers were never overfilled but still as full as possible.
Thus, the example of location #104 can be used to symbolically show how the number of trips can be reduced from 4 to 2 in the same period by emptying later. The capacity of the containers is thus almost fully utilised. This corresponds to an emptying cycle of 4 weeks, i.e. almost 50% less emptying.
In order to further reduce the number of tours, individual containers are emptied earlier to maximise the loading of the vehicles. This leads to a reduction of the optimization potential of the individual containers (Site #102).
However, an evaluation of all 23 locations shows that the total savings potential is still large.
The number of tours was reduced from 11 to 9. At the same time, the average length of the tours was reduced by about 20%.
The total distance travelled fell from over 800 km to approx. 540 km (over 30% decrease) in the evaluated period of 16 weeks.
Reduction of CO2 emissions
This case study is based on the calculation of CO2 values according to the EN 16258 standard. According to this standard, CO2 emissions are calculated based on transport performance. This is determined by the number of kilometres driven and the weight of the load on this route. The transport performance is measured in tonne-kilometres (tkm).
In its guideline "Calculation of greenhouse gases in transport and logistics", the German Freight Forwarding and Logistics Association recommends the following values for "well-to-wheel" greenhouse gas emissions per tonne kilometre:
Thanks to the fill level data, the same quality of service can be provided with 40% less mileage by reducing the number of trips. This results in a reduction in CO2 emissions of over 50%. It corresponds to an annual reduction of 1.5 t CO2 for the containers examined in the proof of concept. The annual CO2 emission per container is thus reduced by 8 kg.
Various factors influence logistics costs. For this study we assume costs of about CHF. 5 /km. These costs include fuel, fees (LSVA), maintenance costs and depreciation. By using the Live Track platform with the Akenza Core a reduction of logistics costs by up to 35% is likely.
Findings and outlook
This study shows that significant improvements in logistics can be achieved by knowing and forecasting the fill levels of glass collection containers and optimising the routes of the waste disposal vehicle accordingly. The result: significantly lower logistics costs and sustainable reduction of CO2 emissions.
A major goal of the study was also to test the measurement quality of the battery-powered sensors under real conditions. The tests have shown that the measurement results are stable enough to allow a cost-effective and data-driven optimization of logistics.
The study also examined whether the communication of the sensors with the platform works reliably. Even in remote locations the coverage of the Swisscom network in the containers is good enough to communicate reliably via the NB-IoT network.
Benefits of using the Akenza Core
Live Track relies on the Akenza Core because it makes an important contribution to the entire value chain in the above use case: the overall connectivity. It ensures that normalized data is always available, independently of the sensor or protocol in use.
The so-called 4D-agnostic software infrastructure ensures that the application is ready for the future, regardless of the device (sensor), connectivity (provider), cloud infrastructure (cloud) and target application (API). Nothing stands in the way of the further development of an IoT project.
«Akenza and Live Track are a powerful combination. Live Track's smart waste platform uses our IoT backend system and allows them to fully focus on their solution and value creation.»