In-situ measurement in the iron ore pellet distribution chain using active RFID technology

Journal Pre-proof In-situ measurement in the iron ore pellet distribution chain using active RFID technology

Bjarne Bergquist, Erik Vanhatalo PII:

S0032-5910(19)31001-0

DOI:

https://doi.org/10.1016/j.powtec.2019.11.042

Reference:

PTEC 14924

To appear in:

Powder Technology

Received date:

4 July 2019

Revised date:

2 November 2019

Accepted date:

14 November 2019

Please cite this article as: B. Bergquist and E. Vanhatalo, In-situ measurement in the iron ore pellet distribution chain using active RFID technology, Powder Technology(2019), https://doi.org/10.1016/j.powtec.2019.11.042

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© 2019 Published by Elsevier.

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In-Situ Measurement in the Iron Ore Pellet Distribution Chain Using Active RFID Technology Bjarne Bergquist1 and Erik Vanhatalo1 1 Quality Technology and Logistics, Luleå University of Technology, Sweden.

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Corresponding author: Bjarne Bergquist Quality Technology and Logistics, Luleå University of Technology, Sweden. Tel: +46 70 5500577 e-mail: [email protected]

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Word count: 8392 words

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Journal Pre-proof Bjarne Bergquist and Erik Vanhatalo

In-Situ Measurement in the Iron Ore Pellet Distribution Chain Using Active RFID Technology

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Abstract—The active radio frequency identification (RFID) technique is used for in-situ measurement of

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acceleration and temperature in the distribution chain of iron ore pellets. The results of this paper are based

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on two experiments , in which active RFID transponders were released into train wagons or product bins. RFID exciters and readers were installed downstream in a harbour storage silo to retrieve data from the

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active transponders. Acceleration peaks and temperatures were recorded. The results imply that in-situ data

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can aid the understanding of induced stresses along the distribution chain to, for example, reduce pellet breakage and dusting. In-situ data can also increase understanding of product mixing behaviour and product

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residence times in silos. Better knowledge of stresses, product mixing and residence times are beneficial to

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process and product quality improvement, to better understand the transportation process, and to reduce

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environmental impacts due to dusting.

Index Terms— Mining industry; RFID tags, Temperature sensors, Accelerometers, Flow production systems, Supply Chain Management

I. INT RODUCT ION HE distribution chain of iron ore pellets includes the shipment of products from the pellet manufacturer to

Tthe steel-producing customer. The distribution chain is essential from the perspectives of production efficiency, the environment, and product quality. Transportation should be energy- and cost-efficient, with transportation steps having minimal negative impact on product properties and the environment. Product quality may, however,

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Journal Pre-proof degrade during transportation. Among the reasons for degradation is that high impact forces or frictional abrasion may cause the pellets to disintegrate before reaching the customers’ blast furnaces [1-3]. The abrasion is caused by the pellets rubbing against each other or the process equipment, such as during storage discharges. High altitude discharges into silos or boats also impact the pellets, which may cause them to break [2]. Particle degradation has, therefore, generated considerable research (e.g., see [4]). Any flow of pellets or loading and unloading step, in which pellets move about neighbouring pellets or vessel surfaces, induces abrasion, reduces pellet diameter, and produces fine material [2]. An increased amount of fine-grained material leads to a loss of gas permeability in blast furnaces

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[5], resulting in reduced production rates and inefficient or unstable blast furnace operations. Producers of iron ore pellets, therefore, increase sintering temperatures [6] and sieve the products before shipment to reduce pellet

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breakage, the amount of fine material that reaches customers, and dusting problems along the transportation chain

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[2]. Higher furnace temperatures and reprocessing of the sieved fine fractions into new products, however, result in

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higher processing costs and lower energy and production efficiency.

Another quality-related transportation concern is traceability and the need for producers of bulk products to

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separate one product from another. Iron ore pellet customers may, for instance, have blast furnaces set up for certain

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chemical properties of the iron ore pellets that may be significantly different from other furnaces. An increased separation accuracy between product types in the distribution chain, therefore also has a large economic potential.

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However, the continuous production process and batch-wise transportation in trains and boats, together with large

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storage warehouses create challenges for handling and separation of different products along the transportation chain. Keeping products separate using separate storage compartments is also costly. Additionally, production process disturbances are, unfortunately, always a possibility. High production rates combined with manual testing risk leading to products with properties outside of specifications being shipped before the analysis results are delivered. At that point, a traceability system can lead to large cost-savings if the residence times and mixing of the product in particle bins can be estimated with high accuracy. [7] The increased dusting due to higher amounts of fine material is problematic from an environmental perspective. The loading and unloading of minerals on conveyors or transportation vessels constitute major sources of dusting [3, 8], while increased fine material fractions in the pellets will increase the need for dust suppressants, such as water sprays [3]. People living near the distribution chain may experience downfalls of iron oxide dust. Dusting is also

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Journal Pre-proof problematic from a work environment perspective. Excessive exposure to iron oxide dust has occasionally led to pulmonary siderosis [9, 10]. Highly dusty air also impairs vision, which in itself can become a work hazard. While the distribution chain induces mechanical stresses that may degrade the product, product temperature is another potential concern. Hot products may generate upwinds from open storage or during open discharges, such as when boats are loaded. Upwinds may lead to increased dusting, which could also lead to environmental hazards. To summarize, quality problems related to the transportation of iron ore pellets can relate to transportation-induced pellet breakage from impacts and mechanical abrasion in the granular flow. These problems do not only impact the

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customer, who receives a subgrade product, but also workers and neighbours near the transportation discharge stations. The problem, as described, could be seen as a natural setting for experimental work into how to overcome

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the stresses and, therefore, the problems created. However, production processes are large, streamlined to handle vast

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volumes of pellets, potentially dangerous, and thus not suitable for most types of experiments. Therefore, research

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into the mechanical stresses exhibited on and exerted by granular material has, to the best of the authors’ knowledge, been confined to off-line, laboratory-based work. While such studies are necessary, for example, to form a general

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understanding of the ability of a pellet to endure different stresses, they do not provide detailed knowledge of the in-

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situ stresses on the products induced by transportation. That is, the stresses acting on the product in a specific distribution chain are seldom known and have, so far, been difficult to measure.

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However, recent technological progress has made it possible to measure acceleration, as well as temperature, in-

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situ in highly difficult and exposed industrial environments. It is reasonable to assume that process steps producing high acceleration peaks in the pellets are the same steps in which impact forces peak. Likewise, acceleration records can help analyse production methods and process designs that minimize internal pellet flow. High pellet temperatures are negative for polymers along the transportation chain, such as in conveyors, and likely increase dusting. In-situ measurement of temperatures may also aid process design and production planning to reduce negative impacts, such as fire hazards. The purpose of this article is, therefore, to illustrate how one can measure acceleration and temperature in-situ in the distribution chain of iron ore pellets and to link the results to events occurring at different steps of the process. Therefore, the article provides a proof of concept for using active RFID for in-situ measurement in the distribution chain of iron ore pellets.

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Journal Pre-proof II. LITERATURE REVIEW A. RFID and in-situ measurements Radio frequency identification (RFID) can be considered one of the foundational technologies for integrating the Internet of Things (IoT) in supply chain management [11]. Full-scale commercial implementation of RFID began around the 1980s [12], and RFID is used extensively for a variety of purposes in the industry today. A passive RFID system automates the identification and is comprised of two main components: a transponder (data-carrying device) and a reader (device to read or write data) [13]. Passive transponders lack onboard power supply. Instead, the

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magnetic or electromagnetic field generated by the reader antenna powers the passive transponders, while active

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transponders carry batteries [13]. Yüksel and Yüksel [14] argue that RFID can vastly increase an organization’s potential for obtaining real-time data about the locations, history, and changing situations of tagged objects.

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Engineers and managers can use this data to improve the effectiveness and efficiency of the distribution chain.

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Bennett [15] lists traceability and monitoring of production through mobile, wireless, and RFID techniques as one of

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the most important future challenges in manufacturing.

There are numerous applications for RFID across many sectors, such as in discrete manufacturing [16], healthcare

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[17, 18], retailing [19, 20], and food traceability systems [21], to name a few. A recent trend is to combine RFID

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technology with active sensors [18, 19].

B. RFID for particulate materials, for mining and pellet distribution

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There are a few examples of using the TFID technology for studies of particulate materials. RFID has been used in studies of agglomerated particle breakage due to impact stresses [23] in fluidized bed reactors. The RFID technology has also been used for residence time measurements in such reactors [24], and in the iron ore transportation chain [35-36]. The use of RFID in the mining industry appears to be more limited than in the previously mentioned sectors. Mishra et al. [24] list challenges in the application of RFID to mining-related services and environments. The environmental characteristics of the mining industry include high temperatures and humidity; varying weather conditions; various hazards, such as explosives, high voltage and dust; and shielding issues due to metal infrastructure, equipment, and ore (ibid.). There are applications for RFID in the mining industry that can be classified as safety oriented. Ruff and Hession-Kunz [25] explore the use of RFID for collision avoidance in the

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Journal Pre-proof mining industry. Bandyopadhyay et al. [26] provide a case study of an active RFID technique for tracking miners and vehicles in an underground mine, and Zhang [27] performs a similar investigation to track miners. Mishra et al. [28] discuss the potential use of passive and active RFID techniques to track explosives and locate detonators in the case of misfires. Zigbee protocol RFID sensors have been used to detect the presence of toxic gases in underground mines [29]. Passive RFID transponders have also been used in the study and control of ores from different ore deposits [3032]. For the distribution chain of iron ore pellets, the focus of this article, the passive RFID technique has been applied

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in the past to try to improve traceability throughout the distribution chain. Kvarnström and Oghazi [33] suggest using passive RFID transponders to achieve traceability after the pelletizing plant stage of the distribution chain.

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Kvarnström and Nordqvist [34], Kvarnström and Vanhatalo [35], and Kvarnström et al. [36] further explore the use

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of passive RFID transponders introduced into the pellet flow to create traceable units in the granular material flow. In

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these studies, the authors varied the shapes, sizes, and coating mixtures of the transponder casings, as well as the antenna positioning (ibid.). Results from these tests show that the read rate of passive transponders increases with

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their size. Larger transponders were not found to segregate significantly from the iron ore pellets. The read rate was

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negatively affected by heat, abrasion, and a low position in the pellet bed when the transponder passed the antenna. Bergquist [7] provides further guidelines for achieving traceability in the iron ore distribution chain and argues that

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business.

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passive RFID transponders in suitable application areas could create traceability in different parts of the iron ore

C. In-situ sensors and RFID in other distribution chains Food industry applications dominate the use of in-situ sensors in combination with RFID technology. In-situ sensors have been used to study logistic process properties important to fresh foods, such as temperature. Amador et al. [37] use RFID sensors to monitor the temperature in a pineapple supply chain. Jedermann et al. [38] use semi-passive RFID sensors to monitor the temperature of refrigerated goods. Mattoli et al. [39] use an in-situ sensor connected to a data logger to measure temperature, humidity, and light conditions of wine bottles during transportation, and infrared for communication. Trebar et al. [40] use RFID-based temperature sensors to study the integrity of the cold chain during fish transport. The interest from the food sector is understandable, given the importance of the distribution chain for food safety and food economics. However, sensors could be of interest to all logistic chains where the

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Journal Pre-proof transportation system affects product properties, such as the mechanical degradation of iron ore pellets at various stages of transportation [41]. From the literature review, we conclude that researchers have previously addressed RFID technology in iron ore production, including in the pellet distribution chain. We also found examples of researchers combining active RFID transponders with sensors. However, as far as we have seen, no other work has tried to attach sensors to RFID pellets to measure properties within the distribution chain of iron ore pellets.

III. MATERIALS AND METHODS

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This section describes the active RFID transponders and their protective casings developed for this specific

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application. The description also includes the RFID exciter antennas, radio receivers, and their installation in a

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harbour storage facility. Last, we describe the experiments performed in the distribution chain of the Swedish mining

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company LKAB. A. Active RFID Transponders

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The active RFID transponder used in this study has the ability to carry sensors, such as accelerometers and

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temperature sensors, register data, such as the signal strength of the exciter antennas (hereafter: exciters) it is communicating with, store data, and transmit these data to radio receivers. These receivers can be mounted

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somewhere in the proximity of a moving transponder in the product flow. Magnetic field signals generated by the

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exciters were in this study designed to wake up the active transponders since preliminary tests had shown that the penetration depth of the magnetic field signal was superior to higher radio frequencies in iron ore pellets. The transponder sizes needed to be small, which prohibited them from magnetic field pulse transmissions. Instead, their responses were recorded through radio waves transmitted to a radio receiver and an external database, as well as logging and storing measurement data locally on-board the transponder. The data log also protected against the loss of measurement data from a lack of radio communication.

The logistics chain of iron ore pellets is a harsh environment for electronics [24], and the transponders were enclosed in casings made of an epoxy and lead oxide powder composite to protect them from mechanical impact. Lead oxide powder was added to give the casings a density similar to that of the iron ore pellets to reduce segregation risks, as

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Journal Pre-proof recommended by Kvarnström et al. [36]. The casings were designed to be sealed with epoxy after the active RFID circuits had been inserted. The casings were sealed close to the experiments to save battery power. The circuits also carried a flashing light-emitting diode visible through the epoxy cap to show whether the transponder was activated. Figure 1 shows an active RFID transponder circuit and passive 22mm glass RFID transponder.

Fig. 1. The active RFID circuit (top left) and, below, a passive 22mm glass RFID transponder. The back of the active circuit contains the battery. The AA battery in the picture

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is only for size comparison.

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Both the hardware and software of the active off-the-shelf RFID circuits (Calypso) were modified by the equipment

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supplier, Electrotech AB, to fit the application. The active circuits include internal accelerometers, temperature

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some additional facts about the active transponders.

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sensors, and sensors for measuring the signal strengths of the exciters and the radio signal antennas. Table 1 contains

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Table 1. Key properties of the Calypso active RFID transponder customized to current use. Radio transmission frequency:

868 MHz

(transponder transmission) Magnetic wave receiver frequency:

125 kHz

(Antenna/Exciter transmission)

8 seconds

Current consumption in three-channel listening mode:

~10 µA

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Exciter radio wake-up frequency:

2.4 – 3.6V (T A = 25ºC)

Operating supply voltage:

-40 to +100 ºC

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Operating temperature range:

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(hi-temp battery)

Accelerometer readout range

0.92 to 155 m/s2

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(wake-up acceleration to maximum detection range)

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The hardware modifications include a battery switch and batteries capable of temperatures slightly beyond 100°C for the circuits to survive the maximum estimated product temperature. The temperature sensors were calibrated by the

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equipment vendor using a two-point calibration in their climate chamber with calibrated gauges.

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The final version of the active RFID transponder also included a 22mm passive pin-shaped RFID transponder, which was fitted together with the active circuit in the protective casing (Figure 2). This adapted design allowed a passive RFID reader mounted at Luleå harbour to detect the casing including the active transponder. The passive transponder acted as a safeguard against active transponders passing the harbour silo undetected due to active circuit failure.

Fig. 2. The active transponder in a closed protective casing (including a 22mm passive transponder), an iron ore pellet, and an AA battery for size reference.

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Journal Pre-proof B. RFID Exciters and Readers in the Product Silo Facility For maintenance purposes, the readers and exciters need to be mounted in environments with sufficient accessibility for maintenance, and as close to the transponders as possible. In the studied case, the harbour silos were the first locations in the supply chain with sufficiently benign accessibility to the transponders. The harbour in Luleå has three parallel silos, and the exciters were installed in one of these (Silo 1), along with a reader. Three RFID exciters were mounted and hung from the silo roof, approximately 120 degrees from each other in the horizontal plane near the silo walls, together with a radio receiver placed at the silo entry at the top of the silo. An exciter and a radio receiver were

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positioned near a conveyor belt below the three silos to download the transponder data log when an active

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transponder was discharged from the silo. Figures 3a-b illustrate the installed active RFID equipment. The layout, using three exciters in the silo, made it reasonable to assume that the active transponders would be

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close to at least one of the silo’s exciters for a long period, and for transponders to be awakened by the closest

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exciter. Field tests ensured that the equipment worked as intended and that the active RFID transponder could sense

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all three exciters at the silo entry. The two reader stations, on top of the silo and in proximity to the conveyor, were fitted with Wi-Fi connectivity to allow access to the data without physical contact with the readers in the dusty

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environment. Each silo exciter emitted magnetic wake-up signals every 10 seconds with a synchronized pattern using a 3.3-second interval between exciters to prevent the transponders from becoming overwhelmed. The awakened

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transponder responds with a data package using radio, allowing for real-time readouts of the accelerometer, the

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temperature, and readouts about the status of the transponder and its communication. Transponder status included battery voltage, the signal strength of the exciter on the transponder’s three internal coordinate axes, radio signal strength, transponder antenna self-tuning numbers, and checksums for the package. The accelerometer readouts included acceleration measurements from the three dimensions related to the sensor’s internal coordinate system. The reader on top of the silo stored the real-time data.

Fig. 3. Schematic view of the exciters mounted at the top of and following the Luleå harbour silo. Illustration courtesy of Johan Carlberg, Electrotech AB.

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Fig. 4. Installation of an exciter on the silo roof. Installation occurred when the silo was nearly full so that the crew could walk on the pellet bed. Photo courtesy of Stefan Englund. The reader placed near the conveyor used for the discharge of the harbour silo (Figure 3) read the transponders’ data logs. These logs contained information about signal strength, acceleration, and temperature. Information about the exciter IDs that had awoken the transponder was also carried by the signal strength package, along with a timestamp.

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The transponder recorded the exciter signal strength for each of the three internal transponder axes.

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The transponder accelerometer triggered the acceleration package. The accelerometer wrote acceleration data to

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the transponder log only when the acceleration exceeded a predetermined value to preserve memory capacity. The

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accelerometer package also included information about acceleration in the transponder coordinate system’s three axes and a timestamp.

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The temperature package delivered the current temperature, as well as the timestamp. These temperature packages

package every hour.

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also included information about battery voltage and were driven by the internal clock, with the log receiving a new

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The equipment supplier, Electrotech AB, also mounted a passive RFID antenna-reader combination around one of the conveyor belts transporting the pellets to the loading dock. In this way, a server monitored the passive RFID

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transponders passing the antenna, as in Figures 5-6.

Fig. 5. Schematic illustration of the passive antenna and the reader’s connection to the server. Illustration by Johan Carlberg, Electrotech AB.

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Fig. 6. Passive RFID antenna connected to the reader. The conveyor belt with the products and RFID transponders runs through the antenna. Photo courtesy of Markus Stenudd, Electrotech AB. C. Two Experiments in the Distribution Chain During the fall of 2017, we performed a series of tests and experiments, in which we introduced active RFID

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transponders into the distribution chain at different locations. This article provides the results from the two most

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extensive of these experiments. Figure 7 shows a simplified overview of the distribution chain, starting from the pelletizing plant. Figure 7 indicates the drop points of the transponders in the two experiments (#1 and #2) and the

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placement of the reading equipment. Note that the preceding steps, (mining, sorting, and concentration), in the

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production of iron ore are not included in the figure.

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Fig. 7. Simplified overview of the distribution chain of iron ore pellets from the LKAB production site in Malmberget,

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Sweden to the end customer. Note that products for the customer in Luleå do not enter the harbour silos.

1) Experiment 1 - Active RFID Transponders Placed on Train Wagons The first experiment was performed on October 26, 2017, and included six active transponders placed directly on train wagons following the pelletizing plant step at LKAB’s production site in Malmberget, Sweden. This specific train included 42 wagons. The first wagon, adjacent to the engine, released its load first when entering the harbour area. We placed active transponders onto the two first, the two middle, and the two last wagons (wagons: 1, 2, 21, 22, 41, 42, counted from the engine), see Figure 8. The active RFID transponders used in this first experiment had regular batteries that were not expected to withstand the high temperature associated with newly produced pellets. The pellets for this experiment were, therefore, produced in a plant with lower product temperatures. When the mining company LKAB had loaded the pellets onto the train, it remained in the train yard for half a day, allowing the

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Fig. 8. Six active transponders were placed onto train wagons of the above train. The photo is from the train station at LKAB’s production site in Malmberget, Sweden.

Readers at the harbour facility retrieved the transponder data logs. Figure 9 provides a simplified illustration of the

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product flow in the harbour facility, where trains are unloaded at an unloading station, after which conveyors transport pellets to one of the harbour silos. The silos are charged from the top and discharged at the bottom. The

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reader antennas for the transponder signals were located near one of the conveyors following the harbour silos.

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Figure 9. Simplified illustration of the harbour silo arrangement. Trains unload to the right in

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this figure and boats are loaded to the left. The arrow indicates the approximate position of

active transponder logs.

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the antennas used in the experiment to measure throughput times and to extract data from

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2) Experiment 2 - Active RFID Transponders Added before the Product Bins and following the Pelletizing Plant

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A second experiment, conducted on November 20, 2017, included another six active transponders. An LKAB engineer dropped the transponders onto a conveyor within the pelletizing plant between the pellet sintering/cooling furnace and the product bins at the LKAB plant during a six-hour interval (see Figure 10). The conveyor belt then loaded the pellets and transponders into product bins. The product bins were discharged into train wagons, and the transponders were expected to follow the pellets to Luleå harbour. Figure 11 shows the harbour discharge from the trains. The active RFID transponders for this experiment had batteries capable of withstanding higher temperatures.

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Figure 10. One active transponder dropped onto a conveyor belt before a product bin at the Malmberget production site. The image also shows passive transponders weight-calibrated according to earlier results (Kvarnström et al. 2011 ), which were used to estimate if the larger active transponders had tendencies to segregate. Photo courtesy of Anders Apelqvist,

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LKAB.

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Figure 11. Schematic image of conveyor belt position for the drop (arrow), before the product

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bin.

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IV. RESULTS A. Acceleration data results

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Figure 12 depicts acceleration data from the accelerometers in the active transponders in experiment 1.

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Acceleration data stemming from the handling of the transponders before they were placed onto the train wagons

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were excluded from the timeline.

Fig. 12. Accelerations from the six active transponders in experiment 1. Acceleration peaks correspond to discharges of pellets into or out of silos/bins. Smaller peaks indicate movement within the silo, or the train or conveyor belt transports. The transponders show some small acceleration readings during train transportation, while the highest acceleration peaks in Figure 12 occurred when train operators discharged the iron ore pellets and transponders into the harbour silo. Recalling that the data in the log are only updated when the acceleration exceeds the predetermined value, see section 3.2. Similar behaviour is also visible in Figure 13, which depicts acceleration data from experiment 2 and for

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Journal Pre-proof active transponders 7, 8, 9 and 11.

Fig. 13. Accelerations from four active transponders in experiment 2. Only four of the transponders in experiment 2 provided acceleration data (transponder logs of transponders 10 and 12 lacked acceleration readings). As can be seen in both Figures 12 and 13, train and silo discharges produce large acceleration peaks for those transponders up to and above 25 m/s 2 . Compared to the behaviour of transponders 1 to 6 in experiment 1, the transponders from experiment 2 have additional acceleration peaks, which are to be expected

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due to the other loading and discharge steps in and out of the product bin before train transportation.

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Acceleration peak data illustrated in Figures 12-13 were available for 10 out of 12 transponders in the two

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experiments. Acceleration data from the transponder logs are not the sole source of acceleration data available. The accelerometer only copied to the transponder log when the measured acceleration surpassed a trigger level, but

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readers in the harbour silo obtained further acceleration data. When the transponders were at the surface in the silo,

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which they are assumed to be at different times due to flow patterns during discharge of the harbour silo, they can communicate with the reader. In these cases, acceleration data were received every eight seconds and stored in the

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reader and could show when the transponders were at rest or when the flow of the pellets set them in motion. The acceleration data shown in Figures 12-13 are the acceleration peaks from the log, but the acceleration data also

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contain information about the acceleration in the transponder’s local coordinate system. A slow slide of the

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transponder on the surface or within the pellets may not have generated a peak for the log, although acceleration on the sensor’s different axes did display changes. Vice versa, stable accelerometer data means that the transponder is still. These additional acceleration data may be used for other analyses not elaborated here, for instance, to improve the accuracy of positioning the transponders in the silo. B. Residence time The residence time at the Luleå harbour silo, i.e., the time between when the trains had discharged the pellets at Luleå harbour to the time when the transponders were detected after the silo, varied significantly between less than 30 minutes (transponder 6 in Experiment 1) to more than two months (transponder 9 in Experiment 2).

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Journal Pre-proof C. Temperature data The temperatures were extracted from the log for the two experiments. Figure 14 depicts results from the first experiment, in which transponders were dropped onto the pellet bed in the train wagons. The lower temperatures, to the left in Figure 14, correspond to train transport. As we placed the active transponders onto already loaded train wagons, they were exposed to both the ambient arctic winter climate, wind, and the heat from the pellets in the wagon.

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Fig. 14. Temperature data from the six transponders dropped onto train wagons. Rapid

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temperature rises or drops indicate that the temperature of the material surrounding the active

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sensor has changed, caused by loading or discharge from the silo.

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It is also evident that the throughput times vary considerably between transponders: transponder 6 was discharged almost immediately (only one temperature reading from the silo), the silo storage for transponder 1 was just under

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one day, and three transponders resided within the silo for nearly two days. Pellets residing for a long time within the

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silo tended to have a lower temperature when discharged from the silo. The lower temperature was expected, at least for as long as newly produced hot pellets flowed into the silo from another train shipment.

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Figure 15 shows the results of the second experiment, in which we dropped transponders onto a conveyor belt prior

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to the product bin in the pelletizing plant (Figure 9). These transponders were first loaded into the product bin at the production site before being loaded onto trains and sent to Luleå harbour.

Fig. 15. Temperature data from three active transponders in experiment 2. Rapid temperature rises or drops indicate that the temperature of the surrounding material around the active sensor has changed due to mixing during loading or discharge. The figure only shows the first three days. Temperature data illustrated in Figures 14-15 were available for 9 of the 12 transponders in the two experiments. The temperature readings shown for the train transport in the left portion of Figure 14 are not representative of the average temperature of the iron ore product, as the transponders were lying on top of the pellet bed in the train wagons. It is reasonable to assume that the transponders were buried in the wagons upon discharge from the product

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Journal Pre-proof bin. The temperature after the increase in the afternoon of October 26 is, therefore, likely to be the same as for the surrounding pellets. Although the temperature measured on the wagons during train transport can be expected to represent the top layer pellets’ temperatures, one could argue that the measurements obtained in the silo should be a better representation of the average product temperature. In experiment 2, the recorded temperature is higher throughout the train transport. A likely explanation is the high probability of transponders being covered by layers of hot pellets when loaded onto train wagons from the product bin. In this second experiment, it was assumed that later pellet products were the first to bury the transponders in a

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product bin in Malmberget, then somewhere in a train wagon, and finally in the Luleå harbour silo. All six transponders were found by the readers, albeit four transponders were discharged six weeks after the transponders

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were activated. The readers could only retrieve logged temperature information from three of the active transponders

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(Figure 12). It should be noted that peak temperatures were higher initially compared to temperatures seen in the first

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experiment, but also that the temperatures dropped relatively fast in the silo storage. Also, the temperatures differ in the silos, bins or during transportation. The temperatures of pellets vary considerably, likely due to where they ended

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up in the train wagons or the product bin. During transport, both transponders 8 and 9 registered increased

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temperature, which was unexpected. A hypothesis is that pellets with higher temperature loaded after the transponder pellet caused the sudden increase in recorded temperature for transponder 9 in Silo 2. Such loading would increase

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the surrounding temperature in the harbour silo. In the Luleå harbour silo, the temperatures seem to converge before

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discharge.

D. Combining acceleration and temperature data Viewing acceleration data alongside temperature measurements can provide further help in understanding the path taken by the transponders in the distribution chain. For example, combining data for transponder 7 from Figures 10 and 12, one can more easily connect the accelerations to the main events in the distribution chain (train loading, silo discharge, etc.) (see Figure 16).

Insert Fig 16 about here

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Journal Pre-proof Fig. 16. Acceleration (left vertical axis) and temperature (right vertical axis) recorded by transponder 7 combined in one graph.

V. DISCUSSION This study introduces active RFID transponders instrumented with sensors for in-situ measurements in the distribution chain of iron ore pellets, and the results show that this methodology is useful for recording in-situ data about accelerations and temperatures.

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The acceleration data are, arguably, the most relevant results. Acceleration is a crucial process parameter that may

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affect the quality of the product, as large accelerations induce large forces that may cause pellet disintegration. Previous researchers may have neglected or been unaware of the long free-fall distances seen in pellet warehouses,

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considering that impact velocities of 1 – 12.5 m/s have been studied [42, 43]. Such speeds correspond to the

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velocities seen when products are transferred from one conveyor to the next, or seen during tumbling tests, but do not

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reach the impact speeds obtained after free-falls of 10-20 meters, where impact speeds would be in the range of 14 – 20 m/s. Online in-situ data could reveal sensitive positions in the process, where different ways of running the

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transportation chain, for instance, by changing silo levels, could affect transport-induced product property degradation, such as fines generation. However, it should be noted that the obtained acceleration readings of this

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study of 25m/s2 are still much lower than the peak acceleration that the accelerometers should handle (155 m/s 2 ). We

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do not believe that peak acceleration remained around twice the gravitational acceleration during impacts from falling to the pellet heaps in the silos, so we consider these readings as indications of high impact places, rather than measurements of acceleration peaks. More likely, the delay of the circuits’ impact wake-up functions also filtered out the accurate peak acceleration readings. The residence time measurements were surprising in the sense that the times varied more than the production engineers had expected. Indeed, the silos are run intermittently, and the harbour contains different silos, so the product may sit in the silo for some time while other silos are engaged. Nonetheless, all transponders from both experiments apart from transponder 9 had left the harbour silo within two days of entering it. In fact, we had assumed that transponder 9 had failed and the RFID technicians were removing readers when they noticed that the reader logs had new entries. At that point, we had expected that the transponder batteries would have been drained, so we were

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Journal Pre-proof surprised by the log entries. Likely, the transponder had been residing in a pocket with stagnant material, too far away from the exciters to be awoken by them and without much movement so that it was not awoken by the onboard accelerometers either, which should have prolonged the battery life. The residence time results are interesting from a traceability perspective. If the residence time variation is representative of the bulk mixing and silo stagnation, it will become difficult to make predictions of when products will appear and what product properties will reach the customers. If we consider the long residence time of transponder 9 to be an outlier, the range of residence times was two days. Since eight out of nine transponders had passed during two days, an estimate is that approximately 90 per

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cent of the products will have passed within two days. This figure is, of course, dependent on if the discharge and filling rate of the silos, as well as the content of the silo during this time. Lower silo levels and increased filling and

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discharge rates will decrease this number, and vice versa, higher levels and lower rates will prolong the residence

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time. Such knowledge can be highly useful, for instance, when a low-grade product has been detected, or when a

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customer has ordered a special product. A suspicion is, however, that the larger sizes of the active transponder casings were, in fact, a factor leading to segregation and that the two months residence time seen from transponder 9,

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was indeed an outlier, and if so, the certainty of assuming that almost all product passing within two days would

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increase.

The temperature readings are not necessary for the product properties, per se. Therefore, the temperature can be

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considered a bonus measurement, as the RFID circuit already contained this sensor, but the temperature proved

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useful for a greater understanding of the process (see Figure 13). However, pellet temperature could be central to the transportation process itself, since high temperatures may degrade conveyor belts, or even generate fires. High pellet temperatures may induce upwinds, which could exacerbate dusting from the distribution chain. The current routine is, therefore, to allow the pellets to cool in the silo before discharge, if possible. More likely, however, in-situ measurement of the temperature may be overly complicated for this purpose. Other sensors mounted to monitor the temperature in sensitive operations could also be used. In our case, the temperature sensor was pre-installed in the active RFID circuit, so measuring the temperature came at no additional “cost.”

VI. FUTURE RESEARCH This study measured the accelerations and temperatures that iron ore pellets were subjected to in the distribution chain. The small size of the pellets makes this application more troublesome, from a segregation perspective, than

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Journal Pre-proof many other materials seen in steel production, such as lump ore or coke. Indeed, ore traceability has been studied before [35]. For such applications, adding sensors, batteries, and larger memories, as well as larger antennas for increased detection rates, would be easier and mean lower segregation risks, as long as the material itself is permeable for magnetic or radio wave communication. Returning to the pellets case and the accelerometer readings, further development of the circuits is needed to obtain accurate acceleration readouts. The development should focus on faster response times of the trigger and the likely higher acceleration limits if the investigator needs more accurate peak accelerations, rather than measurements of

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throughput times between discharges, as was used here. Without better transponder circuitry, more accurate acceleration data and peak forces are likely to be best obtained by simulation, for example, through discrete element

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modelling, rather than by measurements made by pellet transponders [44].

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The residence time variation is important from a traceability perspective, but the results need to be confirmed by

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other experiments, especially considering the long residence time in the silo for transponder 9 in Experiment 2. Another idea for future research is to model the transponder flight in the granular flow within the bulk of the

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pellets in the containers during discharge, that is, the route the transponders take within the bins. The accelerometers

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used in this study had 3D measurement capabilities, but again, the resolution was too low and too infrequent for the generated data packages to calculate the moving patterns of the pellets. The low measurement frequency was

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necessary due to the size restrictions of the on-board memory and the battery of the transponders. If the transponders

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were too large, the segregation risks would increase, yet the batteries needed to last at least until transponders had passed the final reader, which put a limit to the wake-up frequency and accelerometer sensitivity. Therefore, the transponders did not record small accelerations, yet any small but undetected rotations would destroy the flight path calculations. A possible way forward could be to combine the accelerometers with on-board gyros. It is likely possible to add other sensors more directly related to product properties, or their change in the distribution chain, to the RFID circuit, such as sensors measuring frictionally induced wear. A suggestion for future research is to explore the pellets’ moisture content or other sensors that the transponders may carry. Improved location awareness can, for instance, be obtained if the analysis method can link accelerometer information to data from internal gyros that are able to keep track of the circuit’s coordinate system. In the demonstrated case, such location awareness could help the study of macro flows within a silo, or another warehouse with internal particle

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Journal Pre-proof flows. However, to accomplish this, further research is also needed to find out which sensors add the essential process knowledge that is currently missing. Such work also includes interviews with engineers from the pellet producer and the pellet customer about which product properties are affected by the distribution chain and which allow measurement in-situ with this transponder platform. Work to improve the robustness of the sensors in the active transponders may also be needed to reduce the loss of data and to improve data validity. Going from the experimental tests reported in this article to a full-scale online implementation of the results for improved monitoring and control of the distribution chain is another significant step to undertake. For example, data

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processing of in-situ measurements, the timeliness of such data for control purposes, and the placement of antennas

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from process control and signal transfer perspectives need additional research.

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VII. CONCLUSIONS

Iron ore pellets are subjected to considerable stress during transportation from the producer to the customer, which

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may degrade the product and cause environmental concerns. In this article, we have illustrated how an active RFID

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technique can be used to obtain in-situ measurements of acceleration that can help pinpoint process steps critical to particle degradation and residence time calculations. Furthermore, the temperature of particles can exert severe

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problems on the logistic chain itself, and the study also shows where the pellets have the highest temperatures and

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where there are risks of too high temperatures. Transponders experienced different paths and exhibited varying residence times in the distribution chain. Some transponders were discharged a few hours after being loaded into

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silos, while some remained and recorded data for up to six weeks, despite entering the silo at around the same time and with similar silo levels.

An important conclusion is that researchers and practitioners can measure properties in-situ, which may increase the understanding of the distribution chain environment. Such variables may directly or indirectly affect product quality, work hazards, and the environment, and may help in decision making to improve these aspects. The research reported in this article can be considered a proof-of-concept for in-situ measurement of acceleration and temperature during the shipping of pellets by train between the pelletizing plant and the harbour, where the transponders are subjected to high temperatures and a harsh environment.

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Journal Pre-proof ACKNOWLEDGEM ENTS The authors gratefully acknowledge the valuable contributions and assistance from Kristina Andersson, Anders Apelqvist, Kent Tano, Sofia Jonsson, and control room operators at the mining company LKAB. At the RFID equipment supplier Electrotech AB, we thank Markus Stenudd, Juha Rajala, Rickard Hahto, Urban Classon, Johan Carlberg, and Marie Laestander for their valuable contributions. We thank Stefan Englund at Luleå University of Technology for his contributions and assistance during the experimental work. We thank the anonymous reviewers

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and the editor for valuable comments that significantly improved this article.

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Technology, 331 (2018) 20-27.

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Bjarne Bergquist was born in Piteå, Sweden in 1965. He received an MSc in Mechanical Engineering from

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Luleå University of Technology (LTU) in 1991 and a PhD in Materials Science from Linköping University in 1999. From 1999 to 2009, he has been a Senior Lecturer, and from 2009, he has been a Chaired Professor in

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Quality Engineering at LTU. He is the author of 40 articles.

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Dr Bergquist is a vice-president of the European Network for Business and Industrial Statistics (ENBIS).

Erik Vanhatalo was born in Luleå, Sweden in 1979. He received an MSc in Industrial Engineering and

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Management from LTU in 2004 and a PhD in Quality Technology from LTU in 2009. From 2009 to 2017, he worked as an Assistant Professor, and since 2017, as Associate Professor of Quality Engineering at LTU. Dr Vanhatalo’s research interests include the application of statistical improvement methods, such as experimental design and statistical process monitoring, especially in the process industries. Dr Vanhatalo’s awards and hono urs include the 2014 “Søren Bisgaard Award” awarded by the American Society for Quality (ASQ) – Statistics Division and the “best PhD thesis award” at LTU for the 2009 academic year. He is a member of ENBIS.

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In-situ measurements of iron ore pellets using active RFID transponders RFID pellets follow transportation chain and measure acceleration and temperature In-situ data can aid interpretation of transport step induced stresses Results may be used to reduce product degradation and environmental impact Results may also be used for residence time calculations

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