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Continuous bulk-volume measurement in sugar productionContinuous bulk-volume measurement in sugar productionContinuous bulk-volume measurement in sugar production
20 Jan 2023 20. Januar 2023 20 de enero de 2023 · Sachtleben Technology
A 90-day sugar campaign moves more raw material through a single site than most factories see in a year. Beet arrives in waves — sometimes 200 trucks per day — lands on the beet pad (Rübenplatte), accumulates in several beet stockpiles (Rübenhalden) of 50 000 to 300 000 t each, raw sugar fills silos and bays, and pulp leaves the dryer in a near-continuous stream. The industry has historically reconciled all of this on paper, with weekly inventories taken by walking the pad or sticking a rod into a silo.
By the time those numbers reach the controller, the campaign has moved on.
Why beet pads and beet stockpiles are the hardest case
A beet pad is probably the most demanding stockpile geometry in the industry: open-air, no roof, often 100 m or longer, with a constantly shifting form depending on where trucks are tipping or loaders are pulling. Classic measurement methods all fail in some way:
- Drone surveys deliver accurate volumes — but only as a snapshot from last week. During a campaign the pile changes hourly; the value is stale before it lands on the controller's screen.
- Manual surveying ties up a surveyor for half a shift and blocks parts of the pad for other movements.
- Tonnage estimation via weighbridge tickets drifts over the campaign — after 60 days, book stock and reality are typically 3–5 % apart.
The mast-cluster configuration
An OWL EYE® STOCKPILE installation for a beet pad looks like this: 3–6 LiDAR sensors on masts around the pad, together delivering a 360° view of the entire stockpile geometry. Each sensor scans every few minutes; the software fuses the point clouds into a closed 3D model and computes volume per pile section.
| Position | Classic (drone / manual) | OWL EYE® mast cluster |
|---|---|---|
| Measurement frequency | weekly | every few minutes |
| Accuracy | ±2–3 % (drone) / ±5 % (manual) | ±1 % |
| Coverage | snapshot at survey time | continuous, 24/7 |
| Weather sensitivity | high (wind/rain blocks drone) | low (LiDAR works through rain + snow) |
| Person-hours per measurement | 4–6 | 0 |
| Frost / night operation | not possible | runs normally |
| Data history | spreadsheets, gappy | full timeline per pile voxel |
Masts typically stand 8–12 m tall at the corner and mid-points of the pad. The sensors live in IP65+ stainless-steel housings with heated optics (for frost), and connect via a single PoE cable per mast — no cabling running across the active pad.
Where LiDAR changes the math. A fixed OWL EYE® STOCKPILE installation scans the same volume every few minutes, day and night. Output is a 3D point cloud, a calibrated volume figure, and — once the bulk density of the material is known — a tonnage. Accuracy under campaign conditions stays inside ±1 %, which matches what plants used to achieve only with a full belt-scale calibration cycle.
Dust and humidity. Sugar campaigns are not gentle environments. Dryer exhaust, cooling-tower plumes and the fine sugar dust around silo tops all scatter laser light. The OWL EYE® sensor stack uses 905 nm Class 1 LiDAR with multi-return processing, which lets the system reject the front edge of a dust cloud and lock onto the actual material surface behind it. In a Polish sugar plant we ran through the 2022 campaign, the system held its accuracy through a three-day stretch of sub-zero humidity events that would have fogged a conventional optical level sensor.
What plant managers actually do with the data. Three patterns recur across the sugar customers we work with — including the campaign documented in Sugar Industry J48 (2023):
- Live tonnage in the silo feeds straight into the centrifugation schedule. No more "we think we have enough for the next shift."
- Truck-side volume readings (via OWL EYE® TRUCK TERMINAL) cross-check the weighbridge ticket without slowing the line.
- End-of-campaign reconciliation between book stock, scale data and LiDAR volume now closes within a few hours instead of a week of disputes.
What it does not replace. A LiDAR system measures volume, not composition. Polarisation, sugar grade, moisture content — none of that comes from the point cloud. We pair the volume data with the existing lab and near-infrared instruments. The two sources do not compete; they confirm each other.
The integration question. A typical sugar-plant retrofit takes one to two weeks of installation per silo, plus a single calibration pass against a known reference fill. Communication runs over OPC UA or Modbus TCP to the existing control system; no proprietary middleware required. Plants that have already replaced one or two manual measurement points with LiDAR usually expand the rollout in the next campaign break.
Sugar is a campaign business, and the data window matters. A measurement that takes a week to produce is a measurement the campaign has already moved past. Continuous LiDAR shifts that window to minutes.
More on the beet-pad solution at /stockpile/, on the beet-receiving line at /volume-flow/, or write to info@sachtleben-technology.com — we'll come back with a mast-cluster sketch within one business day.
A 90-day sugar campaign moves more raw material through a single site than most factories see in a year. Beet arrives in waves — sometimes 200 trucks per day — lands on the beet pad (Rübenplatte), accumulates in several beet stockpiles (Rübenhalden) of 50 000 to 300 000 t each, raw sugar fills silos and bays, and pulp leaves the dryer in a near-continuous stream. The industry has historically reconciled all of this on paper, with weekly inventories taken by walking the pad or sticking a rod into a silo.
By the time those numbers reach the controller, the campaign has moved on.
Why beet pads and beet stockpiles are the hardest case
A beet pad is probably the most demanding stockpile geometry in the industry: open-air, no roof, often 100 m or longer, with a constantly shifting form depending on where trucks are tipping or loaders are pulling. Classic measurement methods all fail in some way:
- Drone surveys deliver accurate volumes — but only as a snapshot from last week. During a campaign the pile changes hourly; the value is stale before it lands on the controller's screen.
- Manual surveying ties up a surveyor for half a shift and blocks parts of the pad for other movements.
- Tonnage estimation via weighbridge tickets drifts over the campaign — after 60 days, book stock and reality are typically 3–5 % apart.
The mast-cluster configuration
An OWL EYE® STOCKPILE installation for a beet pad looks like this: 3–6 LiDAR sensors on masts around the pad, together delivering a 360° view of the entire stockpile geometry. Each sensor scans every few minutes; the software fuses the point clouds into a closed 3D model and computes volume per pile section.
| Position | Classic (drone / manual) | OWL EYE® mast cluster |
|---|---|---|
| Measurement frequency | weekly | every few minutes |
| Accuracy | ±2–3 % (drone) / ±5 % (manual) | ±1 % |
| Coverage | snapshot at survey time | continuous, 24/7 |
| Weather sensitivity | high (wind/rain blocks drone) | low (LiDAR works through rain + snow) |
| Person-hours per measurement | 4–6 | 0 |
| Frost / night operation | not possible | runs normally |
| Data history | spreadsheets, gappy | full timeline per pile voxel |
Masts typically stand 8–12 m tall at the corner and mid-points of the pad. The sensors live in IP65+ stainless-steel housings with heated optics (for frost), and connect via a single PoE cable per mast — no cabling running across the active pad.
Where LiDAR changes the math. A fixed OWL EYE® STOCKPILE installation scans the same volume every few minutes, day and night. Output is a 3D point cloud, a calibrated volume figure, and — once the bulk density of the material is known — a tonnage. Accuracy under campaign conditions stays inside ±1 %, which matches what plants used to achieve only with a full belt-scale calibration cycle.
Dust and humidity. Sugar campaigns are not gentle environments. Dryer exhaust, cooling-tower plumes and the fine sugar dust around silo tops all scatter laser light. The OWL EYE® sensor stack uses 905 nm Class 1 LiDAR with multi-return processing, which lets the system reject the front edge of a dust cloud and lock onto the actual material surface behind it. In a Polish sugar plant we ran through the 2022 campaign, the system held its accuracy through a three-day stretch of sub-zero humidity events that would have fogged a conventional optical level sensor.
What plant managers actually do with the data. Three patterns recur across the sugar customers we work with — including the campaign documented in Sugar Industry J48 (2023):
- Live tonnage in the silo feeds straight into the centrifugation schedule. No more "we think we have enough for the next shift."
- Truck-side volume readings (via OWL EYE® TRUCK TERMINAL) cross-check the weighbridge ticket without slowing the line.
- End-of-campaign reconciliation between book stock, scale data and LiDAR volume now closes within a few hours instead of a week of disputes.
What it does not replace. A LiDAR system measures volume, not composition. Polarisation, sugar grade, moisture content — none of that comes from the point cloud. We pair the volume data with the existing lab and near-infrared instruments. The two sources do not compete; they confirm each other.
The integration question. A typical sugar-plant retrofit takes one to two weeks of installation per silo, plus a single calibration pass against a known reference fill. Communication runs over OPC UA or Modbus TCP to the existing control system; no proprietary middleware required. Plants that have already replaced one or two manual measurement points with LiDAR usually expand the rollout in the next campaign break.
Sugar is a campaign business, and the data window matters. A measurement that takes a week to produce is a measurement the campaign has already moved past. Continuous LiDAR shifts that window to minutes.
More on the beet-pad solution at /stockpile/, on the beet-receiving line at /volume-flow/, or write to info@sachtleben-technology.com — we'll come back with a mast-cluster sketch within one business day.
A 90-day sugar campaign moves more raw material through a single site than most factories see in a year. Beet arrives in waves — sometimes 200 trucks per day — lands on the beet pad (Rübenplatte), accumulates in several beet stockpiles (Rübenhalden) of 50 000 to 300 000 t each, raw sugar fills silos and bays, and pulp leaves the dryer in a near-continuous stream. The industry has historically reconciled all of this on paper, with weekly inventories taken by walking the pad or sticking a rod into a silo.
By the time those numbers reach the controller, the campaign has moved on.
Why beet pads and beet stockpiles are the hardest case
A beet pad is probably the most demanding stockpile geometry in the industry: open-air, no roof, often 100 m or longer, with a constantly shifting form depending on where trucks are tipping or loaders are pulling. Classic measurement methods all fail in some way:
- Drone surveys deliver accurate volumes — but only as a snapshot from last week. During a campaign the pile changes hourly; the value is stale before it lands on the controller's screen.
- Manual surveying ties up a surveyor for half a shift and blocks parts of the pad for other movements.
- Tonnage estimation via weighbridge tickets drifts over the campaign — after 60 days, book stock and reality are typically 3–5 % apart.
The mast-cluster configuration
An OWL EYE® STOCKPILE installation for a beet pad looks like this: 3–6 LiDAR sensors on masts around the pad, together delivering a 360° view of the entire stockpile geometry. Each sensor scans every few minutes; the software fuses the point clouds into a closed 3D model and computes volume per pile section.
| Position | Classic (drone / manual) | OWL EYE® mast cluster |
|---|---|---|
| Measurement frequency | weekly | every few minutes |
| Accuracy | ±2–3 % (drone) / ±5 % (manual) | ±1 % |
| Coverage | snapshot at survey time | continuous, 24/7 |
| Weather sensitivity | high (wind/rain blocks drone) | low (LiDAR works through rain + snow) |
| Person-hours per measurement | 4–6 | 0 |
| Frost / night operation | not possible | runs normally |
| Data history | spreadsheets, gappy | full timeline per pile voxel |
Masts typically stand 8–12 m tall at the corner and mid-points of the pad. The sensors live in IP65+ stainless-steel housings with heated optics (for frost), and connect via a single PoE cable per mast — no cabling running across the active pad.
Where LiDAR changes the math. A fixed OWL EYE® STOCKPILE installation scans the same volume every few minutes, day and night. Output is a 3D point cloud, a calibrated volume figure, and — once the bulk density of the material is known — a tonnage. Accuracy under campaign conditions stays inside ±1 %, which matches what plants used to achieve only with a full belt-scale calibration cycle.
Dust and humidity. Sugar campaigns are not gentle environments. Dryer exhaust, cooling-tower plumes and the fine sugar dust around silo tops all scatter laser light. The OWL EYE® sensor stack uses 905 nm Class 1 LiDAR with multi-return processing, which lets the system reject the front edge of a dust cloud and lock onto the actual material surface behind it. In a Polish sugar plant we ran through the 2022 campaign, the system held its accuracy through a three-day stretch of sub-zero humidity events that would have fogged a conventional optical level sensor.
What plant managers actually do with the data. Three patterns recur across the sugar customers we work with — including the campaign documented in Sugar Industry J48 (2023):
- Live tonnage in the silo feeds straight into the centrifugation schedule. No more "we think we have enough for the next shift."
- Truck-side volume readings (via OWL EYE® TRUCK TERMINAL) cross-check the weighbridge ticket without slowing the line.
- End-of-campaign reconciliation between book stock, scale data and LiDAR volume now closes within a few hours instead of a week of disputes.
What it does not replace. A LiDAR system measures volume, not composition. Polarisation, sugar grade, moisture content — none of that comes from the point cloud. We pair the volume data with the existing lab and near-infrared instruments. The two sources do not compete; they confirm each other.
The integration question. A typical sugar-plant retrofit takes one to two weeks of installation per silo, plus a single calibration pass against a known reference fill. Communication runs over OPC UA or Modbus TCP to the existing control system; no proprietary middleware required. Plants that have already replaced one or two manual measurement points with LiDAR usually expand the rollout in the next campaign break.
Sugar is a campaign business, and the data window matters. A measurement that takes a week to produce is a measurement the campaign has already moved past. Continuous LiDAR shifts that window to minutes.
More on the beet-pad solution at /stockpile/, on the beet-receiving line at /volume-flow/, or write to info@sachtleben-technology.com — we'll come back with a mast-cluster sketch within one business day.
Want to discuss this topic or share your own experience? Email info@sachtleben-technology.com — we always reply. Möchten Sie über dieses Thema sprechen oder Ihre Erfahrung teilen? Schreiben Sie an info@sachtleben-technology.com — wir antworten immer. Chcą Państwo omówić ten temat lub podzielić się własnym doświadczeniem? Proszę napisać na info@sachtleben-technology.com — zawsze odpowiadamy.