Collapsible
A physical drying unit that can fold, move, store and scale — designed for assembly, disassembly, transport and storage rather than a permanent fixed installation.
Collapsible by design. Controlled by IoT. Monitored by Agentic AI.
A modular drying platform combining a collapsible aluminium structure, hanging rack system, food-grade heat-resistant enclosure, IoT-based environmental control and Agentic AI anomaly monitoring.
Proposed engineering concept. No performance results, certifications or commercial validation are claimed. Dimensions and costs are preliminary estimates only.
Conventional drying relies on fixed racks, flat trays and manual supervision. This proposed concept combines three things that are usually separate: a physical structure that can collapse and scale, a sensing layer that measures the drying environment continuously, and an intelligence layer that watches for abnormal behaviour across the whole process.
A physical drying unit that can fold, move, store and scale — designed for assembly, disassembly, transport and storage rather than a permanent fixed installation.
IoT sensors continuously measure the drying environment — temperature, relative humidity, airflow, product weight, duration and ambient conditions — giving the system a live picture of the process.
Agentic AI monitors the entire process, analyses relationships between sensors and operating conditions, and looks for abnormal behaviour rather than simply displaying readings.
The proposed unit is built around a collapsible aluminium frame with a removable hanging rack system, wrapped in a flexible enclosure. Every major assembly is intended to be folded, removed or added without rebuilding the whole system.
Conventional fucuk drying commonly relies on flat trays. This concept instead proposes hanging the product vertically from a removable rack, so the drying surface is exposed on both sides and the rack itself can be lifted out, folded and stored. Whether hanging improves drying uniformity is a core question this PoC is designed to answer.
The rack, rails and enclosure are treated as separate modules rather than parts of one welded box. That is what allows the unit to be disassembled for transport, stored flat, and expanded by adding more units to the same control system.
Proposed Concept — assembly, hinge geometry, rack load rating and enclosure attachment method are preliminary and require engineering review and prototype validation.
The starting geometry below is a first-pass envelope for a single collapsible unit. It exists to give the prototype a concrete size to engineer against — not as a fixed final specification.
Preliminary engineering dimensions. Final geometry, hanging capacity and airflow configuration require prototype validation.
The material system is deliberately split into layers so each can be specified, sourced and validated independently. Material selection must be validated against actual operating temperature, durability, cleaning requirements and food-contact requirements.
Material compatibility with the actual operating temperature, cleaning and sanitation routine, durability under repeated folding, and food-contact suitability. No food-contact certification is claimed for any proposed material.
Separating structure, enclosure, hanging system and airflow lets the team validate each layer on its own — and swap one material without redesigning the entire unit.
The basic PoC bill of materials below covers the sensing, control, actuation and safety hardware needed to build one functional drying unit and start generating real operating data. Sensor classes and power figures are initial targets, not final selections.
Central edge computer that reads sensors, runs the deterministic control logic and hosts the data/control engine. Also the bridge to the Agentic AI layer.
Measures chamber and airflow temperature. Sensor class and placement are preliminary; accuracy and response time require validation.
Measures relative humidity inside the drying environment — a primary indicator of how the drying process is progressing.
Measures air movement through the chamber. Intended to confirm the blower is producing the airflow the control layer expects.
Tracks product weight over time as an indicator of moisture loss, giving the AI layer a drying-progress signal rather than only environment data.
Moves air through the chamber. Adjustable speed lets the control layer vary airflow as part of the drying cycle. Target figure is preliminary.
Supplies controlled heat within the enclosure. Power target is a starting point for the PoC and depends on chamber volume and insulation performance.
Switches the blower and heater under command from the control layer. Chosen for reliable, repeatable actuation rather than manual control.
Provides regulated DC power for sensors, controller and actuators, with the necessary converters for each component rail.
A separate protection path — over-temperature cutout and electrical protection — that does not depend on the AI layer or the main control software.
Directs intake and exhaust so the airflow path can be measured and adjusted. Final layout depends on measured distribution testing.
Protects the electronics, routes cabling safely and keeps the control module serviceable without dismantling the drying unit.
Ambient temperature, ambient humidity, differential pressure, energy/current measurement and door/opening sensors. Included only where the PoC needs them.
Component classes, sensor accuracy, blower and heater sizing, and the safety protection arrangement are preliminary PoC targets and require engineering validation before procurement or build.
Sensors feed a central edge controller. The deterministic control engine runs the drying cycle, while Agentic AI sits alongside it as a monitoring and reasoning layer. Control outputs drive the blower and heater, and the resulting change is measured again by the sensors — closing the loop.
The architecture deliberately separates what controls the dryer, what monitors it intelligently, and what protects it. Each layer has a distinct responsibility, and the safety layer is independent of both the control software and the AI.
IoT-based deterministic control. Handles the routine drying cycle using fixed, predictable rules — this is the layer that normally keeps the system running.
Agentic AI. Watches behaviour across sensors and operating conditions, looks for patterns that do not fit normal operation, reasons about possible causes, and interprets events for the operator.
Independent protection. Operates separately from the control software and the AI layer, and takes priority for critical conditions.
The separation of control, intelligence and safety is a design intent for the PoC. Final safety architecture, protection ratings and fail-safe behaviour require engineering validation against applicable standards.
The AI layer is not a chart that a person has to read. It observes system behaviour continuously, analyses relationships between sensors and operating conditions, and looks for abnormal behaviour. Where authorised, it can assist with corrective action — but the deterministic control layer handles normal operation, and the safety layer always takes priority.
Read continuous sensor data from every drying unit.
Understand the current drying conditions as a whole, not one reading at a time.
Identify patterns that deviate from expected behaviour.
Evaluate possible causes and relationships between sensors and operating conditions.
Where authorised, initiate an appropriate adjustment through the control layer.
Check whether the system responds as expected after the action.
Alert the operator or initiate safe-state logic when required.
Normal drying operation is handled primarily by the deterministic IoT control layer. The AI observes and reasons — it does not replace routine control logic.
Critical protection and emergency shutdown remain the responsibility of an independent safety layer that does not depend on the AI.
A continuous intelligence layer that watches the process for abnormal behaviour, interprets it, and supports the operator with analysis and, where authorised, corrective action.
The system is expected to spend almost all of its time in normal operation. The value of a separate intelligence layer is that it can recognise the early signs of abnormality before they become a serious condition — and hand the serious cases to independent safety controls.
The IoT system automatically maintains configured drying conditions. No intervention required.
The system observes the drift and may adjust operating parameters within its normal control range.
Agentic AI identifies unexpected behaviour, investigates relationships between signals and raises an interpreted alert.
Independent safety controls take priority and the system can enter a defined safe state.
The four states describe an intended operating model. The thresholds, escalation rules and safe-state definition are preliminary and must be specified and validated during prototype development.
The examples below illustrate the kind of abnormal behaviour the proposed Agentic AI layer is intended to reason about. They describe a proposed function and PoC target — not a validated detection capability.
Blower is commanded on and draws expected current, but measured air velocity through the chamber unexpectedly decreases over time.
AI correlates blower command, current draw and measured airflow, and flags airflow that no longer tracks the command.
Possible causes considered: duct obstruction, filter loading, leakage in the enclosure, sensor drift or blower degradation.
Raise an interpreted alert with likely causes, and where authorised, adjust blower output or pause the cycle pending inspection.
Temperature behaviour does not correspond with the heater command — for example it keeps rising after the heater is switched off.
AI compares commanded heat input against the temperature response curve and the expected rate of change.
Possible causes considered: SSR stuck closed, sensor placement issue, insulation behaviour, or residual heat effects.
Escalate to the operator immediately; the independent safety layer can enforce an over-temperature cutoff.
Humidity remains unusually high despite operating conditions that would normally reduce it.
AI tracks the humidity profile against airflow and temperature and flags a drying environment that is not progressing as expected.
Possible causes considered: insufficient exhaust, recirculation of moist air, wet batch loading or a blocked outlet.
Recommend an airflow or cycle adjustment and notify the operator if the humidity profile does not recover.
The product weight-loss curve deviates from the expected drying profile — too fast, too slow, or stalled.
AI compares the measured weight-loss curve against the expected shape rather than a single threshold value.
Possible causes considered: incorrect loading, uneven airflow distribution, load cell drift or a genuine process change.
Flag the batch for review, propose a drying-time adjustment, and record the deviation for later validation.
One sensor produces readings inconsistent with surrounding sensors measuring the same environment.
AI compares redundant and related sensors to identify readings that do not fit the pattern of its neighbours.
Possible causes considered: sensor failure, wiring fault, calibration drift or a localised physical condition.
Raise a sensor-health warning and, where redundant sensing exists, continue on the remaining sensors where safe to do so.
All anomaly examples above are proposed AI functions and PoC targets. Detection accuracy, false-positive behaviour and the correct response for each case must be established through prototype data, not assumed. Serious abnormalities are handled by the independent safety layer, not by the AI.
To increase drying capacity, additional drying units can be added with their own sensors and drying hardware, while the central IoT and Agentic AI infrastructure can remain shared. The intelligence layer does not need to be rebuilt for every new unit.
The central IoT/AI layer, the dashboard, the data history and the anomaly-monitoring logic are designed to be shared across units. Adding a dryer adds sensors and drying hardware — not another copy of the intelligence stack.
Actual network capacity, controller capacity, communication architecture, sensor addressing and control-loop timing all require validation. This concept does not imply unlimited scalability.
The scaling pattern is a proposed architecture. Practical limits on the number of units per controller, communication reliability and control-loop timing must be established by testing, not assumed.
A single control surface is intended to show every connected unit together — environment readings, drying progress, actuator status, the drying curve, alerts and the event log. The panel below is a design mockup using clearly labelled simulated placeholder values.
The dashboard above is a design mockup. All values, curves, statuses and log entries are simulated placeholders used to show the intended layout. They are not measured results from a prototype or test.
An estimated starting budget for the hardware needed to build a single functional PoC unit. Figures are ranges because component selection and fabrication method are not yet fixed.
| Component | Estimated cost (MYR) |
|---|---|
| Collapsible aluminium frame | RM800–1,500 |
| Collapsible hanging rack | RM300–700 |
| Food-grade heat-resistant enclosure | RM300–700 |
| Blower | RM250–600 |
| Heater | RM150–350 |
| Raspberry Pi 5 + storage | RM450–650 |
| Temperature sensors | RM80–200 |
| Humidity sensors | RM100–250 |
| Airflow sensor | RM150–400 |
| Load cell + HX711 | RM100–250 |
| SSR / relay / controllers | RM100–250 |
| Power / electrical components | RM200–400 |
| Safety protection | RM150–300 |
| Wiring / connectors / enclosure | RM150–300 |
| Air duct / inlet / outlet | RM150–300 |
| Miscellaneous fabrication | RM300–600 |
Preliminary estimate only. Actual cost depends on engineering specifications, component selection, fabrication method and prototype requirements.
The roadmap moves deliberately from the simplest possible proof — sensors and a controller — through the mechanical build, the controlled process, the intelligence layer and finally multi-unit scaling.
Bring up sensors and the edge controller, prove reliable data capture and basic actuation before any mechanical build.
Proposed sequenceBuild and test the folding frame, hanging rack and enclosure attachment. Verify assembly, disassembly and storage.
Proposed sequenceAdd blower, heater and airflow control. Establish a repeatable, monitored drying cycle.
Proposed sequenceIntroduce the intelligence layer, define normal behaviour and implement anomaly observation and reasoning.
Proposed sequenceRun real fucuk drying trials and compare behaviour against expected profiles. Generate operating data.
Proposed sequenceConnect multiple units to the shared IoT/AI layer and test scaling limits, communication and control timing.
Proposed sequencePhases and sequencing are proposed and may change as findings emerge. Durations are intentionally not stated because they depend on budget, fabrication and validation results.
The purpose of the PoC is to produce real operating data. Until those measurements exist, the concept remains a design hypothesis rather than a demonstrated result.
The PoC is intended to generate real operating data before performance claims are made. No drying-time reduction, energy saving, throughput or food-safety claim is asserted on this page. Any future claim should be traceable to measured results from the prototype.
The physical system can collapse. The drying capacity can scale. IoT provides continuous control and visibility. Agentic AI provides an intelligence layer that watches for abnormal behaviour while independent safety controls protect the system.
Fucuk drying is the initial proof-of-concept application because it has a clear product, a known drying requirement and a measurable output. The underlying platform — collapsible structure, modular hanging, IoT control and Agentic AI monitoring — is intended to be adaptable to other drying applications after validation. That adaptability is a hypothesis to be tested, not a claim.
This page describes a proposed engineering concept and proof-of-concept direction. It is prepared for technical discussion and prototype development planning.
Proposed Concept · PoC · Preliminary Design · Estimated Cost · To Be Validated
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