Smart Temperature sensor guide

How LoRaWAN Temperature and Humidity Sensors Enable Cold Chain Equipment Performance Analysis

Short Answer

LoRaWAN wireless temperature and humidity sensors do more than trigger alerts when a cold room goes out of range. When deployed as multi-point arrays and connected to an analytics platform, the continuous time-series data they generate reveals the performance health of every piece of refrigeration equipment in a cold chain facility — compressor efficiency, evaporator heat exchange effectiveness, defrost cycle completeness, door seal integrity, spatial temperature distribution, and energy consumption trends.

The transition from monitoring to performance analysis happens at the analytics layer: raw sensor readings become diagnostic signals when they are trended over time, correlated across sensor points, and compared against established performance baselines. A supply-return temperature differential that narrows by 2°C over 30 days is not an alert condition — it is an early warning of evaporator degradation that no single threshold-based alert would catch. A humidity recovery curve that takes 45 minutes instead of 20 minutes after a door-close event is not a temperature excursion — it is evidence of a failing door seal. These are the insights that transform temperature and humidity data from a compliance tool into a performance intelligence system.

Introduction

There is a significant difference between knowing that a cold room is within temperature range right now and understanding how well the refrigeration system serving that room is performing over time. The first is monitoring. The second is performance analysis. Most cold chain IoT deployments stop at the first. The data infrastructure required for the second is already in place the moment LoRaWAN sensors start transmitting — the gap is analytical, not technological.

Cold chain equipment failure does not happen instantly. A compressor losing valve efficiency, a condenser coil accumulating fouling, an evaporator building up frost between defrost cycles, a door seal gradually failing — all of these degradation processes unfold over days, weeks, or months. During that entire period, the cold room may remain within its temperature setpoint. The equipment is still working — just increasingly inefficiently, at increasing energy cost, and with decreasing margin before a genuine failure causes a temperature excursion.

Without continuous multi-point sensor data and trend analytics, this degradation is invisible. The energy bill climbs. The compressor runs longer and longer to maintain setpoint. And then one night, the equipment crosses its efficiency cliff and the cold room warms. By the time the temperature alert fires, the degradation has been underway for weeks.

LoRaWAN temperature and humidity sensors, generating continuous timestamped data at every monitored point, provide the raw material to detect this degradation long before it becomes a cold chain breach. This article explains exactly how that analysis works — what the data reveals, how each performance diagnostic is derived, what KPIs matter, and how the analytics integrate with the compliance reporting that FSSAI and HACCP frameworks require.

The Gap Between Monitoring and Performance Analysis

Most discussions of cold chain IoT focus on real-time monitoring: sensor reads temperature, compares to threshold, sends alert if exceeded. This is necessary and valuable — but it is the minimum viable use of the data these sensors generate.

Real-time threshold monitoring answers one question: Is the cold room within range right now?

Performance analysis answers a fundamentally different set of questions:

• Is the refrigeration equipment maintaining setpoint as efficiently as it did six months ago?
• Which piece of equipment is showing early signs of degradation?
• Is the defrost cycle completing fully, and how long does temperature recovery take afterward?
• Is spatial temperature distribution uniform, or are there hot zones that put product at risk even when the single monitoring probe reads correctly?
• What is driving the 15% increase in electricity consumption this quarter?
• Which door opening pattern is causing the highest thermal infiltration load?

None of these questions can be answered by a single-point alert system. All of them can be answered by a multi-point LoRaWAN sensor array generating continuous time-series data connected to an analytics platform.

The distinction matters commercially. A monitoring system protects you from the failures you know to watch for. A performance analysis system surfaces the failures you did not know to look for — and surfaces them weeks or months before they become cold chain events.

What Data LoRaWAN Temperature and Humidity Sensors Collect in Cold Chain Environments

Before examining what performance analysis is possible, it is important to be precise about what data a LoRaWAN T&H sensor array actually generates — because the analytical possibilities are constrained by and determined by the data available.

A correctly deployed multi-point LoRaWAN sensor array in a cold storage facility continuously generates:

Derived parameters calculated at the analytics layer:

This derived-parameter layer is where raw sensor readings become performance intelligence. None of it requires additional hardware beyond the LoRaWAN T&H sensors already deployed for compliance monitoring — it requires only that the data be retained in a time-series database and that the analytics layer be configured to compute these metrics continuously.

From Raw Sensor Data to Equipment Performance Intelligence: The Analytics Stack

The data journey from a LoRaWAN sensor transmission to a performance insight on a dashboard involves four distinct layers, each adding analytical value to the raw readings.

Layer 1 — Data Collection (LoRaWAN Network)

The sensor transmits a compact binary payload every 5–15 minutes. The LoRaWAN gateway forwards the packet to the network server (The Things Stack, ChirpStack, or AWS IoT Core for LoRaWAN), where the payload is decrypted and decoded into clean JSON fields: temperature, humidity, battery voltage, timestamp.

Layer 2 — Time-Series Storage

Decoded data is written to a time-series database — InfluxDB, TimescaleDB, or a cloud equivalent such as AWS Timestream or Azure Time Series Insights. Time-series databases are specifically optimised for high-frequency numerical data indexed by timestamp — exactly the data structure that cold chain sensor arrays generate. Every reading from every sensor is stored with nanosecond-precision timestamps, retained for months or years.

This data retention is the foundation of performance analysis. A compressor COP trend requires 30 days of baseline data before it can identify a degrading trend. A seasonal energy efficiency comparison requires a full year. Without time-series storage and retention, there is no trend analysis — only point-in-time snapshots.

Layer 3 — Analytics and Computation

The analytics layer — which may be a purpose-built cold chain platform, a general IoT analytics platform such as ThingsBoard or Grafana with custom dashboards, or a custom data pipeline — performs three types of computation continuously:

Baseline establishment: For each sensor and each derived parameter, the system establishes a healthy-state baseline over an initial 30-day period. The supply-return differential at a particular evaporator under normal load conditions. The post-defrost temperature recovery time in a particular zone. These baselines are the reference points against which future performance is compared.

Deviation detection: Ongoing readings are compared against baseline. When a derived parameter deviates from baseline by more than a defined threshold — for example, the supply-return differential narrows by more than 1.5°C from the 30-day average — the system generates a maintenance alert rather than a temperature exceedance alert. This is predictive maintenance triggered by performance data, not by compliance threshold breaches.

Trend projection: The analytics layer fits trend lines to historical derived parameters. A compressor COP proxy declining at 0.3% per week projects to a maintenance threshold in 8 weeks — generating a scheduled maintenance recommendation before the failure occurs.

Layer 4 — Visualisation and Reporting

Performance dashboards present derived parameters in operator-friendly formats: trend charts for COP proxy and supply-return differential, heat maps for spatial temperature distribution, bar charts for zone-level energy intensity, and compliance reports for FSSAI and HACCP documentation. This layer is where the data becomes operationally useful for the people who make maintenance and operational decisions.

The Old Answer: Run New Cable. Why It No Longer Makes Sense

For decades, "connect this instrument to the control system" meant one answer: run cable. The economics of that answer have not aged well.

The True Cost of Industrial Cabling

When plant managers budget for a new cabling run, they typically think about the cost of the cable itself. That is the smallest part of the total.

The full cost of a new instrument cable run in an existing industrial facility includes:

Material costs:

• Signal cable (screened, armoured for outdoor/underground runs)
• Conduit or cable tray
• Glands, terminals, lugs, junction boxes
• Cable markers and documentation labels

Labour costs:

• Cable routing survey and planning
• Mechanical installation (conduit fitting, tray installation)
• Cable pulling and dressing
• Termination at both ends (instrument and panel)
• Continuity testing and insulation resistance testing
• Loop calibration and commissioning

Civil costs (if underground or through structural elements):

• Concrete or tarmac cutting
• Excavation for buried runs
• Reinstatement of surfaces after cable installation
• Structural penetration sealing (fire-rated, where required)

Operational costs:

• Process shutdown for safe installation in live areas
• Production loss during the shutdown window
• Permit-to-work administration
• Inspection and sign-off by electrical authority (for hazardous area installations)

In Indian industrial facilities, a fully-installed 100-metre screened instrument cable run typically costs between ₹40,000 and ₹1,50,000 depending on environment, routing complexity, and whether civil works are required. Outdoor underground runs, hazardous area installations (ATEX/IECEx zones), or runs through congested cable routes push the figure significantly higher.

For a facility with twenty instruments needing wireless connectivity at an average run length of 80 metres each, the cabling cost alone — before any data platform, software, or integration work — is between ₹8,00,000 and ₹30,00,000.

That is before a single dashboard has been configured or a single alert has been set.

Performance Analysis 1: Compressor Health and COP Degradation

The compressor is the most energy-intensive component in any refrigeration system, accounting for approximately 72% of total refrigeration electricity consumption in a typical cold storage facility. It is also the component whose efficiency degradation is most consequential — and most invisible without continuous sensor trending.

What Compressor Degradation Looks Like in Temperature Data

A healthy compressor maintains a stable supply air temperature at the evaporator outlet for a given ambient load. As the compressor degrades — from worn valve seats, refrigerant charge loss, bearing wear, or oil contamination — its volumetric efficiency falls. It moves less refrigerant per stroke, delivers less cooling per unit of electricity consumed, and must run for longer periods to maintain the same chamber temperature.

In temperature sensor data, early compressor degradation appears as:

• Longer compressor run times to restore setpoint after a door-open event or defrost cycle — detectable by comparing post-event temperature recovery curves to baseline
• Higher supply air temperature at the same load conditions — the evaporator receives less cooling capacity per cycle, so supply air does not cool as far before the cycle ends
• Gradual chamber temperature creep during heavy load periods — the compressor can no longer keep pace with heat infiltration during peak door-opening periods, showing as a rising temperature trend during operating hours even while maintaining setpoint during low-traffic periods

Deriving a COP Proxy From Ambient Sensor Data

A full Coefficient of Performance calculation requires refrigerant pressure measurements — suction and discharge pressures on the refrigerant circuit. These require specialist pressure transducers that are not standard equipment in most cold storage facilities.

However, a useful COP proxy can be derived from ambient temperature sensor data combined with energy metering, using the following approach:

Step 1 — Estimate heat removal rate (Qc): The heat removed from the cold space by the evaporator is proportional to the supply-return air temperature differential multiplied by the air mass flow rate through the evaporator:

Qc ≈ ṁ × Cp × ΔT

Where ΔT is the supply-return temperature differential (measurable from two LoRaWAN sensors positioned at supply air and return air points), ṁ is the estimated air mass flow rate (from fan specifications), and Cp is the specific heat of air (approximately 1.006 kJ/kg·K).

Step 2 — Measure electrical work input (W): A LoRaWAN-compatible energy meter on the compressor circuit measures actual electricity consumption in kWh per interval.

Step 3 — COP proxy: COP proxy = Qc ÷ W

The absolute value of this proxy is less important than its trend over time. A COP proxy declining from 3.2 to 2.7 over 60 days — a 15% efficiency drop — while the cold room continues to maintain setpoint represents a refrigeration system silently consuming 15% more electricity to deliver the same cooling. Without this trending, that efficiency loss is invisible. With it, a maintenance work order can be generated before the efficiency drop becomes a cold chain breach.

⚠️ Important note: The COP proxy derived from ambient temperature sensors is an approximation. It requires calibrated sensor placement at true supply and return air points, known or estimated airflow rates, and concurrent energy metering. For absolute COP measurement, dedicated refrigeration monitoring systems with refrigerant pressure transducers provide more accurate results. The ambient sensor approach is appropriate for trend analysis and early degradation detection — not for precise thermodynamic analysis.

Performance Analysis 2: Evaporator and Defrost Cycle Efficiency

The evaporator coil is the heat exchange surface where refrigerant absorbs heat from the cold room air. Its efficiency is directly reflected in the supply-return temperature differential — a healthy evaporator with clean coils and good airflow maintains a large differential (supply air is much colder than return air). As frost accumulates between defrost cycles, or as coils foul, the differential narrows.

Reading the Post-Defrost Temperature Recovery Curve

Every defrost cycle introduces a thermal disturbance into the cold room: the evaporator heater warms the coils, the surrounding air temperature rises transiently, and then the refrigeration system restores setpoint. The shape of this recovery curve — specifically how long it takes the chamber temperature to return to setpoint after defrost ends — is a direct indicator of both defrost completeness and evaporator condition.

Normal defrost recovery profile:

A well-functioning system in a −18°C deep-freeze room might show the following pattern:

• Defrost starts: chamber temperature begins to rise at ~0.5°C/minute
• Peak temperature during defrost: −12°C to −10°C (depending on defrost duration and chamber load)
• Defrost ends: chamber temperature begins falling immediately
• Recovery to setpoint: 15–25 minutes for a healthy system

Degraded recovery signatures:

Each of these signatures is detectable in temperature and humidity time-series data without any additional instrumentation beyond the sensors already deployed for compliance monitoring. The analytics layer compares each defrost event's recovery profile to the established baseline and generates an alert when the deviation exceeds a defined threshold.

Detecting Ice Bridging and Evaporator Frost Buildup

Evaporator ice bridging — where frost builds across the fin gaps of the evaporator coil before the scheduled defrost cycle, reducing airflow — shows up in temperature data as a progressive narrowing of the supply-return differential between defrost cycles. The trend is gradual: the differential narrows by 0.3–0.5°C per day as frost accumulates, then recovers sharply after defrost, then narrows again — but not quite returning to the pre-frost baseline each time. Over two to three weeks, this pattern is clearly visible in trend data and indicates either that defrost cycle frequency needs to increase or that door-open frequency is driving unusually rapid frost accumulation.

Performance Analysis 3: Condenser Fouling Detection

The condenser rejects heat from the refrigerant to the ambient environment. As the condenser coil fouls — from dust, grease, lint, or biological growth — its ability to reject heat degrades. The refrigerant discharge temperature rises, the compressor works harder, and electricity consumption increases. A condenser fouled to 12°C above its clean-state setpoint forces the compressor to consume approximately 18% more electricity to deliver the same cooling output.

Condenser fouling appears in temperature sensor data as:

• Rising compressor discharge temperature at the same ambient conditions — detectable if a temperature sensor is positioned on the discharge line or near the condenser outlet
• Rising chamber temperature during peak ambient temperature periods — the condenser cannot reject heat fast enough when ambient is high, so chamber temperature creeps upward during hot afternoons even though it recovers at night
• Increasing compressor run-time ratio — the compressor runs for a higher fraction of each hour to maintain the same setpoint

Correlating chamber temperature trends with ambient temperature data (from an external LoRaWAN sensor monitoring outdoor conditions) allows the analytics platform to separate genuine equipment degradation from normal seasonal variation. A chamber that runs warmer every summer is expected. A chamber whose summer performance degrades year-over-year is showing progressive condenser fouling.

Performance Analysis 4: Door Seal Integrity Through Humidity and Temperature Correlation

Every time a cold room door is opened, warm humid ambient air enters the chamber. Two things happen immediately and measurably in sensor data: temperature rises and humidity rises. Both recover as the refrigeration system removes the heat and moisture load. The rate and pattern of this recovery is a precise diagnostic tool for door seal condition.

The Healthy Door Event Signature

In a cold room with a good door seal and a healthy refrigeration system, a standard door-open event (30–60 seconds for a typical forklift entry) produces:

• Temperature rise of 0.5–2°C at the sensor nearest the door, recovering to setpoint within 10–20 minutes
• Humidity spike of 5–15% RH, recovering to baseline within 15–25 minutes as the refrigeration system dehumidifies the infiltrated air
• Both recovery curves are smooth exponential decays — fast initial recovery, then gradual final approach to baseline

The Failing Door Seal Signature

A failing door seal changes this signature in two specific ways:

Continuous low-level infiltration between door events: When the door is closed but the seal is failing, ambient air leaks into the chamber continuously at a low rate. This appears as a slow, steady humidity creep — not correlated with logged door events — that the refrigeration system partially compensates for but cannot fully offset. The baseline humidity in the chamber slowly rises over days or weeks.

Longer post-event recovery times: A failing seal allows infiltrated air to continue entering the chamber even after the door closes, extending the temperature and humidity recovery curves beyond their healthy-state baselines.

The diagnostic check: Compare the humidity reading between door events — during periods of no logged forklift activity, no defrost, and no unusual conditions — over a 30-day window. A well-sealed chamber should show stable humidity at or near the design condition. A slowly rising inter-event humidity baseline indicates seal degradation. The rate of rise is proportional to the severity of the leak.

Catching this early — replacing a door seal as a planned maintenance item — is significantly cheaper than waiting for the humidity infiltration to compromise product packaging, increase compressor load, and eventually cause a temperature excursion during a heat wave.

Performance Analysis 5: Spatial Temperature Mapping and Hot Zone Detection

This is the performance analysis dimension that single-point monitoring is structurally incapable of providing — and the one that most directly affects product integrity in ways that go undetected under standard compliance monitoring.

Why Single-Point Monitoring Misses Product Risk

A cold room with a single temperature probe positioned at the regulatory-required monitoring point — typically at mid-height in the centre of the room, away from the door — reads the temperature that the refrigeration system is designed to produce at that point. This reading complies with FSSAI requirements and generates no alerts.

What it does not reveal:

• Product stored on the shelving unit nearest the entry door may be 3–6°C warmer than the monitoring probe reads, due to thermal infiltration from door openings
• Product stored near the ceiling may be warmer than mid-height product, due to natural thermal stratification in rooms with poor air circulation
• Product stored directly in front of the evaporator air discharge may be overcooled, with surface freezing occurring in a product specified for refrigerated (not frozen) storage
• Corner zones with poor airflow may have localised warm pockets that persist even when the room average is within specification

None of these conditions trigger a single-point alert. All of them affect product quality and shelf life.

Multi-Point LoRaWAN Sensor Array for Spatial Mapping

A multi-point LoRaWAN sensor deployment — five to nine sensors per large cold room, positioned at different heights, depths, and proximity to door and evaporator — generates a spatial temperature dataset that can be visualised as a heat map updated at every sensor transmission interval.

Recommended sensor positions for spatial mapping in a large cold room:

With this array, the spatial temperature map reveals:

• Stratification gradient: The temperature difference between ceiling sensor and floor sensor indicates the degree of thermal stratification. In a well-mixed room, this should be less than 1°C. A gradient above 2°C indicates poor air circulation — typically from blocked evaporator airflow, accumulated product blocking fan discharge, or fan failure.
• Door infiltration radius: The temperature differential between the door-adjacent sensor and the far-zone sensor during and after door events quantifies the thermal infiltration load and identifies how far into the room warm air penetrates.
• Evaporator distribution uniformity: The temperature differential across multiple zones on the same horizontal plane indicates whether the evaporator is distributing cooled air uniformly or creating cold and warm zones.

This spatial dataset also directly supports HACCP critical control point analysis — demonstrating not just that the room was within range, but that specific product storage zones were within range throughout the monitoring period.

Performance Analysis 6: Energy Efficiency Analysis

Cold storage electricity costs are dominated by refrigeration — approximately 70% of total facility energy consumption. Within that, compressors account for roughly 72% of refrigeration electricity. Energy efficiency analysis using temperature and humidity data identifies where electricity is being consumed inefficiently and what operational or maintenance changes would reduce it.

Door Activity and Thermal Load Analysis

Each door-open event imposes a thermal load on the refrigeration system — the compressor must remove the heat and moisture that entered during the door-open period. The magnitude of this load is proportional to door-open duration, ambient temperature, and the temperature differential between chamber and ambient.

By correlating door-event logs with the temperature recovery data from the nearest sensor, the analytics platform can calculate the thermal load imposed by each door event and aggregate this by shift, by day, and by door location. This analysis typically reveals:

• Which doors impose the highest thermal load per event (often loading bay doors with large differential and long open times)
• Which operational shifts create the highest aggregate door load (often early morning shift when loading peaks)
• Whether scheduling high-frequency door activity during cooler periods (early morning, late evening) would meaningfully reduce compressor load

Scheduling peak loading operations during cooler ambient periods can reduce compressor cycling by 15–25%, directly reducing electricity consumption without any change to equipment.

Defrost Timing Optimisation

Fixed-timer defrost cycles — for example, defrost every six hours regardless of frost accumulation — are the default setting on most refrigeration controllers. In practice, frost accumulation rate varies with door opening frequency, ambient humidity, and stored product moisture content. During low-traffic periods, frost accumulation may be minimal; during peak door-opening periods, it may be rapid.

Temperature sensor data reveals the actual frost accumulation rate between defrost cycles through the supply-return differential trend. When this differential narrows faster than baseline between successive defrosts, frost is accumulating more rapidly than the defrost timer accounts for. When it narrows more slowly, the defrost cycle is running more frequently than the actual frost load requires.

Adjusting defrost frequency to match actual measured frost accumulation — rather than a fixed timer — reduces unnecessary defrost energy consumption (each defrost cycle consumes energy to heat the coils and disturbs chamber temperature) while ensuring that frost never accumulates to the point where airflow is restricted.

The KPI Framework: What Metrics Cold Chain Operators Should Track

Effective cold chain performance analytics requires a defined set of KPIs — metrics that are measurable from sensor data, meaningful for equipment health assessment, and actionable when they deviate from baseline.

Primary Equipment Performance KPIs

Key Specifications and What They Mean for Your Deployment

Input Specification

LoRaWAN Specification

Physical and Environmental

⚠️ Pre-publication check: Insert Macnman-specific datasheet values for all specifications above before publishing. Replace any placeholder figures with the actual product specifications.

Frequently Asked Questions

Q: Will connecting the Macnman converter affect my existing flow meter reading or the PLC input?

No. The converter connects in series with the 4-20mA loop and reads the loop current without modifying it. The existing PLC or panel meter continues to receive the same signal it always has, with no change in reading or behaviour. The converter adds a small series resistance (typically 10–50 Ω) to the loop, which may marginally reduce the loop voltage headroom — verify that the loop power supply has sufficient compliance voltage for the additional drop, particularly on loops already operating near their maximum cable resistance budget.

Q: My flow meter outputs a pulse signal, not 4-20mA. Will this converter work?

No — this converter is specifically designed for 4-20mA analogue current loop signals. Pulse-output flow meters (turbine meters with frequency output, water meters with reed switch pulse, etc.) require a pulse-input LoRaWAN converter. Contact us to identify the correct product for pulse-output applications.

Q: What LoRaWAN network server do I need?

Any LoRaWAN-compliant network server works — The Things Stack Community Edition (free for most deployments), ChirpStack (open-source, self-hosted), AWS IoT Core for LoRaWAN, or Azure IoT Hub. For Indian deployments on IN865, verify that your chosen network server is configured with the IN865 frequency plan. The Things Stack Community Edition supports IN865 out of the box.

Q: How far can the converter transmit from the flow meter to the gateway?

In open outdoor environments with the gateway antenna mounted at 10+ metres above ground, reliable transmission ranges of 3–10 km are achievable. Inside industrial buildings with concrete and steel structures, practical range is typically 300–1,500 m depending on the number and type of obstacles between converter and gateway. See our [LoRaWAN Gateway and Sensor Placement Guide] for detailed guidance on gateway siting and antenna height to maximise coverage.

Q: How often does the converter send data, and can I change it?

The default transmission interval is configurable at commissioning — common defaults are 15 or 30 minutes. The interval can be changed after deployment via a LoRaWAN downlink command from the network server, without physically accessing the converter. Shorter intervals (5 minutes) give better data resolution but reduce battery life proportionally. For applications where real-time flow monitoring is critical, external power supply enables continuous high-frequency operation without battery life concerns.

Q: Does the converter work inside a metal junction box or control panel?

The converter's radio signal is significantly attenuated by a metal enclosure. If the converter must be installed inside a metal box, use an external antenna connected to the converter via an SMA extension cable routed through a cable gland in the enclosure. The converter body can be inside the box; the antenna must be outside it with a clear path toward the gateway.

Q: Is LoRaWAN legal to use in India?

Yes. LoRaWAN operating on the IN865 frequency plan (865–867 MHz) falls under India's unlicensed spectrum allocation for Short Range Devices as per WPC (Wireless Planning and Coordination) regulations. No individual licence is required for IN865 LoRaWAN devices operating within the specified power limits. Verify current WPC regulations before deployment, as spectrum rules are subject to revision.

Q: What happens to data if the LoRaWAN network is temporarily unavailable?

The converter continues to sample the 4-20mA loop at the configured interval regardless of network availability. However, standard LoRaWAN Class A devices do not buffer failed uplinks — if the gateway is offline or out of range during a transmission, that reading is lost. For applications where data continuity during network outages is critical, select a converter variant with local data buffering capability, or specify a gateway with cellular failover backhaul. See our [LoRaWAN Gateway Placement Guide] for gateway selection guidance.

Summary: What the Macnman Converter Actually Replaces

The Macnman 4-20mA to LoRaWAN Converter is not a sensor. It does not measure flow. Your existing flow meter does that — accurately, continuously, and with whatever certification it was installed under.

What the Macnman converter replaces is the infrastructure cost of getting that measurement to a system that can use it:

The data that was already being measured — that 4-20mA signal sitting on the loop terminals of your flow meter right now — becomes available to your cloud platform, your SCADA, your energy management dashboard, and your maintenance alert system. Without a single new metre of cable.