In industrial automation and process control, achieving high-precision temperature measurement is critical. Platinum Resistance Thermometers (PRTs), particularly Pt100 RTD sensors, are widely used across engineering fields due to their wide operating temperature range, excellent repeatability, stability, and fast thermal response times.

While standard reference PRTs offer exceptional accuracy, their complex manufacturing processes, long production cycles, and high costs limit their practical application on the factory floor. Consequently, industrial-grade Pt100 sensors have become the go-to solution. However, meeting ultra-high precision requirements within a limited budget remains a challenge. This article explores advanced methods to significantly improve the measurement accuracy of industrial platinum resistance thermometers.

The Accuracy Bottleneck of Industrial Pt100 Sensors

According to the verification regulation JJG 229-2010 (Industrial Platinum and Copper Resistance Thermometers), the Maximum Permissible Error (MPE) for a Class A platinum resistor at 0°C is $\pm0.15^{\circ}\text{C}$.

While Class A sensors are highly cost-effective and suitable for general industrial use, many high-precision applications—such as laboratory testing, pharmaceuticals, and nuclear power plants—require a system error of less than $0.1^{\circ}\text{C}$. This creates a technical bottleneck where standard sensor tolerances limit overall system performance.

Method 1: PC-Based Automated Temperature Detection Systems

To break through the precision limit of standard Class A sensors, engineers have developed a cost-effective, computer-based automated temperature detection system.

How It Works:

1. Hardware Integration: The system utilizes standard Pt100 RTD sensing elements integrated with high-precision data acquisition modules.

2. Software Correction: A computer-driven automated testing rig continuously monitors the temperature environment and applies real-time error-correction algorithms to the raw sensor data.

Key Benefits:

• Enhanced Accuracy: Experimental results demonstrate that this method successfully reduces the total system measurement error to within $0.1^{\circ}\text{C}$.

• Cost-Efficiency: It achieves near-laboratory-grade precision using standard industrial components, making it highly scalable and practical for widespread industrial thermal resistance measurements.

Method 2: Non-Linear Least Squares Curve Fitting (Matlab Lsqnonlin)

For extreme environments requiring ultimate precision—such as bypass temperature monitoring in nuclear power plant reactor coolant systems—hardware upgrades alone are insufficient. Instead, advanced mathematical calibration methods must be applied.

Using Non-Linear Least Squares regression, engineers can map the exact mathematical relationship between resistance and temperature for individual sensors, bypassing the generalized Callendar-Van Dusen standard curves.

[Raw Resistance Data] ➔ [Matlab lsqnonlin Function] ➔ [Custom R-T Formula] ➔ [High-Precision Temperature]

The Implementation Process:

1. Data Collection: The resistance values of the Pt100 sensor are recorded at multiple calibrated temperature points alongside a high-precision standard reference thermometer.

2. Algorithm Fitting: Utilizing the Lsqnonlin function on the Matlab platform, a non-linear curve fitting algorithm optimizes the coefficients of the resistance-temperature ($R\text{-}T$) relationship function.

3. Deviation Analysis: The temperature values calculated via this custom optimized function are compared against the reference thermometer to analyze residuals.

The Result:

Applying the non-linear least-squares relationship function minimizes calibration mathematical errors. Data shows that this software-defined calibration method improves temperature measurement accuracy by an entire order of magnitude across the sensor’s full operating range.