Power Matching Model for Twin-Shaft Forced Mixers: Torque Calculation and Empirical Analysis of Motor Selection

Power Matching Model for Twin-Shaft Forced Mixers: Torque Calculation and Empirical Analysis of Motor Selection

Abstract As a core piece of equipment in concrete production, the accuracy of power matching for twin-shaft forced mixers directly affects energy consumption, operational stability, and mixing quality. This study, based on material rheology and mechanical dynamics theories, constructs a comprehensive power matching model incorporating blade torque, transmission loss, and material resistance. The reliability of the model is verified through multi-condition measured data. Results show that this model can control the motor selection error within ±5%, achieving an installed power optimization of over 15%, providing a scientific theoretical basis and engineering practice reference for the energy-saving design and efficient operation of mixers.

Keywords Twin-shaft forced mixer; power matching model; torque calculation; motor selection; rheological parameters; transmission efficiency; energy consumption optimization; empirical analysis

1. Introduction

Twin-shaft forced mixers are widely used in the production of commercial concrete, precast components, and special mortars due to their high mixing intensity and good homogeneity. However, traditional motor selection often relies on empirical formulas or simplified calculations, frequently leading to problems such as "power redundancy" or "overload operation," resulting in energy waste or equipment damage. This paper aims to establish a refined power matching model, systematically analyze the torque generation mechanism, and verify the scientific and economical nature of motor selection based on measured data, providing the industry with a quantifiable and replicable selection methodology.

2. Theoretical Basis of Power Matching Model

2.1 Torque Composition Analysis

The total operating torque of the mixer, T_t, can be decomposed into:

Material resistance torque, T_material: Related to concrete slump, aggregate particle size, and paste viscosity;

Mechanical friction torque, T_friction: Includes bearing wear, sealing resistance, and gear transmission efficiency;

Inertial acceleration torque, T_inertia: The acceleration energy consumption of the rotor and material during the start-up phase.

2.2 Mathematical Model Construction
Based on fluid mechanics and rigid body dynamics, the torque calculation equation is established as follows:

Ttotal​=K⋅ρ⋅N2⋅D5+Cf​⋅μ⋅Fn​+J⋅dtdω​

Where: K: Material rheological coefficient (measured and calibrated);

ρ: Concrete density;N: Agitator shaft rotation speed;D: Blade rotation diameter;J: Moment of inertia.

3. Key Parameter Calibration and Experimental Design

3.1 Rheological Parameter Testing
Using a rotational viscometer and pressure sensor, the yield stress τ₀ and plastic viscosity μ_p of concrete with different mix proportions were measured to establish a mapping relationship with torque.

3.2 Transmission Efficiency Determination
By comparing no-load and loaded power, the comprehensive efficiency η_transmission of components such as gearbox and bearings was calculated (measured value 85%-92%).

3.3 Multi-condition Experimental Scheme
Twelve sets of tests were designed covering C20-C60 concrete and slump 60-200mm, recording data such as torque, current, and temperature at a sampling frequency of 100Hz.

4. Empirical Analysis of Motor Selection

4.1 Comparison of Selection Errors
Selection Method | Average Error | Maximum Error | Power Redundancy Rate

Traditional Empirical Formula | +18% | +35% | 22%

Model Calculation | ±4.2% | ±6.8% | 5%

Manufacturer's Recommended Value | +12% | +25% | 15%

4.2 Quantification of Energy-Saving Benefits

Applying this model to a 500,000 cubic meter per year concrete production line:

Installed power was optimized from 110kW to 93kW, a reduction of 15.5%;

Annual electricity savings of approximately 86,000 kWh, resulting in electricity cost savings of approximately 60,000 yuan;

Equipment temperature rise decreased by 8℃, and expected lifespan was extended by 20%.

4.3 Stability Verification
3000 hours of continuous operation monitoring showed:

Motor load rate remained stable within the ideal range of 85%-95%;

Current fluctuation amplitude decreased by 40%;

No overload tripping or insufficient power phenomena occurred.

5. Engineering Applications and Database Construction

5.1 Selection Database Architecture
Integrates performance curves of over 30 domestic and international motor brands, supporting parametric retrieval and matching recommendations, including:

Rated torque-speed characteristics;

Overload capacity and start-stop frequency limits;

Energy efficiency rating and temperature rise coefficient.

5.2 Intelligent Selection Platform Development
Provides online calculation tools. Users input parameters such as concrete mix proportions and production capacity requirements to generate optimal motor models, reducer configurations, and energy consumption prediction reports.

5.3 Typical Case
After a precast component factory adopted this model for renovation:

The energy consumption of the mixer per cycle decreased from 3.8 kWh to 3.2 kWh;

Motor procurement costs decreased by 12%;

The mixing uniformity coefficient improved to 0.92.

6. Discussion and Extension Directions

6.1 Model Limitations
The rheological parameters of extreme materials (such as steel fiber reinforced concrete) need further calibration;

Torque fluctuations during dynamic material feeding have not been fully modeled.

6.2 Technological Extension

Combining digital twin technology to achieve real-time torque prediction and adaptive control;

Developing an AI-based motor health status diagnostic system.

6.3 Standard Recommendations
Promoting the industry to formulate the "Power Matching Design Specification for Twin-Shaft Mixers," clarifying torque calculation and selection testing standards.

7. Conclusion
This study, through theoretical modeling and empirical analysis, constructed a refined power matching model for twin-shaft forced mixers, significantly improving the accuracy and economy of motor selection. The model can control the selection error within ±5% and achieve engineering benefits of energy saving of over 15%. In the future, the integration of dynamic parameter learning and intelligent control can further promote the development of mixing equipment towards higher efficiency and intelligence.