# Three-Dimensionally Printed Sensors with Piezo-Actuators and Deep Learning for Biofuel Density and Viscosity Estimation

## TL;DR

A new 3D-printed sensor system with AI is developed to accurately measure biofuel density and viscosity, offering a compact and precise solution for industrial use.

## Contribution

Integration of 3D-printed sensors, piezo-actuators, and deep learning for precise biofuel property estimation.

## Key findings

- The system achieves low calibration and resolution errors for viscosity and density measurements.
- Algorithmic methods improve sensor lifespan and reduce data acquisition time.
- The device is compact, precise, and suitable for various industrial liquids.

## Abstract

What are the main findings?
A novel device integrating 3D-printed sensors with convolutional neural networks for biofuel characterization.Development of sensor optimization strategies and spectral data processing techniques.

A novel device integrating 3D-printed sensors with convolutional neural networks for biofuel characterization.

Development of sensor optimization strategies and spectral data processing techniques.

What are the implications of the main findings?
Successful integration of multiple technologies into a compact, lightweight and highly precise instrument.Providing solutions to typical sensor limitations such as resolution, sensitivity and drift.

Successful integration of multiple technologies into a compact, lightweight and highly precise instrument.

Providing solutions to typical sensor limitations such as resolution, sensitivity and drift.

Biofuels have emerged as a promising alternative to conventional fuels, offering improved environmental sustainability. Nevertheless, inadequate control of their physicochemical properties can lead to increased emissions and potential engine damage. Existing methods for regulating these properties depend on costly and sophisticated laboratory equipment, which poses significant challenges for integration into industrial production processes. Three-dimensional printing technology provides a cost-effective alternative to traditional fabrication methods, offering particular benefits for the development of low-cost designs for detecting liquid properties. In this work, we present a sensor system for assessing biofuel solutions. The presented device employs piezoelectric sensors integrated with 3D-printed, liquid-filled cells whose structural design is refined through experimental validation and novel optimization strategies that account for sensitivity, recovery and resolution. This system incorporates discrete electronic circuits and a microcontroller, within which artificial intelligence algorithms are implemented to correlate sensor responses with fluid viscosity and density. The proposed approach achieves calibration and resolution errors as low as 0.99% and 1.48×10−2 mPa·s for viscosity, and 0.0485% and 1.9×10−4 g/mL for density, enabling detection of small compositional variations in biofuels. Additionally, algorithmic methodologies for dimensionality reduction and data treatment are introduced to address temporal drift, enhance sensor lifespan and accelerate data acquisition. The resulting system is compact, precise and applicable to diverse industrial liquids.