Study and Design of a Machine Learning-Enabled Laser-Based Sensor for Pure and Sea Water Determination Using COMSOL Multiphysics

Abstract

Accurate detection of salt in water is crucial in many applications. Numerous techniques, using direct and indirect methods, have been employed to design seawater sensors. Among the indirect sensing methods, optical sensors are known to be the most accurate, easy to implement, and suitable for application where the chemical properties of the solution to be tested should stay unchanged. This research presents a novel method for real-time label-free biochemical detection of salty water combining various optics concepts with a machine learning system. COMSOL Multiphysics has been employed to design and simulate the proposed sensor. The designed device uses a laser light emitted from the top of a water container, with a sensing part located on the bottom surface. The laser light initially propagates in the air portion, then refracts when it comes into contact with the air-water interface. Different parameters, including the laser beam wavelength and its incident angles, the temperature, and the air-water levels are employed to generate a set of data and the multilayer perceptron classifier (MLP) to model prediction. The obtained results validated the concept of the proposed sensor using machine learning. The sensor’s prediction precision under various temperature conditions is R2 = 0.844, the equivalent of an MSE of 0.155.

Machine-Learning-Based Sensor Design for Water Salinity Prediction: A Conceptual Approach

“…In this study, we use COMSOL Multiphysics, a powerful tool widely used in various fields such as energy harvesting, medical devices, and engineering [42][43][44], to design and analyze our sensor function and obtain the required data. Despite numerous research studies using COMSOL to test seawater sensors, the possibility of integrating machinelearning approaches with COMSOL Multiphysics for seawater sensor development has yet to be thoroughly explored [45,46].…”