Machine Learning Analysis for Phenolic Compound Monitoring Using a Mobile Phone-Based ECL Sensor.

Machine learning (ML) can be an appropriate approach to overcoming common problems associated with sensors for low-cost, point-of-care diagnostics, such as non-linearity, multidimensionality, sensor-to-sensor variations, presence of anomalies, and ambiguity in key features. This study proposes a novel approach based on ML algorithms (neural nets, Gaussian Process Regression, among others) to model the electrochemiluminescence (ECL) quenching mechanism of the [Ru(bpy)]/TPrA system by phenolic compounds, thus allowing their detection and quantification. The relationships between the concentration of phenolic compounds and their effect on the ECL intensity and current data measured using a mobile phone-based ECL sensor is investigated.

Electrochemiluminescence Mechanisms Investigated with Smartphone-Based Sensor Data Modeling, Parameter Estimation and Sensitivity Analysis.

The present study introduces a unified framework combining a mechanistic model with a genetic algorithm (GA) for the parameter estimation of electrochemiluminescence (ECL) kinetics of the Ru(bpy)/TPrA system occurring in a smartphone-based sensor. The framework allows a straightforward solution for simultaneous estimation of multiple parameters which can be, otherwise, time-consuming and lead to non-convergence. Model parameters are estimated by achieving a high correlation between the model prediction and the measured ECL intensity from the ECL sensor.

Data-Driven Modeling of Smartphone-Based Electrochemiluminescence Sensor Data Using Artificial Intelligence.

Understanding relationships among multimodal data extracted from a smartphone-based electrochemiluminescence (ECL) sensor is crucial for the development of low-cost point-of-care diagnostic devices. In this work, artificial intelligence (AI) algorithms such as random forest (RF) and feedforward neural network (FNN) are used to quantitatively investigate the relationships between the concentration of Ru(bpy)32+ luminophore and its experimentally measured ECL and electrochemical data. A smartphone-based ECL sensor with Ru(bpy)32+/TPrA was developed using disposable screen-printed carbon electrodes.

Study of kinetic parameters in a mechanistic model for bioethanol production through a screening technique and optimization.

The accurate description of the kinetics and robust modeling of biotechnological processes can only be achieved by incorporating reliable methodologies to easily update the model when there are changes in operational conditions. The purpose of this work is to provide a systematic approach with which to perform model parameters screening and updating in biotechnological processes. Batch experiments are performed to develop a mechanistic model, considering the effect of temperature on the kinetics, and further experiments (batch fermentations using sugar cane molasses from a different harvest) are used to validate the effectiveness of screening before parameters updating.

Hybrid neural network model of an industrial ethanol fermentation process considering the effect of temperature.

In this work a procedure for the development of a robust mathematical model for an industrial alcoholic fermentation process was evaluated. The proposed model is a hybrid neural model, which combines mass and energy balance equations with functional link networks to describe the kinetics. These networks have been shown to have a good nonlinear approximation capability, although the estimation of its weights is linear.

Estimation of temperature dependent parameters of a batch alcoholic fermentation process.

In this work, a procedure was established to develop a mathematical model considering the effect of temperature on reaction kinetics. Experiments were performed in batch mode in temperatures from 30 to 38 degrees C. The microorganism used was Saccharomyces cerevisiae and the culture media, sugarcane molasses.

Kinetic modeling and parameter estimation in a tower bioreactor for bioethanol production.

In this work, a systematic method to support the building of bioprocess models through the use of different optimization techniques is presented. The method was applied to a tower bioreactor for bioethanol production with immobilized cells of Saccharomyces cerevisiae. Specifically, a step-by-step procedure to the estimation problem is proposed.

Bioethanol production optimization: a thermodynamic analysis.

In this work, the phase equilibrium of binary mixtures for bioethanol production by continuous extractive process was studied. The process is composed of four interlinked units: fermentor, centrifuge, cell treatment unit, and flash vessel (ethanol-congener separation unit). A proposal for modeling the vapor-liquid equilibrium in binary mixtures found in the flash vessel has been considered.

Ethyl alcohol production optimization by coupling genetic algorithm and multilayer perceptron neural network.

In this present article, genetic algorithms and multilayer perceptron neural network (MLPNN) have been integrated in order to reduce the complexity of an optimization problem. A data-driven identification method based on MLPNN and optimal design of experiments is described in detail. The nonlinear model of an extractive ethanol process, represented by a MLPNN, is optimized using real-coded and binary-coded genetic algorithms to determine the optimal operational conditions.