Soft-Sensor for Class Prediction of the Percentage of Pentanes in Butane at a Debutanizer Column

Refineries are complex industrial systems that transform crude oil into more valuable subproducts. Due to the advances in sensors, easily measurable variables are continuously monitored and several data-driven soft-sensors are proposed to control the distillation process and the quality of the resultant subproducts. However, data preprocessing and soft-sensor modelling are still complex and time-consuming tasks that are expected to be automatised in the context of Industry 4.0. Although recently several automated learning (autoML) approaches have been proposed, these rely on model configuration and hyper-parameters optimisation. This paper advances the state-of-the-art by proposing an autoML approach that selects, among different normalisation and feature weighting preprocessing techniques and various well-known Machine Learning (ML) algorithms, the best configuration to create a reliable soft-sensor for the problem at hand. As proven in this research, each normalisation method transforms a given dataset differently, which ultimately affects the ML algorithm performance. The presented autoML approach considers the features preprocessing importance, including it, and the algorithm selection and configuration, as a fundamental stage of the methodology. The proposed autoML approach is applied to real data from a refinery in the Basque Country to create a soft-sensor in order to complement the operators’ decision-making that, based on the operational variables of a distillation process, detects 400 min in advance with 98.925% precision if the resultant product does not reach the quality standards.

Nano-Sensor Modelling for Intra-Body Nano-Networks

In this work, the author has evaluated the propagation of electromagnetic waves inside the human tissue such as blood, skin and fat for single-path and multi-path layers according to nano sensor transmit power calculations. In particular, the propagation characteristics of the Intra-Body Nano-Network communication channel are calculated using a theoretical approach. The analysis in this paper provides an evaluation related to the path loss, bit error rate, signal to noise ratio and the channel capacity. The model is evaluated for each single-path effect and multi-path effect. The effects of human tissue for each blood, skin and fat for single-path effect and multi-path are included in the analysis. The model frequency range is chosen from 0.01 to 1.5 THz frequencies, which are ideal for designing nano sensors antennae and using THz range for communication. This paper will also guide other researchers who are working on the electromagnetic radiation performance of Intra-Body Nano-Network and Nano sensors designed at the THz range.

A Method to Determine Human Skin Heat Capacity Using a Non-Invasive Calorimetric Sensor

A calorimetric sensor has been designed to measure the heat flow dissipated by a 2 × 2 cm2 skin surface. In this work, a non-invasive method is proposed to determine the heat capacity and thermal conductance of the area of skin where the measurement is made. The method consists of programming a linear variation of the temperature of the sensor thermostat during its application to the skin. The sensor is modelled as a two-inputs and two-outputs system. The inputs are (1) the power dissipated by the skin and transmitted by conduction to the sensor, and (2) the power dissipated in the sensor thermostat to maintain the programmed temperature. The outputs are (1) the calorimetric signal and (2) the thermostat temperature. The proposed method consists of a sensor modelling that allows the heat capacity of the element where dissipation takes place (the skin) to be identified, and the transfer functions (TF) that link the inputs and outputs are constructed from its value. These TFs allow the determination of the heat flow dissipated by the surface of the human body as a function of the temperature of the sensor thermostat. Furthermore, as this variation in heat flow is linear, we define and determine an equivalent thermal resistance of the skin in the measured area. The method is validated with a simulation and with experimental measurements on the surface of the human body.