A novel data-driven soft sensor is designed for online product quality prediction and control performance modification in industrial units. A combined approach of time variable parameter (TVP) model, dynamic auto regressive exogenous variable (DARX) algorithm, nonlinear correlation analysis and criterion-based elimination method is introduced in this work. The soft sensor performance validation...

Some crucial process variables in fermentation process could not be measured directly. Soft sensor technology provided an effective way to solve the problem. There has been considerable interest in modeling a soft sensor by using artificial neural network (ANN) in bioprocess. To generate a more efficient soft sensor model, we proposed a novel soft sensor model based on artificial neural network...

A sensor for in vivo biomechanical characterization of buttock soft tissue has been developed and evaluated. The sensor measures interface pressure, applied force, tilt, and rotation angle of the sensor head, and the thicknesses of multiple soft tissue layers. A composite ultrasonic transducer using a 1-3 ceramic-polymer structure was developed for the sensor. The sensor can identify subcutaneo...

Soft Sensors (SSs) are inferential dynamical models employed in industries to perform prediction of process hard-to-measure variables based on their relation with easily accessible ones. They allow implementation real-time control and monitoring the plants present other advantages terms costs efforts. Given complexity industrial processes, these generally designed data-driven black-box machine ...

Soft sensors are the combination of robust on-line sensor signals with mathematical models for deriving additional process information. Here, we apply this principle to a microbial recombinant protein production process in a bioreactor by exploiting bio-calorimetric methodology. Temperature sensor signals from the cooling system of the bioreactor were used for estimating the metabolic heat of t...

In this paper we propose a soft sensor for prediction of NOx emission from the combustion unit of industrial boilers. The soft sensor is based on a dynamical neural network model. A simplified structure of the dynamical neural network model is achieved by grouping the input variables using basic knowledge of the system. Neural network model is trained using real data logs of an industrial boile...

In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are co...

Abstract: In recent decades, micro air vehicles driven by electric propellers have become a hot topic, and developed quickly. The performance of the vehicles depends on the rotational speed of propellers, thus, improving the accuracy of rotational speed measurement is beneficial to the vehicle’s performance. This paper presents the development of a soft sensor for the rotational speed measureme...

Online monitoring humidity in the proton exchange membrane (PEM) fuel cell is an important issue in maintaining proper membrane humidity. The cost and size of existing sensors for monitoring humidity are prohibitive for online measurements. Online prediction of humidity using readily available measured data would be beneficial to water management. In this paper, a novel soft sensor method based...

To cope up with the strict emission and product quality standards the process industries are stern for the maintenance of the product quality and waste emission. For process industries, online hardware analyzers are costly alternate for analyzing the product quality, they are switching to online soft sensors which are cheap and efficient in use. In this work, soft sensor based on artificial neu...