Prediction interval soft sensor for dissolved oxygen content ...

In this study, a novel soft sensor modeling approach using Takagi–Sugeno (TS) fuzzy models and Prediction Intervals (PIs) is presented to ...

In this study, a novel soft sensor modeling approach using Takagi-Sugeno (TS) fuzzy models and Prediction Intervals (PIs) is presented to quantify ...

Enhanced Predictive Modeling of Dissolved Oxygen Concentrations ...

At both stations, DO1 (i.e., the previous day DO) with SHAP values of +0.63 and +1.22, respectively, for the ILL and DP stations showed the ...

A dissolved oxygen levels prediction method based on single ...

A dissolved oxygen levels prediction method based on single-hidden layer feedforward neural network using neighborhood information metric.

Machine Learning Weather Soft-Sensor for Advanced Control of ...

This weather prediction differs from traditional weather forecasting since this soft-sensor predicts the weather conditions as an operator does when controling ...

An online soft sensor method for biochemical reaction process ...

Applying the constructed soft sensor model to the problem of predicting cell concentration and product concentration in Pichia pastoris ...

ANN-Based Soft Sensor to Predict Effluent Violations in Wastewater ...

... concentration because it determines the interval for which the prediction is generated. ... Dissolved oxygen concentrations at tank x (mg/L). S P , e, Total ...

What is a Soft Sensor or Software Sensor? - Körber Pharma

The output of a microbial soft sensor is the biomass concentration, which is predicted in real-time in an interval of 2 minutes. Next to the biomass dry ...

Comparison between I-TS and II-TS model with 1-σ prediction interval.

Prediction interval soft sensor for dissolved oxygen content estimation in an electric arc furnace. Article. Full-text available. Sep 2024; APPL SOFT COMPUT.

Development and application of random forest regression soft ...

In the context of wastewater treatment plants, RFR soft sensor model can be used to predict water quality (COD) through simpler diameters, in ...

Adaptive Soft Sensor for Bioprocess Monitoring - AMA Science

The dissolved oxygen was maintained at 40 %. As cultivation medium. FM22 with an initial glycerol concentration of. 40 g L–1 was used. The pH of the batch ...

Oxygen Uptake Rate Soft-Sensing via Dynamic kLa Computation

In aerobic cell cultivation processes, dissolved oxygen is a key process parameter, and an optimal oxygen supply has to be ensured for ...

Fuzzy Interval Oxygen Estimation in an Electric Arc Furnace from ...

Each measurement of the dissolved oxygen content imposes an operational delay and an unnecessary loss of energy. The fuzzy prediction interval can be used in ...

Water quality soft-sensor prediction in anaerobic process using ...

One of the solutions is using a soft-sensor prediction technique. This article introduces a water quality soft-sensor prediction method based on ...

Development of a Soft Sensor Using Machine Learning Algorithms ...

The soft sensor integrates data that can be obtained from well-established in-line sensors to accurately predict key water quality parameters, ...

Development of Interval Soft Sensors Using Enhanced Just-in-Time ...

These ideas were applied to a wastewater treatment process. The proposed interval soft sensor was seen to be effective for prediction in the ...

Inline Oxygen Sensor: Oxy | Anton Paar

Measuring range (dissolved O₂ in liquids), - (Gas phase only), 0 ppb to 2000 ppb ; Measuring range (gas phase O₂ in CO₂), 0 ppmv to 200 ppmv (0 hPa to 0.2 hPa) ...

Machine learning based soft sensor model for BOD estimation using ...

In this paper, we propose a machine learning-based soft sensor model to estimate biological oxygen demand (BOD), a time-consuming and challenging process to ...

Measuring Dissolved Oxygen In Water - In-Situ

As in any monitoring program, biofouling will grow on the sensors when instruments are left in the field. This can lead to data drift and even damage the sensor ...

A soft sensor modeling method based on the regularized AdaBound ...

And different parameters have bounded adaptive learning rates. Finally, RAIT2FNN is used as a soft sensor model to predict the tail oxygen ...