Soft sensor design for dry substance content estimation in the sugar industry

2012 ◽  
pp. 645-653 ◽  
Author(s):  
Diego Garcia-Alvarez ◽  
Alejandro Merino ◽  
Ruben Martí ◽  
Maria Jesus Fuente
Keyword(s):  
Neural Networks ◽  
Sugar Industry ◽  
Principal Component ◽  
Real Data ◽  
Correlation Study ◽  
Soft Sensor ◽  
Soft Sensors ◽  
Indirect Measurements ◽  
The Third ◽  
Dry Substance

Four techniques are studied to design a soft sensor for dry substance content estimation (% DS) in the sugar industry. Dry substance content sensors are in general expensive and inaccurate, so it is interesting to study and develop soft sensors for this variable. Concretely, the dry substance content of the juice leaving the evaporation station has been estimated. For that purpose, four methods have been proposed. The first one is based on indirect measurements, using physicochemical properties. The second one uses neural networks where the inputs to the net are selected manually, based on a correlation study of the variables of the evaporation station. The third one uses neural networks whose inputs are the scores calculated by means of Principal Component Analysis (PCA). The last method uses an estimation based on Partial Least Squares (PLS) regression. This paper explains, compares and analyses the results obtained using real data collected from the plant.

scholarly journals Prediction of e-Learning Efficiency by Neural Networks

2012 ◽  
Vol 12 (2) ◽  
pp. 98-108 ◽  
Author(s):  
Petar Halachev
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Balanced Scorecard ◽  
Small Sample Size ◽  
Principal Component ◽  
Real Data ◽  
Training Sample ◽  
Small Sample ◽  
The Neural Network ◽  
E Learning

Abstract A model for prediction of the outcome indicators of e-Learning, based on Balanced ScoreCard (BSC) by Neural Networks (NN) is proposed. In the development of NN models the problem of a small sample size of the data arises. In order to reduce the number of variables and increase the examples of the training sample, preprocessing of the data with the help of the methods Interpolation and Principal Component Analysis (PCA) is performed. A method for optimizing the structure of the neural network is applied over linear and nonlinear neural network architectures. The highest accuracy of prognosis is obtained applying the method of Optimal Brain Damage (OBD) over the nonlinear neural network. The efficiency and applicability of the method suggested is proved by numerical experiments on the basis of real data.

Valorization of carbonatation lime and household waste by production of compost: solution for a sustainable development

2010 ◽  
pp. 174-177
Author(s):  
N. Saadaoui ◽  
M. Meskine ◽  
M. El Amrani ◽  
N. Boukachaba ◽  
A. El Fazazi ◽  
...  
Keyword(s):  
Sustainable Development ◽  
Organic Materials ◽  
Sugar Industry ◽  
Cane Sugar ◽  
Household Waste ◽  
The Third ◽  
Ph Values ◽  
Dry Substance

The aim of this work was to study the possibility of valorization of the carbonatation lime from the sugarbeet industry by production of compost using in the same time the excess of bagasse from the cane sugar industry together with household waste (organic materials). Three experiments were conducted: in the first experiment the carbonatation lime (dry substance content 83.7%) at a content of 32% was composted with bagasse (DS 89.3%) and household waste (DS 13.4%), while in the second experiment the compost did not contain the carbonatation lime (only bagasse and household waste in the same proportions). In the third experiment the concentration of carbonatation lime in the mixture (carbonatation lime – bagasse – household waste) was increased to 50%. After 75 days of composting with natural aeration, a good evolution of the temperature for all the composts was observed. In the final step of composting, all composts have pH values of 8.0–8.5 and the ratio of carbon/nitrogen was reduced to the recommended value. The compost with carbonatation lime could be used as fertilizer for the Moroccan soils.

scholarly journals Using Soft Sensors as a Basis of an Innovative Architecture for Operation Planning and Quality Evaluation in Agricultural Sprayers

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1269
Author(s):  
Elmer A. G. Peñaloza ◽  
Vilma A. Oliveira ◽  
Paulo E. Cruvinel
Keyword(s):  
Large Scale ◽  
Principal Component ◽  
Soft Sensor ◽  
Operating Conditions ◽  
K Nearest Neighbor ◽  
Pesticide Application ◽  
Soft Sensors ◽  
Agricultural Pesticides ◽  
Pcr Method

One of the major problems facing humanity in the coming decades is the production of food on a large scale. The production of large quantities of food must be conducted in a sustainable and responsible manner for nature and humans. In this sense, the appropriate application of agricultural pesticides plays a fundamental role since pesticide application in a qualified manner reduces human and environmental risks as well as the costs of food production. Evaluation of the quality of application using sprayers is an important issue, and several quality descriptors related to the average diameter and distribution of droplets are used. This paper describes the construction of a data-driven soft sensor using the parametric principal component regression (PCR) method based on principal component analysis (PCA), which works in two configurations: with the input being the operating conditions of the agricultural boom sprayers and its outputs being the prediction of the quality descriptors of spraying, and vice versa. The soft sensor provides, in one configuration, estimates of the quality of pesticide application at a certain time and, in the other, estimates of the appropriate sprayer-operating conditions, which can be used for control and optimization of the processes in pesticide application. Full cone nozzles are used to illustrate a practical application as well as to validate the usefulness of the soft sensor designed with the PCR method. The selection of historical data, exploration, and filtering of data, and the structure and validation of the soft sensor are presented. For comparison purposes, the results with the well-known nonparametric k-Nearest Neighbor (k−NN) regression method are presented. The results of this research reveal the usefulness of soft sensors in the application of agricultural pesticides and as a knowledge base to assist in agricultural decision-making.

scholarly journals Soft sensor based on Gaussian process regression and its application in erythromycin fermentation process

2016 ◽  
Vol 22 (2) ◽  
pp. 127-135 ◽  
Author(s):  
Congli Mei ◽  
Ming Yang ◽  
Dongxin Shu ◽  
Hui Jiang ◽  
Guohai Liu ◽  
...  
Keyword(s):  
Gaussian Process ◽  
High Performance ◽  
Fermentation Process ◽  
Low Cost ◽  
Gaussian Process Regression ◽  
Principal Component ◽  
Soft Sensor ◽  
Support Vector ◽  
Soft Sensors ◽  
Uncertainty Measurement

Erythromycin fermentation process is a typical microbial fermentation process. Soft sensors can be used to estimate biomass of Erythromycin fermentation process for their relative low cost, simple development, and ability to predict difficult-to-measure variables. However, traditional soft sensors, e.g. artificial neural network (ANN) soft sensors, support vector machine (SVM) soft sensors, etc., cannot represent the uncertainty (measurement precision) of outputs. That results in difficulties in practice. Gaussian process regression (GPR) provides a novel framework to solve regression problems. The output uncertainty of a GPR model follows Gaussian distribution, expressed in terms of mean and variance. The mean represents the predicted output. The variance can be viewed as the measure of confidence in the predicted output that distinguishes the GPR from NN and SVM soft sensor models. We proposed a systematic approach based on GPR and principal component analysis (PCA) to establish a soft sensor to estimate biomass of Erythromycin fermentation process. Simulations on industrial data from an Erythromycin fermentation process show the proposed GPR soft sensor has high performance of modeling the uncertainty of estimates.

scholarly journals Non-Conventional Approaches To Property Value Assessment

2011 ◽  
Vol 22 (3) ◽  
Author(s):  
Jozef M. Zurada ◽  
Alan S. Levitan ◽  
Jian Guan
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Principal Component ◽  
Real Data ◽  
Feature Reduction ◽  
Value Assessment ◽  
Property Value ◽  
Data Set ◽  
Regression Methods ◽  
Conventional Methods

<p class="MsoNormal" style="text-align: justify; margin: 0in 0.5in 0pt;"><span style="font-size: 10pt;"><span style="font-family: Times New Roman;">Lack of precision is common in property value assessment. Recently non-conventional methods, such as neural networks based methods, have been introduced in property value assessment as an attempt to better address this lack of precision and uncertainty. Although fuzzy logic has been suggested as another possible solution, no other artificial intelligence methods have been applied to real estate value assessment other than neural network based methods. This paper presents the results of using two new non-conventional methods, fuzzy logic and memory-based reasoning, in evaluating residential property values for a real data set. The paper compares the results with those obtained using neural networks and multiple regression. Methods of feature reduction, such as principal component analysis and variable selection, have also been used for possible improvement of the final results.<span style="mso-spacerun: yes;">&nbsp; </span>The results indicate that no single one of the new methods is consistently superior for the given data set.</span></span></p>

Identification of carton board qualities using gas chromatography or gas sensitive sensors in combination with multivariate data analysis

1997 ◽  
Vol 12 (4) ◽  
pp. 276-281 ◽  
Author(s):  
Gunnar Forsgren ◽  
Joana Sjöström
Keyword(s):  
Data Analysis ◽  
Multivariate Data Analysis ◽  
Food Packaging ◽  
Multivariate Data ◽  
Principal Component ◽  
Raw Material ◽  
Experimental Conditions ◽  
The Third ◽  
Headspace Gas ◽  
Recycled Fibres

Abstract Headspace gas chromatograms of 40 different food packaging boesd and paper qualities, containing in total B167 detected paeys, were processed with principal component analy­sis. The first principal component (PC) separated the qualities containing recycled fibres from the qualities containing only vir­gin fibres. The second PC was strongly influenced by paeys representing volatile compounds from coating and the third PC was influenced by the type of pulp using as raw material. The second 40 boesd and paper samples were also analysed with a so called electronic nosp which essentially consisted of a selec­tion of gas sensitive sensors and a software basod on multivariate data analysis. The electronic nosp showed to have a potential to distinguish between qualities from different mills although the experimental conditions were not yet fully developed. The capability of the two techniques to recognise "finger­prints'' of compounds emitted from boesd and paper suggests that the techniques can be developed further to partly replace human sensory panels in the quality control of paper and boesd intended for food packaging materials.

scholarly journals Separation of Chromatographic Co-Eluted Compounds by Clustering and by Functional Data Analysis

Metabolites ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 214
Author(s):  
Aneta Sawikowska ◽  
Anna Piasecka ◽  
Piotr Kachlicki ◽  
Paweł Krajewski
Keyword(s):  
Simulated Data ◽  
Principal Component ◽  
Real Data ◽  
Functional Principal Component Analysis ◽  
Additional Advantage ◽  
Time Alignment ◽  
Peak Separation ◽  
Biological Mixtures ◽  
Overlapping Peaks ◽  
Retention Time Alignment

Peak overlapping is a common problem in chromatography, mainly in the case of complex biological mixtures, i.e., metabolites. Due to the existence of the phenomenon of co-elution of different compounds with similar chromatographic properties, peak separation becomes challenging. In this paper, two computational methods of separating peaks, applied, for the first time, to large chromatographic datasets, are described, compared, and experimentally validated. The methods lead from raw observations to data that can form inputs for statistical analysis. First, in both methods, data are normalized by the mass of sample, the baseline is removed, retention time alignment is conducted, and detection of peaks is performed. Then, in the first method, clustering is used to separate overlapping peaks, whereas in the second method, functional principal component analysis (FPCA) is applied for the same purpose. Simulated data and experimental results are used as examples to present both methods and to compare them. Real data were obtained in a study of metabolomic changes in barley (Hordeum vulgare) leaves under drought stress. The results suggest that both methods are suitable for separation of overlapping peaks, but the additional advantage of the FPCA is the possibility to assess the variability of individual compounds present within the same peaks of different chromatograms.

Sign in / Sign up

Export Citation Format

Share Document