Development of a new comprehensive predictive modeling and control framework for multiple-input, multiple-output processes

The increase in the competitiveness of chemical process industries has necessitated the need for lowered energy and raw material consumption and improved quality control with tighter limits. This stronger control of the process conditions has generated interest in the use of advanced process control, and Model Predictive Control (MPC) is one such approach. The idea behind MPC is the use of a model to predict future behavior and use that knowledge to manipulate the input variables so that a cost function is minimized, resulting in optimal control. The predictive model is the core of the MPC method, and the success of a particular strategy hence depends on the accuracy of the model. The task of model building is also very challenging and time-consuming, so an ideal modeling approach would be one that does not require too much data but maintains its accuracy. The semi-empirical modeling approach has the strength that by using an intelligent model form, it has minimal data requirements. The use of semi-empirical models was first demonstrated by Rollins et al., and they coined the term SET (semi-empirical approach) for their method. The accuracy of SET over conventional empirical models was one of the biggest advantages of that approach. The ease of model identification, robustness in parameters, and a novel algorithm were some of the other strengths. Thus SET showed great potential for use in a multivariate situation, and the results of this work have shown that SET has been able to handle this challenge successfully. This extension led to the creation of a comprehensive modeling framework, which is far superior to the current modeling approach using the semi-empirical models. The various components of this approach are: use of statistical design of experiments, model identification, a novel model structure, and finally an algorithm that seeks to maximize accuracy

A Deep Learning Model for Classifying the Hate and Offensive Language in Social Media Text

Recently, we had introduced a model for identifying and removal of toxic content from twitter, using an Information Retrieval (IR) model SOIR (Semantic query Optimization-based Information Retrieval). Based on lexical and semantic analysis, SOIR identifies the class labels of tweets. The result demonstrates the superiority of the SOIR model. This model is accurate but social media is a big data problem and a significant amount of time and memory is required. In this paper the deep learning technique is used to process large-scale social media text data. First uses Natural Language Processing (NLP) based feature extraction to create four different sets of training samples i.e. TF-IDF-based features, POS Tagged Features, a reduced feature vector of POS and the combined vector of TF-IDF and POS tagged features. The deep Convolutional Neural Networks (CNN) is used to train the model and to classify hate and offensive language. The dataset has been obtained from Kaggle. The performance in terms of training accuracy, validation accuracy, training loss and validation loss has been measured with the time complexity. In addition, the class-wise Precision, Recall, F1-score, and Mean accuracy have also been investigated. From experimental results, we found TF-IDF and POS-based combined features provide superior performance

Comparative Analysis of Different Flavonoids on the Immediate Shear Bond Strength of Bleached Enamel Surface: An ex-vivo Study

INTRODUCTION: Bleaching, although considered as the first choice of treatment for discoloured teeth, can be utilized in conjunction with composite resin bonding or veneering and porcelain laminate veneers, to provide a more esthetic result. AIM: The aim of the ex-vivo study is to obtain a comparative analysis to evaluate the effectiveness of antioxidants on the immediate composite bond strength on bleached enamel surface.MATERIALS AND METHOD: Freshly extracted human permanent maxillary central incisors were selected and prepared for the respective study. All the specimens then were randomly divided into two control groups and three experimental groups, each group consisting of 20 specimens each. Among these were three experimental groups 10% Sodium Ascorbate, 5% Grape Seed extracts (Proanthocyanidin, PA) & 10% Green tea extracts (catechins and epigallocatechin gallate, CA and EG) and two control groups (Positive control & Negative control).RESULTS: When compared to Group 1 (positive control, 26.24 ± 0.90 MPa ), Group 3 (5%Grape seed extract; 32.17 ± 1.52 MPa), Group 4 (10% Sodium Ascorbate; 28.91 ±1.50 MPa ) and Group 5 (5% Green tea extract; 24.10 ± 1.21MPa ) showed significantly higher shear bond strength values.CONCLUSION: The present study indicated that the shear bond strength of the antioxidant group (Group3) is higher than all three experimental groups. In addition, the shear bond strength of the bleached group (Group 2) is significantly lower than all the other groups. This implies that immediate use of antioxidants, contributes in reversal the bond strength of bleached enamel

Energy Consumption Minimization in WSN using BFO

The popularity of Wireless Sensor Networks (WSN) have increased rapidly and tremendously due to the vast potential of the sensor networks to connect the physical world with the virtual world. Since sensor devices rely on battery power and node energy and may be placed in hostile environments, so replacing them becomes a difficult task. Thus, improving the energy of these networks i.e. network lifetime becomes important. The thesis provides methods for clustering and cluster head selection to WSN to improve energy efficiency using fuzzy logic controller. It presents a comparison between the different methods on the basis of the network lifetime. It compares existing ABC optimization method with BFO algorithm for different size of networks and different scenario. It provides cluster head selection method with good performance and reduced computational complexity. In addition it also proposes BFO as an algorithm for clustering of WSN which would result in improved performance with faster convergence

Effect of shearing-induced lipolysis on foaming properties of milk

BACKGROUND The attraction of cappuccino-style beverages is attributed to the foam layer, as it greatly improves the texture, appearance, and taste of these products. Typical milk has a low concentration of free fatty acids (FFA), but their concentration can increase due to lipolysis during processing and storage, which is detrimental to the foamability and foam stability of milk. There are contradictory results in reported studies concerning the effects of FFA on the foaming properties of milk due to differences in milk sources, methods inducing lipolysis, and methods of creating foam. In this study, the foaming properties and foam structure of milk samples whose lipolysis was induced by ultra-turraxing, homogenisation, and microfluidisation (1.5–3.5 μ-equiv.mL−1 FFA) were investigated. RESULTS Compared with others, microfluidised milk samples had the smallest particle size, lowest absolute zeta potential, and highest surface tension; thus exhibited high foamability and foam stability, and very small and homogeneous air bubbles in foam structure. For all shearing methods, increasing FFA content from 1.5 to 3.5 μ-equiv.mL−1 markedly decreased the surface tension, foamability, and foam stability of milk samples. The FFA level that led to undesirable foam structure was 1.5 μ-equiv.mL−1 for ultra-turraxed milk samples and 2.5 μ-equiv.mL−1 for homogenised and microfluidised ones. CONCLUSION Shearing-induced lipolysis greatly affected the physical properties of milk samples and subsequently their foaming properties and foam structure. At the same FFA level, lipolysis induced by microfluidisation was much less detrimental to the foaming properties of milk than lipolysis induced by ultra-turraxing and homogenisation.Peer reviewe

Interactions between different forms of bovine lactoferrin and sodium alginate affect the properties of their mixtures

The interactions between different forms (apo-, native- and holo-) of lactoferrin (Lf) and sodium alginate at different ratios in aqueous solution in the pH range of 4-7 were evaluated. Fourier transform infra-red (FTIR) spectra of freeze dried mixtures showed shifts only in the bands arising from the carboxylate groups of alginate relative to physical mixtures; indicating intermolecular interactions involving COO- moieties of alginate. Circular dichroism (CD) spectroscopy showed that Lf retained its tertiary structure in the Lf-alginate mixtures. In the pH range of 4-7, the zeta-potential of Lf-alginate solutions was significantly less negative than that of alginate indicating charge compensation. Native-PAGE results indicated that the extent of binding of Lf by alginate was dependent of the form of Lf with apo-Lf displaying a higher binding affinity. At natural pH, the Lf-alginate mixtures generated higher viscosities than their respective sodium alginate controls indicating the existence of intermolecular interactions between the two components. A mixture of native-Lf and sodium alginate showed the highest increase in the viscosity while increasing level of iron saturation in Lf showed an inverse effect on viscosity. DSC analysis showed that the thermal denaturation temperature of native-and holo-Lf can be enhanced upon interaction with alginate in solution. (C) 2015 Elsevier Ltd. All rights reserved

Comparison of ultra high temperature (UHT) stability of high protein milk dispersions prepared from milk protein concentrate (MPC) and conventional low heat skimmed milk powder (SMP)

This study compared the UHT (145 °C for 5 s) stability and fouling behavior of high protein milk dispersions prepared from reconstituted low heat skimmed milk powder (RSMP) and milk protein concentrate powder (RMPC). It was found that RMPC at 10 and 14% protein content was more UHT stable as compared to lower protein content RSMP (3.25, 6.5, 7, 7.5, 8%). Matching the total solids and mineral composition of 7.5-RMPC with 7.5-RSMP by addition of minerals and lactose markedly reduced its UHT stability (UHT run-time reduced to 66 min from >120 min). The RP-HPLC analysis showed increased casein dissociation but similar whey protein aggregation in 7.5-RSMP as compared to 14-RMPC. UHT processing lead to formation of larger particles in case of 7.5-RSMP (1.84 μm D(0.9)) as compared to 14-RMPC (0.23 μm D(0.9)). It was observed that mineral environment affected protein interactions leading to the differences in UHT behavior of RSMP and RMPC

Visualizing the interaction between sodium caseinate and calcium alginate microgel particles

In this study, the pH dependent adsorption of sodium caseinate onto the surface of micron-sized calcium alginate microgel particles (20-80\ua0μm) was evaluated by electrophoretic mobility measurements (ζ-potential), microscopy, protein assay and a protein dye binding method. ζ-potential measurements and protein assay results suggested that protein adsorption occurred due to electrostatic complexation between sodium caseinate and calcium alginate and was pH dependent. Results of protein dye binding method were in agreement with those of protein assay and ζ-potential measurements. Confocal laser scanning and fluorescence microscopy confirmed the presence of protein layer on the surface of alginate microgel particles at pH 3 and 4. Micrographs from transmission electron microscopy revealed a protein coating with a thickness of ∼206-240\ua0nm on the gel particle surfaces

Properties of functional camel milk powder

Camel milk has a composition and properties quite close to human milk. Camel milk’s composition is considered superior to that of bovine milk in terms of its nutritional and therapeutic value. It contains high concentrations of several bioactive compounds that have health benefits. To achieve long-term stability and usability, many dairy-based products and ingredients are dehydrated to powder form. However, such severe heat treatments eventuate in the loss of heat-labile bioactive compounds. Protecting these bioactive compounds during the production of camel milk powder is a challenge for dairy researchers and manufactures. To maintain the activity of such compounds, low-temperature drying operations such as freeze-drying are preferred, and there are many freeze-dried camel milk powder products available on the market. However, due to the limitations of freeze-drying in the production of milk powder, freeze-drying needs to be replaced with other economic drying approaches such as spray drying. However, the application of spray drying in the production of camel milk powder is still in early stages of research, and there are only a few reported studies. This chapter describes the bioactive properties of camel milk and the potential application of spray drying to produce camel milk powder.Peer reviewe