This document summarizes research on using statistical measures and machine learning techniques to perform sentiment analysis on product reviews. The researchers collected product review data from online sources and analyzed the sentiment and opinions expressed in the text using support vector machine classifiers. They classified reviews as positive or negative and analyzed key product features that were discussed. The results demonstrated that statistical sentiment analysis can help companies better understand customer feedback and identify popular product versions or attributes. Several related works applying techniques like naive Bayes, lexicon-based methods and aspect-based sentiment analysis on reviews from domains like movies, hotels and restaurants are also summarized.
SENTIMENT ANALYSIS ON PRODUCT FEATURES BASED ON LEXICON APPROACH USING NATURA...ijnlc
Sentiment analysis has played an important role in identifying what other people think and what their behavior is. Text can be used to analyze the sentiment and classified as positive, negative or neutral. Applying the sentiment analysis on the product reviews on e-market helps not only the customer but also the industry people for taking decision. The method which provides sentiment analysis about the individual product’s features is discussed here. This paper presents the use of Natural Language Processing and SentiWordNet in this interesting application in Python: 1. Sentiment Analysis on Product review [Domain: Electronic]2. sentiment analysis regarding the product’s feature present in the product review [Sub Domain: Mobile Phones]. It usesa lexicon based approach in which text is tokenized for calculating the sentiment analysis of the product reviews on a e-market. The first part of paper includessentiment analyzer whichclassifiesthe sentiment present in product reviews into positive, negative or neutral depending on the polarity. The second part of the paper is an extension to the first part in which the customer review’s containing product’s features will be segregated and then these separated reviews are classified into positive, negative and neutral using sentiment analysis. Here, mobile phones are used as the product with features as screen, processors, etc. This gives a business solution for users and industries for effective product decisions.
SENTIMENT ANALYSIS ON PRODUCT FEATURES BASED ON LEXICON APPROACH USING NATURA...ijnlc
Sentiment analysis has played an important role in identifying what other people think and what their behavior is. Text can be used to analyze the sentiment and classified as positive, negative or neutral. Applying the sentiment analysis on the product reviews on e-market helps not only the customer but also the industry people for taking decision. The method which provides sentiment analysis about the individual product’s features is discussed here. This paper presents the use of Natural Language Processing and SentiWordNet in this interesting application in Python: 1. Sentiment Analysis on Product review [Domain: Electronic]2. sentiment analysis regarding the product’s feature present in the product review [Sub Domain: Mobile Phones]. It usesa lexicon based approach in which text is tokenized for calculating the sentiment analysis of the product reviews on a e-market. The first part of paper includessentiment analyzer whichclassifiesthe sentiment present in product reviews into positive, negative or neutral depending on the polarity. The second part of the paper is an extension to the first part in which the customer review’s containing product’s features will be segregated and then these separated reviews are classified into positive, negative and neutral using sentiment analysis. Here, mobile phones are used as the product with features as screen, processors, etc. This gives a business solution for users and industries for effective product decisions.
TOWARDS AUTOMATIC DETECTION OF SENTIMENTS IN CUSTOMER REVIEWSijistjournal
Opinions Play important role in the process of knowledge discovery or information retrieval and can be considered as a sub discipline of Data Mining. A major interest has been received towards the automatic extraction of human opinions from web documents. The sole purpose of Sentiment Analysis is to facilitate online consumers in decision making process of purchasing new products. Opinion Mining deals with searching of sentiments that are expressed by Individuals through on-line reviews,surveys, feedback,personal blogs etc. With the vast increase in the utilization of Internet in today's era a similar increase has been seen in the use of blog's,reviews etc. The person who actually uses these reviews or blog's is mostly a consumer or a manufacturer. As most of the customers of the world are buying & selling product on-line so it becomes company's responsibility to make their product updated. In the current scenario companies are taking product reviews from the customers and on the basis of product reviews they are able to know in which they are lacking or strong this can be accomplished with the help of sentiment analysis. Therefore Our objective of our research is to build a tool which can automatically extract opinion words and find out their polarity by using dictionary,This actually reduces the manual effort of reading these reviews and to evaluate them. The research also illustrates the benefits of using Unstructured text instead of training data which expensive . In this research effort we demonstrate a method which is based on rules where product reviews are extracted from review containing sites and analysis is done, so that a person may know whether a particular product review is positive or negative or neutral. The system will utilize a existing knowledge base for calculate positive and negative scores and on the basis of that decide whether a product is recommended or not. The system will evaluate the utility of Lexical resources over the training data.
Product aspect ranking using domain dependent and domain independent revieweSAT Journals
Abstract
In today’s world, internet is the main source of information. There are many blogs and forum sites available where people discuss on different issues and also almost all ecommerce website provide facility to the users to express opinion about their product and services which is important information available on the internet .The problem with this information is that this reviews are mostly not organized therefore creating difficulty for knowledge acquisition. There are many solution exist to resolve this problem but the available existing methods depends on extracting product aspect only considering single domain relevant review corpus. To address this problem, a method is explored to identify product aspect from online review is by taking into account the difference in aspect statistical characteristic across different corpus. This paper shows need of automatically identifying important product aspects from available online customer review and an approach of aspect ranking. This paper also shows the related work on this domain. Our methodology confirmed product aspect which are less nonspecific in domain independent corpus and more domain specific. Then customer opinion expressed on these aspects is determined using sentiment classifier and finally ranking of product aspect is calculated using it’s ranking relevance score of each aspect . Keywords— Product aspect, aspect ranking, sentiment classification, customer review, opinion mining, aspect identification, product ranking.
E-commerce online review for detecting influencing factors users perceptionjournalBEEI
To date, online shopping using e-commerce services becomes a trend. The emergence of e-commerce truly helps people to shop more effectively and efficiently. However, there are still some problems encountered in e-commerce, especially from the user perspective. This research aims to explore user review data, particularly on factors that influence user perception of e-commerce applications, classify, and identify potential solutions to finding problems in e-commerce applications. Data is grabbed using web scraping techniques and classified using proper machine learning, i.e., support vector machine (SVM). Text associations and fishbone analysis are performed based on the classified user review data. The results of this study show that the user satisfaction problem can be captured. Furthermore, various services that should be provided as a potential solution to experienced customers' problems or application users' perception problems can be generated. A detailed discussion of these findings is available in this article.
A Two Stage Classification Model for Call Center Purchase PredictionTELKOMNIKA JOURNAL
In call center [1] product recommendation field, call center as an organization between users and telecom operator, doesn’t have permission to access users’ specific information and the detailed products information. Accordingly, rule-based selection method is common used to predict user purchase behavior by the call center. Unfortunately, rule-based approach not only ignores the user’s previous behavior information entirely, and it is difficult to make use of the existing interaction records between users and products. Consequently, it will not get desired results if we just use the basic selection method to predict user purchase behavior directly, because the problem is that the features straightly extracted from the interaction data records are limited. In order to solve the problem above, this paper proposes a two-stage algorithm that based on K-Means Clustering Algorithm [2] and SVM [3, 4] Classification Algorithm. Firstly, we get the potential category information of products by K-Means Clustering Algorithm, and then use SVM Classification Model to predict users purchasing behavior. This two-stage prediction model not only solves the feature shortage problem, but also gives full consideration to the potential features between users and product categories, which can help us to gain significant performance in call center product recommendation field.
With the rapid growth in ecommerce, reviews for popular products on the web have grown rapidly.
Although these reviews are important for making decisions, it is difficult to read all the reviews.
Automating the opinion mining process was identified as a solution for the problem. Although there are
algorithms for opinion mining, an algorithm with better accuracy is needed. A feature and smiley based
algorithm was developed which extracts product features from reviews based on feature frequency and
generates an opinion summary based on product features.
The algorithm was tested on downloaded customer reviews. The sentences were tagged, opinion words
were extracted and opinion orientations were identified using semantic orientation of opinion words and
smileys. Since the precision values for feature extraction and both precision and recall values for opinion
orientation identification were improved by the new algorithm, it is more successful in opinion mining of
customer reviews.
ASPECT-BASED OPINION EXTRACTION FROM CUSTOMER REVIEWScsandit
Text is the main method of communicating information in the digital age. Messages, blogs,
news articles, reviews, and opinionated information abounds on the Internet. People commonly
purchase products online and post their opinions about purchased items. This feedback is
displayed publicly to assist others with their purchasing decisions, creating the need for a
mechanism with which to extract and summarize useful information for enhancing the decisionmaking
process. Our contribution is to improve the accuracy of extraction by combining
different techniques from three major areas, namedData Mining, Natural Language Processing
techniques and Ontologies. The proposed framework sequentially mines product’s aspects and
users’ opinions, groups representative aspects by similarity, and generates an output summary.
This paper focuses on the task of extracting product aspects and users’ opinions by extracting
all possible aspects and opinions from reviews using natural language, ontology, and frequent
“tag”sets. The proposed framework, when compared with an existing baseline model, yielded
promising results.
USING NLP APPROACH FOR ANALYZING CUSTOMER REVIEWScsandit
The Web considers one of the main sources of customer opinions and reviews which they are represented in two formats; structured data (numeric ratings) and unstructured data (textual comments). Millions of textual comments about goods and services are posted on the web by customers and every day thousands are added, make it a big challenge to read and understand them to make them a useful structured data for customers and decision makers. Sentiment
analysis or Opinion mining is a popular technique for summarizing and analyzing those opinions and reviews. In this paper, we use natural language processing techniques to generate some rules to help us understand customer opinions and reviews (textual comments) written in the Arabic language for the purpose of understanding each one of them and then convert them to a structured data. We use adjectives as a key point to highlight important information in the text then we work around them to tag attributes that describe the subject of the reviews, and we associate them with their values (adjectives).
Opinion pattern mining based on probabilistic principle component analysis re...eSAT Journals
Abstract
Now days, Customer feedback and satisfaction is playing a significant role in commercial product to market. Customer can be reviewed by other customer feedback and collect all the relevant information related to a particular product. Based on that the decision can be taken to purchase the product. In the traditional method, Random forest predicted the impact of the review but not worked with segmentation on the basis of multiple reviewer comments. At the same time, the variable cluster algorithm has been addressed in the market segmentation for retailing the customer’s lifestyle. It has been provided with the segmentation method, but not guide to full proof strategies for different product decision. Instead of that to guide different customers with a variety of product feedback using pattern mining approaches. The product review pattern mining segmentation based on probabilistic principle component analysis is proposed. The opinion mining, segments has categorized into several segments with pattern analysis based on multiple review comments. This mechanism has reduced the dimensionality of the segmentation process using the covariance matrix approach. The experiment uses the opinion rank review dataset information for further process. It increases the segmentation efficient upto9% when compare with traditional and conventional methods. The experimentation has been done with the important factor of opinion decision threshold, false positive rate, segmentation efficiency and customer product ratio level along with customer behavioral feedback.
Keywords: Covariance Matrix ,Opinion Pattern Mining Segmentation, Probabilistic Principle Component Analysis, , Product Review
SENTIMENT ANALYSIS ON PRODUCT FEATURES BASED ON LEXICON APPROACH USING NATURA...kevig
This paper presents the use of Natural Language Processing and SentiWordNet in this interesting application in Python: 1. Sentiment Analysis on Product review [Domain: Electronic]2. sentiment analysis regarding the product’s feature present in the product review [Sub Domain: Mobile Phones]. It usesa lexicon based approach in which text is tokenized for calculating the sentiment analysis of the product reviews on a e-market. The first part of paper includessentiment analyzer whichclassifiesthe sentiment present in product reviews into positive, negative or neutral depending on the polarity. The second part of the paper is an extension to the first part in which the customer review’s containing product’s features will be segregated and then these separated reviews are classified into positive, negative and neutral using sentiment analysis. Here, mobile phones are used as the product with features as screen, processors, etc. This gives a business solution for users and industries for effective product decisions.
TOWARDS AUTOMATIC DETECTION OF SENTIMENTS IN CUSTOMER REVIEWSijistjournal
Opinions Play important role in the process of knowledge discovery or information retrieval and can be considered as a sub discipline of Data Mining. A major interest has been received towards the automatic extraction of human opinions from web documents. The sole purpose of Sentiment Analysis is to facilitate online consumers in decision making process of purchasing new products. Opinion Mining deals with searching of sentiments that are expressed by Individuals through on-line reviews,surveys, feedback,personal blogs etc. With the vast increase in the utilization of Internet in today's era a similar increase has been seen in the use of blog's,reviews etc. The person who actually uses these reviews or blog's is mostly a consumer or a manufacturer. As most of the customers of the world are buying & selling product on-line so it becomes company's responsibility to make their product updated. In the current scenario companies are taking product reviews from the customers and on the basis of product reviews they are able to know in which they are lacking or strong this can be accomplished with the help of sentiment analysis. Therefore Our objective of our research is to build a tool which can automatically extract opinion words and find out their polarity by using dictionary,This actually reduces the manual effort of reading these reviews and to evaluate them. The research also illustrates the benefits of using Unstructured text instead of training data which expensive . In this research effort we demonstrate a method which is based on rules where product reviews are extracted from review containing sites and analysis is done, so that a person may know whether a particular product review is positive or negative or neutral. The system will utilize a existing knowledge base for calculate positive and negative scores and on the basis of that decide whether a product is recommended or not. The system will evaluate the utility of Lexical resources over the training data.
Product aspect ranking using domain dependent and domain independent revieweSAT Journals
Abstract
In today’s world, internet is the main source of information. There are many blogs and forum sites available where people discuss on different issues and also almost all ecommerce website provide facility to the users to express opinion about their product and services which is important information available on the internet .The problem with this information is that this reviews are mostly not organized therefore creating difficulty for knowledge acquisition. There are many solution exist to resolve this problem but the available existing methods depends on extracting product aspect only considering single domain relevant review corpus. To address this problem, a method is explored to identify product aspect from online review is by taking into account the difference in aspect statistical characteristic across different corpus. This paper shows need of automatically identifying important product aspects from available online customer review and an approach of aspect ranking. This paper also shows the related work on this domain. Our methodology confirmed product aspect which are less nonspecific in domain independent corpus and more domain specific. Then customer opinion expressed on these aspects is determined using sentiment classifier and finally ranking of product aspect is calculated using it’s ranking relevance score of each aspect . Keywords— Product aspect, aspect ranking, sentiment classification, customer review, opinion mining, aspect identification, product ranking.
E-commerce online review for detecting influencing factors users perceptionjournalBEEI
To date, online shopping using e-commerce services becomes a trend. The emergence of e-commerce truly helps people to shop more effectively and efficiently. However, there are still some problems encountered in e-commerce, especially from the user perspective. This research aims to explore user review data, particularly on factors that influence user perception of e-commerce applications, classify, and identify potential solutions to finding problems in e-commerce applications. Data is grabbed using web scraping techniques and classified using proper machine learning, i.e., support vector machine (SVM). Text associations and fishbone analysis are performed based on the classified user review data. The results of this study show that the user satisfaction problem can be captured. Furthermore, various services that should be provided as a potential solution to experienced customers' problems or application users' perception problems can be generated. A detailed discussion of these findings is available in this article.
A Two Stage Classification Model for Call Center Purchase PredictionTELKOMNIKA JOURNAL
In call center [1] product recommendation field, call center as an organization between users and telecom operator, doesn’t have permission to access users’ specific information and the detailed products information. Accordingly, rule-based selection method is common used to predict user purchase behavior by the call center. Unfortunately, rule-based approach not only ignores the user’s previous behavior information entirely, and it is difficult to make use of the existing interaction records between users and products. Consequently, it will not get desired results if we just use the basic selection method to predict user purchase behavior directly, because the problem is that the features straightly extracted from the interaction data records are limited. In order to solve the problem above, this paper proposes a two-stage algorithm that based on K-Means Clustering Algorithm [2] and SVM [3, 4] Classification Algorithm. Firstly, we get the potential category information of products by K-Means Clustering Algorithm, and then use SVM Classification Model to predict users purchasing behavior. This two-stage prediction model not only solves the feature shortage problem, but also gives full consideration to the potential features between users and product categories, which can help us to gain significant performance in call center product recommendation field.
With the rapid growth in ecommerce, reviews for popular products on the web have grown rapidly.
Although these reviews are important for making decisions, it is difficult to read all the reviews.
Automating the opinion mining process was identified as a solution for the problem. Although there are
algorithms for opinion mining, an algorithm with better accuracy is needed. A feature and smiley based
algorithm was developed which extracts product features from reviews based on feature frequency and
generates an opinion summary based on product features.
The algorithm was tested on downloaded customer reviews. The sentences were tagged, opinion words
were extracted and opinion orientations were identified using semantic orientation of opinion words and
smileys. Since the precision values for feature extraction and both precision and recall values for opinion
orientation identification were improved by the new algorithm, it is more successful in opinion mining of
customer reviews.
ASPECT-BASED OPINION EXTRACTION FROM CUSTOMER REVIEWScsandit
Text is the main method of communicating information in the digital age. Messages, blogs,
news articles, reviews, and opinionated information abounds on the Internet. People commonly
purchase products online and post their opinions about purchased items. This feedback is
displayed publicly to assist others with their purchasing decisions, creating the need for a
mechanism with which to extract and summarize useful information for enhancing the decisionmaking
process. Our contribution is to improve the accuracy of extraction by combining
different techniques from three major areas, namedData Mining, Natural Language Processing
techniques and Ontologies. The proposed framework sequentially mines product’s aspects and
users’ opinions, groups representative aspects by similarity, and generates an output summary.
This paper focuses on the task of extracting product aspects and users’ opinions by extracting
all possible aspects and opinions from reviews using natural language, ontology, and frequent
“tag”sets. The proposed framework, when compared with an existing baseline model, yielded
promising results.
USING NLP APPROACH FOR ANALYZING CUSTOMER REVIEWScsandit
The Web considers one of the main sources of customer opinions and reviews which they are represented in two formats; structured data (numeric ratings) and unstructured data (textual comments). Millions of textual comments about goods and services are posted on the web by customers and every day thousands are added, make it a big challenge to read and understand them to make them a useful structured data for customers and decision makers. Sentiment
analysis or Opinion mining is a popular technique for summarizing and analyzing those opinions and reviews. In this paper, we use natural language processing techniques to generate some rules to help us understand customer opinions and reviews (textual comments) written in the Arabic language for the purpose of understanding each one of them and then convert them to a structured data. We use adjectives as a key point to highlight important information in the text then we work around them to tag attributes that describe the subject of the reviews, and we associate them with their values (adjectives).
Opinion pattern mining based on probabilistic principle component analysis re...eSAT Journals
Abstract
Now days, Customer feedback and satisfaction is playing a significant role in commercial product to market. Customer can be reviewed by other customer feedback and collect all the relevant information related to a particular product. Based on that the decision can be taken to purchase the product. In the traditional method, Random forest predicted the impact of the review but not worked with segmentation on the basis of multiple reviewer comments. At the same time, the variable cluster algorithm has been addressed in the market segmentation for retailing the customer’s lifestyle. It has been provided with the segmentation method, but not guide to full proof strategies for different product decision. Instead of that to guide different customers with a variety of product feedback using pattern mining approaches. The product review pattern mining segmentation based on probabilistic principle component analysis is proposed. The opinion mining, segments has categorized into several segments with pattern analysis based on multiple review comments. This mechanism has reduced the dimensionality of the segmentation process using the covariance matrix approach. The experiment uses the opinion rank review dataset information for further process. It increases the segmentation efficient upto9% when compare with traditional and conventional methods. The experimentation has been done with the important factor of opinion decision threshold, false positive rate, segmentation efficiency and customer product ratio level along with customer behavioral feedback.
Keywords: Covariance Matrix ,Opinion Pattern Mining Segmentation, Probabilistic Principle Component Analysis, , Product Review
SENTIMENT ANALYSIS ON PRODUCT FEATURES BASED ON LEXICON APPROACH USING NATURA...kevig
This paper presents the use of Natural Language Processing and SentiWordNet in this interesting application in Python: 1. Sentiment Analysis on Product review [Domain: Electronic]2. sentiment analysis regarding the product’s feature present in the product review [Sub Domain: Mobile Phones]. It usesa lexicon based approach in which text is tokenized for calculating the sentiment analysis of the product reviews on a e-market. The first part of paper includessentiment analyzer whichclassifiesthe sentiment present in product reviews into positive, negative or neutral depending on the polarity. The second part of the paper is an extension to the first part in which the customer review’s containing product’s features will be segregated and then these separated reviews are classified into positive, negative and neutral using sentiment analysis. Here, mobile phones are used as the product with features as screen, processors, etc. This gives a business solution for users and industries for effective product decisions.
Extracting Business Intelligence from Online Product Reviews ijsc
The project proposes to build a system which is capable of extracting business intelligence for a manufacturer, from online product reviews. For a particular product, it extracts a list of the discussed features and their associated sentiment scores. Online products reviews and review characteristics are extracted from www.Amazon.com. A two level filtering approach is adapted to choose a set of reviews that are perceived to be useful by customers. The filtering process is based on the concept that the reviewer generated textual content and other characteristics of the review, influence peer customers in making purchasing choices. The filtered reviews are then processed to obtain a relative sentiment score associated with each feature of the product that has been discussed in these reviews. Based on these scores, the customer's impression of each feature of the product can be judged and used for the manufacturers benefit.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
NUMERICAL SIMULATIONS OF HEAT AND MASS TRANSFER IN CONDENSING HEAT EXCHANGERS...ssuser7dcef0
Power plants release a large amount of water vapor into the
atmosphere through the stack. The flue gas can be a potential
source for obtaining much needed cooling water for a power
plant. If a power plant could recover and reuse a portion of this
moisture, it could reduce its total cooling water intake
requirement. One of the most practical way to recover water
from flue gas is to use a condensing heat exchanger. The power
plant could also recover latent heat due to condensation as well
as sensible heat due to lowering the flue gas exit temperature.
Additionally, harmful acids released from the stack can be
reduced in a condensing heat exchanger by acid condensation. reduced in a condensing heat exchanger by acid condensation.
Condensation of vapors in flue gas is a complicated
phenomenon since heat and mass transfer of water vapor and
various acids simultaneously occur in the presence of noncondensable
gases such as nitrogen and oxygen. Design of a
condenser depends on the knowledge and understanding of the
heat and mass transfer processes. A computer program for
numerical simulations of water (H2O) and sulfuric acid (H2SO4)
condensation in a flue gas condensing heat exchanger was
developed using MATLAB. Governing equations based on
mass and energy balances for the system were derived to
predict variables such as flue gas exit temperature, cooling
water outlet temperature, mole fraction and condensation rates
of water and sulfuric acid vapors. The equations were solved
using an iterative solution technique with calculations of heat
and mass transfer coefficients and physical properties.