Oxygen Generator Plants By Linde

Last Updated on May 3, 2021

About

Our PSA oxygen generator plants are based on a reliable, flexible and trouble-free vacuum pressure swing adsorption (VPSA) process. They are the perfect fit for on-stream applications that require low-cost gaseous oxygen with purity levels of up to 95 percent per volume.

Which Linde oxygen generator is right for you?

Our portfolio consists of three different types of oxygen generator (V)PSA plants as following:

  • VPSA: Our customised oxygen VPSA plants range in capacity from around 300 Nm³/h up to 10,000 Nm³/h and can produce oxygen purities between 90 and 95 percent per volume.
  • VPSA C series: We offer several pre-engineered, fully standardised and containerised VPSA plants for capacities between 300 Nm³/h and 2,000 Nm³/h (our C series). The C series plants are easily accessible and easy to maintain. They are quick to set up and commissioned on site and can also be easily relocated.
  • PSA: Furthermore, we offer an alternative oxygen PSA process, without vacuum regeneration for low oxygen production capacities of 50 Nm³/h to 500 Nm³/h.

Our oxygen generator PSA and VPSA plants deliver a host of benefits including:

  • Oxygen on demand
  • Energy efficiency
  • Easy partial load operation
  • High availability
  • Fully automated operation

Linde Engineering – Full flexibility in oxygen production

Linde Engineering is specialized in efficient plant construction. Our focus on customer demands enables us to develop plants with optimum energy efficiency that significantly reduce costs – whether oxygen production demands are high or low in volume.

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Hotel Management System Using Python

Last Updated on May 3, 2021

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This Project is done using python 3.x which depicts a front end interface of the hotel management system which is done using GUI interface and has the menu, where the user has a list of choices to select the food he wants and this interface has the food rating section where the user has to give the rating in which the food he took, and this interface is done using basic components of GUI. The GUI I used here is Tkinter, and by using List boxes, buttons, the text box is deployed in this interface, which is user-friendly. This interface is done because the situation of covid is increasing tremendously, to reduce the people frequently going outside for food, this interface has been developed. In this interface, we can also set background color and

font color. Here we can also set the background dimension and in this application, we can also change font sizes and also with rows and columns. This interface asks the user to enter his name, mobile number, email id and also asks whether a user prefers a choice of veg or nonveg and also gives a chance to give the food specification whether he needs the food spicy, salty, and some other and the user can choose whether he needs to pay cash, or online payment either which he can also give food rating and he can also select coupons and apply in this interface.

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Project - Mercedes-Benz Greener Manufacturing

Last Updated on May 3, 2021

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DESCRIPTION

Reduce the time a Mercedes-Benz spends on the test bench.

Problem Statement Scenario:

Since the first automobile, the Benz Patent Motor Car in 1886, Mercedes-Benz has stood for important automotive innovations. These include the passenger safety cell with the crumple zone, the airbag, and intelligent assistance systems. Mercedes-Benz applies for nearly 2000 patents per year, making the brand the European leader among premium carmakers. Mercedes-Benz cars are leaders in the premium car industry. With a huge selection of features and options, customers can choose the customized Mercedes-Benz of their dreams.

To ensure the safety and reliability of every unique car configuration before they hit the road, Daimler’s engineers have developed a robust testing system. As one of the world’s biggest manufacturers of premium cars, safety and efficiency are paramount on Daimler’s production lines. However, optimizing the speed of their testing system for many possible feature combinations is complex and time-consuming without a powerful algorithmic approach.

You are required to reduce the time that cars spend on the test bench. Others will work with a dataset representing different permutations of features in a Mercedes-Benz car to predict the time it takes to pass testing. Optimal algorithms will contribute to faster testing, resulting in lower carbon dioxide emissions without reducing Daimler’s standards.


I have done Data exploration, checking for Missing values and Outliers. Treat the outliers. Applied Label Encoding on categorical variables. I have scaled the data. Applied PCA to reduce the dimension of data but no effect of it on the result. In the prediction, I used Random Forest, KNN, and XGBoost modelling. In all of them, XGBoost has given good result.


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Bank_Loan_Default_Case

Last Updated on May 3, 2021

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The Objective of this problem is to predict whether a person is ‘Defaulted’ or ‘Not Defaulted’ on the basis of the given 8 predictor variables.

The data consists of 8 Independent Variables and 1 dependent variable. The Independent Variables are I. Age: It is a continuous variable. This feature depicts the age of the person. II. Ed: It is a categorical variable. This feature has the education category of the person converted to numerical form. III. Employ: It is a categorical variable. This feature contains information about the geographic location of the person. This column has also been converted to numeric values. IV. Income: It is a continuous variable. This feature contains the gross income of each person. V. DebtInc: It is a continuous variable. This feature tells us an individual’s debt to his or her gross income. VI. Creddebt: It is a continuous variable. This feature tells us about the debt-to-credit ratio. It is a measurement of how much a person owes their creditors as a percentage of its available credit. VII. Othdebt: It is a continuous variable. It tells about any other debt a person owes. VIII. Default: It is a categorical variable. It tells whether a person is a Default (1) or Not-Default (0).

After performing extensive exploratory data analysis the data is given to multiple models like Logistic Regression, Decision Tree classifier, Random Forest classifier, KNN, Gradient Boosting classifier with and without hyperparameter tuning, the final results are obtained and compared on metrics like precision score, recall score, AUC-ROC score.

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Image Classification Using Machine Learning

Last Updated on May 3, 2021

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This is a prototype that shows the given specific image will belong to which category. Here any images can be taken to classify the difference. The main theme is to predict that the given image will belong to which category we had considered.

In this prototype I downloaded images of three different dog breeds named Doberman, golden retriever and shihtzu. The first step is to preprocess data which basically means converting the images into an numpy array and this process named as flattening the image. This numpy array should be the input of the image.


After preprocessing the data, the next step is to check the best suitable parameters for the machine learning algorithm. After getting the parameters, I passed them into the machine learning algorithm as arguments and fit the model. From Sklearn import classification report, accuracy score, confusion matrix which helps us to get brief understanding about our model. The model can be loaded into file using pickle library.


Now the last step is to predict the output. For this I took a input field which takes a URL as an input. The URL should be the image of the dog for which the output is predicted. In the same way we have to flatten the image into a numpy array and predict output for that. The output will show the predicted output that is which breed that the dog belongs to and the image we are checking the output for.


The main theme of this project is to train the computer to show the difference between different classes considered.

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Emotional Analysis Based Content Recommendation System

Last Updated on May 3, 2021

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As the saying goes, “We are what we see”; the content we see may have an adverse effect on our behavior sometimes. Especially in a country like India, where numerous films and TV series are highly prominent, there are great chances of watching explicit or disturbing content randomly. This may have adverse effects on behavior of people, especially children. And we also know “Prevention is better than cure”. Preventing inappropriate content from going online can be more effective than banning them after release.

To achieve this, we aim to create a content filtering and recommendation system that either recommends a film or TV series or alerts a user with a warning message saying it’s not recommended to watch. Netflix or any other Over-the-top (OTT) platforms perform a filtering process before they buy digital rights for any content. This is where our tool comes handy. It detects absurd or hard emotion inducing content with the help of human emotions. Through this project we aim to create a content detector based on human emotion recognition. We will project scenes to test audience and capture their live emotions.

Then we use “Facebook Deep Face”, a pre-defined CNN based face recognition and facial emotion analysis model to identify faces and analyze their emotions. We use “Deep Learning” methods to recognize facial expressions and then make use of Circumplex Model proposed by James Russell to classify emotions based on arousal and valence values. Based on majority emotion that is projected by audience we would either recommend or not recommend the content for going on-air. This system prevents inappropriate content from going on-air

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