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The heat exchanger is necessary for heat transport and energy conservation. Because of their extensive design possibilities, ease of building, and cheap manufacturing maintenance costs, crossflow and counter-flow heat exchangers are widely used in the petroleum, petrochemical, air conditioning, food storage, and other industries. Shell and tube heat exchangers are frequently used in businesses as chiller plants to transfer waste heat from injection moulding machines to cooling water, hence increasing injection moulding machine efficiency. The heat exchanger capacity determines how waste heat from an injection moulding process is transported to cooling water. Heat exchanger optimization discovers the best heat exchanger parameter combination to maximise heat exchanger capacity. The prefix parameter (tube diameter) is employed as an input variable, while the most significant temperature difference between the shell and tube heat exchanger is used as an output parameter.


The system presented in this project is a solution for monitoring air and sound pollution in a specific location and safeguarding people from dangerous diseases by informing them of pollution rates on a regular basis. The Internet of Objects (IoT), a sophisticated and efficient method for connecting things to the internet and linking the entire universe of things in a network, is the technology underpinning this. Electronic devices, sensors, and automobile electronic equipment can all be used here. The system involves using sensors to monitor and manage environmental variables such as the number of dangerous substances in the air and the frequency of sound played in that area, as well as spreading a protective covering over objects we wish to protect from air and sound pollution. On the monitor, the device shows these readings in real time. This data may be shown on an LCD and sent to the user through the server, where the user can manage the system by choosing on/off, and the shed will respond appropriately. The data updated by the installed system is accessible from any location. We see many harmful diseases caused by air and noise pollution in our daily lives. This approach is developed to address the problem of hazardous illnesses spreading in a certain region as a result of pollution. The project is centered on resolving this issue and has a wide range of applications. The project is developed keeping in mind the best use of resources presents and that it benefits the society and is useful and easy to use by all classes of society. The aim of the project is to save people from harmful diseases that is caused due to gases that causes air pollution and the high frequency sound played in the area that causes sound pollution and incur minimum cost while fulfilling all its functionality. It will be ensured that the project stands for its aims.


Sound plays a crucial part in every element of human life. Sound is a crucial component in the development of automated systems in a variety of domains, from personal security to essential monitoring. There are a few systems on the market now, but their efficiency is a worry for their use in real-world circumstances. Image classification and feature classification are the same as sound classification, just like other classification algorithms like machine learning. We also construct CNN architecture here. Deep learning architectures' learning capabilities can be leveraged to construct sound categorization systems that overcome the inefficiency of standard methods. The goal of this paper is to use deep learning networks to classify environmental sounds based on the spectrograms that are created. The convolutional neural network (CNN) was trained using spectrogram images of environmental noises. For investigation, this paper used one dataset: Urbansound8K.There are 8732 sound clips (<=4s) of urban noises from ten classes in the collection. On this dataset, system was trained, and the accuracy acquired during training and testing was 98 percent and 91.92 percent respectively. The proposed approach for sound classification using spectrogram images of sounds can be efficiently employed to construct sound classification and recognition systems. Which can be used to distinguish audio evidence during crime investigation, remove noises and other useless sounds from music recording, or to classify different animals sounds in the forest.


Today, air pollution is one of the significant environmental issues that causes adverse health effects in human bodies such as cancer, cardiologic disease and, high mortality rate resulting in damaging effects on the welfare of humans, animals and other living organisms of the environment. According to the recent research survey from WHO, India was the third most polluted country globally in 2020. Every year, about 2.5 million Indians, almost 30%, die from air pollution caused by burning fossil fuels. Given this, our group has developed a project based on an air quality monitoring system used to detect the various parameters of air that are perilous to human beings and society. An IoT-based system was developed that detected the various parameters with the help of different sensors such as PM2.5, DHT11, LDR sensor, MQ-135, and the rain sensor. These sensors continuously sense the air quality index, rain, humidity, temperature, and smoke, finally providing all the information on the smart phone. In addition, it also helps us to fetch the data from any location where the device is installed. In this project, the Blynk app is implemented, a platform with IOS and android app to dominate and equate with Arduino Uno using ESP8266wifi controller. This app continuously monitors the value, throws an alert to the user with the help of a buzzer whenever the threshold value is exceeded.


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