The potential role of artificial neural networks in chemical engineering is on the track of research, due to complex derivational problems in chemical engineering we need updated techniques than conventional. Artificial Neural networks in Chemical Engineering may be helpful to tackle the problems.
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What is the Artificial Neural Network (ANN)?
An artificial neural network is a computational model inspired by the structure and function of neurons in the brain. ANNs have been successfully applied to many different problems including speech recognition, handwriting recognition, image processing, and translation. ANN is the piece of a computing system designed to simulate the functioning of a human brain.
- It is the component of Artificial Intelligence (AI).
- Construct a system to perform various computational tasks faster than traditional systems.
- Used to solve problems that would not be possible by a human.
- The pivotal context is borrowed from the analogy of biological neural networks.
- Also called connectionist systems or parallel distributed processing systems.
Machine Learning in ANN
Machine Learning (ML) is a method of data analysis. It is a branch of Artificial intelligence (AI), used to build an automated analytical model. The basic concept of ML is learning ideas, identifying patterns, and building a decision with the least human intervention. See three types of ML strategies:
- Supervised learning: In this task, present a data set. So that algorithm can learn and can use to evaluate its accuracy in the training
- Unsupervised learning: in contrast, present no data set. The algorithm will try to make sense by extracting features and patterns on its own.
- Reinforcement learning: It is based on the observation
NNs in Deep Learning:
Deep learning (DL) is part of machine learning (ML) based on ANN. DL can be supervised, semi-supervised or unsupervised. The three most important neural networks (NN) are:
- Artificial Neural Networks (ANN)
- Convolution Neural Networks (CNN)
- Recurrent Neural Networks (RNN)
Types of ANN
Depending upon the function and human brain neurons, it is broadly classified, but here we discuss only two types of ANN as below:
Feed Forward ANN (FFANN): This type of ANN consists of an input, output, and one or more layers of neurons. The flow of information is unidirectional. The input layer is used to send information to the hidden layer, and then information sends to the output layer without feedback loops. See figure:
Feed Back ANN (FBANN): This type of ANN has the same mechanism as FFANN, but output goes back into the previous layer to attain the best and most efficient results. See figure:
An assortment of feed-forward and feedback network architecture adopted from Jain and Mao, 1996, is shown below:
- Training: These are presented to the network during training, and the network is adjusted according to its error.
- Validation: These are used to measure network generalization, and to halt training when generalization stops improving.
- Testing: These do not affect training and so provide an independent measure of network performance during and after training.
Application of ANN
The wide use of ANN these times is due to the ease and flexibility of the mechanism. ANN performs several tasks in the field of medical, transport, environment, energy, defense, military, meteorology, electronics, etc. Simply, human brains feel difficulties to predict, the ANN can be used.
- Data clustering
- Facial recognition
- Pattern recognition and classification
- Modeling and simulation of solar steam generating plant in the solar energy field
- Mapping and system identification
- Familiar with non-linear problems
- To calculate the building’s heating load
- Weather forecasting
- Prediction of air flows
- Stock prediction
- Prediction of energy consumption
The potential application of artificial neural networks in chemical engineering
Several areas of chemical engineering can be tackled by ANN due to its robustness and efficacy of ANN. Predicting ingenious manner may lead to being a great problem solver in the following areas of chemical engineering:
- Transport phenomenon
- Process design
- Error identification
- Medicinal chemistry
- Prediction of the product (polymer) quality
- Computational chemistry
- Reaction engineering
- Modeling and control
- Data optimization
- Analytical chemistry
- Heat flow
- Curve plotting or fitting
- Process optimization
- Fault detection
Conclusion: Artificial Neural Networks in chemical engineering has a great potential application in chemical engineering-related fields due to their flexibility, robustness, and efficacy of ANNs. The ANN is getting now more attraction to solve complex and non-linear problems.
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