scholarly journals Modeling of Chlorine and Coagulant Dose in a Water Treatment Plant by Artificial Neural Networks

2019 ◽  
Vol 9 (3) ◽  
pp. 4176-4181
Author(s):  
A. S. Kote ◽  
D. V. Wadkar
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Water Treatment ◽  
Treatment Plant ◽  
Water Treatment Plant ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Dose Model ◽  
Artificial Neural

Coagulation and chlorination are complex processes of a water treatment plant (WTP). Determination of coagulant and chlorine dose is time-consuming. Many times WTP operators in India determine the coagulant and chlorine dose approximately using their experience, which may lead to the use of excess or insufficient dose. Hence, there is a need to develop prediction models to determine optimum chlorine and coagulant doses. In this paper, artificial neural networks (ANN) are used for prediction due to their ability to learn and model non-linear and complex relationships. Separate ANN models for chlorine and coagulant doses are explored with radial basis neural network (RBFNN), feed-forward neural network (FFNN), cascade feed forward neural network (CFNN) and generalized regression neural network (GRNN). For modeling, daily water quality data of the last four years are collected from the plant laboratory of WTP in Maharashtra (India). In order to improve performance, these models are established by varying input variables, hidden nodes, training functions, spread factor, and epochs. The best models are selected based on the comparison of performance measures. It is observed that the best performing chlorine dose model using defined statistics is found to be RBFNN with R=0.999. Similarly, the CFNN coagulant dose model with Bayesian regularization (BR) training function provided excellent estimates with network architecture (2-40-1) and R=0.947. Based on the above models, two graphical user interfaces (GUIs) were developed for real-time prediction of chlorine and coagulant dose, which will be useful for plant operators and decision makers.

Contributions to Predict the Malfunction Probability of the Human-Machine-Environment System, Using Artificial Neural Networks

2015 ◽  
Vol 760 ◽  
pp. 771-776
Author(s):  
Daniel Constantin Anghel ◽  
Nadia Belu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Design Experiment ◽  
Manufacturing Processes ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Linear Relationships ◽  
The Neural Network ◽  
Artificial Neural

This paper presents the application of Artificial Neural Networks to predict the malfunction probability of the human-machine-environment system, in order to provide some guidance to designers of manufacturing processes. Artificial Neural Networks excel in gathering difficult non-linear relationships between the inputs and outputs of a system. We used, in this work, a feed forward neural network in order to predict the malfunction probability. The neural network is simulated with Matlab. The design experiment presented in this paper was realized at University of Pitesti, at the Faculty of Mechanics and Technology, Technology and Management Department.

Artificial neural networks for cost optimization of coagulation, sedimentation and filtration in drinking water treatment

2008 ◽  
Vol 8 (4) ◽  
pp. 383-388
Author(s):  
H.-J. Mälzer ◽  
S. Strugholtz
Keyword(s):  
Neural Networks ◽  
Drinking Water ◽  
Artificial Neural Networks ◽  
Water Treatment ◽  
Cost Optimization ◽  
Treatment Plant ◽  
Drinking Water Treatment ◽  
Water Treatment Plant ◽  
Artificial Neural ◽  
Safety And Stability

The applicability of Artificial Neural Networks (ANN) for process and costs optimization in drinking water treatment by coagulation, sedimentation and rapid filtration was investigated. The results showed that besides a considerable cost reduction, an improvement of process safety and stability can be expected. For further testing, the ANN will be installed at a water treatment plant for online coagulation control and process optimization.

scholarly journals PREDIKSI HARGA EMAS MENGGUNAKAN FEED FORWARD NEURAL NETWORK DENGAN METODE EXTREME LEARNING MACHINE

2019 ◽  
Vol 8 (2) ◽  
pp. 171-183
Author(s):  
Nisa Afida Izati ◽  
Budi Warsito ◽  
Tatik Widiharih
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Extreme Learning Machine ◽  
Gold Price ◽  
Good Prediction ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Artificial Neural ◽  
Learning Machine

The prediction of gold price aims to find out the gold price in the future on the basis of historical data on gold prices in the past, so it can be used as a consideration by gold investors to investing in gold. Prediction methods that do not require assumptions, one of which is Artificial Neural Networks. In this study, using Artificial Neural Networks, Feed Forward Neural Network with Extreme Learning Machine (ELM). ELM is a non-iterative algorithm so ELM has advantages in process speed. The input weight and bias for this method are determined randomly. After that, to find the final weight using the Moore-Penrose Generalized Inverse calculation on the hidden layer output matrix. The best model selection criteria uses the Mean Absolute Percentage Error (MAPE). This study shows that the results of the training and testing process from the model 1 input neuron and 7 hidden neurons are very good, because it produces MAPE training = 0.6752% and MAPE testing = 0.8065%. Also gives a very good prediction result because it has MAPE = 0.5499% Keywords: gold price, Extreme Learning Machine, MAPE

Abstract 16866: Artificial Neural Networks as Prediction Tools: Predicting Permanent Pacemaker Implantation (PPMI) in Patients Undergoing Transcatheter Aortic Valve Replacement (TAVR) Utilizing a Shallow Feed-Forward Neural Network

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Anshul Parulkar ◽  
Wasiq Sheikh ◽  
Malik B Ahmed ◽  
Sachit Singal ◽  
Fabio V Lima ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Pacemaker Implantation ◽  
Paravalvular Leak ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Increased Risk ◽  
Test Sets ◽  
Artificial Neural

Introduction: Despite increasing TAVR volumes after a series of recent favorable clinical trials, adverse outcomes remain frequent, including new or worsened conduction disease requiring PPMI, life-threatening bleeding, paravalvular leak, and stroke. PPMI carries a reported incidence ranging from 8.8-14.6% and is of particular concern given the increased risk of mortality and rehospitalization. New techniques in signal processing may inform novel statistical approaches to better predict PPMI from a set of clinical variables. Artificial neural networks (ANN) comprise a family of algorithms that utilize non-linear activation functions to enable improved prediction of PPMI in TAVR patients. Objective: To examine the predictive utility of a feed-forward neural network in classifying PPM implantation in patients undergoing TAVR. Methods: Pre and post-operative data from a single institution were collected for all patients undergoing TAVR without prior pacemaker implantation from January 2016 to December 2019. Data was imported into Matlab, partitioned into training, validation, and test sets, and processed in a two-layer feed-forward neural network with sigmoid hidden and softmax output neurons. Performance data included confusion matrices and receiver operating characteristic (ROC) curves. Results: The total sample size for the cohort was 513 patients with a PPMI incidence of 8.6%. The training set contained 40 variables and 359 patients, while the validation and test sets contained 77 patients each. The final optimized model showed cross-entropy of 0.25 with 6 iterations and an area under ROC curve of 0.73. Overall model accuracy was 92.7% in the validation set and 88.3% in the test set. Conclusions: In summary, we show that feed-forward neural networks can be useful in processing multiple interdependent variables to aid clinical prediction. Our network demonstrated modest discriminatory ability in predicting the need for PPMI after TAVR.

scholarly journals Artificial neural networks to control chlorine dosing in a water treatment plant

2018 ◽  
Vol 40 (1) ◽  
pp. 37275
Author(s):  
André Felipe Librantz ◽  
Fábio Cosme Rodrigues dos Santos ◽  
Cleber Gustavo Dias
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Water Treatment ◽  
Treatment Plant ◽  
Water Treatment Plant ◽  
Artificial Neural

scholarly journals Diagnosing Diabetes Using Artificial Neural Networks

2020 ◽  
Vol 5 (2) ◽  
pp. 221-224
Author(s):  
Joy Oyinye Orukwo ◽  
Ledisi Giok Kabari
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Success Rate ◽  
Medical Diagnosis ◽  
Learning Algorithm ◽  
Diabetes Diagnosis ◽  
Feed Forward Neural Network ◽  
The Neural Network ◽  
Artificial Neural

Diabetes has always been a silent killer and the number of people suffering from it has increased tremendously in the last few decades. More often than not, people continue with their normal lifestyle, unaware that their health is at severe risk and with each passing day diabetes goes undetected. Artificial Neural Networks have become extensively useful in medical diagnosis as it provides a powerful tool to help analyze, model and make sense of complex clinical data. This study developed a diabetes diagnosis system using feed-forward neural network with supervised learning algorithm. The neural network is systematically trained and tested and a success rate of 90% was achieved.

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