scholarly journals The application of artificial neural network for quality prediction of industrial standard water

2021 ◽  
Vol 926 (1) ◽  
pp. 012048
Author(s):  
Y Muharni ◽  
Kulsum ◽  
A Denisa ◽  
Hartono
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Managerial Implication ◽  
Quality Of Water ◽  
Turbidity Level ◽  
Artificial Neural Network Method ◽  
Quality Water ◽  
Artificial Neural ◽  
Input Variables ◽  
Ph Level

Abstract Industrial Standard water is a source of water before being distributed in industry and household in a certain area. For the sake of health, quality water is a must to fulfill and control. Quality of water having several variables as indicators. One indicator, namely, Turbidity. It is defined as the level of cloudiness of water due to the presence of particles, mud or microorganisms. The highest of turbidity value meaning the index of water quality is low. In this study, we apply the Artificial neural network method for predicting the turbidity value. Three input variables are engaged, PH level, color spectrum, and electrical conductivity. As much of 827 data were collected during six months. Seventy percent are used for training and the rest thirty percent were used for testing. The ANN architecture consists of 3-6-1 configuration, 3 input variables, 6 hidden layers, and 1 output variable. The training was set into 1000 epoch and the MSE shows 0,0013, meaning that the ANN has the power of prediction. The prediction of turbidity level has a managerial implication as supporting information for purchasing decision of material in water processing

scholarly journals Determination of the influence of the variation of reducing and non-reducing sugars on coffee quality with use of artificial neural network

2012 ◽  
Vol 32 (2) ◽  
pp. 354-360 ◽  
Author(s):  
José A. T. Messias ◽  
Evandro de C. Melo ◽  
Adílio F. de Lacerda Filho ◽  
José L. Braga ◽  
Paulo R. Cecon
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Sensory Analysis ◽  
Reducing Sugars ◽  
Chemical Analyses ◽  
Artificial Neural Network Method ◽  
Sensory Analyses ◽  
Artificial Neural ◽  
Input Variables

The present study aimed at evaluating the use of Artificial Neural Network to correlate the values resulting from chemical analyses of samples of coffee with the values of their sensory analyses. The coffee samples used were from the Coffea arabica L., cultivars Acaiá do Cerrado, Topázio, Acaiá 474-19 and Bourbon, collected in the southern region of the state of Minas Gerais. The chemical analyses were carried out for reducing and non-reducing sugars. The quality of the beverage was evaluated by sensory analysis. The Artificial Neural Network method used values from chemical analyses as input variables and values from sensory analysis as output values. The multiple linear regression of sensory analysis values, according to the values from chemical analyses, presented a determination coefficient of 0.3106, while the Artificial Neural Network achieved a level of 80.00% of success in the classification of values from the sensory analysis.

scholarly journals Stress Analysis of Segmental Tunnel Lining Using Artificial Neural Network

2017 ◽  
Author(s):  
Armin Rastbood ◽  
Yaghoob Gholipour ◽  
Abbas Majdi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Extreme Values ◽  
Young Modulus ◽  
Coefficient Of Determination ◽  
Network Method ◽  
Different Types ◽  
Artificial Neural Network Method ◽  
Artificial Neural ◽  
Input Variables

The paper describes an artificial neural network method(ANNM) to predict the stresses executed on segmental tunnellining. An ANN using multi-layer perceptron (MLP) is developed.At first, database resulted from numerical analyses wasprepared. This includes; depth of cover (H), horizontal to verticalstress ratio (K), thickness of segment (t), Young modulus ofsegment (E) and key segment position in each ring (θ) on thetunnel perimeter as input variables. Different types of stressesand extreme values of displacement have been considered asoutput parameters. Sensitivity analysis showed that the coverof the tunnel and key position are the most and less effectiveinput variables on output parameters, respectively. Resultsfor coefficient of determination (R2), variance accounted for(VAF), coefficient of efficiency (CE) and root mean squarederror (RMSE) illustrates a high accuracy of the presented ANNmodel to predict the stress types and displacements of segmentaltunnel lining.

scholarly journals Predictive modelling of soils’ hydraulic conductivity using artificial neural network and multiple linear regression

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Charles Gbenga Williams ◽  
Oluwapelumi O. Ojuri
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Hydraulic Conductivity ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Coarse Grained ◽  
Soil Types ◽  
Wide Range ◽  
Artificial Neural ◽  
Input Variables

AbstractAs a result of heterogeneity nature of soils and variation in its hydraulic conductivity over several orders of magnitude for various soil types from fine-grained to coarse-grained soils, predictive methods to estimate hydraulic conductivity of soils from properties considered more easily obtainable have now been given an appropriate consideration. This study evaluates the performance of artificial neural network (ANN) being one of the popular computational intelligence techniques in predicting hydraulic conductivity of wide range of soil types and compared with the traditional multiple linear regression (MLR). ANN and MLR models were developed using six input variables. Results revealed that only three input variables were statistically significant in MLR model development. Performance evaluations of the developed models using determination coefficient and mean square error show that the prediction capability of ANN is far better than MLR. In addition, comparative study with available existing models shows that the developed ANN and MLR in this study performed relatively better.

Modeling of lime production process using artificial neural network

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Abolghasem Daeichian ◽  
Rana Shahramfar ◽  
Elham Heidari
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Production Process ◽  
High Energy ◽  
Ann Model ◽  
Artificial Neural ◽  
Rotary Kilns ◽  
Input Variables ◽  
Artificial Neural Network Ann ◽  
Lime Production

Abstract Lime is a significant material in many industrial processes, including steelmaking by blast furnace. Lime production through rotary kilns is a standard method in industries, yet it has depreciation, high energy consumption, and environmental pollution. A model of the lime production process can help to not only increase our knowledge and awareness but also can help reduce its disadvantages. This paper presents a black-box model by Artificial Neural Network (ANN) for the lime production process considering pre-heater, rotary kiln, and cooler parameters. To this end, actual data are collected from Zobahan Isfahan Steel Company, Iran, which consists of 746 data obtained in a duration of one year. The proposed model considers 23 input variables, predicting the amount of produced lime as an output variable. The ANN parameters such as number of hidden layers, number of neurons in each layer, activation functions, and training algorithm are optimized. Then, the sensitivity of the optimum model to the input variables is investigated. Top-three input variables are selected on the basis of one-group sensitivity analysis and their interactions are studied. Finally, an ANN model is developed considering the top-three most effective input variables. The mean square error of the proposed models with 23 and 3 inputs are equal to 0.000693 and 0.004061, respectively, which shows a high prediction capability of the two proposed models.

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