The accurate estimation of physicochemical properties of ternary mixtures containing ionic liquids via artificial neural networks

2015 ◽  
Vol 17 (6) ◽  
pp. 4533-4537 ◽  
Author(s):  
John C. Cancilla ◽  
Pablo Díaz-Rodríguez ◽  
Gemma Matute ◽  
José S. Torrecilla
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Ionic Liquids ◽  
Physicochemical Properties ◽  
Ternary Mixture ◽  
Ternary Mixtures ◽  
Accurate Estimation ◽  
Mathematical Tool ◽  
Artificial Neural

A graphic scheme of the mathematical tool designed is able to estimate physicochemical properties of a ternary mixture.

scholarly journals SIMULTANEOUS SPECTROPHOTOMETRIC DETERMINATION OF DICLOFENAC SODIUM, PARACETAMOL, AND CHLORZOXAZONE IN TERNARY MIXTURE USING CHEMOMETRIC AND ARTIFICIAL NEURAL NETWORKS TECHNIQUES

2017 ◽  
Vol 10 (11) ◽  
pp. 225 ◽  
Author(s):  
Ramzia I El-bagary ◽  
Ehab F El-kady ◽  
Ahmed A. Al-matari
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Least Squares ◽  
Simultaneous Determination ◽  
Ternary Mixture ◽  
Diclofenac Sodium ◽  
Ternary Mixtures ◽  
Data Matrix ◽  
Artificial Neural

  Objective: The aim of this study is to develop and validate simple, accurate, and precise spectrophotometric methods for the simultaneous determination of diclofenac sodium (DIC), paracetamol (PAR), and chlorzoxazone (CHZ) in ternary mixture using chemometric and artificial neural networks (ANN) techniques.Methods: Three chemometric techniques include classical least squares (CLS), principal component regression (PCR), and partial least squares (PLS) in addition to cascade-forward backpropagation ANN (CFBP-ANN) were prepared using the synthetic mixtures containing the three drugs in methanol. In CLS, PCR, and PLS, the absorbances of the synthetic mixtures in the range 267-295 nm with the intervals Δλ=0.2 nm in their zero-order spectra were selected. Then, calibration or regression was obtained using the absorbance data matrix and concentration data matrix for the prediction of the unknown concentrations of DIC, PAR, and CHZ in their mixtures. In CFBP-ANN, two layers, sigmoid layer with 10 neurons and linear layer were found appropriate for the simultaneous determination of the three drugs in their ternary mixture.Results: The four proposed methods were successfully applied to the analysis of the three drugs in laboratory prepared mixtures and tablets with good percentage recoveries in the range of 98-102%. Relative standard deviation for the precision study was found <1%.Conclusion: The four proposed methods showed simplicity, accuracy, precision, and rapidity making them suitable for quality control and routine analysis of the cited drugs in ternary mixtures and pharmaceutical formulation containing them. 

Predictive performance modeling of Habesha brewery wastewater treatment plant using artificial neural networks

2019 ◽  
Author(s):  
Chem Int
Keyword(s):  
Neural Networks ◽  
Water Quality ◽  
Wastewater Treatment ◽  
Artificial Neural Networks ◽  
Performance Modeling ◽  
Predictive Performance ◽  
Mathematical Tool ◽  
Effluent Water ◽  
Artificial Neural

Recently, process control in wastewater treatment plants (WWTPs) is, mostly accomplished through examining the quality of the water effluent and adjusting the processes through the operator’s experience. This practice is inefficient, costly and slow in control response. A better control of WTPs can be achieved by developing a robust mathematical tool for performance prediction. Due to their high accuracy and quite promising application in the field of engineering, Artificial Neural Networks (ANNs) are attracting attention in the domain of WWTP predictive performance modeling. This work focuses on applying ANN with a feed-forward, back propagation learning paradigm to predict the effluent water quality of the Habesha brewery WTP. Data of influent and effluent water quality covering approximately an 11-month period (May 2016 to March 2017) were used to develop, calibrate and validate the models. The study proves that ANN can predict the effluent water quality parameters with a correlation coefficient (R) between the observed and predicted output values reaching up to 0.969. Model architecture of 3-21-3 for pH and TN, and 1-76-1 for COD were selected as optimum topologies for predicting the Habesha Brewery WTP performance. The linear correlation between predicted and target outputs for the optimal model architectures described above were 0.9201 and 0.9692, respectively.

Density of Ionic Liquids Using Group Contribution and Artificial Neural Networks

2009 ◽  
Vol 48 (6) ◽  
pp. 3254-3259 ◽  
Author(s):  
José O. Valderrama ◽  
Alfonso Reátegui ◽  
Roberto E. Rojas
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Ionic Liquids ◽  
Group Contribution ◽  
Artificial Neural

Viscosity of ionic liquids using the concept of mass connectivity and artificial neural networks

2011 ◽  
Vol 28 (6) ◽  
pp. 1451-1457 ◽  
Author(s):  
José Omar Valderrama ◽  
Jéssica Makarena Muñoz ◽  
Roberto Erasmo Rojas
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Ionic Liquids ◽  
Artificial Neural

scholarly journals Redes Neuronales Artificiales: factores que determinan la cosecha de caña en la industria azucarera.// Artificial Neural Networks: factors that determine the cane harvest in a sugar industry.

Ciencia Unemi ◽  
2019 ◽  
Vol 12 (29) ◽  
pp. 36-50
Author(s):  
Italo Mendoza-Haro ◽  
Hiram Marquetti-Nodarse
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Sugar Industry ◽  
Expert Assessment ◽  
Mathematical Tool ◽  
Development Environment ◽  
Thermal Oscillation ◽  
Historical Database ◽  
Artificial Neural ◽  
The City

La investigación muestra lo importante de las redes neuronales artificiales dentro de la industria azucarera, como una herramienta útil para la predicción del cultivo de la caña de azúcar, tomando como entradas la información climatológica: temperaturas máximas y mínimas, oscilación térmica, precipitaciones, heliofanía, humedad relativa, evaporación y hectáreas de los cultivos sembrados, para obtener una salida: toneladas de caña. Se desarrolló una herramienta de trabajo predictiva con resultados confiables, comparados con métodos tradicionales utilizados, como los aforos de expertos para la cosecha de la caña de azúcar. Se analizó la base de datos histórica de la organización, mediante un software MATLAB, herramienta matemática, que ofrece un entorno de desarrollo integrado (IDE) con lenguaje M de programación propio. La investigación se desarrolló en Compañía Azucarera Valdez S.A. Ubicada en la Ciudad de Milagro-Provincia del Guayas-Ecuador.AbstractThe research shows the importance of artificial neural networks within the sugar industry, as a useful tool for the prediction of the cultivation of sugarcane, taking as input the climatological information: maximum and minimum temperatures, thermal oscillation, rainfall, heliophany, relative humidity, evaporation and hectares of crops planted, to obtain tons of cane as an output. A predictive work tool with reliable results was developed, compared with traditional methods used, such as expert assessment for sugarcane harvesting. The historical database of the organization was analyzed through MATLAB software, a mathematical tool which offers an integrated development environment (IDE) with its own M programming language. The research was developed at Compañía Azucarera Valdez S.A. located in the City of Milagro-Province of Guayas-Ecuador.

scholarly journals The Use of Artificial Neural Networks to Determine the Engine Power and Fuel Consumption of Modern Bulk Carriers, Tankers and Container Ships

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4827
Author(s):  
Tomasz Cepowski ◽  
Paweł Chorab
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fuel Consumption ◽  
Design Stage ◽  
Accurate Estimation ◽  
Ship Propulsion ◽  
Fuel Prices ◽  
Bulk Carriers ◽  
Artificial Neural ◽  
Engine Power

The 2007–2008 financial crisis, together with rises in fuel prices and stringent pollution regulation, led to the need to update the methods concerning ship propulsion system design. In this article, a set of artificial neural networks was used to update the design equations to estimate the engine power and fuel consumption of modern tankers, bulk carriers, and container ships. Deadweight or TEU capacity and ship speed were used as the inputs for the ANNs. This study shows that even a linear ANN with two neurons in the input and output layers, with purelin activation functions, offers an accurate estimation of ship propulsion parameters. The proposed linear ANNs have simple mathematical structures and are straightforward to apply. The ANNs presented in the article were developed based on the data of the most recent ships built from 2015 to present, and could have a practical application at the preliminary design stage, in transportation or air pollution studies for modern commercial cargo ships. The presented equations mirror trends found in the literature and offer much greater accuracy for the features of new-built ships. The article shows how to estimate CO2 emissions for a bulk carrier, tanker, and container carrier utilizing the proposed ANNs.

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