scholarly journals Neural Network Modelling for Prediction of Zeta Potential

Mathematics ◽  
2021 ◽  
Vol 9 (23) ◽  
pp. 3089
Author(s):  
Roman Marsalek ◽  
Martin Kotyrba ◽  
Eva Volna ◽  
Robert Jarusek
Keyword(s):  
Neural Network ◽  
Titanium Dioxide ◽  
Zeta Potential ◽  
Titanium Dioxide Nanoparticles ◽  
Mass Content ◽  
Network Modelling ◽  
The Neural Network ◽  
Ionic Force ◽  
High Prediction ◽  
Increasing Temperature

The study is focused on monitoring the influence of selected parameters on the zeta potential values of titanium dioxide nanoparticles. The influence of pH, temperature, ionic strength, and mass content of titanium dioxide in the suspension was assessed. More than a thousand samples were measured by combining these variables. On the basis of results, the model of artificial neural network was proposed and tested. The authors have rich experiences with neural networks applications and this case shows that the neural network model works with a very high prediction success rate of zeta potential. Clearly, pH has the greatest effect on zeta potential values. The influence of other variables is not so significant. However, it can be said that increasing temperature results in an increase in the value of the zeta potential of titanium dioxide nanoparticles. The ionic force affects the zeta potential depending on the pH; in the vicinity of the isoelectric point, its effect is negligible. The effect of the mass content of titanium dioxide in the suspension is absolutely minor.

scholarly journals UTILIZING SITE CHARACTERISTICS IN NEURAL NETWORK MODELLING OF PERCENTAGE COST-TIME OVERRUN OF BUILDING PROJECTS

2017 ◽  
Vol 8 (1) ◽  
pp. 1-15
Author(s):  
A. O. Ujene ◽  
A. A. Umoh
Keyword(s):  
Neural Network ◽  
Cost Overrun ◽  
Construction Costs ◽  
Network Modelling ◽  
Building Projects ◽  
The Neural Network ◽  
Site Characteristics ◽  
Neural Network Prediction ◽  
The Cost ◽  
Prediction Approach

This study evaluated the site characteristics influencing the time and cost delivery of building projects, determined the range of percentage cost and time overrun and developed a neural network model for predicting the percentage cost and time overrun using the site characteristics of building projects. The study evaluated twelve site characteristics and two performance indicators obtained from records of construction costs, contract documents, and valuation reports of 126 purposively sampled building projects spread across several cities in Nigeria. Analyses were with descriptive and artificial neural network. It was concluded that with fairly favourable site characteristics, cost overrun range reached 77.95% with a mean variation of 44.36%, while time overrun range reached 51.23% with a mean variation of 26.77%. It was found that the accuracy performance levels of 91.93% and 91.43% for the cost and time overrun predictions respectively were very high for the optimum models. Building projects have eight significant site characteristics which can be used to reliably predict the percentage overrun, among which the ground water level, level of available infrastructure and labour proximity around the site are the most important predictors of cost and time overrun. The study recommended that project owners, consultants, contractors and other stakeholders should always use the eight identified site characteristics in predicting percentage cost and time overrun, with more priority on the first three characteristics. The study also recommended the neural network prediction approach due to its prediction accuracy.

Dispersion and stability of titanium dioxide nanoparticles in aqueous suspension: effects of ultrasonication and concentration

2013 ◽  
Vol 67 (1) ◽  
pp. 147-151 ◽  
Author(s):  
J. Qi ◽  
Y. Y. Ye ◽  
J. J. Wu ◽  
H. T. Wang ◽  
F. T. Li
Keyword(s):  
Titanium Dioxide ◽  
Zeta Potential ◽  
Tio2 Nanoparticles ◽  
Titanium Dioxide Nanoparticles ◽  
Aqueous Suspension ◽  
Solution Chemistry ◽  
Hydrodynamic Diameter ◽  
Nanoparticles Dispersion ◽  
Nanoparticles Concentration ◽  
Nanoparticles Suspension

The increasing applications of titanium dioxide (TiO2) nanoparticles raise concerns about their potential environmental impacts. To investigate the fate and transport of TiO2 nanoparticles in aqueous suspension, ultrasonication is widely used for the dispersion of TiO2 nanoparticles in laboratory-scale studies. There is a pressing need for detailed information on the dispersion and stability of TiO2 nanoparticles. This study investigated the change of size, zeta potential, and pH of TiO2 nanoparticles aqueous suspension under different conditions of ultrasonication and concentrations. It was found that the hydrodynamic diameter of TiO2 nanoparticles decreased with increasing suspension concentration and remained stable for more than 1 hour after sonication, which is enough for experimental research. The pH decreased with increasing nanoparticles concentration. Ultrasonication remarkably improved zeta potential to be above 15 mV for all the samples. Therefore, 20 minutes of ultrasonication (180 W) is sufficient for the dispersion of this rutile TiO2 nanoparticles suspension, which can remain stable for more than 1 hour. However, the optimum sonication time for TiO2 nanoparticles dispersion is influenced by many factors, such as TiO2 nanoparticles concentration, solution chemistry, and sonicator parameters.

scholarly journals Selection of the optimal type of thermal insulation structure based on the neural network modelling

2020 ◽  
Vol 216 ◽  
pp. 01037
Author(s):  
Irina Akhmetova ◽  
Elena Balzamova ◽  
Veronika Bronskaya ◽  
Denis Balzamov ◽  
Konstantin Lapin ◽  
...  
Keyword(s):  
Neural Network ◽  
Web Application ◽  
District Heating ◽  
Heat Network ◽  
Network Modelling ◽  
Query Log ◽  
Neural Network Modelling ◽  
The Neural Network ◽  
Selection Of ◽  
Optimal Type

A software package with the user interface for calculating, analyzing and predicting the parameters of cogeneration-based district heating based on the neural network modelling is presented in order to optimize and ensure the reliability of heat networks. The package is the basis for a web-application that allows to calculate the characteristics of the heat network in accordance with the model, keep a query log and provide the possibility of administration.

scholarly journals Innovative management neural network modelling based on logistic theory

2018 ◽  
Vol 239 ◽  
pp. 04021 ◽  
Author(s):  
Olga Kalinina ◽  
Eduard Balchik ◽  
Sergei Barykin
Keyword(s):  
Neural Network ◽  
Research Area ◽  
Network Modelling ◽  
Specific Flow ◽  
Industrial Enterprises ◽  
The Neural Network ◽  
The Social ◽  
Innovative Management ◽  
True Knowledge ◽  
Material Values

Logistical approach assumes the scheme of the variety of flows, including financial resources, material values and information with all of them being united in a specific flow of logistical resources. The paper covers the concept that the stages of the study are likely to form the research area being described as the net of the characteristics of the social and economic system subject to researcher consideration. The neural network considers attitudes of modern scientific thought on obtaining objectively true knowledge about the surrounding reality on the basis of the theory of logistic studies of unique innovative management developing systems. The different principles of training neural networks enable to independently determine the degree of influence of certain factors on the result of operations. Continuous innovative development of production processes, ongoing automation processes in all areas of industrial enterprises and other related changes in the knowledge economy increase the role of information and its processing in providing competitive advantages.

scholarly journals Reliability assurance of the thermal energy sources using the neural network modelling

2020 ◽  
Vol 216 ◽  
pp. 01036
Author(s):  
Irina Akhmetova ◽  
Elena Balzamova ◽  
Veronika Bronskaya ◽  
Denis Balzamov ◽  
Olga Kharitonova
Keyword(s):  
Neural Network ◽  
Software Package ◽  
Heat Supply System ◽  
Supply System ◽  
Heat Sources ◽  
Network Modelling ◽  
Neural Network Modelling ◽  
The Neural Network ◽  
Modelling Analysis ◽  
Heating Networks

A software package for neural network modelling, analysis and decision-making to improve the reliability of the heat supply system is presented. The features of heat sources and heating networks are taken into account when modelling. The trends and recommendations for improving reliability are presented.

Neural networks modelling of pesticides removal by activated carbon for water treatment

2004 ◽  
Vol 4 (5-6) ◽  
pp. 9-19 ◽  
Author(s):  
A. Cougnaud ◽  
C. Faur-Brasquet ◽  
P. Le Cloirec
Keyword(s):  
Neural Network ◽  
Activated Carbon ◽  
Batch Reactor ◽  
Decisive Role ◽  
Operating Conditions ◽  
Batch Reactors ◽  
Network Modelling ◽  
Data Set ◽  
Adsorption Mechanisms ◽  
The Neural Network

The adsorption equilibrium of pesticides (atrazin, atrazin-desethyl and triflusulfuron-methyl) onto activated carbon (AC) carried out in batch reactors has been determined for a large range of concentrations (from 5 μg/L to 21.4 mg/L) to characterise adsorption mechanisms. Single-solute isotherms tend to confirm the decisive role of the adsorbent's microporosity in the adsorption capacity of the AC. These adsorption capacities are high and range between 63 and 509 mg/g. The adsorption of the three pesticides is also studied in a dynamic reactor. The influence of operating conditions (initial concentration Co, flow velocity Uo) and adsorbent's characteristics is investigated. All dynamic experimental results are modelled by a neural network to establish the link between the characteristics of activated carbon materials and the adsorption's results. Parameters related to the adsorbate–adsorbent affinity in a batch reactor are consequently introduced in the input layer of the neural network added to operating conditions whose influence was shown (Co and Uo) and time t. The statistical quality of the neural network modelling is high (R2=0.985 for the static neural network and R2=0.993 for the dynamic neural network between experimental and predicted values for the test data set).

COMPUTATIONAL NEUROGENETIC MODELLING: A PATHWAY TO NEW DISCOVERIES IN GENETIC NEUROSCIENCE

2006 ◽  
Vol 16 (03) ◽  
pp. 215-226 ◽  
Author(s):  
LUBICA BENUSKOVA ◽  
VISHAL JAIN ◽  
SIMEI G. WYSOSKI ◽  
NIKOLA K. KASABOV
Keyword(s):  
Neural Network ◽  
Gene Networks ◽  
Gene Interactions ◽  
Ann Model ◽  
Network Modelling ◽  
Conserved Residues ◽  
The Neural Network ◽  
Internal Gene ◽  
Gene Regulatory ◽  
Target States

The paper presents a methodology for using computational neurogenetic modelling (CNGM) to bring new original insights into how genes influence the dynamics of brain neural networks. CNGM is a novel computational approach to brain neural network modelling that integrates dynamic gene networks with artificial neural network model (ANN). Interaction of genes in neurons affects the dynamics of the whole ANN model through neuronal parameters, which are no longer constant but change as a function of gene expression. Through optimization of interactions within the internal gene regulatory network (GRN), initial gene/protein expression values and ANN parameters, particular target states of the neural network behaviour can be achieved, and statistics about gene interactions can be extracted. In such a way, we have obtained an abstract GRN that contains predictions about particular gene interactions in neurons for subunit genes of AMPA, GABAA and NMDA neuro-receptors. The extent of sequence conservation for 20 subunit proteins of all these receptors was analysed using standard bioinformatics multiple alignment procedures. We have observed abundance of conserved residues but the most interesting observation has been the consistent conservation of phenylalanine (F at position 269) and leucine (L at position 353) in all 20 proteins with no mutations. We hypothesise that these regions can be the basis for mutual interactions. Existing knowledge on evolutionary linkage of their protein families and analysis at molecular level indicate that the expression of these individual subunits should be coordinated, which provides the biological justification for our optimized GRN.

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