scholarly journals Development of a Semi-Empirical Model for Droplet Size Determination of a Three-Channel Spray Nozzle for Pellet Coating Based on the Optical Method Concept

Processes ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 86
Author(s):  
Sara Vidovič ◽  
Alan Bizjak ◽  
Anže Sitar ◽  
Matej Horvat ◽  
Biljana Janković ◽  
...  
Keyword(s):  
Surface Tension ◽  
Droplet Size ◽  
Process Parameters ◽  
Flow Rate ◽  
Empirical Model ◽  
Optical Method ◽  
Spray Nozzle ◽  
Production Scale ◽  
Dispersion Properties ◽  
Semi Empirical

The purpose of this study was to investigate the droplet size obtained with a three-channel spray nozzle typically used in fluid bed devices and to construct a semi-empirical model for prediction of droplet size. With the aid of a custom-made optical method concept, the impact of the type of polymer and solvents used through dispersion properties (viscosity, density, and surface tension), dispersion flow rate, atomization pressure, and microclimate pressure on droplet size was investigated. A semi-empirical model with adequate predictability for calculating the average droplet size (R2 = 0.90, Q2 = 0.73) and its distribution (R2 = 0.84, Q2 = 0.61) was constructed by employing dimensional analysis and design of experiments. Newtonian and non-Newtonian dispersion and process parameters on laboratory and on production scale were included, thereby enabling constant droplet size irrespective of the scale. Based on the model results, it would be possible to scale-up the atomization process (e.g., coating process) from laboratory to production scale in a systematic fashion, regardless of the type of solvent or polymer used. For the system investigated, this can be performed by understanding the dispersion properties, such as viscosity, density, and surface tension, as well as the following process parameters: dispersion flow rate, atomization, and microclimate pressure.

scholarly journals Data-Driven Air-Cooled Condenser Performance Assessment: Model and Input Variable Selection Comparison

2020 ◽  
Vol 197 ◽  
pp. 10003
Author(s):  
Simone Ghettini ◽  
Alessandro Sorce ◽  
Roberto Sacile
Keyword(s):  
Neural Network ◽  
Linear Regression ◽  
Ambient Temperature ◽  
Mass Flow Rate ◽  
Mass Flow ◽  
Flow Rate ◽  
Empirical Model ◽  
Power Plants ◽  
Data Driven ◽  
Semi Empirical

This paper presents a data–driven model for the estimation of the performance of an aircooled steam condenser (ACC) with the aim to develop an efficient online monitoring, summarized by the condenser pressure (or vacuum) as Key Performance Indicator. The estimation of the ACC performance model was based on different dataset from three different combined cycle power plants with a gross power of above 380 MWe each, focusing on stationary condition of the steam turbine. The datasets include both boundary (e.g. Ambient Temperature, Wind Speed) and operative parameters (e.g. steam mass flow rate, Steam turbine power, electrical load of the ACC fans) acquired from the power plants and some derived variable as the incondensable fraction, which calculation is here proposed as additional parameter. After a preliminary sensitivity analysis on data correlation, the paper focuses on the evaluation of different ACC Condenser models: Semi-Empirical model is described trough curves typically based on steam mass flow rate (or condenser load) and the ambient temperature as main parameters. Since monitoring based on ACC design curves Semi-Empirical models, provides biased poor results, with an error of about 15%, the curves parameters were estimated basing on training data set. Other two data driven models were presented, basing on a neural network modelling and multi linear regression technique and compared on the base of the reduced number of input at first and then including aldo the other process variables in the prediction of the condenser back pressure. Estimate the parameters of the Semi-Empirical model, results in a better prediction if just steam mass flow rate and ambient temperature are available, with an error of the 7%, thanks to the knowledge contained within the “curves shapes”, with respect to linear regression (8.3%) and Neural Network models (7.6%). Higher accuracy can be then obtained by considering a larger number of operative parameters and exploiting more complex data-driven model. With a higher number of features, the neural network model has proved a higher accuracy than the linear regression model. In fact, the mean percentage error of the NN model (2.6%), in all plant operating conditions, is slightly lower than the error of the linear regression model, but presents and much lower than the mean error of the Semi-Empirical model thanks to the additional data-based knowledge.

Selective Chemical Liquid Deposition for Fabrication of Mini-Structures and Microsystems

2001 ◽  
Author(s):  
Jack G. Zhou ◽  
Feng Wang
Keyword(s):  
Droplet Size ◽  
Process Parameters ◽  
Flow Rate ◽  
Room Temperature ◽  
Manufacturing Technology ◽  
Layer By Layer ◽  
Chemical Liquid Deposition ◽  
Working Principle ◽  
Selective Chemical ◽  
Liquid Deposition

Abstract Most technologies used in fabrication of MEMS involve very sophisticated and expensive processes, which limit the application of MEMS greatly. This paper presents our research on a new manufacturing technology named Selective Chemical Liquid Deposition for Mini-structures and Microsystems SCLD-MSMS. In SCLD-MEMS, small droplets of solution or liquid reactant are ejected from a nozzle at room temperature, the droplets decompose or react with each other after impinging upon a hot substrate, the reacted solid products will then deposit on the substrate. By controlling the droplet size, the flow rate, the motion of the nozzle, and the temperature of the substrate, as well as other process parameters, a desired 3-D microstructure of deposited material can be formed through layer-by-layer scanning technique. The working principle and some experiment results are discussed in this paper.

Semi-empirical modeling and optimization of process parameters on overcut during MFAPM-EDM of Al6061 alloy

2021 ◽  
pp. 095440892110158
Author(s):  
Arun Kumar Rouniyar ◽  
Pragya Shandilya
Keyword(s):  
Magnetic Field ◽  
Process Parameters ◽  
Empirical Model ◽  
Discharge Current ◽  
Desirability Function ◽  
Experimental Results ◽  
Empirical Modeling ◽  
Machining Process ◽  
Optimum Process ◽  
Semi Empirical

Magnetic field assisted powder mixed electrical discharge machining (MFAPM-EDM) process is a hybrid process machining process which improves the machining characteristics and stability of process using assistive magnetic field and dielectric admixed powder. In this article, study on overcut has been performed on MFAPM-EDM machined Aluminium 6061 alloy. Discharge current, powder concentration, pulse on duration, pulse off duration, and magnetic field strength as process parameters have been varied during experimentation. Box Behnken design approach was employed for experimental design to carry out the experiments. Suitable Semi-empirical model was formulated using dimensional analysis for predicting the overcut. The empirical model developed was also compared with RSM model and was found better in predicting the response. Optimum process parameters for minimal overcut was conducted desirability function approach of RSM. Experimental results divulged discharge current as the most important parameters for overcut as compared to other process parameters on account of higher F-value. Confirmatory experiments revealed good correlation between optimum and experimental results.

scholarly journals A Semi-Empirical Model for Predicting Frost Properties

Processes ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 412
Author(s):  
Shao-Ming Li ◽  
Kai-Shing Yang ◽  
Chi-Chuan Wang
Keyword(s):  
Thermal Conductivity ◽  
Linear Programming ◽  
Numerical Model ◽  
Empirical Model ◽  
Quantitative Method ◽  
Predictive Ability ◽  
Dimensionless Temperature ◽  
Crystal Shape ◽  
Proposed Model ◽  
Semi Empirical

In this study, a quantitative method for classifying the frost geometry is first proposed to substantiate a numerical model in predicting frost properties like density, thickness, and thermal conductivity. This method can recognize the crystal shape via linear programming of the existing map for frost morphology. By using this method, the frost conditions can be taken into account in a model to obtain the corresponding frost properties like thermal conductivity, frost thickness, and density for specific frost crystal. It is found that the developed model can predict the frost properties more accurately than the existing correlations. Specifically, the proposed model can identify the corresponding frost shape by a dimensionless temperature and the surface temperature. Moreover, by adopting the frost identification into the numerical model, the frost thickness can also be predicted satisfactorily. The proposed calculation method not only shows better predictive ability with thermal conductivities, but also gives good predictions for density and is especially accurate when the frost density is lower than 125 kg/m3. Yet, the predictive ability for frost density is improved by 24% when compared to the most accurate correlation available.

scholarly journals Effect of a newly developed pastille on the salivary flow rate in subjects with dry mouth symptoms: a randomized, controlled, monocentric clinical study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
S. Bielfeldt ◽  
D. Wilhelm ◽  
C. Neumeister ◽  
U. Schwantes ◽  
K. -P. Wilhelm
Keyword(s):  
Surface Tension ◽  
Flow Rate ◽  
Gum Arabic ◽  
Salivary Flow ◽  
Salivary Flow Rate ◽  
Dry Mouth ◽  
Main Ingredient ◽  
Control Product ◽  
Swallowing Problems ◽  
The Mean

Abstract Background Xerostomia is associated with several diseases and is a side effect of certain drugs, resulting from reduced saliva secretion. Often, aged and sometimes younger people suffer from (idiopathic) xerostomia. Chewing gum and sucking pastilles may relieve symptoms of xerostomia by increasing the salivary flow rate due to the mechanical effect of sucking and gustatory stimulation. Swallowing problems and the urge to cough or experiencing a tickling sensation in the throat might be alleviated through a reduction in dry mouth symptoms. We investigated whether a pastille containing four polysaccharides increased the salivary flow rate and relieved the symptoms of dry mouth. Methods Participating subjects with xerostomia were randomized into two equally balanced treatment groups. Subjects received the pastille on Day 1 and a control product (Parafilm®) on Day 3, or vice versa. Unstimulated saliva was collected every 2.5 min for 0–10 min. Stimulated saliva was collected after subjects sucked the pastille or the control product. The salivary flow rate was determined gravimetrically, and, in parallel, the feeling of dry mouth was assessed using a visual analog scale. Saliva surface tension was measured in pooled saliva samples (0–5 min of sampling). Additionally, in stimulated saliva from six subjects who sucked the pastille, the presence of the main ingredient—gum arabic—was examined by Raman spectroscopy. Results Chewing the pastille significantly increased the mean salivary flow rate by 8.03 g/10 min compared to the mean changes after chewing the control product (+ 3.71 g/10 min; p < 0.0001). The mean score of dry mouth was significantly alleviated by the pastille (− 19.9 ± 17.9 mm) compared to the control product (− 3.3 ± 18.1 mm). No difference between the two products was seen regarding the saliva surface tension. Gum arabic was present in the saliva of all investigated subjects for up to 10 min after sucking the pastille. Conclusions The pastille was well tolerated and effective in increasing the salivary flow rate and reducing mouth dryness after sucking. These results were in line with the detection of the main ingredient, gum arabic, in saliva for up to 10 min after sucking the pastille. Trial registration German Register Clinical Trials (Deutsches Register Klinische Studien, DRKS) DRKS-ID: DRKS00017393, Registered 29 May 2019, https://www.drks.de/drks_web/navigate.do?navigationId=trial. HTML&TRIAL_ID = DRKS00017393.

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