scholarly journals Process development for isolation of dietary eugenol from leaves of basil (Ocimum sanctum) in combination of optimization of process variables and modeling by artificial neural network

2021 ◽  
pp. 100280
Author(s):  
Susanta Ghanta ◽  
Chanchal Bhaumik ◽  
Mriganka Sekhar Manna
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Process Development ◽  
Ocimum Sanctum ◽  
Process Variables ◽  
Artificial Neural

scholarly journals Optimization and Mathematical Modeling of Biodiesel Production using Homogenous Catalyst from Waste Cooking Oil

2019 ◽  
Vol 9 (1) ◽  
pp. 1733-1739
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Reaction Time ◽  
Waste Cooking Oil ◽  
Process Variables ◽  
Cooking Oil ◽  
Acid Methyl ◽  
Artificial Neural ◽  
Homogenous Catalyst ◽  
Catalyst Load

In the present investigation, the transesterification of waste cooking oil (WCO) to biodiesel over homogenous catalyst KOH have been carried out. To optimize the transesterification process variables both response surface method (RSM) and artificial neural network (ANN) mathematical models were applied to study the impact of process variables temperature, catalyst loading, methanol to oil ratio and the reaction time on biodiesel yield. The experiments were planned with a central composite design matrix using 24 factorial designs. A performance validation assessment was conducted between RSM and ANN. ANN models showed a high precision prediction competence in terms of coefficient of determination (R2 = 0.9995), Root Mean Square Error (RMSE = 0.5702), Standard Predicted Deviation (SEP = 0.0133), Absolute Average Deviation (AAD = 0.0115) compared to RSM model. The concentration of catalyst load was identified as the most significant factor for the base catalyzed transesterification. Under optimum conditions, the maximum biodiesel yield of 88.3% was determined by the artificial neural network model at 60 ºC, 1.05 g catalyst load, 7:1 methanol to oil ratio and 90 min transesterification reaction time. The biodiesel was analyzed by GCMS and it showed the presence of hexadecanoic acid, 9- octadecenoic acid, 9, 12, 15-octadecatrienoic acid, eicosenoic acid, methyl 18-methyl-nonadecanoate, docosanoic acid, and tetracosanoic acid as key fatty acid methyl esters.

Modeling of Drug Release from Matrix Tablets with Process Variables of Microwave-Assisted Modification of Arrowroot Starch Using Artificial Neural Network

2010 ◽  
Vol 152-153 ◽  
pp. 1700-1703 ◽  
Author(s):  
Suchada Piriyaprasarth ◽  
Pornsak Sriamornsak ◽  
Maneerat Juttulapa ◽  
Satit Puttipipatkhachorn
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Drug Release ◽  
Goodness Of Fit ◽  
Matrix Tablets ◽  
Process Variables ◽  
Microwave Assisted ◽  
Feed Forward Back Propagation ◽  
Artificial Neural ◽  
Arrowroot Starch

The objective of this study was to model the drug release property in terms of process variables of microwave-assisted modification of arrowroot starch using artificial neural network (ANN). The water content, microwave power and heating time were used as process variables for modification of arrowroot starch and the mean dissolution time was used as dependent variable. The correlation between process variables and dependent variable was examined using feed-forward back-propagation neural networks. The ANN model was optimized by considering goodness-of-fit and crossvalidated predictability. A “leave-one-out” cross-validation revealed that the neural network model could predict MDT values from matrix tablets with a reasonable accuracy (predictive r2 of 0.824 and predictive root mean square error of 19.53). The predictive ability of these models was validated by a set of 4 formulations that were not included in the training set. The predicted and observed MDT were well correlated.

Artificial neural network-based modelling of optimized experimental study of xylanase production by Penicillium citrinum xym2

2021 ◽  
pp. 095440892110641
Author(s):  
Gaurav Kumar ◽  
Shyama Prasad Saha ◽  
Shilpi Ghosh ◽  
Pranab Kumar Mondal
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Enzyme Production ◽  
Xylanase Activity ◽  
Ann Model ◽  
Process Variables ◽  
Xylanase Production ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Experimental Values

The industrial production of enzymes is generally optimized by one-factor-at-a-time (OFAT) approach. However, enzyme production by the method involves submerged or solid-state fermentation, which is laborious and time-consuming and it does not consider interactions among process variables. Artificial neural network (ANN) offers enormous potential for modelling biochemical processes and it allows rational prediction of process variables of enzyme production. In the present work, ANN has been used to predict the experimental values of xylanase production optimized by OFAT. This makes the reported ANN model to predict further optimal values for different input conditions. Both single hidden layered (6-3-1) and double hidden layered (6-12-12-1) were able to closely predict the actual values with MSE equals to 0.004566 and 0.002156, respectively. The study also uses multiple linear regression (MLR) analysis to calculate and compare the outcome with ANN predicted xylanase activity, and to establish a parametric sensitivity.

scholarly journals Micellar-Enhanced Ultrafiltration to Remove Nickel Ions: A Response Surface Method and Artificial Neural Network Optimization

Water ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1269
Author(s):  
Weiyun Lin ◽  
Liang Jing ◽  
Baiyu Zhang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface ◽  
Sodium Dodecyl ◽  
Rejection Rate ◽  
Permeate Flux ◽  
Process Variables ◽  
Nickel Ions ◽  
Micellar Enhanced Ultrafiltration ◽  
Artificial Neural

Nickel ions from aqueous solutions were removed by micellar-enhanced ultrafiltration (MEUF), using the surfactant sodium dodecyl sulfate (SDS) as a chelating agent. Process variables and indicators were modeled and optimized by a response surface methodology (RSM), using the Box–Behnken design (BBD). The generated quadratic models described the relationship between a performance indicator (nickel rejection rate or permeate flux) and process variables (pressure, nickel concentration, SDS concentration, and molecular weight cut-off (MWCO)). The analysis of variance (ANOVA) showed that both models are statistically significant. To remove 1 mM of nickel ions, the optimal condition for maximum nickel removal and flux were: pressure = 30 psi, CSDS = 10.05 mM, and MWCO = 10 kDa, resulting in a rejection rate of 98.16% and a flux of 119.20 L/h∙m2. Experimental verification indicates that the RSM model could adequately describe the performance indicators within the examined ranges of the process variables. An artificial neural network (ANN) modelling followed to predict the MEUF performance and validate the RSM results. The obtained ANN models showed good fitness to the experimental data.

scholarly journals A Robust Intelligent Framework for Multiple Response Statistical Optimization Problems Based on Artificial Neural Network and Taguchi Method

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Ali Salmasnia ◽  
Mahdi Bastan ◽  
Asghar Moeini
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Optimization Problems ◽  
Multiple Response ◽  
Process Variables ◽  
Multiple Response Optimization ◽  
Response Optimization ◽  
Artificial Neural ◽  
The Relationship ◽  
Response Variables

An important problem encountered in product or process design is the setting of process variables to meet a required specification of quality characteristics (response variables), called a multiple response optimization (MRO) problem. Common optimization approaches often begin with estimating the relationship between the response variable with the process variables. Among these methods, response surface methodology (RSM), due to simplicity, has attracted most attention in recent years. However, in many manufacturing cases, on one hand, the relationship between the response variables with respect to the process variables is far too complex to be efficiently estimated; on the other hand, solving such an optimization problem with accurate techniques is associated with problem. Alternative approach presented in this paper is to use artificial neural network to estimate response functions and meet heuristic algorithms in process optimization. In addition, the proposed approach uses the Taguchi robust parameter design to overcome the common limitation of the existing multiple response approaches, which typically ignore the dispersion effect of the responses. The paper presents a case study to illustrate the effectiveness of the proposed intelligent framework for tackling multiple response optimization problems.

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