scholarly journals Applications of artificial neural network and Box-Behnken Design for modelling malachite green dye degradation from textile effluents using TiO2 photocatalyst

2021 ◽  
Vol 27 (1) ◽  
pp. 200553-0
Author(s):  
Chandrika K.C ◽  
T. Niranjana Prabhu ◽  
R. R. Siva Kiran ◽  
R. Hari Krishna
Keyword(s):  
Malachite Green ◽  
Dye Degradation ◽  
Anatase Phase ◽  
Experimental Studies ◽  
Operating Conditions ◽  
Interactive Effects ◽  
Optimum Operating ◽  
Box Behnken Design ◽  
Degradation Reactions ◽  
Artificial Neural

Most of the photocatalytic studies for pollutant degradation are based on optimizing a single parameter that results in a non-linear relationship between the overall parameters and the photo-degradation reactions. To address this critical problem, herein, we report the use of Response Surface Methodology based on the Box-Behnken Design (BBD) for modeling the photocatalysis degradation of Malachite Green (MG) dye using nano TiO2 as photocatalyst. The catalyst characterizations are carried out using XRD, SEM, and TEM, indicating that the TiO2 prepared by sol-gel synthesis possesses Anatase phase with particles in the nano regime and porous surface morphology. The optimum operating conditions for degradation of MG was identified by the interactive effects of variable factors such as initial dye concentration 10-30 ppm (x1), catalyst dosage 1-3 mg (x2), contact time 20-60 min (x3) using the Box-Behnken method. Furthermore, the degradation reactions are also evaluated by Artificial Neural Networks (ANN). Their predicted results have been validated by the experimental studies and found to be acceptable. Their optimal results to achieve 90% degradation efficiency at TiO2 nanoparticle dosage (3 mg), reaction time (60 min), and initial dye concentration (20 ppm) have been validated by the experimental studies and found to be acceptable.

Thermal Effect of the Spray Drying Process on the Quality of Tongkat Ali Extract

2016 ◽  
Vol 1133 ◽  
pp. 612-616
Author(s):  
Harun Noor Hafiza ◽  
Abdul Aziz Azila ◽  
Wan Zamri Wan Mastura ◽  
Yaakob Harisun ◽  
Aziz Ramlan
Keyword(s):  
Spray Drying ◽  
Operating Conditions ◽  
Optimum Operating ◽  
Powder Size ◽  
Drying Process ◽  
Process Yield ◽  
Box Behnken Design ◽  
Air Inlet ◽  
Process Use

The effect of heat on the quality of spray dried Tongkat Ali extract was investigated at three different air inlet temperatures (100°C, 180°C and 2200C). Response surface methodology employing the Box-Behnken Design was employed to hunt for the optimum operating conditions at these temperatures. Good retentions of eurycomanone, total polysaccharides and glycosaponins were exhibited during the spray drying process. However, protein was found to be susceptible to thermal degradation during the spray drying process. Use of high air inlet temperatures (i.e. 1800C and 2200C) in spray drying led to greater process yield, lower moisture contents, produced non-sticky particles, and resulted in good powder size distribution of Tongkat Ali extract compared to spray drying at 1000C.

scholarly journals Fixed bed utilization for the isolation of xylene vapor: Kinetics and optimization using response surface methodology and artificial neural network

2020 ◽  
Vol 26 (2) ◽  
pp. 200105-0
Author(s):  
Kaushal Naresh Gupta ◽  
Rahul Kumar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Flow Rate ◽  
Operating Conditions ◽  
Optimum Operating ◽  
Operating Parameters ◽  
Bed Height ◽  
Artificial Neural

This paper discusses the isolation of xylene vapor through adsorption using granular activated carbon as an adsorbent. The operating parameters investigated were bed height, inlet xylene concentration and flow rate, their influence on the percentage utilization of the adsorbent bed up to the breakthrough was found out. Mathematical modeling of experimental data was then performed by employing a response surface methodology (RSM) technique to obtain a set of optimum operating conditions to achieve maximum percentage utilization of bed till breakthrough. A fairly high value of R2 (0.993) asserted the proposed polynomial equation’s validity. ANOVA results indicated the model to be highly significant with respect to operating parameters studied. A maximum of 76.1% utilization of adsorbent bed was found out at a bed height of 0.025 m, inlet xylene concentration of 6,200 ppm and a gas flow rate of 25 mL.min-1. Furthermore, the artificial neural network (ANN) was also employed to compute the percentage utilization of the adsorbent bed. A comparison between RSM and ANN divulged the performance of the latter (R2 = 0.99907) to be slightly better. Out of various kinetic models studied, the Yoon-Nelson model established its appropriateness in anticipating the breakthrough curves.

Towards Potential Removal of Malachite Green from Wastewater: Adsorption Process Optimization and Prediction

2020 ◽  
Vol 1008 ◽  
pp. 213-221
Author(s):  
Rehab M. Ali ◽  
Mohamed A. Hassaan ◽  
Marwa R. Elkatory
Keyword(s):  
Malachite Green ◽  
Adsorption Process ◽  
Complete Removal ◽  
Operating Conditions ◽  
Experimental Results ◽  
Optimum Operating ◽  
Batch Adsorption ◽  
Adsorption Processes ◽  
Physical Features ◽  
Full Factorial

Granular activated carbon (GAC) is utilized as an adsorbent for the malachite green (MG) dye removal from aqueous solutions. The GAC was characterized by scanning electron microscopy (SEM) and Fourier transform infrared (FTIR) to realize the GAC chemical and physical features effects on the adsorption efficiency. Batch adsorption processes were carried out with different variables like pH, GAC dose, initial MG concentration and time. The response surface methodology (RSM) was used to design the experiments, model the adsorption process, optimize the operating conditions and predict the response. A 24 full factorial central composite design (CCD) was performed for the experimental design and the analysis of the results. Analysis of variance (ANOVA) was employed to determine the significance of the factors and explore the interaction between the various experimental parameters. An empirical model was derived to correlate the experimental results and predict the behavior of the GAC for the adsorption process. The model showed a good agreement with the experimental results of R2 = 0.9968 and evidenced that the optimum operating parameters are pH 10, 2 g GAC/L, 200 mg/L of MG initial concentration and 113 min adsorption time for complete removal of MG.

The catalytic oxidation of malachite green by the microwave-Fenton processes

2010 ◽  
Vol 62 (6) ◽  
pp. 1304-1311 ◽  
Author(s):  
Huaili Zheng ◽  
Huiqin Zhang ◽  
Xiaonan Sun ◽  
Peng Zhang ◽  
Tiroyaone Tshukudu ◽  
...  
Keyword(s):  
Catalytic Oxidation ◽  
Malachite Green ◽  
Removal Rate ◽  
Operating Conditions ◽  
Dominant Factor ◽  
Optimum Operating ◽  
Fenton Process ◽  
Confirmatory Tests ◽  
Initial Ph ◽  
Cod Removal Rate

Catalytic oxidation of malachite green using the microwave-Fenton process was investigated. 0% of malachite green de-colorization using the microwave process and 23.5% of malachite green de-colorization using the Fenton process were observed within 5 minutes. In contrast 95.4% of malachite green de-colorization using the microwave-Fenton was observed in 5 minutes. During the microwave-Fenton process, the optimum operating conditions for malachite green de-colorization were found to be 3.40 of initial pH, 0.08 mmol/L of Fe2 +  concentration and 12.5 mmol/L of H2O2 concentration. Confirmatory tests were carried out under the optimum conditions and the COD removal rate of 82.0% and the de-colorization rate of 99.0% were observed in 5 minutes. The apparent kinetics equation of −dC/dt = 0.0337 [malachite green]0.9860[Fe2 + ]0.8234[H2O2]0.1663 for malachite green de-colorization was calculated, which implied that malachite green was the dominant factor in determining the removal efficiency of malachite green based on microwave-Fenton process.

Search for Optimum Operating Conditions for a Water Purification Process Integrated to a Heat Transformer with Energy Recycling using Artificial Neural Network Inverse Solved by Genetic and Particle Swarm Algorithms

2012 ◽  
Vol 7 (1) ◽  
Author(s):  
Youness El Hamzaoui ◽  
Bassam Ali ◽  
J. Alfredo Hernandez ◽  
Obed Cortez Aburto ◽  
Outmane Oubram
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Purification ◽  
Particle Swarm ◽  
Optimization Methods ◽  
Operating Conditions ◽  
Optimum Operating ◽  
Purification Process ◽  
Artificial Neural ◽  
Heat Transformer

The coefficient of performance (COP) for a water purification process integrated to an absorption heat transformer with energy recycling was optimized using the artificial intelligence. The objective of this paper is to develop an integrated approach using artificial neural network inverse (ANNi) coupling with optimization methods: genetic algorithms (GAs) and particle swarm algorithm (PSA). Therefore, ANNi was solved by these optimization methods to estimate the optimal input variables when a COP is required. The paper adopts two cases studies to accomplish the comparative study. The results illustrate that the GAs outperforms the PSA. Finally, the study shows that the GAs based on ANNi is a better optimization method for control on-line the performance of the system, and constitutes a very promising framework for finding a set of “good solutions”.

scholarly journals Comparison of Optimization of Exergy Efficiency of a Crude Distillation Unit Using Artificial Neural Network (ANN) and Response Surface Methods (RSM)

2020 ◽  
pp. 1-14
Author(s):  
M. N. Braimah
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Exergy Efficiency ◽  
Operating Conditions ◽  
Distillation Unit ◽  
Optimum Operating ◽  
Base Case ◽  
Operating Variables ◽  
Port Harcourt ◽  
Artificial Neural

The study carried out simulation of the Crude Distillation Unit (CDU) of the New Port Harcourt Refinery (NPHR) and performed exergy analysis of the Refinery. The Crude Distillation Unit (CDU) of the New Port Harcourt refinery was simulated using HYSYS (2006.5). The Atmospheric Distillation Unit (ADU) which is the most inefficient unit and where major separation of the crude occurs was focused on. The simulation result was exported to Microsoft Excel Spreadsheet for exergy analysis. The ADU was optimized using statistical method and Artificial Neural Network. Box-Behnken model was applied to the sensitive operating variables that were identified. The statistical analysis of the RSM was carried out using Design Expert (6.0). Matlab software was used for the Artificial Neural Network. All the operating variables were combined to give the best optimum operating conditions. Exergy efficiency of the ADU was 51.9% and 52.4% when chemical exergy was included and excluded respectively. The optimum operating conditions from statistical optimization (RSM) are 586.1 K for liquid inlet temperature, 595.5 kPa for liquid inlet pressure and condenser pressure of 124 kPa with exergy efficiency of 69.6% which is 33.0% increment as compared to the base case. For the ANN optimization, the exergy efficiency of the ADU was estimated to be 70.6%. This gave an increase of 34.9% as compared to the base case. This study concluded that enormous improvement can be achieved both in design feasibility and improved efficiency if the feed operating parameters and other sensitive parameters are carefully chosen. Furthermore, ANN optimization gave better exergy efficiency of 70.6% than RSM optimization of 69.6%.

Modeling and Analysis of Hot Extrusion Metal Forming Process Using Artificial Neural Network and ANOVA

2010 ◽  
Author(s):  
Iman Zohourkari ◽  
Saeed Assarzadeh ◽  
Mehdi Zohoor
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Analysis Of Variance ◽  
Neural Model ◽  
Operating Conditions ◽  
Optimum Operating ◽  
Extrusion Force ◽  
Data Set ◽  
Artificial Neural ◽  
Anova Technique

In this paper, a feed-forward back-propagation artificial neural network (BP-ANN) and analysis of variance (ANOVA) are applied to a hot metal extrusion process, establishing a black box model as well as analyzing the effects of relevant process parameters on required forging load, under different operating conditions. Some finite element simulation data on extruding ck-45 steel, adopted from a published research paper, were used to train the neural model employing Levenberg-Marquardt learning algorithm. Die angle (15°–75°), friction coefficient between billet-die material pair (0.4–0.8), punch velocity (168–203 mm/s), and billet temperature (1000°C–1260°C) were selected as the inputs, while the extrusion load (tone) was considered as the network’s output. Based on the results during modeling attempts, a 4-10-10-1 size neural network has been decided on as the appropriate architecture of the process model. Testing predictive accuracy of the developed model was also done using a new data set (8 data samples), which has not been used in the training phase. The comparative errors with respect to the desired FEM simulations are all in acceptable ranges (less than 12%) thereby the network’s generalization capabilities were confirmed. Having established the appropriate neural model, analysis of variance (ANOVA) technique was then applied to the original training data base to find and recognize the level of importance of each parameters and their possible dual interactions on the extrusion loading force within 95% of confidence interval (α = 0.05). Based on the obtained inferences, the best optimal combination of parametric settings which leads to the minimum required extruding load was then revealed and recommended. The optimally minimized extrusion force was then predicted by the trained network model. Neural network tool box (NNET) of the Matlab software and design of experiments module of Minitab software were employed as platforms to develop neural simulations and ANOVA technique, respectively. The overall results indicate the feasibility and effectiveness of the proposed approach in a real manufacturing environment and eliminate the need to carry out expensive as well as time consuming trial and error experimentations to reach to the optimum operating conditions.

scholarly journals Optimizing Operating Parameters of High-Temperature Steam for Disinfecting Total Nematodes and Bacteria in Soil: Application of the Box−Behnken Design

2020 ◽  
Vol 17 (14) ◽  
pp. 5029 ◽  
Author(s):  
Da-An Huh ◽  
Woo Ri Chae ◽  
Hong Lyuer Lim ◽  
Joung Ho Kim ◽  
Yoo Sin Kim ◽  
...  
Keyword(s):  
High Temperature ◽  
Removal Efficiency ◽  
Agricultural Research ◽  
Operating Conditions ◽  
Optimum Operating ◽  
Optimum Conditions ◽  
Oil Consumption ◽  
Steam Sterilization ◽  
Box Behnken Design ◽  
High Temperature Steam

Concerns about the widespread use of pesticides have been growing due to the adverse effects of chemicals on the environment and human health. It has prompted worldwide research into the development of a replacement to chemical disinfection of soil. The efficiency of steam sterilization, an alternative to chemical methods, has improved as technology has advanced, and the Agricultural Research and Extension Service in Korea recommends the use of steam sterilization. However, few studies have been conducted on the effects and operating conditions of high-temperature steam disinfection. In this study, we present the optimum operating conditions of a high-steam disinfector, to maximize the cost-effectiveness and removal efficiency of total nematodes and total bacteria in soil using the Box−Behnken design. The experimental data were fitted to a second-order polynomial equation using multiple regression analysis, with coefficients of determination (R2) for each model of 0.9279, 0.9678, and 0.9979. The optimum conditions were found to be a steam temperature of 150.56 °C, running speed of 1.69 m/min, and spray depth of 15.0 cm, with a corresponding desirability value of 0.8367. In the model, these conditions cause the prediction of the following responses: nematode removal efficiency of 93.99%, bacteria removal efficiency of 97.49%, and oil consumption of 70.49 mL/m2. At the optimum conditions for the steam disinfector, the removal efficiencies of nematodes and bacteria were maximized, and the oil consumption was minimized. The results of our study can be used as basic data for efficient soil disinfection using high-temperature steam.

scholarly journals A Strategy for Problem Solving of Filling Imbalance in Geometrically Balanced Injection Molds

Polymers ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 805
Author(s):  
Krzysztof Wilczyński ◽  
Przemysław Narowski
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Problem Solving ◽  
Process Parameters ◽  
Experimental Studies ◽  
Injection Pressure ◽  
Optimization Procedure ◽  
Operating Conditions ◽  
Melt Temperature ◽  
Artificial Neural

Simulation and experimental studies were performed on filling imbalance in geometrically balanced injection molds. An original strategy for problem solving was developed to optimize the imbalance phenomenon. The phenomenon was studied both by simulation and experimentation using several different runner systems at various thermo-rheological material parameters and process operating conditions. Three optimization procedures were applied, Response Surface Methodology (RSM), Taguchi method, and Artificial Neural Networks (ANN). Operating process parameters: the injection rate, melt temperature, and mold temperature, as well as the geometry of the runner system were optimized. The imbalance of mold filling as well as the process parameters: the injection pressure, injection time, and molding temperature were optimization criteria. It was concluded that all the optimization procedures improved filling imbalance. However, the Artificial Neural Networks approach seems to be the most efficient optimization procedure, and the Brain Construction Algorithm (BSM) is proposed for problem solving of the imbalance phenomenon.

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