Competitive Adsorption of Copper, Zinc, and Chromium from Wastewater Using Corn-Cob Ash: Optimization Approach

2022 ◽  
Vol 58 ◽  
pp. 77-94
Author(s):  
Emmanuel Ikechukwu Ugwu ◽  
Jonah Chukwuemeka Agunwamba
Keyword(s):  
Competitive Adsorption ◽  
Metal Removal ◽  
Coefficient Of Determination ◽  
Optimization Approach ◽  
Optimal Conditions ◽  
Corn Cob ◽  
Statistical Parameters ◽  
Copper Zinc ◽  
Predicted Values ◽  
Central Composite

Corn Cob ash was used in competitive adsorption of copper, zinc, and chromium from wastewater. The central composite design; a sub-set of response surface methodology was used to optimize the adsorption of the heavy metals. The result of the statistical parameters showed the coefficient of determination (R2) of 1.000, 0.999, and 1.000 for copper, zinc, and chromium respectively. The optimal conditions obtained for adsorbent dosage, initial concentration, temperature, contact time, and particle size were 13.20 mg, 79.72 mg/l, 34.95 °C, 40.38 min, and 1400 µm, respectively with the desirability of 1.000. The predicted and the actual values of metal removal obtained were 69.41%, 76.37%, as well as 70.44%, 72.50%, 77.90 % and 71.00% for copper, zinc, and chromium respectively. The ressult indicated a good conformity between the model predicted values and the actual values, thus having small errors of 3.09%, 1.53 % and 0.56 % for copper, zinc, and chromium respectively.

scholarly journals Optimization of Operating Conditions of Essential Oil Extraction of Vietnamese Pomelo (Citrus grandis L.) Peels by Hydrodistillation Process

2019 ◽  
Vol 32 (2) ◽  
pp. 237-243 ◽  
Author(s):  
Tan Phat Dao ◽  
Ngo Thi Cam Quyen ◽  
Thien Hien Tran ◽  
Pham Van Thinh ◽  
Pham Quoc Long ◽  
...  
Keyword(s):  
Essential Oil ◽  
Extraction Process ◽  
Operating Conditions ◽  
Gas Chromatography Mass Spectrometry ◽  
Optimal Conditions ◽  
Predicted Values ◽  
Main Component ◽  
Influential Parameters ◽  
Central Composite ◽  
Material Extraction

This study attempted the optimization of the extraction process involving essential oils from Vietnamese pomelo fruits. Three influential parameters including ratio of water and material, extraction time, and temperature were assumed to be impactful to the oil yield and were investigated by establishing a statistical model. A central composite design was adopted to generate dataset required for estimation of the model. Analysis of variance was used to calculate model significance. The results showed that optimum yield of pomelo oil is 4.46 % (v/w) corresponding to water ratio of 507 mL water to 100 g sample, temperature at 119.29 ºC and distillation time of 113.68 min. Predicted values proposed by the Design Expert 11 software well-agreed with the empirical data, suggesting the excellent predictability of the proposed models. In addition, the essential oil obtained under optimal conditions was analyzed by gas chromatography-mass spectrometry. The results indicated that D-limonene is the main component (97.318 %) of essential oil.

scholarly journals Viscosity and Sensory Acceptability of Almond Milkshake as Influenced by Sugar, Almond Paste and Corn Flour-A Response Surface Study

2021 ◽  
Vol 10 (3) ◽  
pp. 2483-2493
Keyword(s):  
Response Surface ◽  
Coefficient Of Determination ◽  
Confirmatory Test ◽  
Corn Flour ◽  
Sensory Score ◽  
Order Polynomial ◽  
Predicted Values ◽  
Experimental Values ◽  
Central Composite

The effect of variables such as sugar, almond paste, and cornflour on viscosity and a sensory score of almond milkshake samples were studied by response surface methodology. The central composite design was used to obtain optimum levels of variables. The values of viscosity and sensory scores obtained from different experiment runs were 170-1085cps and 6.2-7.7. The second-order polynomial model suggested by design expert software for viscosity and a sensory score of almond milkshake showed R2 (coefficient of determination) of 0.9871 and 0.9590, respectively. Whereas model F-values for viscosity and a sensory score of almond milkshake were 84.9 and 26.02, respectively. Optimum levels of sugar, almond paste, and cornflour suggested by models were 8%, 1% & 2%, respectively. Experimental values of responses obtained from the confirmatory test were almost similar to predicted values of responses suggested by models.

Computational simulation applied to improve fuel bioethanol distillation

2013 ◽  
pp. 645-650
Author(s):  
Fabio R.M. Batista ◽  
Antonio J.A. Meirelles
Keyword(s):  
Experimental Validation ◽  
Computational Simulation ◽  
Industrial Plant ◽  
Careful Study ◽  
Optimal Conditions ◽  
Liquid Equilibrium ◽  
Steam Consumption ◽  
Wine Composition ◽  
Standard Solution ◽  
Central Composite

Experimental validation of the process simulation a typical industrial bioethanol unit was conducted, comparing the obtained results with the information collected in an industrial plant. A standard solution containing water, ethanol and 17 congeners was chosen to represent the fermented must, whose composition was selected according to analyses of samples of industrial wines. A careful study of the vapour-liquid equilibrium of the wine components was performed. An attempt to optimise the industrial plant was conducted considering two optimising approaches: the central composite design (CCD) and the Sequential Quadratic Programming (SQP). The process was investigated in terms of bioethanol alcoholic graduation, ethanol recovery, energy consumption and ethanol loss. The results showed that the simulation approach was capable of correctly reproducing a real plant of bioethanol distillation and that the optimal conditions are slightly different from those used at the industrial plant investigated. Substantial fluctuations in wine composition were easily controlled for the two loop controls preventing an off-specification product. The optimised conditions indicate a distillation process able to produce bioethanol according to the legislation requirements and with appropriate steam consumption and loss of ethanol. However, for the production of alcohols with superior qualities, substantial changes in the production system may be required.

Modeling of supercritical fluid extraction of flavonoids from Calycopteris floribunda leaves

2014 ◽  
Vol 68 (3) ◽  
Author(s):  
Xiong Liu ◽  
Dong-Liang Yang ◽  
Jia-Jia Liu ◽  
Kuan Xu ◽  
Guo-Hui Wu
Keyword(s):  
Supercritical Fluid Extraction ◽  
Supercritical Fluid ◽  
Extraction Process ◽  
Optimal Conditions ◽  
Fluid Extraction ◽  
Pressure Method ◽  
Organic Solvent Extraction ◽  
Drug Lead ◽  
Temperature And Pressure ◽  
Central Composite

AbstractThe aim of this study was to obtain flavonoids extracts from Calycopteris floribunda leaves using supercritical fluid extraction (SFE) with CO2 and a co-solvent. Pachypodol, a potential anticancer drug lead compound separated from the extracts, was examined. Classical organic solvent extraction (CE) with ethanol was performed to evaluate the high pressure method. HPLC analysis was introduced to interpret the differences between SFE and CE extracts in terms of antioxidant activity and the concentration of pachypodol. SFE kinetics and mathematical modeling of the overall extraction curves (OEC) were investigated. Evaluation of the models against experimental data showed that the Sovová model performs the best. The supercritical fluid extraction process was optimized using a central composite design (CCD), where temperature and pressure were adjusted. The optimal conditions of SFE were: pressure of 30 MPa and temperature of 35°C.

scholarly journals Understanding the biogeochemical mechanisms of metal removal from acid mine drainage with a subsurface limestone bed at the Motokura Mine, Japan

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Shigeshi Fuchida ◽  
Kohei Suzuki ◽  
Tatsuya Kato ◽  
Masakazu Kadokura ◽  
Chiharu Tokoro
Keyword(s):  
Acid Mine Drainage ◽  
Mine Drainage ◽  
Metal Removal ◽  
Theoretical Calculations ◽  
Exchange Capacity ◽  
Treatment Technique ◽  
Manganese Compound ◽  
Edge Structure ◽  
Acid Mine ◽  
Copper Zinc

AbstractSubsurface limestone beds (SLBs) are used as a passive treatment technique to remove toxic metals from acid mine drainage (AMD). In this study, we investigated the mechanisms and thermodynamics of metal (manganese, copper, zinc, cadmium, and lead) precipitation in the SLB installed at the Motokura Mine. Field surveys in 2017 and 2018 showed that the pH of the SLB influent (initially 5–6) increased to approximately 8 in the drain between 24 and 45 m from the inlet. This increase was caused by limestone dissolution and resulted in the precipitation of hydroxides and/or carbonates of copper, zinc, and lead, as expected from theoretical calculations. Manganese and cadmium were removed within a pH range of approximately 7–8, which was lower than the pH at which they normally precipitate as hydroxides (pH 9–10). X-ray absorption near-edge structure analysis of the sediment indicated that δ-MnO2, which has a high cation-exchange capacity, was the predominant tetravalent manganese compound in the SLB rather than trivalent compound (MnOOH). Biological analysis indicates that microorganism activity of the manganese-oxidizing bacteria in the SLB provided an opportunity for δ-MnO2 formation, after which cadmium was removed by surface complexation with MnO2 (≡ MnOH0 + Cd2+  ⇄  ≡ MnOCd+  +  H+). These findings show that biological agents contributed to the precipitation of manganese and cadmium in the SLB, and suggest that their utilization could enhance the removal performance of the SLB.

Prediction of Design Hourly Volume on Rural Roads

2020 ◽  
pp. 036119812096556
Author(s):  
Miloš Petković ◽  
Vladan Tubić ◽  
Nemanja Stepanović
Keyword(s):  
Coefficient Of Determination ◽  
Rural Roads ◽  
The Road ◽  
Annual Average Daily Traffic ◽  
Proposed Model ◽  
Traffic Volumes ◽  
Implementation Costs ◽  
On The Road ◽  
Predicted Values ◽  
Design Hourly Volume

Design hourly volume (DHV) represents one of the most significant parameters in the procedures of developing and evaluating road designs. DHV values can be accurately and precisely calculated only on the road sections with the implemented automatic traffic counters (ATCs) which constantly monitor the traffic volume. Unfortunately, many road sections do not contain ATCs primarily because of the implementation costs. Consequently, for many years, the DHV values have been defined on the basis of occasional counting and the factors related to traffic flow variability over time. However, it has been determined that this approach has significant limitations and that the predicted values considerably deviate from the actual values. Therefore, the main objective of this paper is to develop a model which will enable DHV prediction on rural roads in cases of insufficient data. The suggested model is based on the correlation between DHVs and the parameters defining the characteristics of traffic flows, that is, the relationship between the traffic volumes on design working days and non-working days, and annual average daily traffic. The results of the conducted research indicate that the application of the proposed model enables the prediction of DHV values with a significant level of data accuracy and reliability. The coefficient of determination (R2) shows that more than 98% of the variance of the calculated DHVs was explained by the observed DHV values, while the mean error ranged from 4.86% to 7.84% depending on the number of hours for which DHV was predicted.

scholarly journals Quality by Design Approach for Formulation and Optimization of Microparticles Based Inhalable Phytopharmaceuticals of Trigonella Foenum-Graecum and Alpinia Galanga

2021 ◽  
Vol 12 (2) ◽  
pp. 2050-2067
Keyword(s):  
Bulk Density ◽  
Herbal Extract ◽  
Chronic Obstructive ◽  
Optimization Approach ◽  
Optimization Strategy ◽  
Alpinia Galanga ◽  
Trigonella Foenum Graecum ◽  
Freeze Dried ◽  
Mean Diameter ◽  
Predicted Values

Chronic obstructive pulmonary disease has been the most widespread worldwide health problem that has influenced millions of people worldwide. The freeze-dried inhalable microparticles (FDIMs) of Trigonella foenum-graecum and Alpinia galanga extracts were synthesized by simple emulsification solvent evaporation technique using polyvinyl pyrrolidone K30 (PVP K30) and polyethylene glycol 6000 (PEG 6000) and were optimized using Box-Behnken design (BBD). Mannitol was utilized for surface modification of FDIM for enhancing their aerodynamic characteristics. The independent parameters which were utilized in the optimization strategy were herbal extract: polymer (w/w, X1), mannitol (% w/v, X2), and surfactant (% v/v, X3). The studied response variables were mean diameter (µm) (Y1) and bulk density (g/cc) (Y2). The present study concluded that optimized FDIMs could be successfully manufactured using herbal extract: polymer (1:2 w/w), mannitol (4.616 % w/v) and surfactant (1.5 % v/v), which had 0.977 desirability functions. The predicted values of response parameters of optimized FDIMs were found at 1.326 µm mean diameter and 0.244 g/cc bulk density. The percentage relative error between actual and model-predicted values of mean diameter and bulk density for optimized FDIM was found 4.09 and 2.45%, respectively (< 5%), which authenticated the articulacy of the optimization approach.

scholarly journals Predictive Algorithm for Handover Decisions between LTE and LTE-A Networks

2021 ◽  
Vol 9 (4) ◽  
pp. 110-126
Author(s):  
Wafa Benaatou ◽  
Adnane Latif ◽  
Vicent Pla
Keyword(s):  
Power Consumption ◽  
Mean Square Error ◽  
Fuzzy Inference ◽  
Hybrid Approach ◽  
Mobile Terminal ◽  
Base Station ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Statistical Parameters ◽  
Inference System

A heterogeneous wireless network needs to maintain seamless mobility and service continuity; for this reason, we have proposed an approach based on the combination of particle swarm optimization (PSO) and an adaptive neuro-fuzzy inference system (ANFIS) to forecast a handover during a movement of a mobile terminal from a serving base station to target base station. Additionally, the handover decision is made by considering several parameters, such as peak data rate, latency, packet loss, and power consumption, to select the best network for handover from an LTE to an LTE-A network. The performance efficiency of the new hybrid approach is determined by computing different statistical parameters, such as root mean square error (RMSE), coefficient of determination (R2), mean square error (MSE), and error standard deviation (StD). The execution of the proposed approach has been performed using MATLAB software. The simulation results show that the hybrid PSO-ANFIS model has better performance than other approaches in terms of prediction accuracy and reduction of handover latency and the power consumption in the network.  

scholarly journals OPTIMIZING THE PROCESS OF TRANSFORMING COFFEE HUSKS INTO BIOCHAR BY MEANS OF HYDROTHERMAL CARBONIZATION

2018 ◽  
Vol 54 (4B) ◽  
pp. 138
Author(s):  
Tran Thi Hien
Keyword(s):  
Maximum Yield ◽  
Hydrothermal Carbonization ◽  
Optimal Conditions ◽  
Coffee Husk ◽  
Optimal Value ◽  
Face Centered ◽  
Composite Face ◽  
Temperature Time ◽  
Central Composite ◽  
Water Ratio

The conditions of the hydrothermal carbonization process to produce biochar from coffee husk will be optimized for maximum yield. Besides, response surface methodology (RSM) and central composite face-centered (CCF) method will be used in designing experiments. Also, the optimal value of factors such as temperature, time and biomass: water ratio which can provide a maximum yield of biochar will be worked out using Modde 5.0. As a result, the optimal conditions for maximum yield of biochar was obtained as temperature of 180 oC, 3.5 h and biomass: water ratio of 15 %. It can also be concluded that temperature has greater impact on the transformation of biochar than time and biomass: water ratio.

scholarly journals A Comparison of Machine Learning Approaches for Prediction of Permeability using Well Log Data in the Hydrocarbon Reservoirs

2021 ◽  
Vol 2 (2) ◽  
pp. 82-99
Author(s):  
Mohsen Talebkeikhah ◽  
Zahra Sadeghtabaghi ◽  
Mehdi Shabani
Keyword(s):  
Neural Network ◽  
Random Forest ◽  
Gamma Ray ◽  
Accurate Determination ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Learning Approaches ◽  
Statistical Parameters ◽  
Southwest Of Iran ◽  
Gamma Ray Log

Permeability is a vital parameter in reservoir engineering that affects production directly. Since this parameter's significance is obvious, finding a way for accurate determination of permeability is essential as well. In this paper, the permeability of two notable carbonate reservoirs (Ilam and Sarvak) in the southwest of Iran was predicted by several different methods, and the level of accuracy in all models was compared. For this purpose, Multi-Layer Perceptron Neural Network (MLP), Radial Basis Function Neural Network (RBF), Support Vector Regression (SVR), decision tree (DT), and random forest (RF) methods were chosen. The full set of real well-logging data was investigated by random forest, and five of them were selected as the potent variables. Depth, Computed gamma-ray log (CGR), Spectral gamma-ray log (SGR), Neutron porosity log (NPHI), and density log (RHOB) were considered efficacious variables and used as input data, while permeability was considered output. It should be noted that permeability values are derived from core analysis. Statistical parameters like the coefficient of determination ( ), root mean square error (RMSE) and standard deviation (SD) were determined for the train, test, and total sets. Based on statistical and graphical results, the SVM and DT models perform more accurately than others. RMSE, SD and R2values of SVM and DT models are 0.38, 1.63, 0.97 and 0.44, 2.89, and 0.96 respectively. The results of the best-proposed models of this paper were then compared with the outcome of the empirical equation for permeability prediction. The comparison indicates that artificial intelligence methods perform more accurately than traditional methods for permeability estimation, such as proposed equations. Doi: 10.28991/HEF-2021-02-02-01 Full Text: PDF

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