A Modelling Study by Response Surface Methodology (RSM) on Turbidity Removal Optimization using Nanocellulose Filter Paper (Neolamarckia Cadamba)

This study observed the influence of initial turbidity, pH and initial temperature on the turbidity removal from the textile wastewater using nanocellulose filter paper from Neolamarckia Cadamba. Response Surface Methodology (RSM) model was employed to optimize and create a predictive model to evaluate the turbidity removal performance on the nanocellulose filter paper. The performance of the RSM model was statistically evaluated in terms of coefficient of determination, R2. The optimum value of turbidity removal of 99.39% were found at 66 NTU, pH 6.4 and 35.9°C. The value of prediction that obtained from modelling (RSM) was in agreement with the experimental values with R2 = 88.23%, AAD = 6.87% and RMSE = 0.18 towards the efficiency of turbidity removal.

Fluid-Solid Interaction Simulation Methodology for Coriolis Flowmeter Operation Analysis

Numerical simulation is a widely used tool for Coriolis flowmeter (CFM) operation analysis. However, there is a lack of experimentally validated methodologies for the CFM simulation. Moreover, there is no consensus on suitable turbulence models and configuration simplifications. The present study intends to address these questions in a framework of a fluid-solid interaction simulation methodology by coupling the finite volume method and finite element method for fluid and solid domains, respectively. The Reynolds stresses (RSM) and eddy viscosity-based turbulence models are explored and compared for CFM simulations. The effects of different configuration simplifications are investigated. It is demonstrated that the RSM model is favorable for the CFM operation simulations. It is also shown that the configuration simplifications should not include the braces neglect or the equivalent flowmeter tube length assumption. The simulation results are validated by earlier experimental data, showing a less than 5% discrepancy. The proposed methodology will increase the confidence in CFM operation simulations and consequently provide the foundation for further studies of flowmeter usage in various fields.

Application of Cogon Grass (Imperata cylindrica) as Biosorbent in Diesel-Filter System for Oil Spill Removal

Imperata cylindrica, often known as cogon grass, is a low-cost and useful sorbent for absorbing oil and optimising processes. The effects of temperature, time, packing density and oil concentration on oil absorption efficiency were investigated and optimised utilising one-factor-at-a-time (OFAT) and response surface methodology (RSM) approaches. Temperature and oil concentration are two important variables in the oil absorption process. Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM) analysis were used to characterise cogon grass. After treatment and oil absorption, the FTIR method indicated new formation and deformation of functional groups, while SEM revealed changes in the surface and texture of cogon grass, including a roughened and jagged surface. Validation of the RSM model yielded 93.54% efficiency with 22.45 mL oil absorbed at 128 °C temperature and 36 (v/v)% oil concentration while keeping packing density and time constant at 30 min and 0.20 g/cm3, respectively. This study may provide an insight into the usefulness of a statistical approach to maximise the oil absorption of cogon grass as an oil sorbent.

Optimization of dissolved proteins in extracts of earthworm (Lumbricus rubellus) with factor adding concentration of papain enzyme and earthworms

Earthworms (Lumbricus rubellus) can be used as an alternative to meet protein needs. This study aimed to obtain optimal N-Amino and Total Soluble Solid (TSS) results in earthworm extract. Efforts to improve the results of N-Amino and TDS (Total Dissolved Solids) in earthworm extract can be done by hydrolyzing the juice of earthworms with the help of enzyme protease papain under certain conditions, so it will facilitate the process of solving protein content. The experimental design in this study was Surface Methodology Response (RSM) model using thirteen models and two factors, namely the addition of papain enzyme concentration (6%, 8% and 10% (b/v)) and the percentage of earthworm base material (10%, 20% and 30% (b/v)). The results of this study showed the highest total protein value of 47.93% (g/L) obtained at the concentration of earthworms by 30% and 10% of papain enzyme. Then the validation results showed the optimum solution at 30% concentration of earthworms and 10% of papain enzyme that produced an N-Amino response of 7.2% and a TDS of 74% (g/L) with a Desirability of 0.906. N-Amino has a quadratic model with the actual variable equation Y 1 = 10 , 66 − 0 , 025 X 1 − 2 , 38 X 2 + 0 , 007 X 1 X 2 + 0 , 004 X 1 2 + 0 , 15 X 2 2 , and TDS has a quadratic model with the actual variable equation Y 2 = 33 , 33 + 1 , 323 X 1 + 2 , 66 X 2 − 0 , 012 X 1 X 2 − 0 , 024 X 1 2 − 0 , 043 X 2 2 .

Applying the Response Surface Methodology (RSM) Approach to Predict the Tractive Performance of an Agricultural Tractor during Semi-Deep Tillage

This study aimed to evaluate the ability of the response surface methodology (RSM) approach to predict the tractive performance of an agricultural tractor during semi-deep tillage operations. The studied parameters of tractor performance, including slippage (S), drawbar power (DP) and traction efficiency (TE), were affected by two different types of tillage tool (paraplow and subsoiler), three different levels of operating depth (30, 40 and 50 cm), and four different levels of forward speed (1.8, 2.3, 2.9 and 3.5 km h−1). Tractors drove a vertical load at two levels (225 kg and no weight) in four replications, forming a total of 192 datapoints. Field test results showed that all variables except vertical load, and different combinations of this and other variables, were effective for the S, DP and TE. Increments in speed and depth resulted in an increase and decrease in S and TE, respectively. Additionally, the RSM approach displayed changes in slippage, drawbar power and traction efficiency, resulting from alterations in tine type, depth, speed and vertical load at 3D views, with high accuracy due to the graph’s surfaces, with many small pixels. The RSM model predicted the slippage as 6.75%, drawbar power as 2.23 kW and traction efficiency as 82.91% at the optimal state for the paraplow tine, with an operating depth of 30 cm, forward speed of 2.07 km h−1 and a vertical load of 0.01 kg.

Toward the accurate estimation of elliptical side orifice discharge coefficient applying two rigorous kernel-based data-intelligence paradigms

In the present study, two kernel-based data-intelligence paradigms, namely, Gaussian Process Regression (GPR) and Kernel Extreme Learning Machine (KELM) along with Generalized Regression Neural Network (GRNN) and Response Surface Methodology (RSM), as the validated schemes, employed to precisely estimate the elliptical side orifice discharge coefficient in rectangular channels. A total of 588 laboratory data in various geometric and hydraulic conditions were used to develop the models. The discharge coefficient was considered as a function of five dimensionless hydraulically and geometrical variables. The results showed that the machine learning models used in this study had shown good performance compared to the regression-based relationships. Comparison between machine learning models showed that GPR (RMSE = 0.0081, R = 0.958, MAPE = 1.3242) and KELM (RMSE = 0.0082, R = 0.9564, MAPE = 1.3499) models provide higher accuracy. Base on the RSM model, a new practical equation was developed to predict the discharge coefficient. Also, the sensitivity analysis of the input parameters showed that the main channel width to orifice height ratio (B/b) has the most significant effect on determining the discharge coefficient. The leveraged approach was applied to identify outlier data and applicability domain.

Improving Formaldehyde Removal from Water and Wastewater by Fenton, Photo-Fenton and Ozonation/Fenton Processes through Optimization and Modeling

This study aimed to assess, optimize and model the efficiencies of Fenton, photo-Fenton and ozonation/Fenton processes in formaldehyde elimination from water and wastewater using the response surface methodology (RSM) and artificial neural network (ANN). A sensitivity analysis was used to determine the importance of the independent variables. The influences of different variables, including H2O2 concentration, initial formaldehyde concentration, Fe dosage, pH, contact time, UV and ozonation, on formaldehyde removal efficiency were studied. The optimized Fenton process demonstrated 75% formaldehyde removal from water. The best performance with 80% formaldehyde removal from wastewater was achieved using the combined ozonation/Fenton process. The developed ANN model demonstrated better adequacy and goodness of fit with a R2 of 0.9454 than the RSM model with a R2 of 0. 9186. The sensitivity analysis showed pH as the most important factor (31%) affecting the Fenton process, followed by the H2O2 concentration (23%), Fe dosage (21%), contact time (14%) and formaldehyde concentration (12%). The findings demonstrated that these treatment processes and models are important tools for formaldehyde elimination from wastewater.

Vertical wind speed extrapolation: Modelling using a response surface methodology (RSM) based on unconventional designs

The main objective of this paper is to develop a predictive model of vertical wind speed profile. Response surface methodology (RSM) is used for this purpose. RSM is a set of statistical and mathematical techniques useful for the development, improvement and optimisation of processes. It is mainly used in industrial processes and is successfully applied in this paper to model the wind speed at the hub height of the wind turbine. An unconventional model is adopted due to the nature of the input parameters which cannot be controlled or modified. The model validation indicators, namely correlation coefficient ([Formula: see text]) and root mean square error (RMSE = 1.02), give excellent results when comparing predicted and measured wind speeds. For the same data, the RSM model gives a better RMSE compared to the conventional power law and the artificial neural network.

A Cooks Distance Approach to Control the Haz and Arc Length of Tig Mild Steel Weld

The heat affected zone and arc length parameters have a vital role to play in determining the integrity of a weld structure. The cooks distance is a statistical diagnostic employed in this study to select the best optimum combination of welding process parameters. Mild steel plate was the choice material used to produce the weld specimen, which was welded with the Tungsten inert gas method. The RSM model was used to develop an optimal solution that can explain the behavior of the welded joint with respect to the heat affected zone and arc length, different diagnostic techniques were employed which includes the normal probability plot and cooks distance plot. The model developed has sufficient merit as the results obtained shows that the cooks distance values is within the range of 0 and 1 indicating the absence of outlier in the data making the optimal solution highly acceptable.

Computational Study of Operating Parameters on Performance of Compound Hydrocyclone

The dynamics of hydro cyclones is complex, because it is a multiphase flow problem that involves interaction between a discrete phase and multiple continuum phases. The performance of hydro cyclones is evaluated by using Computational Fluid Dynamics (CFD), and it is characterized by the pressure drop, split water ratio, and particle collection efficiency. In this paper, a computational model to improve and evaluate hydro cyclone performance is proposed. Computational turbulence models (renormalization group (RNG) k-ε, Reynolds’s stress model (RSM), and large-eddy simulation (LES)) are implemented, and the accuracy of each for predicting the hydro cyclone behavior is assessed. Four hydro cyclone configurations were analyzed using the RSM model. By analyzing the streamlines resulting from those simulations, it was found that the formation of some vortices and saddle points affect the separation efficiency. Furthermore, the effects of inlet width, cone length, and vortex finder diameter were found to be significant. The cut-size diameter was decreased compared to the Hsieh experimental hydro cyclone. An increase in the pressure drops leads to high values of cut-size and classification sharpness. If the pressure drop increases to twice its original value, the cut-size and the sharpness of classification are reduced to their initial values, respectively.