scholarly journals Adsorptive of Nickel in Wastewater by Olive Stone Waste: Optimization through Multi-Response Surface Methodology Using Desirability Functions

Water ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1320 ◽  
Author(s):  
Marina Corral Bobadilla ◽  
Rubén Lostado Lorza ◽  
Fátima Somovilla Gómez ◽  
Rubén Escribano García
Keyword(s):  
Heavy Metals ◽  
Response Surface ◽  
Regression Models ◽  
Industrial Effluents ◽  
Kinetic Studies ◽  
Nickel Ions ◽  
Desirability Functions ◽  
Biosorption Process ◽  
Olive Stone ◽  
Input Variables

Pollution from industrial wastewater has the greatest impact on the environment due to the wide variety of wastes and materials that water can contain. These include heavy metals. Some of the technologies that are used to remove heavy metals from industrial effluents are inadequate, because they cannot reduce their concentration of the former to below the discharge limits. Biosorption technology has demonstrated its potential in recent years as an alternative for this type of application. This paper examines the biosorption process for the removal of nickel ions that are present in wastewater using olive stone waste as the biosorbent. Kinetic studies were conducted to investigate the biosorbent dosage, pH of the solution, and stirring speed. These are input variables that are frequently used to determine the efficiency of the adsorption process. This paper describes an effort to identify regression models, in which the biosorption process variables are related to the process output (i.e., the removal efficiency). It uses the Response Surface Method (RSM) and it is based on Box Benken Design experiments (BBD), in which olive stones serves as the biosorbent. Several scenarios of biosorption were proposed and demonstrated by use of the Multi-Response Surface (MRS) and desirability functions. The optimum conditions that were necessary to remove nickel when the dosage of biosorbent was the minimum (0.553 g/L) were determined to be a stirring speed of 199.234 rpm and a pH of 6.369. The maximum removal of nickel under optimized conditions was 61.73%. Therefore, the olive stone waste that was investigated has the potential to provide an inexpensive biosorbent material for use in recovering the water that the nickel has contaminated. The experimental results agree closely with what the regression models have provided. This confirms the use of MRS since this technique and enables satisfactory predictions with use of the least possible amount of experimental data.

scholarly journals OPTIMIZATION OF EDTA ENHANCED SOIL WASHING ON MULTIPLE HEAVY METALS REMOVAL USING RESPONSE SURFACE METHODOLOGY

2018 ◽  
Vol 26 (4) ◽  
pp. 241-250 ◽  
Author(s):  
Liwei He ◽  
Bin Li ◽  
Ping Ning ◽  
Xiao Gong
Keyword(s):  
Heavy Metals ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Regression Models ◽  
Ethylenediaminetetraacetic Acid ◽  
Soil Washing ◽  
Liquid Ratio ◽  
Metals Removal ◽  
Edta Concentration ◽  
Multiple Heavy Metals

This research presents the optimization of soil washing conditions in the removal of multiple heavy metals (Cu-Pb-Zn-Cd) under the using of ethylenediaminetetraacetic acid (EDTA). The optimum combination of washing parameters in a bench-scale soil washing experiments is determined by response surface methodology (RSM). Central composite design is applied after single factor experiment, EDTA concentration, solid-to-liquid ratio and washing time are evaluated variables for the removal processes, and the regression models of HMs are constructed. The results show that, EDTA concentration and solid-to-liquid ratio are significant factors for this process. Subsequently, 50% of Cu removal was set as the optimum target to optimize the combined conditions, through the building of multiple quadratic regression models, the optimal condition combination is determined that EDTA concentration is 0.0026 mol·L-1, solid-to-liquid ratio is 1:22, washing time is 3.89 h, the extraction rate of Pb, Zn, Cd is predicted to be 78%, 75% and 71%, respectively.

scholarly journals Coagulation: Determination of Key Operating Parameters by Multi-Response Surface Methodology Using Desirability Functions

Water ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 398 ◽  
Author(s):  
Marina Corral Bobadilla ◽  
Rubén Lorza ◽  
Rubén Escribano García ◽  
Fátima Somovilla Gómez ◽  
Eliseo Vergara González
Keyword(s):  
Waste Water ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Regression Models ◽  
Suspended Solids ◽  
Colloidal Particles ◽  
Total Suspended Solids ◽  
Natural Coagulant ◽  
Desirability Functions ◽  
Coagulant Dosage

The clarification process removes colloidal particles that are suspended in waste water. The efficiency of this process is influenced by a series of inputs or parameters of the coagulation process, of which the most commonly used are initial turbidity, natural coagulant dosage, temperature, mixing speed and mixing time. The estimation of the natural coagulant dosage that is required to effectively remove these total suspended solids is usually determined by a jar test. This test seeks to achieve the highest efficiency of removal of the total suspended solids while reducing the final turbidity of waste water. This is often configured in iterative fashion, and requires significant experimentation and coagulant. This paper seeks to identify regression models that relate the clarification process parameters to the process outputs (final turbidity and total suspend solid) by the Response Surface Methodology (RSM) based on experiments of Central Composite Design (CCD) of experiments that involve three emerging natural coagulants. Several clarification process scenarios also were proposed and demonstrated using the Multi-Response Surface (MRS) with desirability functions. The experimental results were found to be in close agreement to what are provided by the regression models. This validates the use of the MRS-based methodology to achieve satisfactory predictions after minimal experimentation.

scholarly journals XANTHATION OF SAWDUST WASTE FOR ADSORPTION OF LEAD IONS FROM AQUEOUS SOLUTIONS

2017 ◽  
Vol 14 (27) ◽  
pp. 131-138
Author(s):  
Jaider Enrique NUÑEZ-HERNANDEZ ◽  
Fredy COLPAS-CASTILLO ◽  
Roberto FERNANDEZ-MAESTRE
Keyword(s):  
Heavy Metals ◽  
Infrared Spectroscopy ◽  
Kinetic Model ◽  
Environmental Remediation ◽  
Natural Waters ◽  
Raw Materials ◽  
Industrial Effluents ◽  
Kinetic Studies ◽  
Alternative Methods ◽  
Pseudo Second Order

Heavy metals in industrial effluents have contaminated natural waters compromising the health of humans and animals. Traditional methods eliminate these metals from wastewater but they are costly or ineffective at low metal concentrations for which alternative methods are required. We investigated the adsorption of lead on xanthated sawdust (CS2 treatment). Kinetic and pH tests, adsorption isotherms and infrared spectroscopy were performed. Kinetic studies indicated that adsorption equilibrated at 120 min following a kinetic model of pseudo-second order. The adsorption capacity was 72 mg Pb2+ g-1 (maximum at pH 5, Freundlich type isotherm, 98% adsorption). This product obtained from raw materials without commercial value, can be used for environmental remediation as an alternative to its expensive dumping into landfills.

Demulsification of a Water-in-crude Oil Emulsion with a Corn Oil BasedDemulsifier using the Response Surface Methodology: Modelling and Optimization

2021 ◽  
Vol 14 ◽  
Author(s):  
Abed Saad ◽  
Nour Abdurahman ◽  
Rosli Mohd Yunus
Keyword(s):  
Response Surface Methodology ◽  
Crude Oil ◽  
Water Content ◽  
Response Surface ◽  
Separation Efficiency ◽  
Corn Oil ◽  
Oil Emulsion ◽  
Optimal Values ◽  
Input Variables ◽  
Oil Emulsions

: In this study, the Sany-glass test was used to evaluate the performance of a new surfactant prepared from corn oil as a demulsifier for crude oil emulsions. Central composite design (CCD), based on the response surface methodology (RSM), was used to investigate the effect of four variables, including demulsifier dosage, water content, temperature, and pH, on the efficiency of water removal from the emulsion. As well, analysis of variance was applied to examine the precision of the CCD mathematical model. The results indicate that demulsifier dose and emulsion pH are two significant parameters determining demulsification. The maximum separation efficiency of 96% was attained at an alkaline pH and with 3500 ppm demulsifier. According to the RSM analysis, the optimal values for the input variables are 40% water content, 3500 ppm demulsifier, 60 °C, and pH 8.

scholarly journals Modeling and Sensitivity Analysis of the Forward Osmosis Process to Predict Membrane Flux Using a Novel Combination of Neural Network and Response Surface Methodology Techniques

Membranes ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 70
Author(s):  
Jasir Jawad ◽  
Alaa H. Hawari ◽  
Syed Javaid Zaidi
Keyword(s):  
Response Surface Methodology ◽  
Experimental Design ◽  
Response Surface ◽  
Forward Osmosis ◽  
Feed Solution ◽  
Operating Conditions ◽  
Ann Model ◽  
Membrane Flux ◽  
Input Variables ◽  
Rsm Model

The forward osmosis (FO) process is an emerging technology that has been considered as an alternative to desalination due to its low energy consumption and less severe reversible fouling. Artificial neural networks (ANNs) and response surface methodology (RSM) have become popular for the modeling and optimization of membrane processes. RSM requires the data on a specific experimental design whereas ANN does not. In this work, a combined ANN-RSM approach is presented to predict and optimize the membrane flux for the FO process. The ANN model, developed based on an experimental study, is used to predict the membrane flux for the experimental design in order to create the RSM model for optimization. A Box–Behnken design (BBD) is used to develop a response surface design where the ANN model evaluates the responses. The input variables were osmotic pressure difference, feed solution (FS) velocity, draw solution (DS) velocity, FS temperature, and DS temperature. The R2 obtained for the developed ANN and RSM model are 0.98036 and 0.9408, respectively. The weights of the ANN model and the response surface plots were used to optimize and study the influence of the operating conditions on the membrane flux.

scholarly journals Metal(loid) speciation and transformation by aerobic methanotrophs

Microbiome ◽  
2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Obulisamy Parthiba Karthikeyan ◽  
Thomas J. Smith ◽  
Shamsudeen Umar Dandare ◽  
Kamaludeen Sara Parwin ◽  
Heetasmin Singh ◽  
...  
Keyword(s):  
Heavy Metals ◽  
Ecosystem Health ◽  
Toxic Metal ◽  
Industrial Effluents ◽  
Environmental Cost ◽  
Toxic Heavy Metal ◽  
Food Web Interactions ◽  
Climate Regulation ◽  
Aerobic Methanotrophs ◽  
Long Term Impact

AbstractManufacturing and resource industries are the key drivers for economic growth with a huge environmental cost (e.g. discharge of industrial effluents and post-mining substrates). Pollutants from waste streams, either organic or inorganic (e.g. heavy metals), are prone to interact with their physical environment that not only affects the ecosystem health but also the livelihood of local communities. Unlike organic pollutants, heavy metals or trace metals (e.g. chromium, mercury) are non-biodegradable, bioaccumulate through food-web interactions and are likely to have a long-term impact on ecosystem health. Microorganisms provide varied ecosystem services including climate regulation, purification of groundwater, rehabilitation of contaminated sites by detoxifying pollutants. Recent studies have highlighted the potential of methanotrophs, a group of bacteria that can use methane as a sole carbon and energy source, to transform toxic metal (loids) such as chromium, mercury and selenium. In this review, we synthesise recent advances in the role of essential metals (e.g. copper) for methanotroph activity, uptake mechanisms alongside their potential to transform toxic heavy metal (loids). Case studies are presented on chromium, selenium and mercury pollution from the tanneries, coal burning and artisanal gold mining, respectively, which are particular problems in the developing economy that we propose may be suitable for remediation by methanotrophs.

An Efficient Response Surface Method Using Givens Transformation for Structural Reliability Analysis

2012 ◽  
Vol 532-533 ◽  
pp. 408-411
Author(s):  
Wei Tao Zhao ◽  
Yi Yang ◽  
Tian Jun Yu
Keyword(s):  
Response Surface ◽  
Response Surface Method ◽  
Structural Reliability ◽  
Limit State ◽  
State Function ◽  
Limit State Function ◽  
Surface Method ◽  
Mathematical Techniques ◽  
Input Variables ◽  
Improved Accuracy

The response surface method was proposed as a collection of statistical and mathematical techniques that are useful for modeling and analyzing a system which is influenced by several input variables. This method gives an explicit approximation of the implicit limit state function of the structure through a number of deterministic structural analyses. However, the position of the experimental points is very important to improve the accuracy of the evaluation of failure probability. In the paper, the experimental points are obtained by using Givens transformation in such way these experimental points nearly close to limit state function. A Numerical example is presented to demonstrate the improved accuracy and computational efficiency of the proposed method compared to the classical response surface method. As seen from the result of the example, the proposed method leads to a better approximation of the limit state function over a large region of the design space, and the number of experimental points using the proposed method is less than that of classical response surface method.

scholarly journals Adsorption of heavy metals by road deposited solids

2013 ◽  
Vol 67 (11) ◽  
pp. 2622-2629 ◽  
Author(s):  
Chandima Gunawardana ◽  
Ashantha Goonetilleke ◽  
Prasanna Egodawatta
Keyword(s):  
Heavy Metals ◽  
Kinetic Studies ◽  
Field Studies ◽  
Water Soluble ◽  
Trivalent Cation ◽  
Urban Road ◽  
High Charge Density ◽  
Road Surfaces ◽  
Adsorption Desorption ◽  
Exchangeable Form

The research study discussed in the paper investigated the adsorption/desorption behaviour of heavy metals commonly deposited on urban road surfaces, namely, Zn, Cu, Cr and Pb, for different particle size ranges of solids. The study outcomes, based on field studies and batch experiments, confirmed that road deposited solids particles contain a significantly high amount of vacant charge sites with the potential to adsorb additional heavy metals. Kinetic studies and adsorption experiments indicated that Cr is the most preferred metal element to associate with solids due to the relatively high electronegativity and high charge density of trivalent cation (Cr3+). However, the relatively low availability of Cr in the urban road environment could influence this behaviour. Comparing total adsorbed metals present in solids particles, it was found that Zn has the highest capacity for adsorption to solids. Desorption experiments confirmed that a low concentration of Cu, Cr and Pb in solids was present in water-soluble and exchangeable form, whilst a significant fraction of adsorbed Zn has a high likelihood of being released back into solution. Among heavy metals, Zn is considered to be the most commonly available metal among road surface pollutants.

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