Central Composite Design Method Research Articles

Sludge deep dewatering by liquefied dimethyl ether: selection of operating conditions based on response surface methodology

ABSTRACT Sludge is an inevitable by-product of the sewage treatment process and its high moisture content poses significant challenges for its treatment and disposal. This study focuses on the technology of sludge deep dewatering using liquefied dimethyl ether (DME) and explores the relationship between operating parameters (DME/sludge ratio, extraction time and stirring speed) and the water content of the sludge after deep dewatering. After deep dewatering, the sludge's lower heating value (LHV) was significantly increased. The dehydrated filtrate is highly biodegradable and could be treated together with sewage. Based on the response surface method of central composite design, a second-order regression model of the above three variables and sludge water content as the response was established. Finally, the operating conditions diagram was drawn by target water content (36.96 wt.%) which meets the requirement of self-sustained incineration and model equation. This study provides a valuable perspective on sludge drying and fuelisation.

Modelling and optimization of electrodeposited amorphous Fe-P alloys using central composite design and response surface method

Central composite circumscribed (CCC) design and response surface methodology (RSM) were used to model and optimize the electrodeposition of amorphous Fe-P alloys. Based on our previous single-factor experimental results, the significance of influencing factors was ranked using analysis of variance (ANOVA) method. Three factors, significantly impacting the P content in deposits, hardness and corrosion current density, were identified, including bath temperature, pH values and H2PO2− concentration. The statistic relationships between process parameters and individual responses were established based on the CCC experimental data and RSM. The optimal parameters for each response were derived and the influences of interaction terms were investigated. The predicted maximum P content of 26.35 wt% was achieved at a bath temperature of 60℃, a pH of 1.2 and H2PO2− concentration of 60 g/L. The predicted highest microhardness was 534 HV0.1 under the optimal conditions of temperature of 60 ℃, pH value of 1.6 and H2PO2− concentration of 49 g/L. The lowest corrosion current density was predicted to be 1.34 μA·cm−2 under the conditions of a bath temperature of 60℃，a pH value of 1.2, and a H2PO2− concentration of 53 g/L. A desirability function was applied to explore the optimal comprehensive performance of both high hardness and low corrosion current density. The optimal parameter combination for comprehensive performance was bath temperature of 60℃, pH of 1.4, and H2PO2− concentration of 49 g/L, and the resulting hardness and corrosion current density were 527 HV0.1 and 2.40 μA·cm−2, respectively. Due to the complex electrodeposition mechanism of amorphous Fe-P alloys, the predicted P content in deposits largely deviated from the experimental result, but the hardness, corrosion current density and comprehensive performances show high prediction accuracy.

Development of a Genetic Algorithm – Artificial Neural Network model to optimize the Dimensional Accuracy of parts printed by FFF

PurposeFused Filament Fabrication (FFF) is one of the growing technologies in additive manufacturing, that can be used in a number of applications. In this method, process parameters can be customized and their simultaneous variation has conflicting impacts on various properties of printed parts such as dimensional accuracy (DA) and surface finish. These properties could be improved by optimizing the values of these parameters.Design/methodology/approachIn this paper, four process parameters, namely, print speed, build orientation, raster width, and layer height which are referred to as “input variables” were investigated. The conflicting influence of their simultaneous variations on the DA of printed parts was investigated and predicated. To achieve this goal, a hybrid Genetic Algorithm – Artificial Neural Network (GA-ANN) model, was developed in C#.net, and three geometries, namely, U-shape, cube and cylinder were selected. To investigate the DA of printed parts, samples were printed with a central through hole. Design of Experiments (DoE), specifically the Rotational Central Composite Design method was adopted to establish the number of parts to be printed (30 for each selected geometry) and also the value of each input process parameter. The dimensions of printed parts were accurately measured by a shadowgraph and were used as an input data set for the training phase of the developed ANN to predict the behavior of process parameters. Then the predicted values were used as input to the Desirability Function tool which resulted in a mathematical model that optimizes the input process variables for selected geometries. The mean square error of 0.0528 was achieved, which is indicative of the accuracy of the developed model.FindingsThe results showed that print speed is the most dominant input variable compared to others, and by increasing its value, considerable variations resulted in DA. The inaccuracy increased, especially with parts of circular cross section. In addition, if there is no need to print parts in vertical position, the build orientation should be set at 0° to achieve the highest DA. Finally, optimized values of raster width and layer height improved the DA especially when the print speed was set at a high value.Originality/valueBy using ANN, it is possible to investigate the impact of simultaneous variations of FFF machines’ input process parameters on the DA of printed parts. By their optimization, parts of highly accurate dimensions could be printed. These findings will be of significant value to those industries that need to produce parts of high DA on FFF machines.

Enhancing resource utilization: A novel method for effective separation of residual film and impurities in cotton fields

This study addresses the challenge of incomplete separation of mechanically recovered residual films and impurities in cotton fields, examining their impact on resource utilization and environmental pollution. It introduces an innovative screening method that combines pneumatic force and mechanical vibration for processing crushed film residue mixtures. A double-action screening device integrating pneumatic force and a key-type vibrating screen was developed. The working characteristics of this device were analyzed to explore the dynamic characteristics and kinematic laws of the materials using theoretical analysis methods. This led to the revelation of the screening laws of residual films and impurities. Screening tests were conducted using the Central Composite Design method, considering factors such as fan outlet, fan speed, vibration frequency of the screen, and feeding amount, with the impurity-rate-in-film (Q) and film-content-in-impurity (W) as evaluation indexes. The significant influence of each factor on the indexes was determined, regression models between the test factors and indexes were established, and the effect laws of key parameters and their significant interaction terms on the indexes were interpreted. The optimal combination of working parameters for the screening device was identified through multivariable optimization methods. Validation tests under this optimal parameters combination showed that the impurity-rate-in-film was 3.08% and the film-content-in-impurity was 1.94%, with average errors between the test values and the predicted values of 3.36% and 5.98%, respectively, demonstrating the effectiveness of the proposed method. This research provides a novel method and technical reference for achieving effective separation of residual film and impurities, thereby enhancing resource utilization.

Design and Parameter Optimization of a Dual-Disc Trenching Device for Ecological Tea Plantations

This paper addresses challenges in the application of existing colters in Chinese ecological tea plantations due to abundant straw roots and insufficient tillage depth. Aligned with the agronomic requirements of hilly eco-tea plantations, our study optimizes the structural advantages of the joint use of rotary tillage blades and double-disc colters to design an efficient trenching device. Our investigation explores the motion characteristics of a double-disc colter during deep trenching operations, in conjunction with rotary tillage blades. Employing discrete element method (DEM) simulations, this paper aims to minimize the working resistance and enhance the tillage depth stability. Single-factor experiments are conducted to determine the impact of key structural parameters on the tillage depth stability and working resistance. The optimal parameters are determined as a relative height of 80 mm to 120 mm, a 280 mm to 320 mm diameter for the double-disc colter, and a 10° to 14° angle between the two discs. The central composite design method is used to optimize the structural parameters of the double-disc colter. The results indicate that when the relative height is 82 mm, the diameter of the double-disc colter is 297 mm, and the angle between the two discs is 14°, the tillage depth stability performance reaches 91.64%. With a working resistance of merely 93.93 N, the trenching device achieves optimal operational performance under these conditions. Field validation testing shows a tillage depth stability coefficient of 92.37% and a working resistance of 104.2 N. These values deviate by 0.73% and 10.93%, respectively, from the simulation results, confirming the reliability of the simulation model. A field validation test further confirms that the operational performance of the colter aligns with the agronomic requirements of ecological tea plantations, offering valuable insights for research on trenching devices in such environments.

Optimisation of cationic dye dyeing process of Porel fabrics using response surface methodology

AbstractPolyethylene terephthalate (PET) fibre, widely utilised across diverse industries, suffers from inherent drawbacks such as inadequate moisture absorption, limited hydrophilicity, and susceptibility to static electricity. To address these limitations, Porel fibre has emerged as a novel modified polyester fibre that incorporates flexible aliphatic polyester into its macromolecular chain. This innovative design not only overcomes the deficiencies of PET fibre but also enables it to be dyed using cationic dyes or disperse dyes under atmospheric pressure conditions. However, the investigation of the dyeing process of cationic dyes on Porel textiles remains relatively scarce. In this work, response surface methodology (RSM) was used to optimise the dyeing process of Porel fabrics with C.I. Basic Blue 162. The effects of dyeing temperature, dyeing time, and dye concentration on exhaustion percentage and colour strength (K/S value) were investigated using the Central Composite Design (CCD) method. The optimal dyeing process condition was as follows: a dyeing temperature of 96°C, a dyeing time of 51 min, a dye concentration of 3.6% owf, a dye solution pH value of 4–5, and a liquor ratio of 1:20. Under the optimal dyeing process conditions, the Porel fabrics achieved an exhaustion percentage of 96.2% and a K/S value of 36.0. The aforementioned efforts endow Porel fibre with high‐quality dyeing capabilities and broaden the application prospects of polyester textiles.

Research on mix design and mechanical performances of MK-GGBFS based geopolymer pastes using central composite design method

In order to alleviate environmental problems and reduce CO2 emissions, geopolymers had drew attention as a kind of alkali-activated materials. Geopolymers are easier access to raw materials, green and environment friendly than traditional cement industry. Its special reaction mechanism and gel structure show excellent characteristics such as quick hardening, high strength, acid and alkali resistance. In this paper, geopolymer pastes were made with metakaolin (MK) and ground granulated blast furnace slag (GGBFS) as precursors. The effects of liquid–solid ratio (L/S) and modulus of sodium silicate (Ms) on the performances of MK-GGBFS based geopolymer paste (MSGP) were characterized by workability, strength and microstructural tests. The regression equations were obtained by central composite design method to optimize the mix design of MSGP. The goodness of fit of all the equations were more than 98%. Based on the results of experiments, the optimum mix design was found to have L/S of 0.75 and Ms of 1.55. The workability of MSGP was significantly improved while maintaining the strength under the optimum mix design. The initial setting time of MSGP decreased by 71.8%, while both of the fluidity and 28-d compressive strength increased by 15.3%, compared with ordinary Portland cement pastes. Therefore, geopolymers are promising alternative cementitious material, which can consume a large amount of MK and GGBFS and promote green and clean production.

Modeling the adsorption of ibuprofen on the Zn-decorated S,P,B co-doped C2N nanosheet: Machine learning and central composite design approaches

The ibuprofen (IB) residuals in water resources are classified as toxic and non-biodegradable contaminants. In the previous study, the Zn-decorated S,P,B co-doped C2N (Zn-SPB@C2N) nanosheet exhibited significant effectiveness in adsorbing IB from aqueous solutions. However, the operating conditions were not optimized for the adsorption process. The current study modeled the adsorption of IB onto Zn-SPB@C2N nanosheets employing central composite design (CCD) and machine learning (ML) methods under various operating conditions. The operating conditions included adsorbent mass, initial IB concentration, and pH. The CCD model revealed a mean squared error (MSE) value of 36.56. The ML investigations showed MSE values of 28.12 for the artificial neural network (ANN), 10.12 for the decision tree (DT), 8.68 for the linear regression (LR), and 3.70 for the random forest (RF) models. The RF model demonstrated high reliability in predicting IB removal across various conditions compared to other methods. Using the RF model, a maximum removal efficiency of 98 % was achieved under the optimized operating conditions, containing a pH of 7, an initial concentration of 59 mg/L, and an adsorbent mass of 0.020 g.

Optimizing heat source distribution in sintering molds: Integrating response surface model with sequential quadratic programming

The sintering mold imposes strict requirements for temperature uniformity. The mold geometric parameters and the power configuration of heating elements exert substantial influence. In this paper, we introduce an optimization approach that combines response surface models with the sequential quadratic programming algorithm to optimize the geometric parameters and heating power configuration of a heating system for sintering mold. The response surface models of the maximum temperature difference, maximum temperature, and minimum temperature of the sintering area are constructed utilizing the central composite design method. The model's reliability is rigorously confirmed through variance analysis, residual analysis, and generalization capability validation. The models demonstrate remarkable predictive accuracy within the design space. A nonlinear constrained optimization model is established based on the response surface models, and the optimal parameters are obtained after 9 iterations using the sequential quadratic programming algorithm. Under the optimal parameters, the maximum temperature difference is maintained at less than 5 °C, confirming exceptional temperature uniformity. We conduct parameter analysis based on standardized effects to determine the main influencing factors of temperature uniformity, revealing that the distance between adjacent heating rods and the power density of the inner heating rods exert significant influence. Enhanced temperature uniformity can be achieved by adopting a larger distance between heating rods and configuring the power density of the heating rods to a relatively modest level. This work introduces a practical approach to optimize the heating systems for sintering molds, with potential applications in various industrial mold optimization.

Amide-functionalized g-C3N4 nanosheet for the adsorption of arsenite (As3+): Process optimization, experimental, and density functional theory insight

Long-term exposure to arsenite (As3+) from water resources poses a global health concern. In the current study, g-C3N4 (C3N4) nanosheets were functionalized with amide groups to increase their adsorption capacity to remove As3+ from aqueous environments. Experimental assessments and density functional theory (DFT) calculations have been utilized to explore the adsorption characteristics of As3+ on C3N4 and amide-functionalized C3N4 (AC3N4) nanosheets. The central composite design (CCD) method unveiled the optimal operating conditions, including a pH level of 6.5, an adsorbent mass of 0.12 g, and an As3+ initial concentration of 250 mg/L. The AC3N4 nanosheet showed a higher adsorption capacity (205.8 mg/g) than the pristine C3N4 nanosheet (27.7 mg/g). The experimental data demonstrated conformity with the Freundlich isotherm in the adsorption study. DFT calculations revealed that the As3+/AC3N4 complex exhibited a higher adsorption energy (-40.98 eV) and stronger covalent bonds than the As3+/C3N4 complex (28.34 eV). The outstanding adsorptive properties of AC3N4 present it as an effective adsorbent for eliminating heavy metal ions from water, highlighting its capability for environmental remediation.

Innovatively-synthesized α-Fe2O3/Ti3C2Tx MXene nanocomposite by dry-impregnation method: Photocatalyst characterization and influence of operational parameters on highly efficient tetracycline degradation

Herein, a facile and cost-effective dry-impregnation method was employed to impregnate α-Fe2O3 onto Ti3C2Tx MXene (Ti3C2). The synthesized α-Fe2O3/Ti3C2Tx MXene (FeTM) photocatalyst with a narrow band gap (2.1 eV) was characterized using XRD, EDS, SEM, UV–Vis, TEM, and BET techniques. The photocatalyst was applied for the optimization degradation condition of tetracycline (TC) under visible light irradiation by measuring the absorbance at a wavelength of 357 nm via Response Surface Methodology (RSM)-Central Composite Design (CCD) method. Maximum degradation rate of pollutant (99.13 %) was achieved under optimized condition (Catalyst dosage: 0.606 g/L, tetracycline concentration: 21.67 mg/L, irradiation time: 93 min, and pH: 3.4). The correlation coefficients, F-value, and p-value are determined to be 99.35, 163.35, and < 0.0001, respectively, confirming the validity of the proposed model. Moreover, the reusability test demonstrates the favorable nanocomposite’s stability in the degradation of tetracycline from wastewater. After four cycles, a degradation efficiency of up to 81.4 % is maintained. The photodegradation kinetics was investigated and the result showed R2 = 0.9887 and K value equal to 0.0183 min−1, which represents a reaction with a quick kinetic. The α-Fe2O3/Ti3C2Tx MXene nanocomposite demonstrates remarkable degradation efficiency and high stability, positioning it as a highly promising candidate for wastewater treatment process.

Recovery of uranium from phosphate ore of Iran mine: Part II- solvent extraction of uranium from wet-process phosphoric acid

The current study in laboratory scale was aimed at developing an optimal two-stage extraction process for production of yellow cake from the phosphoric acid solution that was extracted from the Sheikh Habil phosphate rocks in central Iran. To attain an optimized extraction process, different mixtures of D2EHPA + TOPO + TBP were examined. A total extraction cycle including extraction, stripping and yellow cake precipitation was run to realistically evaluate the performances of the selected mixtures-conditions. As for optimization of the operational conditions, we exploited central composite design (CCD) method, that is a branch of the surface response methodology (RSM), and analyzed the results with standard statistical methods. The O/A (organic to aqueous ratio) = 2, 11 min extraction time, 5 wt% TOPO concentration, 10–15 wt% TBP concentration, 20 wt% D2EHPA concentration, 800 mv oxidation-reduction potential, and 25 °C temperature were obtained as optimum extraction parameters. The O/A = 15, 11 min stripping time, 50 wt% phosphoric acid concentration, 250 mv oxidation-reduction potential, and 40 °C temperature were obtained as optimum stripping parameters. Finally on the last uranium extraction from the H3PO4 strippant on the optimum conditions, the concentration of uranium was more than 4500 ppm with extraction efficiency near to 90 %. Scrubbing of organic phase was done by 2 wt% of sulfuric acid solution at 40 °C and precipitation of ammonium uranyl carbonate (AUC, (NH4)4UO2(CO3)3) was done with 2 M ammonium carbonate solution at an O/A ratio of 1/2 with efficiency more than 95 % in the uranium precipitation process.

Optimization of value-added products using response surface methodology from the HDPE waste plastic by thermal cracking

The current research aims at the thermal degradation of waste plastic in the presence of bentonite solid catalyst to produce value added products such as fuel oil, gas and charcoal. The thermal cracking of High-Density Polyethylene plastic produces the liquid fuel with conversion around 84.46 % with the characteristics: heating value 35.5 MJ/Kg, density 0.749 g/cc, viscosity 1.43cSt, a boiling point 200 °C and flash point 16 °C. The response surface methodology using the central composite design (CCD) method have been investigated to evaluate the effect of independent variables such as heating rate, temperature, time of operation on the production of value-added products. The optimal operating values for temperature, heating rate and batch time are 473.74 K, 24.9 °C/min and 159.90 min respectively. At the optimal operating conditions, the value-added products produced are fuel oil 91.16 %, gaseous products 8 % and solids 2 %. The experimental results were best fitted with quadratic polynomial model with the appreciable regression coefficient using the response surface method of analysis.

Determination of Thermal Conductivity Constant for Spray-Dried Mung Bean Protein Isolate using Series-Parallel “Block” Method and Central Composite Design

The efficient production of mung bean protein isolate (MBPI) is essential for sustainable food processing, yet it is energy-intensive due to drying phase required to convert raw mung beans into a fine powder. This work was carried out to calculate the thermal conductivity constant (k) of protein (A), moisture (B) and residual carbohydrate (C) of spray-dried MBPI. The protein slurry was prepared by dissolving MBPI in water at the ratio of 1:3, 1:4, and 1:5 prior to spray drying. The protein slurry was then spray dried at three different inflow temperatures of 140, 160, and 180 °C. A Series and Parallel Combination "Block" Model in tandem with a Central Composite Design (CCD) were selected to calculate the thermal conductivity constant of A, B and C. Eight arrangement components on A, B, and C component, measurements were derived from the initial three data points by utilizing the CCD method, in the preliminary study. By utilizing the series-parallel approach and statistical combination, there are a total of forty study arrangements. The thermal conductivity of MBPI with arrangements of A, B, and C (pABC); and a series of combinations of A and C in parallel to B (sAC pB) ranged from 0.1964-0.224 Wm-1 ℃ -1, with R2 = 98.73%-99.42%. Therefore, the energy requirement of spray drying process for MBPI could be predicted by the thermal properties of each component selected in the protein isolate.

Optimization and experiment research of the cylinder block of the roller piston pump

The roller piston pump is a new type of axial piston pump. The pump has the advantages of high limiting speed, low noise, and vibration compared to the traditional axial piston pump. However, the structure and flow channel of the cylinder block of the roller piston pump are more complicated. When the cylinder block is subjected to high hydraulic pressure, its radial compressive deformation is large, which will lead to wear of the cylinder block and distribution shaft. For this problem, the paper will optimize the structural parameters of the cylinder block. Eight key parameters of the cylinder block are selected as the design variables, and the maximum radial deformation and mass of the cylinder block are taken as the optimization objectives. The central composite design method is used to generate 81 sets of sample data, and the second-order response surface model is obtained by regression analysis. The optimal structural parameters of the cylinder block are obtained through multi-objective optimization, and the material of the cylinder block is optimized. Finally, an experiment bench was built for experimental validation, and the efficiency of the pump was further investigated. The optimization results show that the maximum compression deformation of the inner diameter surface of the cylinder block decreases from 15.865 to 4.903 µm, and the mass of the cylinder block increases from 434.951 to 489.892 g. The experimental results show that the load-bearing pressure of the optimized roller piston pump increases from less than 15 to 35 MPa, and compared with the traditional axial piston pump, there is an increase in both mechanical efficiency and volumetric efficiency.

Determination of the effectiveness of porcelain fine waste to enhance the performance of geopolymer concrete with recycled waste using central composite design and Taguchi method

This study aims to evaluate the effectiveness of incorporating bone china fine aggregate (BCA) in geopolymer concrete (GPC) with recycled coarse aggregate (RCA). A total of 25 experiments were conducted to optimize the mix design of GPC with coarse RCA and fine BCA. The suitability of a response surface model using the central composite design approach and the Taguchi method with an L25 orthogonal array was assessed. Both methods provide valuable insights and recommendations for achieving ideal mix proportions to enhance strength. The resulting model, with a higher coefficient of determination, successfully predicted the mechanical properties of GPC in fresh and hardened states. The findings suggest that GPC with up to 50% RCA and up to 100% BCA demonstrates optimal performance in terms of hardened mechanical properties. Furthermore, a strong correlation was observed between the predicted and actual values, indicating the reliability of the model.

Adsorption of heavy metals from aqueous solutions by magnetic barium hydroxyapatite nanoparticles

ABSTRACT Magnetic barium hydroxyapatite (Fe3O4@BaHAP) nanoparticles were synthesised and used as a sorbent for the removal of several heavy metal ions from aqueous solutions. The sorbent characterisation was performed by scanning electron microscopy (SEM), X-ray diffraction (XRD), Brunauer–Emmett–Teller (BET), vibrating-sample magnetometer (VSM) and Fourier transform infrared spectroscopy (FTIR) techniques. A homemade device, named continuous collection of magnetic sorbent (CCMS), was designed for efficient removal of heavy metal ions from aqueous solutions and simultaneous collection of the sorbent by a permanent magnet at the end of the process. Results of BET analysis show that average pore diameter, average pore width, pore volume and specific surface area of the Fe3O4@BaHAP NPs were 10.9 nm, 11.6 nm, 0.16 cm3/g and 55.7 m2/g, respectively. Influential parameters on the removal efficiency were investigated and optimised using a central composite design method. The optimised values for solution pH, sorbent amount, stirring and circulating times were 6.2, 150 mg, 13 and 25 min, respectively. Magnetic BaHAP nanoparticles were shown to have higher removal efficiency than BaHAP or Fe3O4 alone. Under optimised conditions, adsorption capacities for Pb2+, Cr3+, Cd2+ and Cu2+ were 99.0, 66.5, 91.2 and 92.1 mg/g, respectively. The results demonstrated that the synthesised magnetic BaHAP nanoparticles can effectively remove several metal ions from contaminated water sources.

Reduction of sulfur in fuel oil using Fe2O3 hybrid nanoadsorbent by solvent deasphalting and optimization of operational parameters with CCD

The present study investigated and tested the effect of adding three types of nanoadsorbents (multi-walled carbon nanotubes (MWCNT)) in pure form, multi-walled carbon nanotubes with Fe2O3 particles (MWCNT-Fe2O3) hybrid, and Silanated-Fe2O3 hybrid to heavy fuel oil to reduce sulfur using a deasphalting process with solvent. First, all three types of nanoadsorbents were synthesized. Then, the Central Composite Design (CCD) method was used to identify the parameters effective in deasphalting, such as the type of nanoadsorbent, the weight percentage of nanoadsorbent, and the solvent-to-fuel ratio, and to obtain their optimal values. Based on the optimization result, under laboratory temperature and pressure conditions, the highest percentage of sulfur reduction in deasphalted fuel (DAO) was obtained by adding 2.5% by weight of silanated-Fe2O3 nano-adsorbent and with a solvent-to-fuel ratio of 7.7 (The weight percentage of sulfur in DAO decreased from 3.5% by weight to 2.46%, indicating a decrease of 30%). Additionally, by increasing the temperature to 70 °C, in optimal conditions, the results revealed that the remaining sulfur percentage in DAO decreased to 2.13% by weight, indicating a decrease of 40%. Synthesized nanoadsorbents and asphaltene particles adsorbed on the surfaces of nanoadsorbents were evaluated by XRD, FTIR, FESEM, and TEM techniques.

Optimization of atmospheric leaching parameters for cadmium and zinc recovery from low-grade waste by response surface methodology (RSM)

This study is focused on the optimization of effective parameters on Cadmium and Zinc recovery by atmospheric acid leaching of low-grade waste by response surface methodology (RSM) and using the Central Composite Design (CCD) method. The effects of parameters including time (0.5–2.5 h), temperature (40–80 °C), solid/liquid (S/L) (0.05–0.09 g/cc), particle size (174–44 mic), oxygen injection (0–1%) and pH (0.5–4.5) were statistically investigated at 5 surfaces. The sample of low-grade waste used in this study was mainly zinc factory waste. Two quadratic models for the correlation of independent parameters for the maximum recovery were proposed. The properties of waste were evaluated by X-ray diffraction (XRD) and X-ray fluorescence (XRF). Atomic absorption spectroscopy was used to determine the amount of Cadmium and Zinc in the leaching solution. The correlation coefficient (R2) for the predicted and experimental data of Cadmium and Zinc are 0.9837 and 0.9368, respectively. Time, S/L and size were the most effective parameters for the recovery efficiency of cadmium and zinc. 75.05% of Cadmium and 86.13% of Zinc were recovered in optimal conditions of leaching: S/L 0.08, pH 2.5, size 88 µm, 70 °C and 2.5 h. with air injection.

Degradation of phenol in wastewater through an integrated dielectric barrier discharge and Fenton/photo-Fenton process

In this study, a non-thermal dielectric barrier discharge-Fenton/photo-Fenton process was investigated to remove phenol from synthetic wastewater. The changes and optimal values of influencing parameters, including treatment time, iron concentration, phenol initial concentration, and pH, were investigated based on the central composite design (CCD) method. The presence of 0.4 mmol/L of iron in the phenol solution with a concentration of 100 mg/L increased the removal efficiency and pseudo-first-order kinetic constant compared to dielectric barrier discharge cold plasma (DBDP) alone from 0.0824 min−1 and 56.8% to 0.2078 min−1 and 86.83%, respectively. The phenol removal efficiency was reduced to 52.9%, 45.6% and 31.8% by adding tert-butyl alcohol (TBA) with concentrations of 50, 100, and 200 mg/l, respectively. After 12 min of DBDP irradiation, the pH of the sample decreased from 5.95 to 3.42, and the temperature of the sample increased from 19.3 to 37.2 degrees Celsius. The chemical oxygen demand (COD) of the sample containing 100 mg/L phenol under plasma-Fenton/photo-Fenton irradiation decreased from 241 mg/L to 161 mg/L. Phenol removal efficiency after 10 min of treatment in the presence of 0.4 mmol/L of iron with the reactor volume of 50 mL was 87%, but the efficiency decreased to 76%, 47%, and 9% by increasing the volume to 100, 200, and 400 mL, respectively. Reducing the power led to a decrease in the removal efficiency from 56.8% for 100 W power to 10.8% for 40 W. The energy efficiency for 50% removal by DBDP and plasma-Fenton/photo-Fenton systems was 5.86×10−3 kWh/mg and 1.27×10−3 kWh/mg, respectively.