# Removal of Reactive Red 120 in a Batch Technique Using Seaweed-Based Biochar: A Response Surface Methodology Approach

Authors: Gokulan Ravindiran; Kalyani Gaddam; Killi Sunil
Journal: Journal of Nanomaterials
 (2022)

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## Abstract

The elimination of Reactive Red 120 (RR120) from synthetic solution using biochar generated fromUlva prolifera was investigated. The process parameters sorbent dose (A), pH (B), initial dye concentration (C), and temperatures (D) were optimized using response surface methodology (RSM). To estimate the removal effectiveness of dye with the best 27 trails, the Box-Behnken method (blocked) was utilised. Analysis of variance (ANOVA), main effect plot, interaction plot, and surface plot were used to establish the significance of the model, and the confidence level of the model was set at 95%. To maximise the objective, the RSM optimizer was utilised with a desirability of 1. Furthermore, the batch investigation was supplemented by isotherm models, kinetic models, and thermodynamic analyses to understand the adsorption mechanism.


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## Body

## 1. Introduction

Water is the primary source of survival for humans and aquatic life, and it has recently become a big concern. Many chemicals are introduced into water bodies as a result of home and industrial activities, resulting in water pollution [1]. Dyes cause significant water contamination. There are currently about 10,000 different types of dyes available in the industry [2]. Over the last three decades, the use of dyes in the textile, paint, paper and pulp, and leather sectors has steadily increased. Dyes are classified into two types: natural dyes and synthetic dyes. Natural dyes have various drawbacks, including limited availability, dyeing processing time, and expense. Synthetic dyes are created by colouring naturally occurring dyes. Dyes are further subdivided into reactive dyes, acid dyes, and cationic dyes [3]. Reactive dyes have various distinct properties. Reactive dyes will strongly bond to the cloth through the production of covalent bonds, resulting in good colouring over the fabric.Ion exchange, membrane filtration, sedimentation, electrocoagulation, and chemical precipitation are some of the various treatment methods for separating contaminants from the effluent [4]. These traditional processes are time-consuming, and the colour treatment costs a lot of money. The adsorption technique is used to eliminate several hazardous contaminants. The key advantage of the adsorption method is that it takes less time and has a lower treatment cost, and regeneration of the sorbent will result in a lower overall treatment cost [5]. Activated carbon is one of the most common sorbents used in industry. Because of the heterogeneous nature of the activated carbon, a wide range of contaminants was absorbed [6]. Many researchers advocated the biosorption approach to solve the shortcomings of activated carbon. Biosorption is a method that uses either live or dead biomass to remove contaminants [7]. Many sorbents derived from waste leaf compost, neem seeds, tamarind seeds, industrial sludge, date seeds, orange peels, powdered nut shell, and rice husk have proven to be effective.Biochar is a new sorbent created through the pyrolysis process in an oxygen-limited environment [8]. Because of its improved properties, biochar has a high potential for harmful pollutant sorption. Because of the pyrolysis process, biochar is rich in carbon and has improved functional groups. Heavy metals and colour molecules are removed from effluents using biochar [9]. The spent biochar is then utilised to enhance the soil. In recent years, biochar has been seen as an environmental management tool capable of mitigating environmental concerns [10]. There are numerous approaches for improving the properties of biochar. Heat treatment, acid treatment, alkaline treatment, and the addition of foreign material to biochar result in the creation of a new unique biomaterial [11, 12].RSM is a statistical tool for optimizing process conditions [13]. To obtain a single output-dependent variable, several input parameters are examined at various levels (response). The Box-Behnken approach is used to develop the design of tests, and it has intervals between -1 and +1, with the design predicting in-between values. RSM will reduce the number of trials required to obtain the output [14, 15]. The current work employed 27 experimental trails based on four input variables to predict one output variable (biochar removal efficiency) for dye molecules. Ulva prolifera is widely available throughout the world’s coastal regions. Because these marine seaweeds are naturally occurring, they can be obtained at a very low or no cost. The use of overgrown biomass as an environmental mitigation method for eutrophication in water bodies will be beneficial to the environment. Many experts believe Ulva prolifera biochar can be beneficial in the sorption of heavy metals, antibiotics, and other harmful substances [16]. The current work investigated the RR120 adsorption mechanism in a single solute system using various influencing parameters.


## 2. Materials and Methods

### 2.1. Biochar Preparation and Batch Adsorption Studies

The biochar was created using the biomass ofUlva prolifera. A preliminary treatment of the biomass was performed prior to the synthesis of biochar. Deionized water was used to eliminate dust particles and other contaminants (three times). To achieve a uniform size, the washed biomass was naturally dried and shredded into 7.5 mm pieces. To ensure the absence of moisture content, the shredded biomass was heated to 103°C. Finally, the dried biomass was placed in a muffle furnace to produce biochar. To begin, the muffle furnace was cleaned using nitrogen gas to verify that there was no oxygen present. The necessary amount of dried biomass was preserved in the crucible, which was then covered with aluminium foil and pierced with two small holes. The pyrolysis temperature was held at 300°C for three hours, and the biochar was retained in the furnace until it reached room temperature. The batch adsorption study was carried out in the same manner as described in our prior research [17].


### 2.2. Response Surface Methodology

RSM used Minitab to create a Box-Behnken study design. The batch technique takes into account the biochar dosage (A), pH (B), initial RR120 concentration (C), and temperature (D) as input parameters. Table1 summarises the variable’s various levels.Table 1
Design of experiment input factors with different levels.
LevelsVariablesCode10-1123Biochar dose (g/L)A234pHB250500750Initial dye concentration (mg/L)C303540Temperature (°C)D


## 2.1. Biochar Preparation and Batch Adsorption Studies

The biochar was created using the biomass ofUlva prolifera. A preliminary treatment of the biomass was performed prior to the synthesis of biochar. Deionized water was used to eliminate dust particles and other contaminants (three times). To achieve a uniform size, the washed biomass was naturally dried and shredded into 7.5 mm pieces. To ensure the absence of moisture content, the shredded biomass was heated to 103°C. Finally, the dried biomass was placed in a muffle furnace to produce biochar. To begin, the muffle furnace was cleaned using nitrogen gas to verify that there was no oxygen present. The necessary amount of dried biomass was preserved in the crucible, which was then covered with aluminium foil and pierced with two small holes. The pyrolysis temperature was held at 300°C for three hours, and the biochar was retained in the furnace until it reached room temperature. The batch adsorption study was carried out in the same manner as described in our prior research [17].


## 2.2. Response Surface Methodology

RSM used Minitab to create a Box-Behnken study design. The batch technique takes into account the biochar dosage (A), pH (B), initial RR120 concentration (C), and temperature (D) as input parameters. Table1 summarises the variable’s various levels.Table 1
Design of experiment input factors with different levels.
LevelsVariablesCode10-1123Biochar dose (g/L)A234pHB250500750Initial dye concentration (mg/L)C303540Temperature (°C)D


## 3. Result and Discussion

### 3.1. Batch Adsorption Studies

The biochar dose (1 to 10 g/L), pH (1.75 to 5), initial dye concentration (100 to 1000 g/L), and temperature (20 to 45°C) were all varied in real-time batch tests. At ideal conditions, the batch adsorption results showed that 79.8 percent of the colour was removed [17]. The batch results came to the following conclusions. The removal effectiveness increased with an increase in biochar dose, initial dye concentration, and temperature but decreased with an increase in pH. The isotherm model determined that the Langmuir model best fit the experimental data with the highest correlation coefficient. The Langmuir isotherm indicates that the sorption was caused via chemisorption. Because of the accessibility of uniform binding sites, the results determined that the sorption of the dye molecules over the surface of the sorbent was even with single layers (monolayer adsorption). Between the adsorbate and the adsorbent, a strong equilibrium condition was achieved. The kinetic study is carried out to assess the sorption process at different time intervals with a constant initial dye concentration. The kinetic investigation indicated that the results predicted by the pseudo-second-order kinetic model are superior to the results predicted by the pseudo-first-order kinetic model, and the correlation coefficient for the PSO model was also good. PSO models fit only when the sorption was caused by a chemisorption mechanism. The thermodynamic analysis determined that the reactions are spontaneous and endothermic. The free energy value grew as the temperature rose. The negative Gibbs free energy and positive enthalpy values found that at high temperatures, reactions are spontaneous The adsorbate and adsorbent’s randomness is represented by the positive entropy value. Furthermore, the desorption studies validated the biochar’s ability to sequentially adsorb and desorb for three cycles [17].


### 3.2. RSM-Design of Experiments

Equation (1) shows the model equation produced by the Box-Behnken approach for determining removal efficiency. The model was built using the linear, two-way interaction, and quadratic form of the various components. Table 2 shows the anticipated and experimental elimination efficiency of the 27 trails. The correlation value (R2) was found to be 0.9912, indicating an excellent match for the model [18]. The adjusted correlation coefficient (adj R2) was calculated to be 0.9902, with just a 0.01 difference between R2 and adj R2 [19]. The adj R2 is used to determine the variance of the mean created by the model, and a small difference indicates that the model is significant.
(1)RemovalEfficiency=3.1+23.98A+7.21B+0.0125C+2.245D−4.634A∗A–1.779B∗B−0.000023C∗C−0.0298D∗D+0.208A∗B–0.00185A∗C+0.0000A∗D+0.00119B∗C−0.0350B∗D.Table 2
Comparison of RSM and experimental removal efficiency with residual errors.
Run orderABCDEfficiency (%)Residual errorExperimentalRSM predicted1335003077.6577.680.032345003572.8572.30-0.553235003577.1076.06-1.044247503568.5068.25-0.255237503073.3073.720.426135003067.1566.96-0.197242503573.5373.36-0.168125003571.2371.990.769237504073.7574.050.3010232503078.3378.23-0.0911225003078.7078.56-0.1412227503575.4075.29-0.1113235003578.1278.10-0.0214325003579.7579.16-0.5915222503581.6281.60-0.0216137503563.8563.10-0.7517337503574.3574.740.3918145003563.5064.300.8019132503568.8868.55-0.3320245004072.2572.450.2021235003579.2080.261.0622135004067.6067.30-0.3023225004079.8579.950.1024245003071.8071.77-0.0325332503581.2382.040.8126335004078.1078.01-0.0927232504078.7878.57-0.21The results of the analysis of variance are reported in Table3. The p and F values were used to determine the model significance for linear, quadratic, and two-way interactions of distinct components. The model assumes a 95 percent confidence level, and a p value of less than 0.05 indicates that the model is significant [20]. The p values of numerous components in Table 3 were less than 0.001, indicating that the model was significant. Temperature has a p value of 0.197, which is not statistically significant. The nonsignificance was due to the interaction of other factors with temperature and the quadratic shape of temperature. The higher the F value, the better the model. The relevance of a model is shown by a F value of 96.17 [21].Table 3
ANOVA for the developed model.
SourceDFAdj SSAdj MSF valuep valueRemarksModel14693.63949.54696.16<0.001SignificantLinear4572.566143.141277.80<0.001SignificantA1317.498317.498616.19<0.001SignificantB1162.251162.251314.89<0.001SignificantC191.85391.853178.27<0.001SignificantD10.9630.9631.870.197—Square4119.56129.89058.01<0.001SignificantA∗A1114.505114.505222.23<0.001SignificantB∗B116.87016.87032.74<0.001SignificantC∗C111.41411.41422.15<0.001SignificantD∗D12.9542.9545.730.034—2-way interaction61.5120.2520.490.805—A∗B10.1720.1720.330.574—A∗C10.8600.8601.670.221—A∗D10.0000.0000.001.000—B∗C10.3570.3570.690.421—B∗D10.1230.1230.240.635—C∗D10.0000.0000.001.000—Error126.1830.515———Lack-of-fit103.9780.3980.360.890—Pure error22.2061.103———Total26699.822————Figure1 shows a normal probability map that can be used to determine if the distribution of expected values is normal or not. If the developed model is a good fit, the normalcy probability plot should stay close to a straight line. Many points are close to the expected normal probability plot, while those points away from the reference line are termed outliers, as seen in Figure 1. Fitted vs. residual values are compared, with fitted values on the x-axis and residual values on the y. The results show that the fits are unevenly spread out with fanning, implying that the variables are not constant. The order vs. residual method was used to determine if the data obtained had any residual. The results are inconsistent and unrelated to one another, and there is no clear justification for the residuals for each element. The amount of orders categorised concerning the error was often analysed using a histogram. According to the results, eight observations have an error between 0.1 and -0.1, and this histogram shows that approximately one-third of the data have insignificant errors, indicating the model’s quality.Figure 1
Probability plot for the observation of different trails.Figure2 shows the Pareto chart for several factors. The Pareto chart creates a standard line to identify the variables that have a substantial impact on the answer. From the largest to the smallest effect, a Pareto chart is used to determine the standardised effect of all variables. The statistically significant level is indicated by the reference line, which is based on the significant level. The significance threshold is usually kept at 95 percent, or 0.05, which means the results are 95 percent correct. The importance is indicated by the individual and interaction factors crossing the reference line at 2.18. Factors and interactions that do not pass the 2.18 reference line are not significant. With the created models, the factors representing the bars at A, B, C, AA, BB, and CC suggest that these factors are important with a 95 percent accuracy. Temperature was not a statistically significant factor. For D and DD, the coded coefficient of the p value was 0.197 and 0.34, respectively. To be considered significant, the p value must be less than 0.05. The p value for all remaining temperature interactions was more than 0.05 and not significant. Temperature and the interaction of other parameters with temperature are not significant for the created model, according to the Pareto chart.Figure 2
Pareto chart for different variables and their significance.


### 3.3. Main Effect and Interaction Plot of Variables

The major effect plot of all factors was depicted in Figure3. The results showed that removal efficiency improved with increasing biochar dose, decreased with increasing pH, and decreased with increasing dye concentration. The temperature dropped at first, but a subsequent rise in temperature increased the removal efficiency. Biochar with a larger dosage may have more binding sites for the colour molecules available. The pH determines the effectiveness of elimination. The biochar’s point of zero charges was found to be 6.72. At a pH of 6.72, the available positive and negative charges on the surface are neutral. Positive charges develop more quickly in pH changes less than a point of zero change, while negative charges develop more quickly in pH changes higher than a point of zero change [22]. Because reactive dyes are negatively charged ions, sorption will increase when pH is reduced. This could lead to superior elimination efficiency at pH 2 rather than pH 4. The initial dye concentration indicates how many dye molecules are accessible to bind to available binding sites. A higher dosage may have resulted in more dye molecules being deposited at the binding sites [23]. Temperature is thought to be a significant influencing factor in the sorption process. Initially, increasing the temperature from 30 to 35°C reduced the removal efficiency, but increasing the temperature from 35 to 40°C improved the efficiency. It implies that sorption can take place as a result of physical or chemical adsorption.Figure 3
Main effect plot of different variables for responses.The interaction pot for many variables is depicted in Figure4. The continuous response (removal efficiency) to the first- and second-category components is determined using an interaction plot. The outcomes of interaction plots can be parallel or nonparallel lines. There was no interaction between the variables, as indicated by the parallel line. The nonparallel line implies that there is a lot of interaction between the variables. Figure 4 depicts the nonparallel lines, which proved that the variables had a substantial interaction. The following conclusions can be drawn from the graph, for example. When biochar is the first factor, a dose of 3 g/L of biochar produced the best results with all other interacting variables. At 2, 250 mg/L, and 40°C, the secondary parameters pH (B), initial dye concentration (C), and temperature (D) showed the best responses. When pH was taken into account as the initial consideration, a pH of 2 produced the best results. At 3, 250 mg/L, and 40°C, the secondary variables of biochar, initial dye concentration, and temperature showed maximal responses. So, based on the interaction plot, A=3g/L, B=2, C=250mg/L, and D=40°C were determined to be the best conditions for maximal response yield.Figure 4
Interaction plot of different variables for the response (removal efficiency).


### 3.4. Surface Plot of Different Variables

The surface plot is used to visualise the relationship between two independent variables and the desired result. Surface plots usually include three axes:X, Y, and Z. The X- and Z-axes are used to input independent variables, while the Y-axis is used to display desired replies. Figures 5–10 show how different variables interact to get a desired response (removal efficiency). Figure 5 shows the X- and Z-axes as the biochar dose and pH, respectively. The removal efficiency is allocated to the Y-axis. The surface plot of these two parameters revealed that a pH of 2 to 2.5 and a biochar dose of 1.5 to 3 g/L resulted in the highest removal effectiveness of 78-80 percent. It was also determined that an increase in pH and a decrease in biochar dose both reduced efficiency. From the surface plot, it can be deduced that a lower pH and a higher biochar dose are required for maximum removal effectiveness. The surface contact between biochar dosage and initial dye concentration was depicted in Figure 6. With a biochar dose of 2.5 to 3 g/L and an initial dye concentration of 200 to 300 mg/L, a removal efficiency of 79 to 81 percent was achieved. The surface plot of temperature vs. biochar dose is shown in Figure 7. Between a biochar dose of 2.5 to 3 and a temperature of 35 to 40°C, a maximum removal effectiveness of 78 to 79 percent was achieved. The surface plot between pH and initial dye concentration is shown in Figure 8. When the pH was kept between 2 and 2.5 and the initial dye concentration was 200 to 300 mg/L, the removal efficiency was 80 to 82 percent. At a pH of 2 and a temperature of 40°C, Figure 9 depicts the surface interaction between pH and temperature, with an efficiency of 79 to 80 percent. The surface interactions of initial dye concentration and temperature are depicted in Figure 10. With a dye concentration of 200 to 300 mg/L and a temperature of 35 to 40°C, a removal efficiency of 78 to 79 percent was achieved.Figure 5
Surface plot: biochar (a) vs. pH (b).Figure 6
Surface plot: biochar (a) vs. initial dye concentration (c).Figure 7
Surface plot: biochar (a) vs. temperature (d).Figure 8
Surface plot: pH (b) vs. initial dye concentration (c).Figure 9
Surface plot: pH (b) vs. temperature (d).Figure 10
Surface plot: initial dye concentration (c) vs. temperature (d).


### 3.5. RSM Optimizer

The optimization plot is used to increase the range of selected elements with a high attractiveness level or to maximise the response. The goal of this study was to boost responsiveness by taking into account all aspects at a desired level of 1. Setting the goal to maximise the reaction enhanced the response. The actual lower and higher responses were 63.5 and 81.62 percent, respectively. At process circumstances of A of 2.5 g/L, B of 2, C of 250 mg/L, and D of 36.5°C, the composite desirability of 1 was attained, with a response of 83.28 percent. Using the RSM optimization plot, a progressive increase of 1.66 percent was obtained. A boundary layer was created by varying the prediction interval (PI) between 81.16 and 85.40. To have a significant model, the RSM optimal predictor must fall inside this range. An optimization graphic was used to establish the process conditions for a batch experiment. Three sets of trials were carried out, resulting in an average removal efficiency of 82.90 percent. The RSM optimization plot for the reactive red 120 elimination is shown in Figure11.Figure 11
RSM optimization plot.


## 3.1. Batch Adsorption Studies

The biochar dose (1 to 10 g/L), pH (1.75 to 5), initial dye concentration (100 to 1000 g/L), and temperature (20 to 45°C) were all varied in real-time batch tests. At ideal conditions, the batch adsorption results showed that 79.8 percent of the colour was removed [17]. The batch results came to the following conclusions. The removal effectiveness increased with an increase in biochar dose, initial dye concentration, and temperature but decreased with an increase in pH. The isotherm model determined that the Langmuir model best fit the experimental data with the highest correlation coefficient. The Langmuir isotherm indicates that the sorption was caused via chemisorption. Because of the accessibility of uniform binding sites, the results determined that the sorption of the dye molecules over the surface of the sorbent was even with single layers (monolayer adsorption). Between the adsorbate and the adsorbent, a strong equilibrium condition was achieved. The kinetic study is carried out to assess the sorption process at different time intervals with a constant initial dye concentration. The kinetic investigation indicated that the results predicted by the pseudo-second-order kinetic model are superior to the results predicted by the pseudo-first-order kinetic model, and the correlation coefficient for the PSO model was also good. PSO models fit only when the sorption was caused by a chemisorption mechanism. The thermodynamic analysis determined that the reactions are spontaneous and endothermic. The free energy value grew as the temperature rose. The negative Gibbs free energy and positive enthalpy values found that at high temperatures, reactions are spontaneous The adsorbate and adsorbent’s randomness is represented by the positive entropy value. Furthermore, the desorption studies validated the biochar’s ability to sequentially adsorb and desorb for three cycles [17].


## 3.2. RSM-Design of Experiments

Equation (1) shows the model equation produced by the Box-Behnken approach for determining removal efficiency. The model was built using the linear, two-way interaction, and quadratic form of the various components. Table 2 shows the anticipated and experimental elimination efficiency of the 27 trails. The correlation value (R2) was found to be 0.9912, indicating an excellent match for the model [18]. The adjusted correlation coefficient (adj R2) was calculated to be 0.9902, with just a 0.01 difference between R2 and adj R2 [19]. The adj R2 is used to determine the variance of the mean created by the model, and a small difference indicates that the model is significant.
(1)RemovalEfficiency=3.1+23.98A+7.21B+0.0125C+2.245D−4.634A∗A–1.779B∗B−0.000023C∗C−0.0298D∗D+0.208A∗B–0.00185A∗C+0.0000A∗D+0.00119B∗C−0.0350B∗D.Table 2
Comparison of RSM and experimental removal efficiency with residual errors.
Run orderABCDEfficiency (%)Residual errorExperimentalRSM predicted1335003077.6577.680.032345003572.8572.30-0.553235003577.1076.06-1.044247503568.5068.25-0.255237503073.3073.720.426135003067.1566.96-0.197242503573.5373.36-0.168125003571.2371.990.769237504073.7574.050.3010232503078.3378.23-0.0911225003078.7078.56-0.1412227503575.4075.29-0.1113235003578.1278.10-0.0214325003579.7579.16-0.5915222503581.6281.60-0.0216137503563.8563.10-0.7517337503574.3574.740.3918145003563.5064.300.8019132503568.8868.55-0.3320245004072.2572.450.2021235003579.2080.261.0622135004067.6067.30-0.3023225004079.8579.950.1024245003071.8071.77-0.0325332503581.2382.040.8126335004078.1078.01-0.0927232504078.7878.57-0.21The results of the analysis of variance are reported in Table3. The p and F values were used to determine the model significance for linear, quadratic, and two-way interactions of distinct components. The model assumes a 95 percent confidence level, and a p value of less than 0.05 indicates that the model is significant [20]. The p values of numerous components in Table 3 were less than 0.001, indicating that the model was significant. Temperature has a p value of 0.197, which is not statistically significant. The nonsignificance was due to the interaction of other factors with temperature and the quadratic shape of temperature. The higher the F value, the better the model. The relevance of a model is shown by a F value of 96.17 [21].Table 3
ANOVA for the developed model.
SourceDFAdj SSAdj MSF valuep valueRemarksModel14693.63949.54696.16<0.001SignificantLinear4572.566143.141277.80<0.001SignificantA1317.498317.498616.19<0.001SignificantB1162.251162.251314.89<0.001SignificantC191.85391.853178.27<0.001SignificantD10.9630.9631.870.197—Square4119.56129.89058.01<0.001SignificantA∗A1114.505114.505222.23<0.001SignificantB∗B116.87016.87032.74<0.001SignificantC∗C111.41411.41422.15<0.001SignificantD∗D12.9542.9545.730.034—2-way interaction61.5120.2520.490.805—A∗B10.1720.1720.330.574—A∗C10.8600.8601.670.221—A∗D10.0000.0000.001.000—B∗C10.3570.3570.690.421—B∗D10.1230.1230.240.635—C∗D10.0000.0000.001.000—Error126.1830.515———Lack-of-fit103.9780.3980.360.890—Pure error22.2061.103———Total26699.822————Figure1 shows a normal probability map that can be used to determine if the distribution of expected values is normal or not. If the developed model is a good fit, the normalcy probability plot should stay close to a straight line. Many points are close to the expected normal probability plot, while those points away from the reference line are termed outliers, as seen in Figure 1. Fitted vs. residual values are compared, with fitted values on the x-axis and residual values on the y. The results show that the fits are unevenly spread out with fanning, implying that the variables are not constant. The order vs. residual method was used to determine if the data obtained had any residual. The results are inconsistent and unrelated to one another, and there is no clear justification for the residuals for each element. The amount of orders categorised concerning the error was often analysed using a histogram. According to the results, eight observations have an error between 0.1 and -0.1, and this histogram shows that approximately one-third of the data have insignificant errors, indicating the model’s quality.Figure 1
Probability plot for the observation of different trails.Figure2 shows the Pareto chart for several factors. The Pareto chart creates a standard line to identify the variables that have a substantial impact on the answer. From the largest to the smallest effect, a Pareto chart is used to determine the standardised effect of all variables. The statistically significant level is indicated by the reference line, which is based on the significant level. The significance threshold is usually kept at 95 percent, or 0.05, which means the results are 95 percent correct. The importance is indicated by the individual and interaction factors crossing the reference line at 2.18. Factors and interactions that do not pass the 2.18 reference line are not significant. With the created models, the factors representing the bars at A, B, C, AA, BB, and CC suggest that these factors are important with a 95 percent accuracy. Temperature was not a statistically significant factor. For D and DD, the coded coefficient of the p value was 0.197 and 0.34, respectively. To be considered significant, the p value must be less than 0.05. The p value for all remaining temperature interactions was more than 0.05 and not significant. Temperature and the interaction of other parameters with temperature are not significant for the created model, according to the Pareto chart.Figure 2
Pareto chart for different variables and their significance.


## 3.3. Main Effect and Interaction Plot of Variables

The major effect plot of all factors was depicted in Figure3. The results showed that removal efficiency improved with increasing biochar dose, decreased with increasing pH, and decreased with increasing dye concentration. The temperature dropped at first, but a subsequent rise in temperature increased the removal efficiency. Biochar with a larger dosage may have more binding sites for the colour molecules available. The pH determines the effectiveness of elimination. The biochar’s point of zero charges was found to be 6.72. At a pH of 6.72, the available positive and negative charges on the surface are neutral. Positive charges develop more quickly in pH changes less than a point of zero change, while negative charges develop more quickly in pH changes higher than a point of zero change [22]. Because reactive dyes are negatively charged ions, sorption will increase when pH is reduced. This could lead to superior elimination efficiency at pH 2 rather than pH 4. The initial dye concentration indicates how many dye molecules are accessible to bind to available binding sites. A higher dosage may have resulted in more dye molecules being deposited at the binding sites [23]. Temperature is thought to be a significant influencing factor in the sorption process. Initially, increasing the temperature from 30 to 35°C reduced the removal efficiency, but increasing the temperature from 35 to 40°C improved the efficiency. It implies that sorption can take place as a result of physical or chemical adsorption.Figure 3
Main effect plot of different variables for responses.The interaction pot for many variables is depicted in Figure4. The continuous response (removal efficiency) to the first- and second-category components is determined using an interaction plot. The outcomes of interaction plots can be parallel or nonparallel lines. There was no interaction between the variables, as indicated by the parallel line. The nonparallel line implies that there is a lot of interaction between the variables. Figure 4 depicts the nonparallel lines, which proved that the variables had a substantial interaction. The following conclusions can be drawn from the graph, for example. When biochar is the first factor, a dose of 3 g/L of biochar produced the best results with all other interacting variables. At 2, 250 mg/L, and 40°C, the secondary parameters pH (B), initial dye concentration (C), and temperature (D) showed the best responses. When pH was taken into account as the initial consideration, a pH of 2 produced the best results. At 3, 250 mg/L, and 40°C, the secondary variables of biochar, initial dye concentration, and temperature showed maximal responses. So, based on the interaction plot, A=3g/L, B=2, C=250mg/L, and D=40°C were determined to be the best conditions for maximal response yield.Figure 4
Interaction plot of different variables for the response (removal efficiency).


## 3.4. Surface Plot of Different Variables

The surface plot is used to visualise the relationship between two independent variables and the desired result. Surface plots usually include three axes:X, Y, and Z. The X- and Z-axes are used to input independent variables, while the Y-axis is used to display desired replies. Figures 5–10 show how different variables interact to get a desired response (removal efficiency). Figure 5 shows the X- and Z-axes as the biochar dose and pH, respectively. The removal efficiency is allocated to the Y-axis. The surface plot of these two parameters revealed that a pH of 2 to 2.5 and a biochar dose of 1.5 to 3 g/L resulted in the highest removal effectiveness of 78-80 percent. It was also determined that an increase in pH and a decrease in biochar dose both reduced efficiency. From the surface plot, it can be deduced that a lower pH and a higher biochar dose are required for maximum removal effectiveness. The surface contact between biochar dosage and initial dye concentration was depicted in Figure 6. With a biochar dose of 2.5 to 3 g/L and an initial dye concentration of 200 to 300 mg/L, a removal efficiency of 79 to 81 percent was achieved. The surface plot of temperature vs. biochar dose is shown in Figure 7. Between a biochar dose of 2.5 to 3 and a temperature of 35 to 40°C, a maximum removal effectiveness of 78 to 79 percent was achieved. The surface plot between pH and initial dye concentration is shown in Figure 8. When the pH was kept between 2 and 2.5 and the initial dye concentration was 200 to 300 mg/L, the removal efficiency was 80 to 82 percent. At a pH of 2 and a temperature of 40°C, Figure 9 depicts the surface interaction between pH and temperature, with an efficiency of 79 to 80 percent. The surface interactions of initial dye concentration and temperature are depicted in Figure 10. With a dye concentration of 200 to 300 mg/L and a temperature of 35 to 40°C, a removal efficiency of 78 to 79 percent was achieved.Figure 5
Surface plot: biochar (a) vs. pH (b).Figure 6
Surface plot: biochar (a) vs. initial dye concentration (c).Figure 7
Surface plot: biochar (a) vs. temperature (d).Figure 8
Surface plot: pH (b) vs. initial dye concentration (c).Figure 9
Surface plot: pH (b) vs. temperature (d).Figure 10
Surface plot: initial dye concentration (c) vs. temperature (d).


## 3.5. RSM Optimizer

The optimization plot is used to increase the range of selected elements with a high attractiveness level or to maximise the response. The goal of this study was to boost responsiveness by taking into account all aspects at a desired level of 1. Setting the goal to maximise the reaction enhanced the response. The actual lower and higher responses were 63.5 and 81.62 percent, respectively. At process circumstances of A of 2.5 g/L, B of 2, C of 250 mg/L, and D of 36.5°C, the composite desirability of 1 was attained, with a response of 83.28 percent. Using the RSM optimization plot, a progressive increase of 1.66 percent was obtained. A boundary layer was created by varying the prediction interval (PI) between 81.16 and 85.40. To have a significant model, the RSM optimal predictor must fall inside this range. An optimization graphic was used to establish the process conditions for a batch experiment. Three sets of trials were carried out, resulting in an average removal efficiency of 82.90 percent. The RSM optimization plot for the reactive red 120 elimination is shown in Figure11.Figure 11
RSM optimization plot.


## 4. Conclusion

The following conclusions were reached as a result of the current inquiry.(i)
The chemisorption method was responsible for the sorption of reactive red 120 employingUlva prolifera(ii)
Thermodynamic tests demonstrated that the reactions are endothermic and spontaneous(iii)
Using the response surface approach, the maximum removal efficiency was determined to be 81.62 percent(iv)
The major effect plot revealed that as pH and initial dye concentration increased, removal efficiency declined, whereas it increased as biochar dose and temperature increased(v)
RSM’s optimization plot revealed that by adjusting the parameters, a removal effectiveness of 83.28 percent may be achieved



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*Source: 3621807-2022-05-19.xml*