Quadratic Response Surface Model Research Articles

Phytoinhibitory activities and extraction optimization of potent invasive plants as eco-friendly weed suppressant against Echinochloa colona (L.) Link

Parasitic Echinochloa colona (L.) Link has globally invaded paddy fields that could devote to severe grain yield loss. Prolonged extensive application of commercial synthetic herbicides could pose the weed evolved resistance, bioaccumulation and environmental deterioration. Hence, this paper pertains to the aim of investigating potent natural products which have shown considerable phytotoxic activities, subsequent phytotoxin studies and extraction optimization for pre-emergent control of E. colona. In the phytotoxicity study, a suite of invasive plants was procured and the leaf, stem and root extracts were evaluated by in vitro germination and seedling growth assays. The invasive plant extracts exhibited multiple magnitudes of inhibition against E. colona in the plant species-, plant part- and concentration-dependent manners. Along with the dose-response study, the leaf extracts of Mikania micrantha Kunth ex H.B.K., Clidemia hirta (L.) D. Don , Dicranopteris linearis (Burm.f.) Underw. and Ageratum conyzoides L. showed the attainable half maximal effective doses (ED50) below the concentration 100g biomass dry weight equivalent (BDWE)/L, indicating the top-ranked inhibition of E. colona among the investigated plant extracts. The top-ranked phytotoxic leaf extracts were subjected to qualitative and quantitative characterizations including extraction yield, total phenolic content, phytochemical screening and spectroscopic analysis of the phytochemicals with possible inhibitory effect. Joint putative allelochemicals consisting of 16 phenolics and 5 aromatics were detected concurrently by liquid chromatography-mass spectrometry (LC-MS), were found contributed to the appreciable germination inhibition. Amongst the abundant phenolic acids were protocatechuic acid, gallic acid, p-hydroxyphenylacetic acid and chlorogenic acid. To suit the industrial desire in product development, the phytotoxic leaves were processed through extraction optimization using response surface methodology (RSM). A multivariate face-centered cube design (FCCD) was established to seek the functional relationship between the process variables (extraction time, agitation speed and solvent consumption) and the response (germination inhibition). Quadratic response surface models were obtained with the models significance and significant effect of the process factors at 95% confidence interval were examined by various statistical analyses. Verification experiments at the optimal conditions were well matched with the predicted values, along with absolute errors in the range 3.63% to 6.70%, indicating the proposed quadratic models were valid and useful prediction of the extraction conditions. These outcomes provide the valuable findings on the possible use of potent invasive plants as bioeffective, economical and eco-friendly herbicide alternative towards fostering the sustainable weed management.

Magnetic nanocomposite of multi-walled carbon nanotube as effective adsorbent for methyl violet removal from aqueous solutions: Response surface modeling and kinetic study

Magnetic nanocomposite of multi-walled carbon nanotube (m-MWCNT) was synthesized for adsorptive removal of methyl violet (MV) from aqueous solutions. The experiments were conducted using a central composite design (CCD) with the variables of adsorbent dosage (0.4-1.2 g/L), solution pH (3-9), contact time (10-42 min) and ionic strength (0.02-0.1mol L−1). Regression analysis showed good fit of the experimental data to a quadratic response surface model whose statistical significance was verified by analysis of variance. By applying the desirability functions, optimum conditions of the process were predicted as adsorbent dosage of 0.99g/L, pH=4.92, contact time of 40.98 minutes and ionic strength of 0.04 mol L−1 to achieve MV removal percentage of 101.19. Experimental removal efficiency of 99.51% indicated that CCD along with the desirability functions can be effectively applied for optimizing MV removal by m-MWCNT. Based on the study, the adsorption process followed Langmuir isotherm model and pseudo-second-order kinetic model could realistically describe the dye adsorption onto m-MWCNT.

Treatability study using novel activated carbon prepared from rice husk: Column study, optimization using response surface methodology and mathematical modeling

In this present study, activated carbon was synthesized from rice husk and was used for the removal of fluoride present in water. The adsorption of fluoride from aqueous solution by activated carbon (ACRH) was investigated in a laboratory-scale fixed-bed column. A two-level three factor (23) full factorial central composite design with the help of Design Expert Version 7.1.6 (Stat Ease, USA) was used for optimization of the fluoride removal process and evaluation of interaction effects of different operating parameters: initial fluoride concentration (20–50mgL−1), flow rate (5–10mLmin−1) and bed height (3–7cm) were studied. The higher correlation coefficient (R2) value of 0.9893 and low p value (<0.0001) indicating the fitness of the response surface quadratic model developed by the Design expert optimization process. Numerical optimization was used to identify the optimum conditions for achieving the targeted breakthrough time of 17.26h. The optimum conditions were found to be initial fluoride concentration of 50mgL−1, flow rate of 10mLmin−1and bed height of 3cm. A confirmatory experiment was conducted to evaluate the accuracy of the optimized procedure. The Thomas model showed excellent fit to the dynamic fluoride adsorption data obtained from the experimental results. Therefore, it can be concluded that the current investigation provided valuable insights for designing and establishing a continuous waste water treatment process.

STATISTICAL OPTIMIZATION OF MEDIUM COMPONENTS BY PLACKETT BURMAN DESIGN AND RESPONSE SURFACE METHODOLOGY FOR ENHANCED ANTIOXIDANT ACTIVITY BY XYLARIA FEEJEENSIS HMJAU22039

Objective: To enhance the antioxidant potential of Xylaria feejeensis by statistical optimization. The components of potato dextrose yeast extract (PDYEB) medium: potato infusion, dextrose and yeast extract were investigated to optimize antioxidant activity by Plackett-Burman design (P-B design). The interaction between the critical components of the medium and incubation time was further investigated by Response Surface Method (RSM) and the culture conditions were optimized for enhancing antioxidant activity. Central composite design (CCD) was employed and quadratic response surface model was used to analyze the data using analysis of variance (ANOVA).Methods: The antioxidant potential was measured by 1-1diphenyl-2-picryl-hydrazil (DPPH) radical scavenging assay, nitric oxide (NO) scavenging assay. The antioxidant activity was compared with total phenolic content measured by Folin-Ciocalteau (FC) reagent based assay.Results: Plackett-Burman design revealed dextrose and yeast extract to be the most significant components of PDYEB medium (p&lt;.0001). ANOVA analysis showed that the model was highly significant (p&lt;0.0001) for antioxidant activity measured by DPPH, NO scavenging and TPC (total phenolic content). The model was justified by applying the optimized conditions and values of 87.38%, 56.43%, 27.28 mg/g for DPPH, NO scavenging and TPC respectively were obtained. The estimated optimum conditions of the variables for the antioxidant activity and phenolic content are 12.72g of dextrose, 1.67g yeast extract, and incubation period of 23.43 d.Conclusion: The study highlights the importance of various components of the medium and the physiochemical limitations in antioxidant potential and phenol production of the fungal strain.

Optimization of twin gear-based pretreatment of rice straw for bioethanol production

A laboratory twin-gear reactor (TGR) was investigated as a new means for the pretreatment of high solid lignocelluloses. Response surface methodology based on Box Behnken Design was used to optimize the enzymatic digestibility with respect to the pretreatment process variables: temperature of 50–90°C, NaOH concentration of 2–6% and no. of cycles of 30–60. The results revealed that the TGR-based pretreatment led to the significant structural alterations through increases in pore size, pore volume, cellulose crystallinity and surface area. SEM images also confirmed the surface modifications in the pretreated rice straw. A response surface quadratic model predicted 90% of the enzymatic digestibility, and it was confirmed experimentally and through the analysis of variance (ANOVA) as well. The TGR extrusion proved to be an effective means for exceedingly high solids lignocellulose.

Optimal design of longitudinal conformal cooling channels in hot stamping tools

A new longitudinal CCC (conformal cooling channels) design in a B-pillar tool is proposed in this study. Three kinds of design parameters, the radius (Rad) of cooling channels, the distance (H) from channel center to tool work surface and the ratio (rat) of each channel center, are defined in a parameterized CAD model with the new design. For optimizing the layout of the longitudinal CCC which is determined by the above design parameters, an integrated process established by multiple software platforms based on the response surface methodology and multi-objective optimization is put forward. A design matrix with 17 factors and 50 levels is generated through OLH (optimal Latin hypercube) method. The average temperature and temperature deviation of work surface are used to evaluate the cooling performance. Quadratic response surface models are established to describe the relationship between design parameters and evaluation objectives. The accuracy of fitting models is checked by error analysis. The layout of the longitudinal CCC is optimized to find the Pareto-optimal frontier which consists of some optimal combinations of design parameters. Besides, the influence of initial design is discussed and a novel manufacture method of hot stamping tools with longitudinal CCC is briefly introduced.

Optimization of Manganese Reduction in Biotreated POME onto 3A Molecular Sieve and Clinoptilolite Zeolites.

Availability of quality-certified water is pertinent to the production of food and pharmaceutical products. Adverse effects of manganese content of water on the corrosion of vessels and reactors necessitate that process water is scrutinized for allowable concentration levels before being applied in the production processes. In this research, optimization of the adsorption process conditions germane to the removal of manganese from biotreated palm oil mill effluent (BPOME) using zeolite 3A subsequent to a comparative adsorption with clinoptilolite was studied. A face-centered central composite design (FCCCD) of the response surface methodology (RSM) was adopted for the study. Analysis of variance (ANOVA) for response surface quadratic model revealed that the model was significant with dosage and agitation speed connoting the main significant process factors for the optimization. R(2) of 0.9478 yielded by the model was in agreement with predicted R(2). Langmuir and pseudo-second-order suggest the adsorption mechanism involved monolayer adsorption and cation exchanging.

Modeling of Finned-Tube Evaporator Using Neural Network and Response Surface Methodology

A new response surface methodology (RSM) based neural network (NN) modeling method is proposed for finned-tube evaporator performance evaluation under dry and wet conditions. Two RSM designs, Box–Behnken design (BBD) and central composite design (CCD), are applied to collect a small but well-designed dataset for NN training, respectively. Compared with additional 7000 sets of test data, for all the evaporator performance including total cooling capacity, sensible heat ratio and pressure drops on both refrigerant and air sides, the standard deviation (SD) and coefficient of determination of trained NNs are less than 2% and higher than 0.998, respectively, under dry conditions while those are less than 4% and greater than 0.974, respectively, under wet conditions. Classic quadratic polynomial response surface models were also included for reference. By comparison, the proposed model achieves higher accuracy. Finally, parametric study based on the trained NNs is conducted. This new method can remarkably downsize the training dataset and mitigate the over-fitting risk of NN.

Response surface single loop reliability-based design optimization with higher-order reliability assessment

Reliability-based design optimization (RBDO) aims at determination of the optimal design in the presence of uncertainty. The available Single-Loop approaches for RBDO are based on the First-Order Reliability Method (FORM) for the computation of the probability of failure, along with different approximations in order to avoid the expensive inner loop aiming at finding the Most Probable Point (MPP). However, the use of FORM in RBDO may not lead to sufficient accuracy depending on the degree of nonlinearity of the limit-state function. This is demonstrated for an extensively studied reliability-based design for vehicle crashworthiness problem solved in this paper, where all RBDO methods based on FORM strongly violates the probabilistic constraints. The Response Surface Single Loop (RSSL) method for RBDO is proposed based on the higher order probability computation for quadratic models previously presented by the authors. The RSSL-method bypasses the concept of an MPP and has high accuracy and efficiency. The method can solve problems with both constant and varying standard deviation of design variables and is particularly well suited for typical industrial applications where general quadratic response surface models can be used. If the quadratic response surface models of the deterministic constraints are valid in the whole region of interest, the method becomes a true single loop method with accuracy higher than traditional SORM. In other cases, quadratic response surface models are fitted to the deterministic constraints around the deterministic solution and the RBDO problem is solved using the proposed single loop method.

Research on optimization design of conformal cooling channels in hot stamping tool based on response surface methodology and multi-objective optimization

In order to optimize the layout of the conformal cooling channels in hot stamping tools, a response surface methodology and multi-objective optimization technique are proposed. By means of an Optimal Latin Hypercube experimental design method, a design matrix with 17 factors and 50 levels is generated. Three kinds of design variables, the radius Rad of the cooling channel, the distance H from the channel center to tool work surface and the ratio rat of each channel center, are optimized to determine the layout of cooling channels. The average temperature and temperature deviation of work surface are used to evaluate the cooling performance of hot stamping tools. On the basis of the experimental design results, quadratic response surface models are established to describe the relationship between the design variables and the evaluation objectives. The error analysis is performed to ensure the accuracy of response surface models. Then the layout of the conformal cooling channels is optimized in accordance with a multi-objective optimization method to find the Pareto optimal frontier which consists of some optimal combinations of design variables that can lead to an acceptable cooling performance.

Transient probabilistic analysis for turbine blade-tip radial clearance with multi-component and multi-physics fields based on DCERSM

Against the background of the probabilistic analysis for High Pressure Turbine (HPT) Blade-tip Radial Running Clearance (BRRC) to achieve the high-performance and high-reliability of aeroengine, Distributed Collaborative Extremum Response Surface Method (DCERSM) was proposed for the dynamic probabilistic analysis of complex turbomachinery on the foundation of quadratic polynomials response surface model. On the basis of deeply investigating Extremum Response Surface Method (ERSM), the mathematical model of DCERSM was established based on quadratic polynomial function. As illustrated in BRRC transient probabilistic analysis with multiple components and multi-physics fields based on DCERSM, blade-tip radial static clearance δ=1.82 mm is advisable synthetically considering the reliability and working efficiency of gas turbine. The reliability, distribution characteristics and failure probability of BRRC are obtained. Besides, rotational speed ω and gas temperature T are the most important factors and expansivity coefficients and surface coefficients of heat transfer show also important influence on BRRC variation. Through the comparison of three methods (DCERSM, ERSM, Monte Carlo method), it is demonstrated that DCERSM reshapes the possibility of complex turbomachinery probabilistic analysis and improves computing efficiency while preserving the accuracy. DCERSM offers a useful insight for BRRC dynamic reliability design and optimization with multi-object and multi-discipline. The efforts of this study also enrich the theory and method of mechanical reliability design.

A highly efficient robust design under data contamination

In robust design, the general assumption is that the experimental data are normally distributed without contamination. Under this assumption, the sample mean and variance are often used to estimate the process mean and the process variance at each design point. The process mean and variance response functions are then fitted based on the sample mean and variance obtained at each design point.However, the non-contamination component of the assumption may not hold in practice. If this assumption is violated in a serious manner, the optimal operating conditions of the control factors may not be estimated properly. Thus, an outlier-resistant approach to the robust design problem is clearly warranted.In this article, we estimate a dual quadratic response surface model for the expected value and logarithm-transformed variance of the target response. The response surface is estimated from a designed experiment with a common number of replications at each design point. Then, given the observed responses at each design point, the following outlier-resistant statistics are computed at each design point: (i) the median and Hodges Lehmann estimators of the mean response, and (ii) the natural logarithm of the squared normal-consistent MAD, IQR and Shamos estimators of the response standard deviation.Through extensive Monte Carlo simulations, we show that alternative estimators exist which are quite efficient when the data are normally distributed yet also outlier-resistant when the data are contaminated.

Impact indicators for caprock integrity and induced seismicity in CO2 geosequestration: insights from uncertainty analyses

The geological sequestration (geosequestration) of carbon dioxide (CO2) is a mitigation method for reducing greenhouse gas emission into the atmosphere. The security and safety of CO2 geosequestration are strongly dependent on the mechanical stability of the caprock overlying the reservoir. Underground injection of CO2 increases the pore pressure and thus decreases the effective stress. It may lead to caprock failure, as well as the subsequent leakage of sequestered CO2. In particular, geothermal exploitation and the underground disposal of hazardous liquid wastes have demonstrated a risk of induced seismicity. We performed an uncertainty analysis using a novel response surface methodology and a two-step statistical experimental design, evaluated the statistical significance of operator choices and subsurface uncertainties to caprock integrity, and quantified the moment magnitude of the induced seismicity. Furthermore, the optimal combination (i.e., the worst-case scenario) with the desired properties was forecast. A series of numerical experiments was well designed, and 130 combinations were statistically determined. Based on the results from the analysis of variance for the response surface quadratic model, the impact indicators were presented in histograms according to their significances to the Coulomb failure stress and moment magnitude of the induced seismicity. Lastly, the values of the selected independent impact indicators were predicted to obtain optimal compositions for object function of both Coulomb failure stress and moment magnitude, and the desired properties were being picked out. The optimal combinations had desirability values of 1.000, demonstrating the fitness of the selected statistical models in analyzing the experimental data.

EMERGING TREND IN ADRESSING THE CHALLENGES TO ORAL NANOCURCUMIN DELIVERY TO IMPROVE QUALITY OF LIFE OF PATIENTS SUFFERING FROM CANCER

Present work was investigated for enhancement of solubility, bioavailability and anti-cancer activity of curcumin, a potent natural anticancer agent. Solid microdispersion of curcumin was prepared by melt granulation technique using Gelucire® 50/13 as a hydrophilic carrier followed by adsorbtion on Aeroperl® 300 Pharma. Compatibility among curcumin and excipients were checked using FT-IR, DSC and XRD analysis. The prepared microdispersion was characterized for percent drug content, entrapment efficiency, surface morphology, particle size, solubility, bio-availability and anticancer activity. DSC analysis study had shown complete encapsulation of raw curcumin which was further supported by XRD study showing flat peaks. A 32 full factorial response surface quadratic model demonstrated the positive effect upon change in the concentration of independent variables viz. Gelucire® 50/13 and Aeroperl® 300 Pharma on dependent variables particle size; % entrapment efficiency. TEM analysis confirmed the nano particulate dispersion upon dilution in distilled water with many fold increased in solubility. Maximum solubility was observed in acidic buffer pH 1.2. In vivo study revealed increased in bio-availability in Wistar rats as compared to raw curcumin by HPLC analysis. In MTT assay study, curcumin microdispersion has shown more prominent anti-cancer activity than raw curcumin on breast cancer cell line culture, MCF-7. From above study, it may be concluded that implementation of this innovative approach could be a better alternative for oral nanocurcumin delivery to improve the quality of life patients suffering from cancer.

Investigation on the fuel injection quantity fluctuation of a common-rail fuel injection system using response surface methodology

Variations in the parameters affect the injection characteristics of a common-rail injection system and leads to fluctuation in the fuel injection quantity. This paper is aimed at combining the numerical modelling and design of experiments to investigate the significant effects of the interactions between the common-rail injector parameters on the fluctuation in the fuel injection quantity using response surface methodology. A numerical model of a common-rail injector was developed. The model was validated by comparing simulation results with experimental measurements, which showed that it can accurately predict the fuel injection quantity of the system. The design of experiments was performed using a two-level five-factor D-optimal design. The factors studied were the pre-tension of the control valve spring, the diameter of the outlet orifice, the diameter of the inlet orifice, the needle lift and the diameter of the nozzle holes. A quadratic response surface model was suggested by means of partial least-squares regression analysis. The distribution of the standardized residuals, the relation between the predicted fluctuation and the observed fluctuation in the fuel injection quantity, the coefficient R2 of determination and the adjusted coefficient [Formula: see text] of determination for the response surface model were analysed, which demonstrated the significance of the model. Analysis of the F value and the p value for the model terms at the 95% confidence level revealed that the interactions between the control valve spring pre-tension and the nozzle hole diameter, between the outlet orifice diameter and the nozzle hole diameter, between the inlet orifice diameter and the nozzle hole diameter and between the needle lift and the nozzle hole diameter had significant effects on the fluctuation in the fuel injection quantity of the system. Furthermore, the significant effects of the interactions between these parameters were analysed in detail.

Experimental design and response surface modeling for optimization of humic substances removal by activated carbon: A kinetic and isotherm study

The presence of humic acid (HA) in water treatment processes is very harmful and the cause of undesirable color, taste, and smell. Drinking water containing high concentrations of humic substances can be the cause of many health problems. Therefore, the removal of these compounds from water resources is a very important topic. In this research, response surface methodology (RSM) has been used to optimize the effect of main operational variables responsible for higher HA removal by activated carbon (AC). A three-level Box–Behnken factorial design (BBD) was used to optimize initial concentration of HA, time, pH, and AC dose for humic substances removal. The characterization of AC was carried out using scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), and X-ray diffraction (XRD) analysis. A coefficient of determination (R 2 ) value of 0.98, model F-value of 82.32 and its low P-value (F < 0.0001), and low value of coefficient of variation (9.94%) indicated the fitness of the response surface quadratic model during the present study. At initial optimum concentration (5.25 mg HA/L), pH (5.85), contact time (36.01 minutes), and dose (1.38 g AC/L), the model predicted 1.90 mg HA/L. Equilibrium adsorption of HA onto AC had best fitness with the Freundlich isotherm and pseudo-second-order kinetic model.

Fin-and-tube condenser performance modeling with neural network and response surface methodology

This paper presents a new approach of combining response surface methodology and neural network for performance evaluation of fin-and-tube air-cooled condensers which are widely used in refrigeration, air-conditioning and heat pump systems. Box–Behnken design (BBD) and Central Composite design (CCD) are applied to collect a small dataset for neural network training, respectively. It turns out that 41 sets of data are collected for heating capacity and refrigerant pressure drop, and 9 sets of data are collected for air pressure drop. Additional 2000+ sets of data are served as the test data. Compared with the test data, for the heating capacity, the average deviation (A.D.), standard deviation (S.D.) and coefficient of determination (R2) of trained neural network are −0.43%, 0.98% and 0.9996, respectively; for the refrigerant pressure drop, those are −2.09%, 4.98% and 0.996, respectively; and for the air pressure drop, those are 0.11%, 1.96% and 0.992, respectively. Classical quadratic polynomial response surface models were also included for reference. By comparison, the developed neural networks gave much better results. Moreover, the proposed method can remarkably downsize the neural network training dataset and mitigate the over-fitting risk.

Uncertainty analysis of impact indicators for the integrity of combined caprock during CO2 geosequestration

CO2 capture and geologic sequestration represent one of the most promising options for reducing atmospheric emissions of CO2. Operator choices and subsurface uncertainties each have significant effects on avoiding the risk of triggering fault failure or intact rock failure that could lead to CO2 leakage. With the application of response surface methodology, a Box–Behnken design of experiments was utilized to evaluate the statistical significance of impact indicators, and uncertainty analysis was performed using a robust model to forecast the optimal combination of impact indicators for maintaining caprock integrity during CO2 injection in saline aquifers of the Ordos Basin, China. The Coulomb failure stress and vertical deformation of 130 statistically designed combinations were experimentally determined using numerical simulation. Then, according to the analysis of variance conducted for the response surface quadratic model, the burying depth, friction angle, Young's modulus, Poisson's ratio, and stress field, as well as quadratic terms such as burying depth∗friction angle and Poisson's ratio∗Poisson's ratio, were found to be highly significant with respect to the level of Coulomb failure stress. Among the variables, only burying depth was highly significant with respect to the level of vertical deformation. Furthermore, the results were analyzed and an optimal combination of impact indicators for Coulomb failure stress was determined according to the minimum Coulomb failure stress. The optimum combination had a desirability value of 1.000, which demonstrated the fitness of the selected statistical model in analyzing the experimental data.

Optimization and Modelling of Process Conditions Using Response Surface Methodology (RSM) for Enzymatic Saccharification of Spent Tea Waste (STW)

Spent tea waste (STW) is an important lignocellulosic waste as a cost-effective feedstock for ethanol production. The enzymatic hydrolysis of acid pretreated STW was investigated in this study. The effects of process parameters, including acid pretreatment time 26.4–93.6 min, β-glucosidase loading from 20 to 80 IU/g and cellulase loading from 11 to 45 IU/g on reducing sugar yield, were optimized by using central composite design of response surface methodology. The analysis of variance of data was examined by using response surface quadratic model. The valid model was used for optimization of the reducing sugar concentration during enzymatic hydrolysis. The optimum conditions for enzymatic saccharification were found to be acid pretreatment time of 27 min, β-glucosidase loading of 49 IU/g and cellulase loading of 12 IU/g. Maximum concentration of the reducing sugar under the optimum conditions was determined as 29.0 g reducing sugar/L.

Prediction and optimization of the laccase-mediated synthesis of the antimicrobial compound iodine (I2)

An artificial neural network (ANN) and genetic algorithm (GA) were applied to improve the laccase-mediated oxidation of iodide (I−) to elemental iodine (I2). Biosynthesis of iodine (I2) was studied with a 5-level-4-factor central composite design (CCD). The generated ANN network was mathematically evaluated by several statistical indices and revealed better results than a classical quadratic response surface (RS) model. Determination of the relative significance of model input parameters, ranking the process parameters in order of importance (pH>laccase>mediator>iodide), was performed by sensitivity analysis. ANN-GA methodology was used to optimize the input space of the neural network model to find optimal settings for the laccase-mediated synthesis of iodine. ANN-GA optimized parameters resulted in a 9.9% increase in the conversion rate.