Box-Behnken Design Of Response Surface Methodology Research Articles

Preparation of curcumin submicron particles by supercritical antisolvent method with external adjustable annular gap nozzle

The supercritical antisolvent (SAS) method can effectively improve the bioavailability of poorly water-soluble drugs. However, the current supercritical equipment and processes were not fully developed, making industrialization difficult to achieve. Therefore, an externally adjustable annular gap nozzle and its supporting equipment were designed. Curcumin was used as a model drug, ethanol as the solvent, and supercritical carbon dioxide (SC-CO2) as the antisolvent. Building on single-factor experiments, a Box-Behnken Design-Response Surface Methodology (BBD-RSM) was employed to systematically investigate the effects of four process parameters—crystallizer pressure (12–16 MPa), crystallizer temperature (313–323 K), solution concentration (1–2 mg/mL), and CO2/solution flow rate ratio (133–173 g/g)—on the morphology and particle size of curcumin particles. Using scanning electron microscopy (SEM) and dynamic light scattering (DLS) analyses, morphologies and mean diameter ranges were examined. To look into how the SAS process affects TML’s chemical and physical characteristics, X-ray diffraction analysis (XRD) and Fourier-transform infrared spectroscopy (FT-IR) were further performed. Experimental results show that, flow ratio of CO2/solution had the greatest effect of particle size, followed by crystallizer temperature and solution concentration, while crystallizer pressure had the least influence. The optimum process conditions are operational conditions were set with a crystallizer pressure of 15 MPa, crystallizer temperature of 320 K, solution concentration of 1.2 mg/mL, and flow ratio of CO2/solution of 134 g/g, resulting in curcumin submicron particles with an average particle size of 808 nm being obtained. This study demonstrated the feasibility of an externally adjustable annular gap nozzle and its associated equipment in the SAS process, showcasing significant potential for reducing particles size and enhancing the bioavailability of poorly water-soluble drugs.

Photocatalytic Degradation of Tetracycline Hydrochloride Using TiO2/CdS on Nickel Foam Under Visible Light and RSM–BBD Optimization

This study investigates the photocatalytic degradation of tetracycline hydrochloride (TCH) using a TiO2/CdS composite nanocatalyst synthesized on flexible nickel foam via a dipping–pull method. By comparing the photocatalytic degradation of TCH by TiO2/CdS with different precursor ratios, it was found that TiO2/CdS-1.43% exhibited better photocatalytic degradation performance. The X-ray diffraction (XRD) pattern of the TiO2/CdS composite retains the characteristic peaks of both TiO2 and CdS, indicating the successful formation of the composite. According to the analysis of ultraviolet–visible spectroscopy (UV–Vis), the absorption edge of TiO2/CdS is approximately 530 nm. The transmission electron microscopy (TEM) images show Cd and S evenly, densely distributed in TiO2/CdS, further validating its successful synthesis. X-ray photoelectron spectroscopy (XPS) analysis reveals that Cd and Ti elements exist in the forms of Cd2+ and Ti4+, respectively. TiO2/CdS loading uniformity on the nickel foam was assessed using super-depth microscopy. The removal efficiency of 10 L of 20 mg/L TCH solution achieved 53.89%, respectively, under response surface methodology—Box–Behnken design (RSM–BBD) optimal conditions (28 g catalyst, 325 rpm, pH = 9.04 within 150 min). Furthermore, five successive cycling experiments demonstrated strong stability, with a catalyst loss of only 4.44%. Finally, free radical scavenging experiments revealed that ·O2− radicals are the primary active species. This study highlights the potential of TiO2/CdS composites supported on nickel foam for efficient photocatalytic degradation of antibiotic pollutants in water.

From Experimentation to Optimization: Surface Micro-Texturing for Low-Friction and Durable PTFE-Steel Interfaces Under Full Film Lubrication.

To enhance the sliding tribological performance between PTFE and 40#steel (AISI 1040) under full film lubrication conditions, laser surface texturing (LST) technology was employed to prepare micro-dimples on the contact surfaces of 40# steel discs. The Box-Behnken design response surface methodology (BBD-RSM) was applied to optimize the micro-dimple parameters. Coefficients of friction (COFs), wear losses and worn contact surfaces of the PTFE-40# steel tribo-pairs were researched through repeated wear tests, as lubricated with sufficient anti-wear hydraulic oil. The influencing mechanism of micro-dimples on the tribological behavior of tribo-pairs was also discussed. The results proved that micro-dimples can significantly improve the tribological properties of PTFE-40#steel tribo-pairs. The deviation between the final obtained average COF and the prediction by the BBD-RSM regression model was only 0.0023. Following optimization, the average COF of the PTFE-40# steel tribo-pair was reduced by 39.34% compared to the smooth reference. The wear losses of the PTFE ring and 40# steel disc decreased by 91.8% and 30.3%, respectively. This study would offer a valuable reference for the optimal design of key seals used in hydraulic cylinders.

Maximizing waste cooking oil biodiesel production employing novel Brotia costula derived catalyst through statistical and machine learning optimization techniques

The limitations of homogeneous catalysts associated with biodiesel production such as non-reusability, emulsification, and saponification have led to the increased cost of biodiesel synthesis. To overcome these limitations, the current work aims to investigate the applicability and effectiveness of green catalyst made from Brotia costula shells (BCS) for waste cooking oil biodiesel production. The catalyst was synthesized by calcinating BCS at 800℃−1000℃ for 4 h. Catalyst characterization showed that BC900 (BCS calcined at 900℃) contained 98.15 CaO wt% and exhibited the smallest grain size of 39.23 nm with the highest specific surface area of 14.28 m2/g and highest catalytic activity. Optimization of biodiesel synthesis was carried out using response surface methodology - Box-Behnken design (RSM-BBD), artificial neural network (ANN) coupled with genetic algorithm (GA), and adaptive neuro-fuzzy inference system (ANFIS) coupled with GA. Maximum experimental biodiesel yield of 97.78±0.59 % at optimum conditions of methanol: WCO molar ratio 9.73: 1, reaction time 2.12 h, catalyst dose wt% 7.59 and reaction temperature 69.3°C was obtained with ANFIS-GA optimization model. The prediction accuracy of the ANFIS model (R2 = 0.988) was better than those of the RSM-BBD (R2 = 0.982) and ANN (R2 = 0.955) models. The synthesized catalyst exhibited high catalytic activity up to four cycles of reusability (yield > 88 %). It is environment-friendly, inexpensive, renewable, and performs on par with catalysts synthesized from other mollusk shells. Using this catalyst for biodiesel production will contribute to the biorefinery's ability to explore sustainable raw materials and may lower overall biodiesel cost.

Hybrid Optimization Approach Using Multiobjective Genetic Algorithm NSGA‐II, SCAPS‐1D Simulation, and Response Surface Methodology for Organic Solar Cell Analysis

In the field of simulation, it is difficult to find the relevant values for the properties of materials and in this context this approach has been proposed on optimizing the performance of organic solar cells, a promising technology in the field of renewable energy, to increase their efficiency. It adopts a hybrid approach combining the response surface methodology (RSM) with a Box–Behnken design (BBD) and the nondominated sorting genetic algorithm II (NSGA‐II). The RSM BBD method is used to identify objective functions to be optimized, considering interactions between selected parameters such as the thickness of the active layer, electron‐transport layer (ETL), hole‐transport layer (HTL), and the doping of these layers. Concurrently, the NSGA‐II genetic algorithm aims to maximize the performance of the solar cell based on these parameters. The specific importance of NSGA‐II lies in its ability to solve complex multiobjective optimization problems. Indeed, NSGA‐II is designed to simultaneously manage several performance objectives, which is crucial for organic solar cells. Its ability to generate a diverse set of optimal solutions enables efficient configurations to be found that may not be obvious with simpler optimization approaches. The results of this study show that optimum solar cell performance is achieved with active layer, ETL layer, and HTL layer thicknesses of 100.86, 79.9, and 20.24 nm, respectively, and active layer doping of 8.71E + 21 cm−3, HTL layer doping of 9.90E + 21 cm−3, and ETL layer doping of 9.49E + 21 cm−3. Analysis using Solar Cell Capacitance Simulator‐1D (SCAPS‐1D) software shows that optimum performance is achieved with these specific parameter values. After optimization with NSGA‐II, the power conversion efficiency increases by 39% compared to previous work. This study provides evidence of the effectiveness of the proposed hybrid approach for optimizing the performance of organic solar cells. By showing remarkable agreement between the results obtained by NSGA‐II and SCAPS‐1D, this approach opens up promising prospects for the future of renewable energy.

Efficient mercury ion abatement through highly porous cellulose nanofibrils combined with microporous organic polymer enhancements

Pristine microporous organic polymer (p-MOP), owing to the presence of heteroatoms, has emerged as a significant platform for sensing and adsorption of heavy metal ions. The present work is a novel approach for developing highly porous hybrid architectures with trimesic acid and phenylene diamine-based p-MOP embedded over rice straw-derived cellulose nanofibers (ACNFs/MOP) for the sensing and remediation of mercury ions in the aqueous medium. The ACNFs/MOP were successfully characterized by various techniques, such as FTIR spectroscopy, BET surface area analysis, X-ray diffraction, XPS, HR-TEM, and TGA. The hybrid exhibited excellent porosity and crystallinity. The ACNFs/MOP hybrid was highly selective for Hg(II) ions, displaying substantial enhancement in fluorescence intensity with an LOD of 3.927 nM while also facilitating simultaneous adsorption. The adsorption showed a strong fit with pseudo-second-order kinetics and Langmuir isotherm models with an excellent adsorption capacity of 416.18 mg g−1, attributed to electrostatic interactions, coordination surface complexation, and metal-π interactions, as confirmed by XPS studies. Thermodynamic studies indicated an endothermic adsorption process. Box-Behnken Design-Response Surface methodology with Design Expert Software-13 was applied to model the process parameters. The hybrids were 97 % efficient even after five cycles of reusability, exhibiting their excellent potential for removing perilous Hg(II) ions from wastewater.

Ultrasonic-Assisted Water-Rich Natural Deep Eutectic Solvents for Sustainable Polyphenol Extraction from Seaweed: A Case Study on Cultivated Saccharina latissima.

This case study introduces a green, 1 h single-step method using water-rich natural deep eutectic solvent (WRNADES) for ultrasound-assisted extraction (UAE) of polyphenols fromSaccharina latissima, a commercially cultivated brown seaweed. The extraction efficiency was evaluated using a selective quantitative NMR method (s-qNMR) and the traditional nonselective colorimetric total phenolic content assay (TPC). Initial 6 h extractions in traditional solvents (methanol, ethanol, acetone, and ethyl acetate) showed a 40-60% increase in polyphenolic yields in 50% aqueous solutions measured by the TPC method. Six different water-rich (50%) NADES (WRNADES) combinations were tested (choline chloride/betaine with lactic acid, citric acid, and 1,3-butanediol), with betaine and 1,3-butanediol (1:1) proving most effective. Parameters for the WRNADES were optimized using Box-Behnken design response surface methodology, resulting in a 1:20 w/w biomass to solvent ratio and a 1 h extraction time at 50 °C. The WRNADES extraction process was refined into a scalable, single-step procedure and compared with traditional solvent extractions (6 h, 50% aqueous methanol and acetone). A final XAD-7 polyphenol recovery step was included in all extractions. The optimized WRNADES extraction yielded 15.97 mg GAE/g of the dry weight recovered polyphenolic extract (s-qNMR), exceeding the 6 h 50% aqueous methanol (12.4 mg GAE/g) and acetone (11.4 mg GAE/g) extractions. Thus, the UAE-WRNADES method presented in this case study provides a cost-effective, sustainable, and eco-friendly alternative for the extraction of phenolic compounds from seaweed. It promotes the development of environmentally friendly production processes within the seaweed biorefinery.

Multi-Response Optimization of Mechanical Properties of MWCNTs Fused GFRPs Using RSM-Based GRA and Mother Optimization Algorithm

This study delves into enhancing the mechanical properties of glass fiber-reinforced polymers (GFRPs) by integrating multi-walled carbon nanotubes (MWCNTs) using the hand layup method. Integrated response surface methodology (RSM) combined with grey relational analysis (GRA) and mother optimization algorithm (MOA) was employed to predict multiple responses simultaneously. Fabrication parameters: MWCNT loading, sonication time, oven curing temperature and output responses, ultimate tensile strength (UTS), and shear beam strength in longitudinal and traverse directions are considered. The experiments are planned as per the Box-Behnken design of RSM, and responses have been noted. GRA applications are used to convert multi-responses into a single response, i.e. GRG. RSM is applied to postulate the mathematical equation to create a relationship between the fabrication parameters and GRG. The MOA is then used to maximize GRG value, which indicates enhanced multi-responses. Research shows that adding MWCNTs considerably strengthens GFRPs’ mechanical aspects. The predicted fabricating setting is validated with confirmatory tests, showing considerable improvements in mechanical properties.

Optimization In-vitro Evaluation and Scratch Wound Healing Assay of Hexacosane Topical Gel for Wound Healing.

The compounds hexocosane, a sterol hydrocarbon identified in the dichloromethane extract and isolated from marine sponges of the genus Agelas. Hexocosane possesses anti-inflammatory and antioxidant activities that reduce inflammation and oxidative stress, they promote cell proliferation and angiogenesis, facilitating tissue regeneration and repair. This multifaceted approach makes this secondary metabolite a promising candidate for developing advanced wound care therapies that effectively address both infection control and wound healing. On the basis of viscosity, in-vitro release, and skin retention, optimal gel formulations were developed with hexocosane (1%) to achieve the best results. A response surface methodology Box-Behnken design was applied on three factors and three levels [carbopol 940 (1, 1.25 and 1.5%), hydroxypropyl methylcellulose (HPMC) (1,1.5 and 1.2%), and propylene glycol (0–1–1.5% and 1–2%, respectively] using three factors and three levels. In order to assess the spreadability and viscosity, a glass slide and a Brookfield viscometer were used. An assay was conducted to determine how topical gel affects wound healing. The optimization study of the gel formulation, involving variations in adhesive polymer (Carbopol 940), release retarding polymer (HPMC K4M), and penetration enhancer (Surfactant), has identified three noteworthy formulations (F4, F8 & F14). Each formulation is characterized by specific attributes such as viscosity, in-vitro drug release, and skin retention. All samples were found to elicit significant wound healing efficacy as evidenced from the representative photomicrographs. The maximum efficacy was elicited by formulation (F4) with 100 mg drug at the time duration of 36 hours.

Sustainable Biodiesel Production via Biogenic Catalyzed Transesterification of Baobab Oil Methyl Ester and Optimization Process

Biomass diesel is one of the sustainable and renewable sources of energy envisaged to hold a prominent position in the world energy infrastructure. In this study, biodiesel was production from baobab seed oil by transesterification using biogenic heterogeneous catalyst, derived from mixed wastes of white chicken eggshells and banana fruit peels. The production process was analyzed and statistically analyzed using Box-Behnken Design-Response Surface Methodology (BBD-RSM). The influential transesterification reaction parameters investigated with their ranges include reaction time (40–80 min), molar ratio of oil to methanol (1:9–1:15) and catalyst weight (3–5 wt%). The nano-catalyst (CaO-BFP-850 NPs) was prepared by calcination at high temperature of 850 °C for 4 h, and its properties were found to contain majorly the basic elements of Ca and K when investigated with analytical instruments such as SEM, EDS, DSC-TGA, FT-IR, and XRD. The regeneration test of the CaO-BFP-850 NPs conducted showed it could be reused for more than four cycles with less catalytic efficiency reduction. The ideal conditions instituted by BBD-RSM was 75 min of reaction time, 12.8:1 molar ratio of oil to methanol, and 4.08 wt% CaO-BFP-850 at 65 °C and 650 rpm constant temperature and agitation speed respectively, with the validated biodiesel yield of 96.70 wt%. The assessment of the quality of the biodiesel produced showed compliance with the standard specifications of ASTM D6751, EN 14241, and SANS 833.

Sustainability-driven application of waste steel and tyre rubber fibres as reinforcement in concrete: An optimization study using response surface methodology

Innovative use of waste materials has proven to be effective for improving concrete’s technical properties while eliminating the threat of environmental pollution. There is paucity of research regarding the combined use of Waste Steel Fibres (WSFs) and Waste Tire Rubber Fibres (WTRFs) in concrete. Thus, this study aimed to investigate the influence of WSFs and WTRFs as reinforcement in concrete and determine their optimal fractions for enhancing the concrete strength properties using Response Surface Methodology (RSM). WSFs of 0.3-1.5% with a 0.6% increment and WTRFs of 0.3-1.5% with a 0.6% increment by concrete volume as reinforcement in concrete with water-to-cement ratio (W/C) of 0.25–0.65 with 0.2 increment were designed using the Box-Behnken Design of RSM. A total of fourteen (14) concrete mixes with a design strength of 25 N/mm2 were prepared. The fresh and hardened properties (slump, density, water absorption, 7- and 28-day compressive and split tensile strengths) of concrete were determined using standard procedures. The RSM was used to evaluate the interaction between the concrete variables and identify their optimum combination which gave the peak values of responses. Furthermore, the characteristics and sustainability of the concrete under optimum variables were compared with that of the conventional concrete. The outcomes revealed that the inclusion of WSFs and WTRFs yielded concretes with maximum slump, density, water absorption and 28-day compressive and split tensile strengths of 25 mm, 2633 kg/m3, 5%, 41 N/mm2 and 4.54 N/mm2, respectively. The optimization technique showed the optimal variables combination which yielded the peak response values of WSFs (1.40%), WTRFs (0.66%) and W/C (0.54). The nominal variance with the absolute percent error of less than (9%) between the actual experimental verified results and predicted results for all the responses validates the predictability of the model. The split tensile strength and compressive strength increased by 28.33% and 48.85%, respectively at the optimum setpoint of variable with respect to the reference concrete. In addition, the WSFs and WTRFs-reinforced concrete exhibited lower embodied CO2 emission but the embodied energy and cost are slightly higher relative to conventional concrete. Nevertheless, the embodied energy of the concrete was significantly lesser than that of concrete that used individual fibers for reinforcement. Thus, the enhancement of concrete strength properties with the prospect for sustainable fibre-reinforced concrete production through the use of waste steel and tyre rubber fibres is feasible.

Efficient removal of Eosin Yellow dye using solvothermal synthesised Ni/Al layered double hydroxide and layered double oxide: insights into adsorption kinetics and thermodynamics

ABSTRACT The accumulation of organic pollutants in the environment is swelling steadily and raising the alarm for their redressal. Layered double hydroxides (LDH) and layered double oxides (LDO) are gaining prominence for their potential to address this challenge. This work includes an eco-friendly facile solvothermal synthesis of Ni/Al LDH and their calcination to Ni/Al LDO. The prepared nanomaterials were characterised by XRD, FE-SEM, HR-TEM, FT-IR, BET and TGA techniques, which confirm the highly porous structure of synthesised nanomaterials with a high specific area of 109.94 m2g−1 and 188.94 m2g−1 for Ni/Al LDH and Ni/Al LDO, respectively. A systematic study was conducted to explore the adsorptive potential of these materials towards the removal of organic dye Eosin Yellow (EY). The adsorptive efficiency was found to be 95% and 99% for Ni/Al LDH and Ni/Al LDO, respectively, under the optimised conditions of pH, adsorbent dose, contact time, dye concentration and temperature on adsorptive removal of EY dye, which was analysed by BBD-RSM (Box-Behnken Design Response surface methodology). The adsorption data fits best with the Freundlich isotherm model and pseudo-second-order kinetics. The thermodynamic study confirms the exothermic adsorptive process. The durability and reusability of the synthesised adsorbents were ascertained by their retention of the adsorptive efficiency even after five adsorption-desorption cycles which were as high as 91% and 85% for Ni/Al LDH and Ni/Al LDO, respectively. The mechanism of adsorption involves electrostatic interaction and hydrogen bonding between the surface of adsorbent and adsorbate as well as intercalation of dye molecules.

Optimization of Microbial Fuel Cell Operational Parameters for the Treatment of Brewery Sludge

AbstractThis study investigates the potential of a salt bridge‐mediated microbial fuel cell (MFC) for power generation and wastewater sludge treatment in breweries. Unlike traditional “one‐parameter‐at‐a‐time” methodologies, this study uses a three‐variable Box–Behnken design response surface methodology to optimize critical MFC operational parameters. The effects of parameters such as solution pH, salt bridge molarity, and temperature were studied in the range of 4 to 10, 1 to 5 M, and 20 to 45 g L−1. The optimum operating parameters were found to be solution pH of 5.853, salt bridge molarity of 3.343 M, and temperature of 32.5 °C for chemical oxygen demand and biological oxygen demand removal efficiencies of 92.485 % and 88.51 %, respectively. Temperature was found to be the most significant factor affecting the reactor's performance.

Green diesel synthesis from palm fatty acid distillate using a nickel phosphide catalyst: Optimization by box behnken design

The persistent global energy crisis is propelling significant innovations, including the use of palm fatty acid distillate (PFAD) as a renewable resource for producing green diesel (GD). Therefore, this study aimed to intensify GD synthesis from PFAD by optimizing the process using a Response Surface Methodology-Box Behnken Design (RSM-BBD). Optimization was achieved with a nickel phosphide catalyst supported by natural zeolite through hydrodeoxygenation method. RSM-BBD was used to design the process, considering time (1−3 h), temperature (300–350 °C), catalyst concentration (5–15 %), and pressure (20–60 bar). The results showed that the optimum conditions consisted of 11 % catalyst concentration, 3 h of reaction time, 342 °C temperature, and 29 bar pressure, leading to a 96.35 % GD yield. All conversion parameters, except pressure, significantly influenced GD yield. The quality of the synthesized GD showed high quality and compliance with Indonesian as well as European diesel fuel standards.

Green, environmentally friendly synthesis of MnO Nanoparticles for corrosion inhibition in mild steel

<p>This study uses green, environmentally friendly methods to synthesize manganese oxide nanoparticles (MnO-NPs) that can be applied on mild steel as a safe and eco-friendly material with corrosion inhibition attributes. Mukia madaraspatana leaf extract is effective in the green synthesis of Mno-NPs. Several critical parameters can influence the corrosion rate and inhibition efficiency of the MnO-NPs, including the dose of nanoparticles in parts per million (PPM), acid concentration, temperature, and exposure time. Response Surface Methodology-Box Behnken Design (RSM-BBD) was employed to optimize these parameters. An optimal dosage of 550 parts per million (ppm) of nanoparticles, an acid concentration of 0.9 M, a temperature of 37°C, and an exposure time of 3.2 hours resulted in an inhibition efficiency of 92.5%. It reduced the corrosion rate to 0.652 (mm/Y) in an independent validation run. These findings demonstrate that the environmentally friendly synthesized MnO-NPs have great potential as an anti-corrosive agent for mild steel.</p>

Removal of methylene blue and Congo red from the wastewater in a jet loop reactor using ozone and activated carbon

The textile industry consumes a large amount of fresh water, resulting in colored effluent due to the incomplete fixation of certain dyes. Traditional textile wastewater treatment such as coagulation-flocculation, membrane filtration, thermal separation and electrochemical processes are expensive and pose significant environmental challenges. To address these issues an alternative technique for decolorizing textile effluent using a modified sparged loop reactor was proposed in this study. The jet loop reactor basically a multiphase reactor was utilized to treat textile wastewater with ozone. In this study, the textile wastewater containing water-soluble basic dyes such as Methylene blue and Congo red was simulated at a concentration of 50 ppm and the color removal efficiency of the reactor was analyzed. The effect of process parameters such as effluent flow rate (0.2– 0.4 Liter/min), sparger openings (2−4), adsorbent weight (0.5 – 1.5 g) and ozone dosage time (10–30 min) on the decolorization efficiency of the reactor were examined and optimized using Response Surface Methodology. In this study, a maximum color removal of 93 % and 85 % for Congo red dye and Methylene blue dye simulated wastewater was achieved. A correlation was developed using Response Surface Methodology-Box Behnken Design and the developed model was successfully interpreted with experimental values. Based on our results, we believe that it is possible to replace the energy-intensive thermal separation process with jet loop sparged reactor to treat the textile effluent. As a sustainable process, loop reactor may result in a huge reduction of freshwater consumption.

Optimizing injection molding parameters to minimize and prediction potential for flashing defects

The injection molding process is a manufacturing process that can produce products in a short time in large quantities, in the injection molding process the factor of setting process parameters plays a significant role in product quality, so it requires special treatment. The purpose of this study is to find the optimal parameters in the injection moulding process of yogurth container lid with polypropylene material, so that the process can reduce the incidence of flashing defects that result in the emergence of initial waste in the industrial environment. The method used in this research is to create a Response Surface Methodology Box-Behnken Design (RSM - BBD) optimization model and an Artificial Neural Network (ANN) model approach in analyzing optimal parameters and predicting the appearance of flashing defects in a designed cycle. The results obtained from this research are the optimal parameters from the RSM and ANN model recommendations, namely the clamping force setting of 70 tons, holding time 0.1 seconds, and holding pressure. The ANN model provides the highest level of prediction accuracy with an R2 value of 100% and a prediction error rate of 7.9689E-09. In comparison, the RSM model obtains a prediction accuracy level with an R2 of 71% with an error rate of 0.24315.

Response surface methodology and artificial intelligence modeling for in vitro regeneration of Brazilian micro sword (Lilaeopsis brasiliensis)

In this study, response surface methodology (RSM) was used to optimize in vitro regeneration of the Brazilian micro sword (Lilaeopsis brasiliensis) aquatic plant, followed by data prediction and validation using machine learning algorithms. The basal salt, sucrose and Benzyaminopurine (BAP) concentrations were derived from Box-Behnken design of RSM. The response surface regression analysis revealed that 1.0 g/L MS + 0.1 mg/L BAP + 25 g/L sucrose was optimized for maximum regeneration (100%), shoot counts (63.2), and fresh weight (1.382 g). The RSM-based predicted scores were fairly similar to the actual scores, which were 100% regeneration, 63.39 shoot counts, and 1.44 g fresh weight. Pareto charts analysis illustrated the significance of MS for regeneration and fresh weight but remained insignificant. Conversely, MS × BAP was found to be the most crucial factor for the shoot counts, with MS coming in second and having a major influence. The analysis of the normal plot ascertained the negative impact of elevated MS concentration on shoot counts and enhanced shoot counts from the combination of MS × BAP. Results were further optimized by constructing contour and surface plots. The response optimizer tool demonstrated that maximum shoot counts of 63.26 and 1.454 g fresh weight can be taken from the combination of 1.0 g/L MS + 0.114 mg/L BAP + 23.94 g/L. Using three distinct performance criterias, the results of machine learning models showed that the multilayer perceptron (MLP) model performed better than the random forest (RF) model. Our findings suggest that the results may be utilized to optimize various input variables using RSM and verified via ML models.Key messageOptimization of in vitro whole plant regeneration of Brazilian sword wood using response surface methodologyData analysis through ANOVA, response surface regression anlaysis and machine learningGraphical presentation of data via Pareto charts, normal plots, contour plots and surface plots for optimizationBetter performance of ANN-based MLP model compared to decision tree based RF modelGraphical abstract

Simultaneous Extraction and Decaffeination Process Optimization of Green Coffee Bean-Based Beverages Using Response Surface Methodology

Simultaneous Extraction and Decaffeination Process Optimization of Green Coffee Bean-Based Beverages Using Response Surface Methodology

Hydrothermal carbonization of organic waste using faecal sludge as a water source: Response surface methodology-Box Behnken design

In this research, organic waste was subjected to hydrothermal carbonization (HTC) using faecal sludge as the water source. HTC is a sustainable method for converting organic waste into valuable hydrochar. However, the use of portable water as a reaction medium in HTC is environmentally unsustainable and increases operational costs. The aim of this study was to optimize HTC operating parameters such as residence time, temperature, and biomass to water (BTW) ratio for maximizing both higher heating value (HHV) and hydrochar yield and also to determine the effects of these operating parameters on the hydrochar properties. This study utilized a Box Behnken design within the response surface methodology to identify optimal HTC conditions. Temperature was varied between 180 and 250 °C, residence time between 30 min and 120 min, and BTW ratio between 1 and 10. Volatile matter percentage was reduced in the hydrochar compared to that of the feedstock. A progressive rise in carbon percentage and a decline in oxygen percentage were observed with increasing temperatures. Temperature and residence time were the most significant factors affecting HHV, on the other hand, temperature and BTW ratio were the most significant factors that affected hydrochar yield. Numerical optimization of the factors revealed that a maximum HHV of 28.409 MJ/kg was obtained at 250 °C, 116 min, and a 1:9 BTW ratio, while an ideal hydrochar yield of 57.491% was obtained at 242 °C, 95 min and a BTW ratio of 1:9. This study highlights the environmental sustainability of utilizing faecal sludge as a water source in HTC, presenting a promising possibility for transforming organic waste into valuable hydrochar while addressing concerns related to water use.