Response Surface Methodology Technique Research Articles

Oxidative degradation of erythromycin using calcium carbonate under UV and solar light irradiation: Condition optimized by response surface methodology

In the present investigation, CaCO3 (nano-calcite) was prepared using a microwave-assisted sol-gel route and photocatalyst efficiency was investigated by degrading antibiotic, Erythromycin (Ery). The prepared sample was characterized by XRD, SEM, EDX, FTIR and UV–vis techniques. The process variables such as Ery concentration, irradiation time and catalyst dose were optimized by RSM (response surface methodology) techniques. The Ery degradation was achieved up to 93% (6 h sunlight) and 73% (2 h UV irradiation) at Ery concentration 30 mg/L and catalyst load 20 mg. The water quality parameters (COD, BOD and TOC) were also evaluated pre- and post-treatment and the COD, BOD and TOC values were reduced up to 78.5, 77.6, 64.5 (%), respectively. The reusability/recyclability of CaCO3 was also performed and up to 3 cycles the activity did not change, which reduced slightly for 4–9 cycles slightly at each run. The results indicate that CaCO3 is an efficient catalyst for the degradation of Ery under sunlight irradiation, which could be used for Ery degradation in effluents.

An investigation on cutting sound effect on power consumption and surface roughness in CBN tool-assisted hard turning

In machining activities, sound emission is one of the key factors toward the operator's health and safety. Sound generation during cutting is the outcome of the interaction between tool and work. The intensity of sound greatly influences the cutting power consumption and surface finish obtained during machining. Therefore, the current work emphasized the analysis of sound emission, power consumption, and surface roughness in hard turning of AISI 4340 steel using a CBN tool which was rarely found in the literature. Response surface methodology (RSM) and artificial neural network (ANN) techniques were utilized to formulate the model for each response. The results indicated that the maximum value of input parameters exhibited the highest level of sound due to the creation of vibration in the machine and tool. Higher sound level indicates the generation of lower power consumption but at the same instant surface roughness was leading with increment in sound level. The feed rate exhibited the utmost noteworthy consequence on surface quality with 87.71% contribution. The cutting power can be decreased by choosing the high level of cutting parameters. The RSM and ANN have a good correlation with experimental data, but the accuracy of the ANN is better than the RSM.

Optimization of Photooxygenation of Dihydroartemisinic Acid in a Continuous Micro-reactor utilizing Physically Immobilized Methylene Blue on Polystyrene Micro-particles

Extensive studies reported upon the application of microfluidic devices for photooxygenation reactions. Only a few researches, however, have studied the optimization of experimentally controllable parameters towards achieving the best reaction conversion. Accordingly, in the current study, the optimum values of operating parameters towards achieving the highest dihydroartemisinic acid (DHAA) conversion were determined through the response surface methodology (RSM) technique. In order to do that, first, heterogenized micro-photosensitizers were synthesized during a super facile procedure not requiring any chemical reaction or modifications. Then, these micro-photosensitizers were utilized to perform continuous photooxygenation of the DHAA by means of a glass micro-reactor at room temperature and atmospheric pressure. Finally, the impacts of experimental parameters such as the flow ratios of solution to oxygen as well as DHAA and micro-photosensitizers’ concentrations upon the DHAA conversions were examined by using Design of Experiments (DOE) software in order to optimize and statistically model the DHAA conversion. The optimum value for this conversion was predicted by the RSM technique to be 70 % which was in a very good agreement with that of the experimental value of 69 % obtained in this study. The optimal values for the flow rates ratio, DHAA initial concentration, and micro-photosensitizer’s concentration were obtained to be 0.5, 0.5 mg/ml, as well as 1.5 mg/ml, respectively.

Study on the performance and emission characteristics of pyrolytic waste plastic oil operated CI engine using response surface methodology

Depletion of fossil fuels, strict pollution rules, and potential generation of waste plastics strive to focus another facet of value addition by conversion of plastic wastes into alternate fuel for a diesel engine. The current study is intended to optimize the combined influences of engine load, fuel blend, and compression ratio on the performance and emission of a normal diesel engine fueled with waste plastic oil diesel (WPO) blend by using response surface methodology (RSM). Optimum engine performance i.e.maximum brake thermal efficiency and minimum NOx emissions are established using the technique of response surface methodology concerning three different engine input parameters engine load, compression ratios, and fuel blend ratio. The compression ratio of 17, 10% of WPO-diesel blending at full load conditions are found to be optimum input parameters. And accordingly, the brake thermal efficiency of 28.8% and NOx emission of 162.45 ppm are obtained under multi-response optimization techniques. A sensitivity analysis has also been accomplished to clarify the significant contribution of different parameters and their orders of importance. The compression ratio has resulted as the most significant factor followed by load and % fuel blend.

Optimization of FDM 3D Printing Process Parameters on ABS based Bone Hammer using RSM Technique

Rapid prototyping (RP) uses a cycle where a real model is made by explicitly adding material as thin cross-sectional layers. Fused deposition modelling (FDM) 3D printer is being use for synthesis of ABS based bone hammer. Response surface methodology (RSM) based L27 design of experiment were adopted to perform the experiment using four influencing parameters such as layer thickness, infill percentage, orientation and nozzle temperature for the three responses deflection, hardness and weight. Response surface methodology was used for modelling and optimization of considered process parameters. In present investigation, it is evident that bone hammer fabrication process parameters have been optimized on data such as bone hammer weight 19.8091g, hardness 104.5921 BHN, and force of 15 degree deflection 36.0681 N has been produced with RSM prediction with influence of process parameters such as layer thickness 0.250 mm, infill percentage 63.3333, orientation 60 degree, nozzle temperature 240°C.

Optimization and modeling of axial strength of concrete-filled double skin steel tubular columns using response surface and neural-network methods

The main objective of this study is to optimize and model the ultimate strength of axially loaded concrete-filled double skin steel tubular (CFDST) composite columns having a circular hollow section . By performing the multi-objective optimization analysis through the response surface methodology (RSM), an ultimate strength-based optimal design solution for CFDST composite columns was proposed. Besides, artificial neural networks (ANN) together with RSM techniques were used to develop design formulations that can be involved in the prediction of the ultimate axial strength of such types of composite columns. An experiment data repository compiled from the studies available in the literature was used in both optimization and modeling analysis. The aim of the optimization was to achieve the optimum values for the design parameters based on the maximum capacity. However, in the modeling, attaining a design formula having an accurate and reliable prediction performance was the purpose. Furthermore, a verification study in which the developed models were compared with the modified design formulas of the AISC and Eurocode 4 and some existing models proposed by the researchers was carried out. It was observed that the generated models gave a good estimation capability with a considerably high R-squared value and fewer errors. After statistically analyzing the outputs of the models, it was proved that the models proposed in the current study yielded more reliable, accurate, and consistent prediction performance than the existing formulas. Besides, it was indicated the optimization analysis has a desirability value of 0.942, meaning an almost perfect optimization. • The CFDST composite columns were optimized based on ultimate strength. • The ultimate strength-based modeling of the CFDST columns was carried out. • The axial loading condition was considered in the modeling. • Reliable and accurate numerical models were developed. • Optimum properties for the CFDST column with the CHS were achieved.

Sorptive removal of malachite green from aqueous solution by magnetite/coir pith supported sodium alginate beads: Kinetics, isotherms, thermodynamics and parametric optimization

The present study deals with facile synthesis of magnetite/coir pith supported sodium alginate beads from naturally available waste materials which was further employed for removal of malachite green dye from synthetic aqueous solution underaparametric optimized condition of the adsorption process. Several operating parameters such as stirring speed (50–250 rpm), adsorbent weight (0.5–7 g/L), pH (2–9), temperature (35–55 °C), and initial malachite green dye concentration (50–250 mg/L) were used to study the adsorption in a batch operation. The central composite design approach of response surface methodology in design-expert software revealed maximum removal efficiency of malachite green at pH of 7; initial malachite green dye concentration of 150 mg/L; adsorbent weight of 3g and temperature of 35 °C which showed adsorption efficiency (>98%). Analysis of Variance (ANOVA) suggested the equation to be significant for the process with a greater impact of adsorbent weight and malachite green concentration rather than the other three contributing parameters. Extensive kinetic, isotherm, thermodynamic, and eco-toxicological studies were performed to understand the behavior and sustainability of the developed materials. Final observation assured the reusability of the processed beads and hence showed gigantic potential for implementation on industrial-scale evaluation for the treatment of wastewater. • Malachite green (MG) dye was adsorbed using low-cost composite material. • Facile synthesis followed by application of Fe/coir pith supported sodium alginate beads for MG dye removal. • Developed materials has intrinsic capability for high degree of MG dye removal (>97%). • Optimizations of the operating parameters were validated by response surface methodology technique (R 2 >0.98). • Toxicity assessment of the developed material showed the non-harmful effects on non-specific targets.

Biofuels production from pine needles via pyrolysis: Process parameters modeling and optimization through combined RSM and ANN based approach

The current study determined the pyrolysis potential of pine needles with the aim to assess the influence of process parameters (namely temperature, heating rate and inert gas flow rate) along with their modeling and optimization through combination of response surface methodology (RSM) and artificial neural network (ANN) technique. Pyrolysis process output was predicted by ANN, while interaction and significance/insignificance along with optimization of process parameters was determined by RSM. A combined approach was employed to accomplish the individual limitations of both the modeling methods. R2 close to 1 and low error demonstrates the viability of the developed models. Results showed comparatively higher R2 and lower MSE value for ANN model, thereby elucidating superior capability of ANN for predicting process yield over RSM modeling; while RSM accurately predicted the process parameters interaction and significance. The study revealed that such a combinational approach has the better ability to model the pine needle pyrolysis process compared to the individual ones. Temperature had been determined as the most predominant variable influencing the yield of the products. Optimized condition had been predicted at 552.06 °C temperature, 50 °C/min heating rate and 164.40 mL/min inert flow rate by yielding maximum bio-oil as 51.11 and 51.70% from RSM and ANN modeling, respectively. Detailed characterization advocates high end-use of all the three products. GC–MS and FTIR techniques predicted that bio-oil was composed of different organic compounds and can be utilized in place of hydrocarbon fuels after certain upgradation processes and can also be extracted into various chemicals. Characterization of biochar and non-condensable gases also demonstrate their potential application as fuel and in several other fields. The study revealed that pine needles can be effectively employed for the production of bioenergy precursors.

Proportional impact prediction model of animal waste fat-derived biodiesel by ANN and RSM technique for diesel engine

Instead of many experimental studies made for the suitability of biodiesel for use in diesel engine, it has become easier to determine by fewer experiments with the development of computer applications. In this research, it was aimed to determine the optimum ratio of animal waste fat biodiesel (AWFBD) and the corresponding engine responses by using artificial neural network (ANN) and response surface methodology (RSM). In addition, a comparison was made with test results to evaluate the performance of ANN and RSM. According to the regression results obtained from RSM, absolute fraction of variance (R2) values greater than 0.95 emerged for all answers. Correlation coefficient (R) values obtained from ANN were found to be higher than 0.97. The developed ANN model was able to predict engine responses with mean absolute percentage error (MAPE) in the range of 3.787–10.730%. MAPE values for RSM were obtained between 2.004 and 11.461%. Combined desirability factor obtained from RSM was found as 0.72288% and optimum engine parameters were found as 22% AWFBD ratio and 1350-Watt engine load. In addition, according to the verification test between the optimum results and the prediction results, it was concluded that there is a good agreement with a maximum error rate of 3.863%.

Optimized Biodiesel Preparation from Waste Cooking Oil as a Fuel for Unmodified Diesel Engines

The continuous fluctuation in the price of crude oil in the international market during the Covid-19 situation forced all the nation to work for self-sustainability in the energy sector. This pandemic condition also teaches all to utilize available sources effectively. So to deal with dual problems the optimized conversion of waste into an energy source is the most effective solution. In the present work waste cooking oil is converted into biodiesel and the production process is optimized using the response surface methodology technique. The central composite design approach of RSM is selected for optimization in the present work which provides a better result in limited experiments. The yield of waste cooking oil biodiesel is optimized through four parameters i.e. catalyst concentration, temp., time, and alcohol to oil molar ratio. The effect of all these parameters is analyzed exhaustively with the help of design expert software. The physicochemical properties of optimized WCOB are measured and the results are compared with petrodiesel fuel and normally prepared WCOB. It is found that the yield of WCOB is increased by more than 4% while prepared with optimized parameter values. The physicochemical properties of optimized WCOB were also found better as compared to normally prepared WCOB and comparable to petrodiesel. Hence it can be concluded that the optimization of biodiesel production not only improves the yield but also improves the quality of the biodiesel.

Inconvenient truth: unsafely managed fecal sludge after achieving MDG for decades in Thailand

Abstract In most low- and middle-income countries, due to financial constraints and improper management practices, the expansion of conventional centralized systems to enhance wastewater management services is not practical. Despite the fact that the majority of the world population has met the Millennium Development Goals 7c (MDG7c) targets, more than 2.5 billion are still using unimproved sanitation facilities and affecting the safety of drinking water and public health. Because of this problem, a novel approach on ‘Non-Sewered Sanitation’ with decentralized treatment systems is being recognized as a promising alternative to accomplish the Sustainable Development Goal No. 6 (SDG6) targets on ‘Safe Water and Sanitation’ and bringing health benefits to the people. This study aimed to investigate the current status of access to improved sanitation facilities in relation to the MDG7c and SDG6 in Thailand and comparing with other countries, including the effects of unsafely managed fecal sludge (FS) on the prevalence of diarrhea and liver fluke infections. The prevailing constraints and weaknesses were identified, and effective sanitation management measures to accomplish SDG6 targets were recommended. Based on data collected from 20 cities located in the north and northeastern regions of Thailand during the period of 2015–2020, the sanitation facilities data on conventional centralized systems and FS management facilities that affect the prevalence of diarrhea and liver infections were analyzed and interpreted using statistical and response surface methodology techniques. The findings showed that most of the surveyed cities have achieved the MDG7c targets, but 70% of the FS generated from on-site sanitation systems are still unsafely managed, caused by limited expansion of sewage systems for wastewater collection and conveyance (with inadequate operation and maintenance) as well as the financial constraints, especially in areas outside of urban neighborhoods. The effective sanitation management practices to accomplish the SDG6 targets were proposed such as increasing the capacity of sanitation facilities to be 55,000 m3/day/30,000 households, designing appropriate FS collection program and treatment technology, and providing more awareness programs for safe sanitation management and health risk protection.

A box behnken design approach for parametric optimization in processing of aluminum 6061 tubes

ABSTRACT In this paper, bonded magnetic abrasives-based media were applied in magnetic field-assisted abrasive flow machining (MFAAFM) for the processing of aluminum 6061 material tubes to improve the surface quality. The effects of different parameters including magnetic field strength (300, 450, 600 mT), number of working cycles (15, 20, 25), and hydraulic pressure (30, 45, 60 bars) on surface roughness improvement rate (SRIR) were studied, and optimization was done utilizing the response surface methodology technique. A Box–Behnken design (BBD) was employed to maximize the SRIR, which was taken as a response variable. A maximum SRIR (72.2%) was attained when the aluminum 6061 tubes was processed at 450 mT of MFS, 25 working cycles, and 60 bars of hydraulic pressure. The model is well fitted and represented utilizing 2nd order polynomial equations. Furthermore, the model validation revealed a 99.84% fit among the actual and experimental values. The surface roughness (Ra) = 0.20 μm of the aluminum 6061 tube samples can be obtained, and the surface morphology after finishing has been greatly improved as revealed by SEM micrographs and Ra peak-valley diagram.

Optimization of cutting parameters while turning Ti-6Al-4 V using response surface methodology and machine learning technique

Optimization of cutting parameters while turning Ti-6Al-4 V using response surface methodology and machine learning technique

Curve Fitting for Damage Evolution through Regression Analysis for the Kachanov-Rabotnov Model to the Norton-Bailey Creep Law of SS-316 Material.

In a number of circumstances, the Kachanov–Rabotnov isotropic creep damage constitutive model has been utilized to assess the creep deformation of high-temperature components. Secondary creep behavior is usually studied using analytical methods, whereas tertiary creep damage constants are determined by the combination of experiments and numerical optimization. To obtain the tertiary creep damage constants, these methods necessitate extensive computational effort and time to determine the tertiary creep damage constants. In this study, a curve-fitting technique was proposed for applying the Kachanov–Rabotnov model into the built-in Norton–Bailey model in Abaqus. It extrapolates the creep behaviour by fitting the Kachanov–Rabotnov model to the limited creep data obtained from the Omega-Norton–Bailey regression model and then simulates beyond the available data points. Through the Omega creep model, several creep strain rates for SS-316 were calculated using API-579/ASME FFS-1 standards. These are dependent on the type of the material, the flow stress, and the temperature. In the present work, FEA creep assessment was carried out on the SS-316 dog bone specimen, which was used as a material coupon to forecast time-dependent permanent plastic deformation as well as creep behavior at elevated temperatures and under uniform stress. The model was validated with the help of published experimental creep test data, and data optimization for sensitivity study was conducted by applying response surface methodology (RSM) and ANOVA techniques. The results showed that the specimen underwent secondary creep deformation for most of the analysis period. Hence, the method is useful in predicting the complete creep behavior of the material and in generating a creep curve.

Determination of optimal performance in wire electrical discharge machining of Al/Al2O3/22p composites using response surface methods and grey relational analysis

ABSTRACT In this study, wire electrical discharge machining of aluminium matrix composites has been experimentally investigated. A metal matrix composite Al/Al2O3/22 p has been machined at various combinations of machining parameters such as pulse on-time, off-time, wire speed and wire tension. Empirical models have been developed to predict the cutting rate, surface roughness and kerf width of the machined composite material. The models were developed by using the technique of response surface methodology. The experiments were conducted in standard roughing mode by using carefully planned response surface design matrix (central composite design). The model predictions are in good agreement with the experimental results. The R 2 values of the proposed cutting rate and kerf width models are above 0.99, where as it is 0.98 for the surface roughness model. The pulse on-time was found to influence cutting rate and surface finish significantly. Kerf width was found to be affected by pulse on-time, off-time, wire speed and wire tension. In addition, a model was developed to correlate the multiple performance characteristic called as grey relational grade and the process parameters. The grey relational grade was significantly affected by pulse on-time and off-time time. The R 2 value for the grey relational grade model was 0.94. Optimal parameter setting was determined for the multiple performance characteristic. The improvement in cutting rate was significant with reasonably smooth surfaces and narrow kerf width. However, the surface finish was found to be dominated by the presence of protruding ceramic particles on the machined surfaces.

Application of deep eutectic solvent in biodiesel reaction: RSM optimization, CI engine test, cost analysis and research dynamics

In this study, biodiesel was produced from waste cooking oil (WCO) in one step process with crude glycerol-ChCl based deep eutectic solvent (DES) and NaOH catalyst. Response surface methodology (RSM) technique was employed in predicting the optimal operating conditions and to study the relationship between reaction parameters and the yield of fatty acid methyl ester (FAME). The RSM coefficient of determination (R2) was observed to be 0.9965. The highest yield of biodiesel was 95% at the optimum reaction temperature of 65 °C with a reaction time of 90 min. The FAME produced was in compliance with the American standard for testing materials (ASTM) D6751-07 (2007) standards. CI engine test of B100 biodiesel fuel emitted lesser CO (2.01 g/kWh), HC (0.02 g/kWh) for engine load of 100% with little increase in CO2 when compared to petroleum-based diesel (PBD). Lower calorific value of biodiesel resulted in higher brake specific fuel consumption (BSFC) compared to the PBD. Computed biodiesel production cost was approximately 833.35 USD/ton. This investigation opens new pathway for utilizing crude glycerol-ChCl based DES as co-solvent for transesterification and other chemical reactions.

NOVEL FLOATING AGENT SACCHAROMYCES BOULARDII FORMULATION BASED FLOATING DRUG DELIVERY SYSTEM

Objective: The objective is to design and optimize a floating tablet of furosemide using a novel floating agent Saccharomyces boulardii. Methods: In this study floating tablet based on principle of combination of floating and swelling prepared by direct compression technique. Saccharomyces boulardii probiotics preparation is used as a floating agent due to its bloating property i.e. production of CO2 gas and hydrophilic polymer HPMC E LV 15 used as swellable polymer. Furosemide is a BCS class IV drug selected as model drug which shows pH dependent solubility and permeability and it is better absorbed from the gastric region, hence to improve dissolution and residence at absorption site of such drug, floating drug delivery system is needed. Calcium hydroxide used as pH modifier which increase rate of dissolution of furosemide and also maintain integrity of tablet matrix. Formulation designed and developed using central composite design response surface methodology technique, so as to explore the effect of formulation variables such as amount of Saccharomyces boulardii preparation and calcium hydroxide on floating lag time and % drug release after 12h. Results: The numerical and graphical optimization technique were used to choose the optimal formulation. Floating lag time was found to be 12.6 min and 88.18% drug release for the optimized formulation. In vivo buoyancy studies depicted that formulation stay more then 6h in stomach. Conclusion: Study indicate that Saccharomyces boulardii is a promising floating agent, and the formulation containing this novel floating agent is suitable for gastro retention and it increases bioavailability of furosemide.

Azolla pinnata methyl ester production and process optimization using a novel heterogeneous catalyst

This study concentrated on utilizing a novel heterogeneous dolomite catalyst in transesterification of Azolla pinnata algae oil with methanol to convert Azolla pinnata methyl ester (AME). The thermophysical properties of the catalyst were characterized by XRF, XRD, FTIR, and BET analysis. The optimized AME yield of 88.7% was obtained for the methanol to oil molar ratio (30:1), catalyst weight% (4 wt%), and operating temperature of (70 °C) through central composite design (CCD) in response surface methodology (RSM) technique. Five different proportions of Azolla pinnata methyl ester (AME) viz., 10%, 20%, 30%, 40% and 100% by volume were blended with 90%, 80%, 70%, 60% and 0% by volume of diesel. These AME test fuel blends were named AME10, AME20, AME30, AME40, and AME100. American Society for Testing and Materials (ASTM D6751) standards followed to testing the thermophysical properties of prepared biodiesel. AME fuel blends were tested in the single-cylinder variable compression ratio (VCR) engine with varied compression ratios (CR) of 16:1, 17:1, and 18:1 for different loadings at a constant speed of 1500 rpm. The performance, in-cylinder combustion, and exhaust emission results were concluded among five different diesel-AME blends at varied compression ratios. The obtained results for AME blends were compared with the diesel fuel under the same working conditions. At peak load condition, AME30 test fuel with CR18:1 gives a reduction of CO (14.0%), HC (12.06%) and smoke opacity (5.88%) and slight increment in NOx (2.46%) emissions as well as reduced BTE (10.20%) and increased BSEC (17.33%) were obtained related to diesel fuel. Better diffusion phase combustion was recorded for AME blends due to their higher cetane value than neat diesel.

Artificial Neural Network Led Optimization of Oxyhydrogen Hybridized Diesel Operated Engine

The prevailing massive exploitation of conventional fuels has staked the energy accessibility to future generations. The gloomy peril of inflated demand and depleting fuel reservoirs in the energy sector has supposedly instigated the urgent need for reliable alternative fuels. These very issues have been addressed by introducing oxyhydrogen gas (HHO) in compression ignition (CI) engines in various flow rates with diesel for assessing brake-specific fuel consumption (BSFC) and brake thermal efficiency (BTE). The enrichment of neat diesel fuel with 10 dm3/min of HHO resulted in the most substantial decrease in BSFC and improved BTE at all test speeds in the range of 1000–2200 rpm. Moreover, an Artificial Intelligence (AI) approach was employed for designing an ANN performance-predicting model with an engine operating on HHO. The correlation coefficients (R) of BSFC and BTE given by the ANN predicting model were 0.99764 and 0.99902, respectively. The mean root errors (MRE) of both parameters (BSFC and BTE) were within the range of 1–3% while the root mean square errors (RMSE) were 0.0122 kg/kWh and 0.2768% for BSFC and BTE, respectively. In addition, ANN was coupled with the response surface methodology (RSM) technique for comprehending the individual impact of design parameters and their statistical interactions governing the output parameters. The R2 values of RSM responses (BSFC and BTE) were near to 1 and MRE values were within the designated range. The comparative evaluation of ANN and RSM predicting models revealed that MRE and RMSE of RSM models are also well within the desired range but to be outrightly accurate and precise, the choice of ANN should be potentially endorsed. Thus, the combined use of ANN and RSM could be used effectively for reliable predictions and effective study of statistical interactions.

Valorization of Banana Peel Waste Used as Filler in Castor Oil Polyurethane Foam for Vegetal Oil Sorption

ABSTRACT This research describes the influence of fiber content from the banana peel waste used as a filler in castor oil polyurethane foam (PU), obtaining eco-sorbent for vegetal oil sorption. Biocomposites were prepared with banana peel waste (BP) in different content: 0, 5, 10, 15, and 20 wt.%, to improve the oil sorption capacity (system PU/BP-oil). Results indicated that banana peel waste was hydrogen-bonded to PU chains, increasing the foams’ density and decreasing the pore size with an increase in fiber content, from 444 to 123 μm. The addition of fibers from banana peel to PU maintained the foams’ hydrophobicity and decreased the foams’ crystallinity. Compared to pure PU, the biocomposites showed greater thermal stability (Tonset from 247.6 to 264°C). All modified foams improved vegetal oil sorption capacity, but PU/BP20% presented better sorption capacity due to high pore size, which directly influenced the oil sorption. The response surface methodology (RSM) technique confirmed the influence of content fibers and oil sorption time.