Response Surface Methodology Approach Research Articles

Data center server workload and infrastructure control based on a joint RSM and CFD approach

The rapid expansion of data centers and cloud computing has exacerbated the issue of high energy consumption and localized overheating in servers. Consequently, the identification of effective cooling methods and resource allocation strategies for server rooms has become pivotal in enhancing overall airflow distribution and optimizing thermal performance within data centers. Time-efficient Computational Fluid Dynamics (CFD) tools offer an alternative to resource-intensive experimental measurements. However, it is difficult to monitor the information in real time and optimize the design of DC. In this study, an innovative application of Response Surface Methodology (RSM) is introduced to prediction thermal environment information and optimization performance of data center. In contrast to traditional single-factor optimization methods, this approach incorporates multiple factors, such as server workloads and air supply conditions, as optimization parameters. Furthermore, maximum server temperatures, temperature differences, and other metrics are defined as key performance indicators (KPIs). A predictive model was developed with the aim of offering comprehensive information about the thermal environment of the data center. Moreover, the study qualitatively analyzes the influence of each parameter on various indicators. Fitting equations are solved based on actual conditions to determine optimized configuration schemes in real time for the entire data center or specific scenarios. This approach effectively reduces cooling energy consumption and optimizes data center thermal management. The optimized configuration resulted in a significant reduction of approximately 30% in the maximum server temperature and approximately 20% in the temperature difference. Additionally, the optimization methodology established in this study facilitates the implementation of a real-time control mechanism for data center cooling systems, enabling energy demand and management optimization. The findings of this study offer valuable insights and generalized methods for enhancing the performance of air-cooled servers.

Optimization of methylene blue removal from aqueous solutions using activated carbon derived from coffee ground pyrolysis: A response surface methodology (RSM) approach for natural and cost-effective adsorption

This study aimed to examine the impact of operational factors on the adsorption capacity of methylene blue (MB) using a natural and cost-effective adsorbent, activated carbon from coffee grounds (CAP). The three-factor Box-Behnken design of the response surface methodology (RSM) was employed to optimize this economically viable process with maximum efficiency. Through extensive experiments, the factors influencing the adsorption process were identified, their interactions were measured, and a mathematical model was developed. The experiment evaluated the quantity of MB adsorbed by CAP based on pH (2.5–10), initial MB concentration (10–100 mg/L), and CAP adsorbent amount (0.05–0.1 g/L). The results revealed that both concentration and mass significantly influenced the decoloration enhancement. Optimal conditions for achieving a 91 % degradation efficiency were determined as 0.05 g/L adsorbent weight, 100 mg/L dye concentration, and pH 2.5, with a desirability score of approximately 0.986, aligning closely with the predictions of the BBD model. In conclusion, this research addresses a research gap by demonstrating the high effectiveness of the CAP adsorbent in removing dyes from textiles.

Process Optimization of Biodiesel from Used Cooking Oil in a Microwave Reactor: A Case of Machine Learning and Box–Behnken Design

In the present investigation, response surface methodology (RSM) and machine learning (ML) are applied to the biodiesel production process via acid-catalyzed transesterification and esterification of triglyceride (TG). In order to optimize the production of biodiesel from used cooking oil (UCO) in a microwave reactor, these models are also compared. During the process, Box–Behnken design (BBD) and an artificial neural network (ANN) were used to evaluate the effect of the catalyst content (3.0–7.0 wt.%), methanol/UCO mole ratio (12:1–18:1), and irradiation time (5.0–9.0 min). The process conditions were adjusted and developed to predict the highest biodiesel yield using BBD with the RSM approach and an ANN model. With optimal process parameters of 4.94 wt.% catalyst content, 16.76:1 methanol/UCO mole ratio, and 8.13 min of irradiation time, a yield of approximately 98.62% was discovered. The coefficient of determination (R2) for the BBD model was found to be 0.9988, and the correlation coefficient (R) for the ANN model was found to be 0.9994. According to the findings, applying RSM and ANN models is advantageous when optimizing the biodiesel manufacturing process as well as making predictions about it. This renewable and environmentally friendly process has the potential to provide a sustainable route for the synthesis of high-quality biodiesel from waste oil with a low cost and high acid value.

Optimization of process parameters for waste motor oil pyrolysis towards sustainable waste-to-energy utilizing a combinatorial approach of response surface methodology and desirability criteria

Sustainable management of hazardous waste and alternate energy generation needs to occur concurrently. Therefore, the objective of the present study is to investigate the optimum operating conditions for the pyrolysis of waste motor oil in a semi-batch scale robust pyrolyzer to generate transport grade fuels. Process parameter optimization was done using the Box-Behnken design and the desirability criteria to maximize the pyro-oil yield. Reactor temperature, holding time, and heating rate affected the pyro-oil yield the most. A maximum pyro-oil yield of 92.55 % with a desirability of 0.843 was obtained for 477 °C, and 10 °C/min of reactor temperature, holding time, and heating rate, respectively, at a constant nitrogen flowrate of 0.6 LPM. The confirmation and validation of the optimized condition were also performed. The paraffinic groups were abundantly found in the optimized pyro-oil, methylene possessing the highest fraction, followed by methyl and methine paraffinic groups. Besides, the (H/C)eff ratio surged from 1.85 to 1.95, whereas the O/C ratio noticeably decreased from 0.69 to 0.02 after the pyrolysis indicating improvement in the igniting characteristics and oxidative stability of the pyro-oil. Further, the decline in kinematic viscosity and density was also observed. Furthermore, a considerable improvement in the physiochemical properties of the generated pyro-oil with reduced moisture and enrichment in higher heating value was also observed. Altogether, the generated pyro-oil could be used as transport-grade fuel.

Evaluation of the photodegradation activity of bismuth oxoiodide/bismuth sub-carbonate nanocatalyst: Experimental design and the mechanism study

In this study, a binary BiOI/(BiO)2CO3 catalyst was prepared and used for sulfasalazine (SSZ) photodegradation in an aqueous phase. The semiconductors were identified by XRD, SEM-EDX, and UV–Vis diffuse reflectance spectroscopy (DRS) methods. Applying the Kubelka-Munk model on DRS results, the band gap energies of 2.09, 3.5, and 2.07 eV were obtained for BiOI, (BiO)2CO3, and BiOI/(BiO)2CO3 samples. pHpzc values of 6.3, 10.1, and 8.1 were estimated for BiOI, (BiO)2CO3, and BiOI/(BiO)2CO3, respectively. After observing the boosted photocatalytic activity by the coupled system, the interaction effects of the influencing variables in SSZ photodegradation were evaluated via the response surface methodology (RSM) approach. The optimal RSM-run conditions were 8.5 ppm SSZ at pH 8, which contained 0.28 g/L of the BiOI/(BiO)2CO3 catalyst and 29 min illumination time, resulting in 87% SSZ photodegradation. The effects of some scavenging agents were also studied to elucidate the relative roles of the reactive species in the SSZ photodegradation by the proposed catalyst, that is, hydroxyl radicals ∼ photoinduced electrons > superoxide radicals ∼ photoinduced holes. The proposed catalyst retained good activity after 5 successive reusing runs.

Effects of different nanoparticles Cu, TiO2, and Ag on fluid flow and heat transfer over cylindrical surface subject to non-fourier heat flux model

The significance of this investigation is to optimize the heat transfer rate of magnetohydrodynamic bioconvective hybrid nanofluid flow in the presence of heat source and thermal convection. The main focus is to examine the two-dimensional incompressible MHD and / hybrid nanofluid flow across the stretching cylinder with the Cattaneo–Christov heat flux model. The response surface methodological approach and sensitivity analysis have been employed to statistically scrutinize the influences of concentration, bioconvection, and thermo-diffusion on heat transfer rate. By applying suitable similarity vectors in controlling the partial differential equations, a system of equations (ODEs) is formed. A well-known method Runge Kutta with shooting has used for numerical simulations. The role of various involving factors on heat and mass transfer rate and skin friction factor is illustrated using tables, figures, and surface plots. The three-dimensional graphs displayed the synchronized effect of involving factors on physical quantities. The heat transfer rate is least responsive to variations in the magnetic parameter and most sensitive to the fluctuations of the Biot number. It is perceived that the nanoparticle concentration and motile concentration profiles declined with the increasing effect of chemical reaction parameters. The legitimacy of the current conclusions is established by the excellent agreement concerning present and previous consequences.

Performance Optimization of Chemical and Green Coagulants in Tannery Wastewater Treatment: A Response Surface Methodology Approach

Vegetable tannery wastewater, highly laden with recalcitrant organics, is not easily treatable through biological processes. This study focuses on the use of response surface methodology in optimizing a coagulation-flocculation process for pretreatment of vegetable tannery wastewater. This study also assessed the possibility of replacing chemical coagulants such as aluminum sulphate with green alternatives such as cassava starch and orange peel powder. The effects of coagulant dosage and pH on three key wastewater quality parameters (chemical oxygen demand (COD) and total suspended solids (TSS)’s removal efficiencies as well as sludge volume index (SVI)) were also assessed. Quadratic models developed for all the three responses were adequate. The optimal conditions were attained at a pH of 3.17 and a dosage of 2.76 g/L for cassava starch coagulant, pH of 3.74 and a dosage of 5.16 g/L for orange peel powder coagulant, and pH of 6.09 and a dosage of 11.60 g/L for aluminum sulphate. The COD and TSS removal efficiencies as well as SVI achieved under these optimal conditions were 37.25%, 73.95%, and 14.80 mL/g, respectively, for cassava starch coagulant; 17.97%, 66.08%, and 19.87 mL/g, respectively, for orange peel powder coagulant; and 38.51%, 76.06%, and 29.57 mL/g, respectively, for aluminum sulphate. The outperformance of cassava starch over orange peel powder and its comparable results with aluminum sulphate makes the former a more environment-friendly alternative to aluminum sulphate for treatment of tannery wastewater.

Effect of Grain Size Distribution of Soil on Immobilization of Cadmium and Nickel in Contaminated Soil Using Nano Zerovalent Iron: A Factorial Design and Response Surface Methodology Approach

Urban soils get contaminated due to the unscientific disposal of industrial effluents and solid waste, contaminating the soil with heavy metals. Contaminated soils need to be remediated to avoid the ingestion of toxic heavy metals. The immobilization of heavy metals is one of the methods to remediate contaminated soils. The primary purpose of this study was to explore the effect of grain size distribution (GSD) of soil on the efficiency of immobilization of toxic heavy metals in soil when nano zerovalent iron (nZVI) is used to immobilize the metals. Preliminary studies conducted with the 23 factorial design method confirmed that GSD is a significant factor along with the level of contamination and nZVI dosage in the immobilization of heavy metals in soil. Experiments were conducted with cadmium (Cd) and nickel (Ni), the toxic heavy metals, and three soils with different finer particles than 75 µm. Immobilization efficiency was obtained by comparing the leachability of contaminated and treated soil samples using the toxicity characteristic leaching procedure. Reduction in available fraction and variation in the speciation of heavy metals was observed through the sequential extraction procedure. The response surface methodology was adopted for analysis with three levels of GSD, contamination, and nZVI dosage. For Cd, a reduced cubic model was the best fit with a coefficient of determination (R-square) of 0.93, and for Ni, a quadratic model was the best fit with an R-square of 0.92. Both these models were validated experimentally. The efficiency variation followed an optimum curve concerning GSD, contamination level, and the nZVI dosage. For multimetal contamination, the immobilization efficiency gradually decreased from 10% finer to 90% finer soil, both in the case of Cd and Ni. It was observed that for both single metal and multimetal contamination, the interaction between soil and the nZVI plays a vital role in immobilization along with the factors considered.

Comparative study of process parameters on dross properties by laser machining of AISI 316L material

The main aim of this paper is to examine the impact of process parameters on the dross height formation for CO2 and fiber laser machining of SS 316L material. CO2 and fiber laser machining has a small spot size, narrow beam, high intensity, and depth of focus that are conveniently absorbed by the metal surfaces. The work is concentrated on the experimental study of laser machining of SS 316L material for 20 mm thickness. The cut qualities of the workpiece are analysed by measuring the dross height by changing the process parameters like assist gas pressure, laser power, and cutting speed. The experimentation is performed on Bystronic laser cutting machine. In this study, the DOE technique is used implementing the response surface methodology approach. Three factors and two levels are selected, and the total numbers of test runs are 17. Mathematical calculations are carried out using ANOVA. The optimum values of performance parameters are defined to achieve the required minimum dross height. The experimental analysis identified that the CO2 laser process produces higher cut quality as compared to the fiber laser process. The major inference that is drawn from this study is change in cutting speed affects the dross height. Gas pressure did not show much effect on change in dross height formation.

Modeling and optimization of CO2 capture into mixed MEA-PZ amine solutions using machine learning based on ANN and RSM models

Carbon dioxide (CO2) sequestration by chemical absorption is widely regarded as the most effective method for its reduction in natural gas streams or flue gases from fossil fuel power plants. In this article, modeling and optimization of CO2 mass transfer flux (NCO₂) are investigated. A combination of Piperazine (PZ) and Monoethanolamine (MEA) amines has been used for CO2 absorption. Artificial neural networks (ANN) and Response Surface Methodology (RSM) were used to achieve goals. The dimensionless numbers for the input of ANNs and RSM was obtained using Buckingham's Pi theory. The resulting models can provide acceptable results in an effect of independent variables and the interaction between them by the impact on the objective function, to optimize the process of CO2 capture. In the RSM approach, the quadratic model is used. Optimized ANNs and a structure with the least error and the most matching with the experimental data were obtained. Both ANNs and RSM models showed acceptable prediction of experimental data with maximum R2 value of 0.9974 and 0.9723 respectively. Due to the mean squared error of5.2 × 10−4, the ANN is recommended for the development of absorption simulation models.

Optimizing the Powder Metallurgy Parameters to Enhance the Mechanical Properties of Al-4Cu/xAl2O3 Composites Using Machine Learning and Response Surface Approaches

This study comprehensively investigates the impact of various parameters on aluminum matrix composites (AMCs) fabricated using the powder metallurgy (PM) technique. An Al-Cu matrix composite (2xxx series) was employed in the current study, and Al2O3 was used as a reinforcement. The performance evaluation of the Al-4Cu/Al2O3 composite involved analyzing the influence of the Al2O3 weight percent (wt. %), the height-to-diameter ratio (H/D) of the compacted samples, and the compaction pressure. Different concentrations of the Al2O3 reinforcement, namely 0%, 2.5%, 5.0%, 7.5%, and 10% by weight, were utilized, while the compaction process was conducted for one hour under varying pressures of 500, 600, 700, 800, and 900 MPa. The compacted Al-4Cu/Al2O3 composites were in the form of cylindrical discs with a fixed diameter of 20 mm and varying H/D ratios of 0.75, 1.0, 1.25, 1.5, and 2.0. Moreover, the machine learning (ML), design of experiment (DOE), response surface methodology (RSM), genetic algorithm (GA), and hybrid DOE-GA methodologies were utilized to thoroughly investigate the physical properties, such as the relative density (RD), as well as the mechanical properties, including the hardness distribution, fracture strain, yield strength, and compression strength. Subsequently, different statistical analysis approaches, including analysis of variance (ANOVA), 3D response surface plots, and ML approaches, were employed to predict the output responses and optimize the input variables. The optimal combination of variables that demonstrated significant improvements in the RD, fracture strain, hardness distribution, yield strength, and compression strength of the Al-4Cu/Al2O3 composite was determined using the RSM, GA, and hybrid DOE-GA approaches. Furthermore, the ML and RSM models were validated, and their accuracy was evaluated and compared, revealing close agreement with the experimental results.

Optimization of waste lubricating oil regeneration using acid‐clay process

AbstractWaste lubricating oil is non‐biodegradable materials. It is released into the environment and its disposal has become a global environmental issue due to its impact on all life aspects. The production of waste lubricating oil has recently increased rapidly. The aim of this work is to recover, optimize oil production process from waste lubricating oil. This was investigated using the acid‐clay process. Response Surface Methodology (RSM) approach was used to optimize the desludging reaction. The studied parameters were acid to oil ratio, contact time, temperature. The mixing ratio of acid to waste oil were selected in the range of 1:10 to 2:10 (v/v). Reaction time was in the range of 60–120 min. The predicted maximum viscosity index of 117.54 was obtained at the temperature of 40°C, 20% acid to oil ratio, and reaction time of 60 min. The results were assessed experimentally using five runs and the average value of viscosity index was 111.76 with standard deviation of 0.739 and error 4.91%. The samples obtained at optimum conditions were characterized by measuring flash point at 180°C, carbon residue of 0.487, kinematic viscosity at 100°C of 17cSt, the water content of 0.01%, ash content of 0.02%. Viscosity index of was found in complies with the Egyptian standards.

Solar photocatalytic degradation of polyethylene terephthalate nanoplastics: Evaluation of the applicability of the TiO2/MIL-100(Fe) composite material

For the first time, TiO2/MIL-100(Fe) photocatalysts supported on perlite mineral particles prepared by the solvothermal/microwave methods and post-annealing technique were tested in the degradation of polyethylene terephthalate nanoplastics (PET NPs). Powder X-ray diffraction, Fourier transform infrared spectroscopy, thermogravimetric analysis, scanning electron microscopy, X-ray photoelectron spectroscopy, UV–vis diffuse reflectance spectroscopy, N2 physisorption, photoluminescence emission spectroscopy, photocurrent response, and electrochemical impedance spectroscopy were used to characterize the as-prepared materials. The response surface methodology approach was used to study the effects: pH of the NPs suspension and incorporated amount of MIL-100(Fe) on the TiO2/MIL-100(Fe) catalyst to optimize the photocatalytic degradation of the PET NPs under simulated solar light. The degradation of the PET NPs was evaluated by measuring turbidity and carbonyl index (FTIR) changes. The total organic carbon (TOC) in the solution during the degradation of the PET NPs was assessed to measure NPs oxidation into water-soluble degradation by-products. The active species involved in the photocatalytic degradation of PET NPs by the TiO2/MIL-100(Fe) composite was further examined based on trapping experiments. The use of 12.5 wt% TiO2/MIL-100(Fe) catalyst showed improved photocatalytic efficacy in the oxidation of PET NPs at pH 3 under simulated sunlight compared to bare TiO2. The increase in the carbonyl index (CI = 0.99), the reduction in the turbidity ratio (0.454), and the increase in the content of TOC released (3.00 mg/L) were possible with 12.5 wt% TiO2/MIL-100(Fe) material. In contrast, the PET NPs were slowly degraded by TiO2-based photocatalysis (CI = 0.96, turbidity ratio = 0.539, released TOC = 2.12 mg/L). The mesoporous TiO2/MIL-100(Fe) composites with high specific surface area, capacity to absorb visible light, and effective separation of photogenerated electron-hole charges clearly demonstrated the enhancement of the photocatalytic performance in the PET NPs degradation under simulated solar light.

Phenol removal by electro-Fenton process using a 3D electrode with iron foam as particles and carbon fibre modified with graphene

The 3D electro-Fenton technique is, due to its high efficiency, one of the technologies suggested to eliminate organic pollutants in wastewater. The type of particle electrode used in the 3D electro-Fenton process is one of the most crucial variables because of its effect on the formation of reactive species and the source of iron ions. The electrolytic cell in the current study consisted of graphite as an anode, carbon fiber (CF) modified with graphene as a cathode, and iron foam particles as a third electrode. A response surface methodology (RSM) approach was used to optimize the 3D electro-Fenton process. The RSM results revealed that the quadratic model has a high R2 of 99.05 %. At 4 g L-1 iron foam particles, time of 5 h, and 1 g of graphene, the maximum efficiency of phenol removal of 92.58 % and chemical oxygen demand (COD) of 89.33 % were achieved with 32.976 kWh kg-1 phenol of consumed power. Based on the analysis of variance (ANOVA) results, the time has the highest impact on phenol removal efficiency, followed by iron foam and graphene dosage. In the present study, the 3D electro-Fenton technique with iron foam partials and carbon fiber modified with graphene was detected as a great choice for removing phenol from aqueous solutions due to its high efficiency, formation of highly reactive species, with excellent iron ions source electrode.

Production and characterization of Cymbopogon citratus biofuel and its optimization study for efficient and cleaner production blended with water and cetane improver: A response surface methodology approach

The use of low viscous biofuel derived from grasses has a better prospect of turning into a renewable source of diesel fuel. Cymbopogon citratus, a profoundly unsaturated fatty acid grass, is proposed as feedstock for biofuel production as none of the assessments tried to explore the Cymbopogon citratus bio-oil (CCB) as an alternative source for diesel fuel with reasonable fuel change. The current study intends to extract low viscous biofuel from Cymbopogon citratus grass and work on its energy and environmental features by adding an optimum amount of water and Di-tert-butyl peroxide (DTBP). A steam refining process was employed to extract the CCB, and the fuel characterizations were assessed and quantified by FTIR and GC–MS investigations. Since the individual impact of CCB, water, and DTBP on energy and environmental boundaries is unique, a response surface methodology was applied to achieve the optimum level of individuals for overall efficient and cleaner production. The fuel characteristics report that the extricated CCB's chemical substances are tantamount to ordinary diesel and can be suggested as an alternative source for fuel applications. The optimization results demonstrate that the coefficient of determination is significantly closer to one, and the chance of error is less than 0.1%. The optimum value of CCB, water, and DTBP is determined as 22.08%, 12.23%, and 1.31%, respectively, and the energy and environmental parameters agree well with the predicted values. The progress in the combustion parameters is also documented for the test fuel while the fuel modification parameters approach their optimal state. The proposed fuel blend would lessen the demand for fossil fuel energy sources by around 35%.

Electrospun nanofiber membrane diameter prediction using a combined response surface methodology and machine learning approach

Despite the widespread interest in electrospinning technology, very few simulation studies have been conducted. Thus, the current research produced a system for providing a sustainable and effective electrospinning process by combining the design of experiments with machine learning prediction models. Specifically, in order to estimate the diameter of the electrospun nanofiber membrane, we developed a locally weighted kernel partial least squares regression (LW-KPLSR) model based on a response surface methodology (RSM). The accuracy of the model's predictions was evaluated based on its root mean square error (RMSE), its mean absolute error (MAE), and its coefficient of determination (R2). In addition to principal component regression (PCR), locally weighted partial least squares regression (LW-PLSR), partial least square regression (PLSR), and least square support vector regression model (LSSVR), some of the other types of regression models used to verify and compare the results were fuzzy modelling and least square support vector regression model (LSSVR). According to the results of our research, the LW-KPLSR model performed far better than other competing models when attempting to forecast the membrane's diameter. This is made clear by the much lower RMSE and MAE values of the LW-KPLSR model. In addition, it offered the highest R2 values that could be achieved, reaching 0.9989.

Computational optimization of engine emissions and performance of a CI engine powered with biogas and NiO nanoparticles doped diesel

AbstractTo counter the concern of the future availability of fossil fuel due to ever‐increasing energy demand and the emission generated due to its utilization, alternative renewable fuels, such as biogas and fuel additives in the form of nanoparticles emerge as viable alternative fuels. To date, no research has been carried out on the optimization of the co‐addition rate of nanoparticles and biogas. In this study, engine load (0.7–3.5 kW), Nickel oxide (NiO) Nanoparticles doped rate (NDR, 0–50 ppm), and Biogas Flow Rate (BFR, 0.5–1 kg/h) were selected as independent input variables to optimize compression ignition (CI) engine emission and performance outputs through the response surface methodology (RSM) approach. The F‐value of the RSM model indicates that the engine load variable has the greatest impact on engine output responses, with the BFR and NDR variables following in importance. At 2.54 kW engine load, 47.48 ppm NDR, and 0.747 kg/h BFR, the best engine output response was seen when the RSM model including the desirability function was optimized. Based on the optimization results, an assertion can be made that the use of nanoparticles in conjunction with biogas has a substantial impact on the emissions and efficiency of CI engines.

Optimisation of the machining time required by insole orthotic shoes for patients with clubfoot using the Taguchi and response surface methodology approach

In this study, the application of the computer-aided reverse engineering system (CARE) to the novel design and manufacture of a comfortable insole for a clubfoot patient is presented. The Taguchi method (TM) and response surface methodology (RMS) were used to predict the machining time of the orthotic boot insole during both computer-aided manufacturing (CAM) simulation and computer numerical control (CNC) machining. Taguchi’s experimental design, presented as a matrix orthogonal array L2736, was acquired for controlling parameters, namely tool path strategy (A), spindle speed (B), step-down (C), step-over of the cutter (D), cutter diameter (E), and dimensional tolerance (F) of the insole size. In this method, the model generated by the RMS method evaluates the six parameters influencing the machining time. The objective of this study is to develop a regression model that demonstrates the relationship between the cutting parameters and insole machining time. The optimal parameters are A1B1C3D2E1F2, where A1 denotes raster finishing, B1 denotes a spindle speed of 10,000 rpm, C3 denotes a step-down of 850 mm, D2 denotes a step-over of 0.25 mm, E1 denotes a cutter diameter of 20–35 mm, and F2 deontes a tolerance of 0.75 mm. The experimental and calculated machining time (tm) results were 236 and 125.4 min, respectively. However, the real machining results were 334 and 152.25 min with error values of 46.86% and 54.42%, respectively. Meanwhile, with the tm RMS method, the simulated and calculated machining time results were 189.22 and 236.35 min, whereas the real tm values were 236.52 and 334.86 min with error values of 19.94% and 29.37%, respectively. This research obtains improvements of 19.82% (simulation time) and 29.19% (real-time).

Process optimization of electrocoagulation reactor for treatment of distillery effluent using aluminium electrode: Response surface methodology approach

Process optimization of electrocoagulation reactor for treatment of distillery effluent using aluminium electrode: Response surface methodology approach

Optimization of microfibrillated cellulose isolation from cocoa pod husk via mild oxalic acid hydrolysis: A response surface methodology approach

Theobroma cacao L. species, cultivated worldwide for its valuable beans, generates up to 72% weight of the fruit as waste. The lack of reutilization technologies in the cocoa agroindustry has hindered the exploitation of valuable bio-components applicable to the generation of high value added bioproducts. One such bioproduct is microfibrillated cellulose (MFC), a biopolymer that stands out for its desirable mechanical properties and biocompatibility in biomedical, packing, 3D printing, and construction applications. In this study, we isolated microfibrillated cellulose (MFC) from cocoa pod husk (CPH) via oxalic acid hydrolysis combined with a steam explosion. MFC isolation started with the Solid/Liquid extraction via Soxhlet, followed by mild citric acid hydrolysis, diluted alkaline hydrolysis, and bleaching pre-treatments. A Response Surface Methodology (RSM) was used to optimize the hydrolysis reaction at levels between 110 and 125 °C, 30–90 min at 5–10% (w/v) oxalic acid concentration. The cellulose-rich fraction was characterized by Fourier-Transform Infrared Spectroscopy (FTIR), Thermogravimetric Analysis (TGA), Differential Scanning Calorimetry (DSC), X-Ray Diffraction (XRD), and Scanning Electron Microscopy (SEM) analyses. Characterization analyses revealed a cellulose-rich polymer with fibers ranging from 6 to 10 μm, a maximum thermal degradation temperature of 350 °C, and a crystallinity index of 63.4% (peak height method) and 29.0% (amorphous subtraction method). The optimized hydrolysis conditions were 125 °C, 30 min, at 5% w/v oxalic acid: with a 75.7% yield. These results compare with MFC obtained through highly concentrated inorganic acid hydrolysis from different biomass sources. Thus, we show a reliable and greener alternative chemical treatment for the obtention of MFC.