Application of response surface methodology for optimization of biodiesel production parameters from waste cooking oil using a membrane reactor

Artificial Neural Network and Response Surface Methodology for Predicting and Maximizing Biodiesel Production from Waste Oil with KI/CaO/Al2O3 Catalyst in a Fixed Bed Reactor

Biodiesel from waste oil is produced using heterogeneous catalyzed transesterification in a fixed bed reactor (FBR). Potassium iodide/calcium oxide/alumina (KI/CaO/Al2O3) catalyst was prepared through the processes of calcination and impregnation. The novel catalyst was analyzed with X-ray diffraction (XRD), scanning electron microscopy (SEM), and energy dispersive X-ray spectrometer (EDX). The design of experiment (DoE) method resulted in a total of 20 experimental runs. The significance of 3 reaction parameters, namely catalyst bed height, methanol to waste oil molar ratio, and residence time, and their combined impact on biodiesel yield is investigated. Both the artificial neural network (ANN) based on artificial intelligence (AI) and the Box-Behnken design (BBD) based on response surface methodology (RSM) were utilized in order to optimize the process conditions and maximize the biodiesel production. A quadratic regression model was developed to predict biodiesel yield, with a correlation coefficient (R) value of 0.9994 for ANN model and a coefficient of determination (R2) value of 0.9986 for BBD model. The maximum amount of biodiesel that can be produced is 98.88 % when catalyst bed height is 7.87 cm, molar ratio of methanol to waste oil is 17.47 : 1, and residence time is 3.12 h. The results of this study indicate that ANN and BBD models can effectively be used to optimize and synthesize the highest %yield of biodiesel in a FBR.

Hazardous waste management (Buxus papillosa) investment for the prosperity of environment and circular economy: Response surface methodology-based simulation

Dealing with the current defaults of environmental toxicity, heating, waste management, and economic crises, exploration of novel non-edible, toxic, and waste feedstock for renewable biodiesel synthesis is the need of the hour. The present study is concerned with Buxus papillosa with seeds oil concentration (45% w/w), a promising biodiesel feedstock encountering environmental defaults and waste management; in addition, this research performed simulation based-response surface methodology (RSM) for Buxus papillosa bio-diesel. Synthesis and application of novel Phyto-nanocatalyst bimetallic oxide with Buxus papillosa fruit capsule aqueous extract was advantageous during transesterification. Characterization of sodium/potassium oxide Phyto-nanocatalyst confirmed 23.5 nm nano-size and enhanced catalytic activity. Other characterizing tools are FTIR, DRS, XRD, Zeta potential, SEM, and EDX. Methyl ester formation was authenticated by FTIR, GC-MS, and NMR. A maximum 97% yield was obtained at optimized conditions i.e., methanol ratio to oil (8:1), catalyst amount (0.37 wt%), reaction duration (180 min), and temperature of 80 °C. The reusability of novel sodium/potassium oxide was checked for six reactions. Buxus papillosa fuel properties were within the international restrictions of fuel. The sulphur content of 0.00090% signified the environmental remedial nature of Buxus papillosa methyl esters and it is a highly recommendable species for biodiesel production at large scale due to a t huge number of seeds production and vast distribution.

Artificial Intelligence Techniques and Response Surface Methodology for the Optimization of Methyl Ester Sulfonate Synthesis from Used Cooking Oil by Sulfonation

Herein, the impacts of sulfonation temperature (100-120 °C), sulfonation time (3-5 h), and NaHSO₃/methyl ester (ME) molar ratio (1:1-1.5:1 mol/mol) on methyl ester sulfonate (MES) yield were studied. For the first time, MES synthesis via the sulfonation process was modeled using the adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), and response surface methodology (RSM). Moreover, particle swarm optimization (PSO) and RSM methods were used to improve the independent process variables that affect the sulfonation process. The RSM model (coefficient of determination (R²) = 0.9695, mean square error (MSE) = 2.7094, and average absolute deviation (AAD) = 2.9508%) was the least efficient in accurately predicting MES yield, whereas the ANFIS model (R² = 0.9886, MSE = 1.0138, and AAD = 0.9058%) was superior to the ANN model (R² = 0.9750, MSE = 2.6282, and AAD = 1.7184%). The results of process optimization using the developed models revealed that PSO outperformed RSM. The ANFIS model coupled with PSO (ANFIS-PSO) achieved the best combination of sulfonation process factors (96.84 °C temperature, 2.68 h time, and 0.92:1 mol/mol NaHSO₃/ME molar ratio) that resulted in the maximum MES yield of 74.82%. Analysis of MES synthesized under optimum conditions using FTIR, ¹H NMR, and surface tension determination showed that MES could be prepared from used cooking oil.

Production and Optimization of Biodiesel in a Membrane Reactor, Using a Solid Base Catalyst

The commercial Calcium oxide was successfully embedded on activated carbon surfaces to increase the reactive surface area of a composite catalyst material CaO/AC. The composite catalyst material was also successfully packed in the tubular titanium dioxide/Aluminum dioxide ceramic membrane reactor used to separate the biodiesel produced. Virgin soybean oil was used as precursor feedstock for the reaction. Using a central composite approach, response surface methodology (RSM) was employed to obtain the optimum conditions for producing biodiesel from soybean oil. A total of four process factors were examined (2⁴ experimental designs). 30 experiments were derived and run to investigate the effects of temperature, reaction time, methanol to oil molar ratio, and catalyst concentration (calcium oxide attached on activated carbon). 96.9 percent of soybean oil methyl ester (SOME/biodiesel) was produced at 65 °C temperature, 90 min of reaction time, 4.2:1 molar ratio of methanol to oil, and 3.0 wt.% catalyst concentration. The measured yield and expected biodiesel production values were correlated in a linear sequence. The fuel qualities of SOME/biodiesel were tested, including kinematic viscosity, density, flash point, copper corrosion, calorific value, cloud point, pour point, ash content, and carbon residue.

The preference of the most appropriate radical-based regeneration process for spent activated carbon by the PROMETHEE approach

In this study, regeneration of spent granular activated carbon (GAC) with reactive dye by hydroxyl and sulfate radical-based advanced oxidation processes (microwave (MW) +persulfate (PS)), (Fe(II)+ PS), and (O₃ + H₂O₂) were evaluated. The adsorption of the dye to the GAC surface was characterized by chemisorption and Langmuir isotherm. Regeneration processes have been optimized by the response surface methodology to determine the operating conditions that will provide the highest adsorptive capacity. The optimum conditions of (MW + PS), (Fe (II) + PS), and (O₃ + H₂O₂) processes were process PS anion of 45.52 g/L, pH of 11.4, MW power of 126 W, and duration of 14.56 min; Fe (II) of 3.58 g/L, PS anion of 73.5 g/L, duration of 59.8 min, and pH of 10.9; and H₂O₂ of 2.8 mole/L, flow rate of 8.14 mg ozone/L, duration of 32.8 min, and pH of 5.3, respectively. For (MW + PS), (Fe (II) + PS), and (O₃ + H₂O₂) processes, the adsorptive capacity under optimum conditions was found as 4.36, 8.89, and 8.12 mg dye/g GAC, respectively. For (Fe (II) + PS) and (O₃ + H₂O₂) processes, these values are approximately equal to the adsorptive capacity of raw GAC (8.01 mg dye/g GAC). The predicted values of the adsorption capacities by the obtained models were in good agreement with the actual experimental results. Preference Ranking Organization Method for Enrichment Evaluation approach was used in the preference of the appropriate regeneration process. The adsorptive capacity of regenerated GAC, operating cost of the regeneration process, change in the adsorptive capacity during the regeneration cycle, and carbon mass loss criteria were taken into account. The order of preference of regeneration processes was determined as (Fe (II) + PS)> (MW + PS)> (O₃ + H₂O₂) considering all criteria.

Evaluation of a New Cerium Oxide-Bismuth Oxide-Based Nanobiocomposite as a Biocatalyst for Biodiesel Production

Biodiesel is a promising renewable energy source that can be used together with other biofuels to help meet the growing energy needs of the rapidly increasing global population in an environmentally friendly way. In search for new and more efficient biodiesel production methods, this work reports on the synthesis and use of a novel biocatalyst that can function in a broader range of pH and temperature conditions, while producing high biodiesel yields from vegetable oils. Biodiesel was synthesized by transesterification of non-edible Eruca sativa oil using a lipase from Aspergillus niger that was immobilized on cerium oxide bismuth oxide nanoparticles. The synthesized nanoparticles were first grafted with polydopamine which facilitated the subsequent anchoring of the enzyme on the nanoparticle support. The enzyme activity, pH and temperature stability, and reusability of the immobilized lipase were superior to those of the free enzyme. Following response surface methodology optimization, the highest biodiesel yield of 90.6% was attained using 5 wt% biocatalyst, methanol to oil ratio of 6:1, reaction temperature of 40 °C, pH of 7, and reaction time of 60 h. The produced biodiesel was characterized by Fourier transform infrared spectroscopy and its fatty acid methyl ester composition was determined by gas chromatography-mass spectrometry. Erucic acid methyl ester was identified as the major component in biodiesel, with 47.7 wt% of the total fatty acid methyl esters content. The novel nanobiocatalyst (Bi2O3·CeO2@PDA@A.niger.Lipase) has the potential to produce high biodiesel yields from a variety of vegetable oils.