Response Surface Methodology Technique Research Articles

Utilising Pareto efficiency and RSM to adjust binder content in clay stabilisation for Yttre Ringvägen, Malmö

In this paper, we present a new framework for improving soil strength using an advanced method of engineering statistics. The materials included clay till collected in Yttre Ringvägen, southern Sweden. Binders included quicklime, slag and ordinary Portland cement used as pure binders and blended mixtures. We first applied the Response Surface Methodology techniques aimed at binder blend optimisation: 1) Central Composite Design; 2) Box-Behnken Design; 3) Simplex Lattice Design. The Pareto charts were presented for modelling responses from tests with different binders and estimating their effects on soil strength. Finally, to examine the variables important for soil stabilisation, we also evaluated the effect of the amount of binder and the interaction between cement/lime/slag in different ratios: 30-50-20 %; 50-50-0 %; 100-0-0 % The paper highlights the major opportunities and challenges of engineering statistics as a cross-cutting research direction for the issues of civil engineering.

Artificial Neural Network and Response Surface-Based Combined Approach to Optimize the Oil Content of Ocimum basilicum var. thyrsiflora (Thai Basil).

Ocimum basilicum var. thyrsiflora is valuable for its medicinal properties. The barriers to the commercialization of essential oil are the lack of requisite high oil-containing genotypes and variations in the quantity and quality of essential oils in different geographic areas. Thai basil's essential oil content is significantly influenced by soil and environmental factors. To optimize and predict the essential oil yield of Thai basil in various agroclimatic regions, the current study was conducted. The 93 datasets used to construct the model were collected from samples taken across 10 different agroclimatic regions of Odisha. Climate variables, soil parameters, and oil content were used to train the Artificial Neural Network (ANN) model. The outcome showed that a multilayer feed-forward neural network with an R squared value of 0.95 was the most suitable model. To understand how the variables interact and to determine the optimum value of each variable for the greatest response, the response surface curves were plotted. Garson's algorithm was used to discover the influential predictors. Soil potassium content was found to have a very strong influence on responses, followed by maximum relative humidity and average rainfall, respectively. The study reveals that by adjusting the changeable parameters for high commercial significance, the ANN-based prediction model with the response surface methodology technique is a new and promising way to estimate the oil yield at a new site and maximize the essential oil yield at a particular region. To our knowledge, this is the first report on an ANN-based prediction model for Ocimum basilicum var. thyrsiflora.

Effect of torrefaction on steam-enhanced co-gasification of an urban forest and landfill waste blend: H2 production and CO2 emissions mitigation

This study aims to simulate the steam-enhanced co-gasification of hybrid blends (HB) composed of municipal solid waste (MSW) and urban forest waste (UFW), with torrefaction as pretreatment. Experimental (torrefaction), numerical (gasification thermodynamic equilibrium model), and optimization (response surface methodology) techniques evaluated the gasification process to produce (i) hydrogen-rich gas or (ii) enhanced calorific value gas. The torrefaction (225–275 °C), steam-to-biomass (0.4–1.2 S/B) ratio, and HB proportion (0–50% of MSW) influences were investigated to assess H2%, exergy efficiency of H2-production (ηH2), lower heating value (LHV), cold gas efficiency (CGE), and CO2%. The hybrid methodology defined the optimal conditions for 600 °C gasification as (i) 0.9 S/B and an HB comprising 31% MSW and 69% torrefied UFW at 275 °C, presenting an ηH2 of 49%; and (ii) 0.4 S/B and an HB containing 23% MSW and 77% torrefied UFW at 275 °C, showing an LHV of 6126 kJ.Nm−3.

Preparation of TiO2-Ag heterostructure via tannic acid-assistance and its immobilization on PVDF membrane for the degradation of dye under visible light

In this work, PVDF/TiO2-Ag hybrid membranes were fabricated by immobilizing TiO2-Ag nanoparticles on the Polyvinylidene fluoride (PVDF) membrane surface via covalent bonding. Interestingly, not only Ag nanoparticles were in-situ reduced on the TiO2 surface by tannic acid (TA) and lead to the construction of tightly coupled TiO2-Ag heterostructures, but also the TiO2-Ag nanoparticles and the membrane matrix were bridged by TA. The photo-response range of the hybrid membrane was broadened to visible light through the surface plasmon resonance (SPR) effect. The impact of the Ag precursor solution species on the microstructure of the TiO2-Ag nanoparticles was compared and the photocatalytic properties was thoroughly studied. The smaller particle size and more uniform distribution of Ag further enhanced the SPR effect, reduced the band gap, and inhibited the photogenerated electron-hole recombination. Finally, the photocatalytic degradation efficiency (η) for RhB reached 97.8 % under visible light, while the reaction rate constant was as high as 0.156 mL min−1 cm−2. In addition, the value of η remained above 90 % even after 8 recycling cycles. The fabrication conditions of TiO2-Ag nanoparticles were optimized using the response surface methodology technique. Our work provides valuable insights for the purposeful design and optimization of TiO2-based photocatalytic membranes.

Computational fluid dynamics and multi-objective response surface methodology optimization of perforated-finned heat sinks

BackgroundThis study analyses turbulent flow and heat transfer characteristics of a three-dimensional perforated finned heat sink using computational fluid dynamics (CFD) and response surface methodology (RSM) methods. MethodsThe effects of perforation geometry, including size (1.85 ≤ σ ≤ 2) and cross-sectional shape (square, circular, hexagonal, and triangular), as well as the Reynolds number (25, 000 ≤ Re ≤ 40, 000) as design variables, on parameters such as friction drag force, pressure drag force, total drag force, and Nusselt number were investigated. In addition, using the well-known RSM technique, three accurate models were proposed for the percentage of heat transfer enhancement (PHTE), percentage of drag reduction (PDR), and percentage of weight reduction (PWR) as the most significant design objectives for any heat sink. RSM models served as the foundation for two- and three-objective optimizations. Significant FindingsResults indicate that fins with square perforations in high σ and fins with circular perforations in low σ could achieve exceptional thermal performance (PHTE > 70%). Compared to solid fins, fins with square perforations demonstrated improved PDR performance with a 20–40% reduction in total drag force. In addition, using fins with circular perforations can reduce the heat sink's weight by 45–65% compared to solid fins. Moreover, the optimal condition for the perforated finned heat sink can be attained by considering the values 40,000 and 1,946 for Reynolds number and σ, respectively, and by selecting a circular shape for the perforations. PHTE, PDR, and PWR increase in the optimal case by 68.98%, 35.87%, and 58.86%, respectively, compared to the base case (solid fin).

Bayesian Regularization Neural Network-Based Machine Learning Approach on Optimization of CRDI-Split Injection with Waste Cooking Oil Biodiesel to Improve Diesel Engine Performance

The present study utilized response surface methodology (RSM) and Bayesian neural network (BNN) to predict the characteristics of a diesel engine powered by a blend of biodiesel and diesel fuel. The biodiesel was produced from waste cooking oil using a biocatalyst synthesized from vegetable waste through the wet impregnation technique. A multilevel central composite design was utilized to predict engine characteristics, including brake thermal efficiency (BTE), nitric oxide (NO), unburned hydrocarbons (UBHC), smoke emissions, heat release rate (HRR), and cylinder peak pressure (CGPP). BNN and the logistic–sigmoid activation function were used to train the experimental data in the artificial neural network (ANN) model, and the errors and correlations of the predicted models were calculated. The study revealed that the biocatalyst was capable of producing a maximum yield of 93% at 55 °C under specific reaction conditions, namely a reaction time of 120 min, a stirrer speed of 900 rpm, a catalyst loading of 7 wt.%, and a molar ratio of 1:9. Further, the ANN model was found to exhibit comparably lower prediction errors (0.001–0.0024), lower MAPE errors (3.14–4.6%), and a strong correlation (0.984–0.998) compared to the RSM model. B100-80%-20% was discovered to be the best formulation for emission property, while B100-90%-10% was the best mix for engine performance and combustion at 100% load. In conclusion, this study found that utilizing the synthesized biocatalyst led to attaining a maximum biodiesel yield. Furthermore, the study recommends using ANN and RSM techniques for accurately predicting the characteristics of a diesel engine.

Experimental Investigation for Determining an Ideal Algal Biodiesel-Diesel Blend to Improve the Performance and Mitigate Emissions Using a Response Surface Methodology.

The ongoing depletion of the world's fossil fuel sources and environmental damage has compelled the quest for alternative energy. Excellent characteristics of biodiesel include its renewable nature, safety, absence of sulfur, environmental advantages, and biodegradability, which can eradicate the above problems. In this study, algal oil was characterized to obtain the fatty acid profile, and the free fatty acid value of algal oil suggested a two-step process of esterification and transesterification for efficient biodiesel production. The performance and emission results of biodiesel and its blends (B10, B20, and B30) were investigated in a constant speed, single-cylinder, 4-stroke, 3.5 kW compression ignition engine at different loads for arriving at an appropriate fuel blend ratio. The response surface methodology technique is used to predict the ideal composition of microalgae-diesel using the experimental data with due weightage for the optimization criterion. The predicted blend ratio of B25 was tested on the engine and authenticated. The findings recorded an improvement in brake thermal efficiency to 31.42% and reduction in brake specific energy consumption to 9.82 MJ/kW h, unburned hydrocarbon to 85 ppm, carbon monoxide to 0.164% v/v, carbon dioxide to 4.115% v/v, nitrogen oxides to 691 ppm, and smoke opacity to 16.93%.

Degradation of antibiotic amoxicillin from pharmaceutical industry wastewater into a continuous flow reactor using supercritical water gasification

In recent years the concern with emerging pollutants in water has become more prominent, especially pharmaceutical residues, such as antibiotics due to the influence to increase antibacterial resistance. Further, conventional wastewater treatment methods have not demonstrated efficiency for the complete degradation of these compounds, or they have limitations to treat a large volume of waste. In this sense, this study aims to investigate the degradation of amoxicillin, one of the most prescribed antibiotics, in wastewater via supercritical water gasification (SCWG) using a continuous flow reactor. For this purpose, the process operating conditions of temperature, feed flow rate, and concentration of H2O2 was evaluated using Experimental Design and Response Surface Methodology techniques and optimized by Differential Evolution methodology. Total organic carbon (TOC) removal, chemical oxygen demand (COD) degradability, reaction time, amoxicillin degradation rate, toxicity of degradation by-products, and gaseous products were evaluated. The use of SCWG for treatment achieved 78.4% of the TOC removal for the industrial wastewater. In the gaseous products, hydrogen was the majority component. Furthermore, high-performance liquid chromatography analyses demonstrated that the antibiotic amoxicillin was degraded. For a mass flow rate of 15 mg/min of amoxicillin fed into the reaction system, 14.4 mg/min was degraded. Toxicity tests with microcrustacean Artemia salina showed slight toxicity to treated wastewater. Despite that, the outcomes reveal the SCWG has great potential to degrade amoxicillin and may be applied to treat several pharmaceutical pollutants. Aside from this, carbon-rich effluents may lead to a significant energy gaseous product, especially, hydrogen and syngas.

Statistical analysis of stone mastic asphalt containing steel fiber

The use of various fibers in asphalt mixes has been developed and applied to enhance pavement performance. The conventional asphalt mixture can be improved by including steel fibre as an additive in the asphalt mixture. However, the use of steel fibre in SMA mixtures was not widely studied. Consequently, the objective of this study was to evaluate the effect of steel fibre on the volumetric properties and characterize the performance of steel fibre in SMA mixture. Different steel fiber proportions of 0%, 0.3%, 0.5%, and 0.7% by the total weight of the mixture were used in this study. The test conducted includes the Marshall stability and Flow test, and the Resilient Modulus Test. Response Surface Methodology (RSM) from Design Expert Software was then performed to determine the most optimum properties from the experimental data. The test results revealed that adding steel fiber greatly improved the SMA mixture’s resistance to deformation while also increasing its stability, reducing the abrasion loss, and increasing the stiffness of the SMA mixture. Optimized performance was observed at 0.3% steel fiber content and 1598 MPa of resilient modulus. The relative error results show acceptable agreement between the prediction and the experiment which is considered tolerable and acceptable. This demonstrates that the software optimization of the specific SMA with steel fiber characteristics by the RSM technique has the desired precision for laboratory tests.

Investigation of Wear and Wire Electrical Discharge Machining Characteristics of Al-Mg-MoS2 Composites Using Response Surface Method

Abstract Aluminum metal matrix composites remain sophisticated materials with a broad series of applications because of their high strength-to-weight ratio, better resistance to wear and corrosion, improved high-temperature zone applications, and reasonable machinability. Furthermore, when manufactured through the powder metallurgy process, the tribological and machinability properties of aluminum-magnesium–based composites are found to be improved with the inclusion of molybdenum disulfide reinforcement. The response surface methodology technique was used for estimating the wear loss and coefficient of friction to study the tribological property. Also, it is employed in the wire electrical discharge machining (WEDM) technique, to predict material removal rate, surface roughness, and overcut. The projected values in both the wear and WEDM parameters are well within the minimal error value, according to the perturbation plot, and sufficient validation has been performed. The created model reveals that at 15 N, 2 m/s, 750 m, and 15 A, 90 µs, and 45 V, better tribology and WEDM characteristics are acquired.

Guinea Grass (Megathyrsus maximus) Fibres as Sorbent in Diesel Bioremediation

In this study, the ability of a natural grass named Megathyrsus maximus (Guinea grass) as a sorbent for cleaning up diesel spills in water was investigated. Fourier transform infrared (FTIR) spectroscopy was used to identify the physicochemical properties of untreated and treated GG. Several parameters influencing the efficiency of oil absorbed by guinea grass were optimised using established One Factor at a Time (OFAT) and followed by Response Surface Methodology (RSM) approaches. The optimised parameters include heat treatment, time of heating, packing density, and diesel concentration, with only the significant factors proceeded to statistical optimisation through RSM. As a result of OFAT analysis, 18.83 mL of oil was absorbed at 110 °C for 15 min time of heating, with packing density of 14 g/cm3 and oil concentration of 10% (v/v). Through RSM, the predicted model was significant, confirming that packing density and oil concentration significantly influenced the efficiency of oil absorption by GG. The software predicted an oil absorption efficiency of 16.64 mL, whereas the experimental model validated the design with 22.33 mL of oil absorbed at a constant temperature and time, respectively. The RSM technique has proven better efficiency and effectiveness in absorbing oil compared to OFAT. This research advances our understanding of the utilisation of natural sorbents as a diesel pollution remediation strategy.

Investigating Variation in Compressional Behavior of a Ternary Mixture from a Plastic, Elastic and Brittle Fracture Perspective in the Context of Optimum Composition of a Pharmaceutical Blend

The choice of optimum composition of a mixture of binary and ternary excipients for optimum compressional properties was investigated in this work. Excipients were chosen based on three types of excipients: plastic, elastic, and brittle fracture. Mixture compositions were selected based on a one-factor experimental design using the response surface methodology technique. Compressive properties comprising Heckel and Kawakita parameters, work of compression, and tablet hardness were measured as the main responses of this design. The one-factor RSM analysis revealed that there exist specific mass fractions that are associated with optimum responses for binary mixtures. Furthermore, the RSM analysis of the 'mixture' design type for the three components revealed a region of optimal responses around a specific composition. The foregoing had a mass ratio of 80:15:5 for microcrystalline cellulose: starch: magnesium silicate, respectively. Upon comparison using all RSM data, ternary mixtures were found to perform better in compression and tableting properties than binary mixtures. Finally, the finding of an optimal mixture composition has proven effective in its applicability in the context of the dissolution of model drugs (metronidazole and paracetamol).

Experimental Investigation on the PCCI Engine Fueled by Algal Biodiesel Blend with CuO Nanocatalyst Additive and Optimization of Fuel Combination for Improved Performance and Reduced Emissions at Various Load Conditions by RSM Technique.

Fossil fuel depletion and environmental pollution are paramount problems the world faces. Despite several measures, the transportation industry is still battling to manage these issues. A combined approach of fuel modification for low-temperature combustion with combustion enhancers could offer a breakthrough. Due to their properties and chemical structure, biodiesels have piqued the interest of scientists. Studies have asserted that microalgal biodiesel might be a viable alternative. Premixed charge compression ignition (PCCI) is an easily adoptable promising low-temperature combustion strategy in compression ignition engines. The objective of this study is to identify the optimal blend and catalyst measure for improved performance and reduced emissions. Microalgae biodiesel at various proportions (B10, B20, B30, and B40) was amalgamated with CuO nanocatalyst and tested to arrive at the right concoction of biodiesel with nanoparticles in a 5.2 kW CI engine for different load conditions. The PCCI function warrants that about 20% of the fuel supplied is vaporized for premixing. Finally, the interplay factors of the independent variables of the PCCI engine were then explored by response surface methodology (RSM) to determine the optimal level of desired dependent and independent variables. The RSM experiment findings suggest that the best biodiesel and nanoparticle concoctions at 20%, 40%, 60%, and 80% loads were B20CuO76, B20Cu60, B18CuO61, and B18CuO65, respectively. These findings were experimentally validated.

Use of Response Surface Methodology to Optimize the crystalline size of rice husk derived graphene produced by microwave process

In this work, the microwave process is optimized for graphene preparation from rice husk using RSM (Response Surface Methodology) as the graphene has exceptional qualities and is highly used in many industrial applications. The experiments are designed by using Box-Behnken Design (BBD) approach. The characterization of prepared graphene is done by FESEM (Field Emission Scanning Electron Microscope), UV–Visible spectroscopy, and FTIR (Fourier transform infrared Spectroscopy). The best route to finding good-quality graphene is the objective of this research. The higher value R2 describes the satisfactory correlation between experimental and predicted data. The obtained optimized minimum crystallite size of graphene is 28.39 nm. The characterization, prediction, and process optimization are done, and the validation of the developed model confirms the model can be used for the preparation of graphene from rice husk. The prediction and optimization of the process parameters are done for the synthesis of graphene from rice husk. RSM is used as a statistical technique to obtain a quadratic model for the response. As the properties of graphene mostly depend upon the size of the particle, so the prediction of the crystalline size is done by this RSM technique.

Optimization of Reducing Sugar Concentration from Ulva fasciata Using Cellulase via Response Surface Methodology Techniques

The enzymatic hydrolysis of a cellulose-bearing substrate such as green seaweed Ulva fasciata for the release of reducing sugars for subsequent conversion to bioethanol is generating considerable research interest. However, a major challenge in this process is the cost of enzymes. Crude enzyme extracts from Aspergillus inseitus isolated from indigenous U. fasciata was employed in this study to compare its hydrolytic efficiency with commercial cellulase CTec 2. The aim of the study was to model and optimize the hydrolysis process to predict and determine the concentration of reducing sugars obtained using response surface methodology with the application of the statistical tool, central composite design. Two most important influencing process parameters—hydrolysis time and enzyme dosage—were varied and used to conduct the optimization. The results showed that an optimal enzyme dosage of 0.25 U/g DM could yield a maximum reducing sugar concentration of 1.61 g/L at 6 h hydrolysis time for the commercial enzyme CTec 2 and 0.61 g/L at 12 h for enzyme extracts from A. inseitus, making the hydrolytic efficiency of CTec 2 only approximately 3 times better than crude enzyme extracts from A. inseitus based on reducing sugar concentration. A satisfactory coefficient of R2 of 0.9415 and 0.9493 for A. inseitus and CTec 2 respectively was obtained for the models which were experimentally verified. Careful concentration and purification of the crude enzyme extract from A. inseitus could make it an effective cellulase for hydrolysis processes resulting in cost reduction.

Comparison of neural network and response surface methodology techniques on optimization of biodiesel production from mixed waste cooking oil using heterogeneous biocatalyst

The present research aims to optimize the process parameters of biodiesel production from mixed waste cooking oil, consisting of sunflower and palm oil. The biocatalyst was synthesized from Chinese broccoli, Cauliflower and Napa cabbage vegetable wastes and potassium hydroxide-impregnated biocatalyst was synthesized through the incipient wet impregnation method. Artificial neural networks (Scaled Conjugate Gradient, Bayesian Regularization and Levenberg Marquardt Neural Network) and RSM-based central composite design are used to optimize the parameters in biodiesel production. The obtained predicted results from ANN and RSM models are compared with experimental results. The experimental results showed that biocatalyst impregnated with KOH has produced a maximum yield of 94.7 % at a reaction temperature of 60 °C, time of 120 min, catalyst loading of 7 wt%, 1:9 M ratio and 900 rpm stirrer speed. In addition, among the three algorithms of ANN, BRNN neural network had better prediction results in biodiesel production. At the same time, LMNN, and SCGNN are nearly similarly efficient on the other hand the BRNN model has lower error values. The ANN and CCD models have demonstrated lower RMSE and MAPE such as 0.001 to 0.018, and 3.22–4.37 % respectively, and high R2 values in the ranges of 0.97 to 0.99, indicating the excellent reliability of these two approaches for process optimization. Hence, the above study recommended a biocatalyst impregnated with KOH as a catalyst to achieve maximum biodiesel yield. Further, ANN and RSM techniques are suggested for biodiesel process optimization and yield prediction.

Response surface methodology application on lubricant oil degradation, performance, and emissions in SI engine: A novel optimization of alcoholic fuel blends.

For evaluating the significance of renewable alternative fuels for optimized engine performance and lower emissions, methanol has been extensively utilized as a blend with gasoline in spark-ignition engines. However, rare attempts have been rendered to examine the consequence of methanol-gasoline fuel blends (M6, M12, and M18) on lubricant oil operating for a longer period in engines. The highest and least decrease of 9.62% and 6.68% in kinematic viscosity (KV) was observed for M0 and M18, respectively. However, the flash point (FP) of degraded lubricant oil for M6, M12, and M18 was 3%, 5%, and 7% higher than that of M0, respectively. Total acid number (TAN) and ash content of degraded lubricant oil for M18 were the highest among M0, M6, and M12. An inclusive optimization of engine performance, emissions, and lubricant oil properties has been made for various methanol-gasoline fuel blends at distinct operating conditions by employing the response surface methodology (RSM) technique. RSM-based optimization portrayed the composite desirability value of 0.73 for 2137.13 watt brake power (BP), 6.08 N-m torque, 0.37 kg/kwh brake-specific fuel consumption, 22.10% brake thermal efficiency, 4.02% carbon monoxide emission, 7.15% carbon dioxide emission, 134.12 ppm hydrocarbon emission, 517.02 ppm nitrogen oxides emission, 12.44 cst KV, 203.77°C FP, 2.23 mg/g KOH TAN, and 2.65%wt ash content as responses for fuel blend M8 at 3400 rpm and higher loading condition. RSM predicted results demonstrated significant compliance with empirical findings, with absolute percentage error (APE) below 5% for each response. However, the highest APE of 4.68% was obtained for FP owing to inefficient desirability as a consequence of manual testing. The least APE of 1.57% was obtained for torque because of the highest desirability. Overall, the RSM predicted results of the designed models are effective and viable. RSM technique was found to be effective for the optimization of the broader engine characteristics spectrum.

Enhancing Rutting Resistance in Bituminous Concrete: A Response Surface Methodology Approach for Optimizing Bitumen and Hydrochar-Based Geopolymer Content

In recent time, there has been a wave in concern in enhancing the durability and performance of bituminous pavements, particularly considering rising traffic volumes and environmental concerns. Thus, modifying bitumen with biowaste materials such as hydrochar-based geopolymer composites is one new technique for resolving these challenges. Thus, this study investigates how the bitumen and hydrochar-based geopolymer composites (HBGC) affect the rutting resistance (RR) of bituminous pavements, utilizing an optimization known as the Response Surface Methodology (RSM). Three main design factors were investigated: asphalt binder content, hydrochar content, and geopolymer content. The asphalt binder content varied from 4% to 6%, whereas the biochar and geopolymer content ranged from 0% to 4%. The RSM technique was used to analyze the interplay between various design elements and develop a prediction model for optimizing the RR. The ANOVA analysis demonstrated strong statistical performance, indicating a high link between actual and projected outcomes. Notably, the synergistic effect of increasing bitumen content and the use of a hydrochar-based geopolymer composite modifier resulted in improved RR in bituminous concrete. Furthermore, the study indicated ideal input content of 5.75% for bitumen, 3.4% for hydrochar, and 3.24% for the geopolymer modifier for improve RR. Also, the model has a 5% percentage error, showing a significant correlation between predicted and actual data. This study demonstrates the effectiveness of using RSM to optimize and predict HBGC bituminous concrete RR, by providing durability and sustainability through HBGC utilization in bituminous concrete

Towards integrated sustainable biofuel and chemical production: An application of banana pseudostem ash in the production of biodiesel and recovery of lignin from bamboo leaves

This study investigated an integrated approach to the biowaste transformation and valorization of byproducts. Biochar obtained from the banana pseudostem was calcined to synthesize a heterogeneous catalyst and sustainably prepare a highly alkaline solution. The ash was utilized directly as a heterogeneous catalyst in biodiesel production from waste cooking oil. At the same time, an alkaline solution prepared from the ash was used for delignification and recovery of lignin from bamboo leaves by the hydrothermal reaction. Techniques like Fourier-transform infrared spectroscopy (FTIR), Field emission scanning electron microscopy (FESEM), Brunauer-Emmett-Teller (BET), Transmission electron microscopy (TEM), and Energy dispersive X-ray (EDX) were applied to characterized the catalyst. The alkaline solution was analyzed with Atomic absorption spectroscopy (AAS). The Response surface methodology (RSM) technique was considered for the optimization of different parameters in the transesterification and hydrothermal reaction. Under the optimized condition, waste cooking oil (WCO) to Fatty acid methyl ester (FAME) conversion was 97.56 ± 0.11%, and lignin recovery was 43.20 ± 0.45%. While at the best operating pyrolysis temperature, the liquid fraction yield from the banana pseudostem (500 °C) was 38.10 ± 0.31 wt%. This integrated study approach encourages the inexpensive, sustainable, and environment-friendly pathway for synthesizing catalysts and preparing a highly alkaline solution for the valorization of biowaste into biofuel and biochemicals.

Optimization of α-Amylase Production by Enterobacter cloacae Strain D1 Isolated from Cassava Effluent-impacted Soil Using Response Surface Methodology

Background: Production of amylase by Enterobacter cloacae D1 was optimized in this study using central composite design (CCD) of response surface methodology (RSM).
 Methodology: Effects of five numeric factors (pH, temperature, inoculum concentration, peptone and yeast extract) on the production of amylase were examined. Amylase production was first screened using plate technique and amylase assay thereafter carried out using the dinitrosalicylic acid (DNSA) method. The CCD-RSM experimental set-up involved 30 runs with 5 levels of independent variables.
 Results: The amylase-producing bacterium Enterobacter cloacae strain D1 was identified based on the phylogenetic tree analysis of its sequence. The sequence has been submitted to GenBank under the accession number: MZ477010. The isolate had 98% similarity to the GenBank match Enterobacter cloacae strain ATCC 13182. Optimum conditions that yielded maximum amylase (34.43 U/mL) were pH 5; temperature 40 ℃; inoculum concentration 3%; peptone 1.2% and yeast extract 0.5%.
 Conclusion: This study has demonstrated efficient amylase production from Enterobacter cloacae strain D1 isolated from cassava effluent-impacted soil from Rumuosi, Port Harcourt, Rivers State. In addition, optimization of the critical factors of amylase production resulted in 3.4 fold increase in amylase activity. The enhancement of amylase production by the RSM techniques shows that amylase from this strain can be scaled-up for industrial application.