Statistical approach to optimization of the transesterification reaction from sorrel (hibiscus sabdariffa) oil

In an effort to optimize the reaction conditions of biodiesel production from Sorrel seed oil, Response Surface Methodology (RSM) was applied and the effects of reaction temperature, catalyst amount, reaction time and methanol/oil molar ratio, and their reciprocal interactions were ascertained. A total of 30 experimental runs were designed by Central Composite Rotatable Design (CCRD) and carried out. A quadratic polynomial was obtained for predicting the Transesterification process and the ANOVA test showed the model to be significant (p<0.05). The validity of the predicted model was confirmed by carrying out three independent replicates experiments. The actual maximum biodiesel yield obtained was 99.23% (w/w) at methanol/oil molar ratio 6.21, catalyst amount 1.03 (% wt.), reaction temperature 51 oC, and reaction time 63 min. The fuel properties of Hibiscus sabdariffa methylester (HSME) produced were found to be within the ASTM D6751 and DIN EN 14214 biodiesel standards. The fatty acid profile of the HSME revealed that the dominant fatty acids were oleic (58.34%), arachidic (1.55%), palmitic (18.28%) and linoleic (21.19%). Emission assessment revealed 70% reduction of CO at B80, 80% reduction of NO concentration at B40

Substrate Channelling and Energetics of Saccharomyces cerevisiae DSM 2155 Grown on Glucose in Fed-Batch Fermentation Process

Data collected during the high-cell-density cultivation of Saccharomyces cerevisiae DSM 2155 on glucose in a simulated five-phase feeding strategy of fed-batch process, executed on the Universal BIoprocess CONtrol (UBICON) system using 150L bioreactor over a period of 24h have been analysed. The consistency of the data set was checked using both the available electron and carbon balances. Estimates of the true energetic yields and cell maintenance requirements were obtained through the application of a multivariate statistical procedure known as covariate adjustment technique. A low value of maintenance coefficient, me = 0.004h-1, and a high average value of the true biomass energetic yield, hmax = 0.745, were obtained for the bioreactor system, which showed that the organism was in no danger of ethanol produced during this cultivation. A simple model for estimating the distribution of substrate consumed between the fermentative and the respiratory pathways in the oxido-reductive process was developed based on the respiratory quotient (RQ) values. The fraction of substrate consumed for respiratory metabolic activities (qsresp/qs) was virtually 1.0 for the first three phases of the feeding strategy, which accounted for the first sixteen hours of the 24h operation. This was an indication that ethanol formation was avoided during this period. (African Journal of Biotechnology: 2003 2(5): 96-103

Fermentation parameter optimization of microbial oxalic acid production from cashew apple juice

AbstractThe potential of cashew apple juice (CAJ) as a carbon source for oxalic acid (OA) production via fermentation process was investigated in this study. The effects and interactions of CAJ concentration, time, pH, NaNO3 concentration and methanol concentration on OA production were determined in a central composite design (CCD) and the process was modelled and optimized using response surface methodology (RSM). The results showed that OA fermentation can be described significantly (p < 0.05) by a quadratic model giving regression coefficient (R2) of 0.9964. NaNO3 concentration was the most significant positive variable while methanol was not a significant variable. A maximum OA concentration of 122.68 g/l could be obtained using the optimum levels of CAJ of 150.0 g/l, pH of 5.4, time of 7.31 days, NaNO3 of 2 g/l and methanol of 1% volume. The production of OA was found to increase from 106.75 to 122.68 g/l using the statistically design optimization. This study revealed that CAJ could serve as an inexpensive and abundant feedstock for fermentative OA production, the resulting model could be used in the design of a typical pilot plant for a scale up production

A home made kit for plasmid DNA mini-preparation

Many methods have been used to isolate plasmid DNA, but some of them are time consuming especially when extracting a large number of samples. Here, we developed a rapid protocol for plasmid DNA extraction based on the alkaline lysis method of plasmid preparation (extraction at pH 8.0). Using this new method, a good plasmid preparation can be made in approximately one hour. The plasmids are suitable for any subsequent molecular applications in the laboratory. By applying the recommendations to avoid contaminations and to maximize the plasmid yield and quality during extraction, this protocol could be a valuable reference especially when analyzing a large number of samples. (African Journal of Biotechnology: 2003 2(4): 87

Cellulase Production by Aspergillus flavus Linn Isolate NSPR 101 fermented in sawdust, bagasse and corncob

Bagasse, corncob and sawdust were used as lignocellulosic substrates for the production of cellulase enzyme using Aspergillus flavus after ballmilling and pretreatment with caustic soda.  From the fermentation studies, sawdust gave the best result with an enzyme activity value of 0.0743IU/ml while bagasse and corncob gave 0.0573IU/ml and 0.0502IU/ml respectively. The three lignocellulosics gave their maximum enzyme activities at about the twelfth hour of cultivation, suggesting that the 12th hour is the optimum time when the enzyme may be harvested. (African Journal of Biotechnology: 2003 2(6): 150-152

Methanolysis optimization of sesame (Sesamum indicum) oil to biodiesel and fuel quality characterization

Statistical approach was employed to optimize biodiesel production from sesame oil in this work. Precisely, response surface methodology was applied, and the effects of four variables, viz. reaction temperature, catalyst amount, reaction time, and methanol/oil molar ratio, and their reciprocal interactions were determined. Central composite rotatable design was used to generate a total of 30 individual experiments, which were designed to study the effects of these variables during alkali-catalyzed methanolysis of sesame oil. A statistical model predicted the highest conversion yield of sesame biodiesel to be 99.71% at the following optimized variable conditions: reaction temperature of 63°C, catalyst amount of 1.04 wt.%, and methanol/oil molar ratio of 6.24, with a reaction time of 51.09 min. Using these variables under experimental condition in four independent replicates, an actual biodiesel yield of 98.36% was accomplished. The fuel properties of biodiesel produced were found to be within the ASTM D6751 and DIN EN 14214 biodiesel specifications. Keywords: Biodiesel, Sesame oil, Methanolysis, Optimization, Response surface methodolog

Banana peels as a biobase catalyst for fatty acid methyl esters production using Napoleon's plume (Bauhinia monandra) seed oil: A process parameters optimization study

The potential of banana peels as a suitable catalyst for conversion of Bauhinia monandra seed oil (BMSO) to fatty acidy methyl ester (FAME) in a transesterification reaction was investigated. The FAME was produced through a two-step method of esterification and transesterification. The high free fatty acid (FFA) content of BMSO was reduced in the esterification reaction to less than 1% using reaction conditions of methanol/FFA molar ratio of 46:1, Fe2(SO4)3 of 12 wt.%, and reaction time of 75 min. The design of experiments (DoE) was applied in the transesterification step to investigate the effect of pertinent process parameters on the yield of BMME (Bauhinia monandra methyl esters). The results showed that BMME, which is consistent with ASTM D-6751 and EN 14214 standards, can be obtained at an optimum yield of 98.5 ± 0.18 wt.% using catalyst loading of 2.75 wt.%, methanol/oil molar ratio of 7.6:1 and reaction time of 69.02 min. FT-IR, XRD, SEM and elemental analysis revealed that the catalytic action of banana peels was as a result of the potassium content and the microstructural formation when calcined at 700 °C. The study revealed the possibility of developing heterogeneous catalyst from banana peels for FAME production, which may reduce the overall cost of production

Machine learning approaches to modeling and optimization of biodiesel production systems: State of art and future outlook

One of the main limitations to the economic sustainability of biodiesel production remains the high feedstock cost. Modeling and optimization are crucial steps to determine if processes (esterification and transesterification) involved in biodiesel production are economically viable. Phenomenological or mechanistic models can simulate the processes. These methods have been used to simulate and manage the processes, but their broad use has been constrained by computational complexity and numerical difficulties. Therefore, it is necessary to use quick, effective, accurate, and resilient modeling methodologies to simulate and regulate such complex systems. Data-driven computational and machine-learning (ML) techniques offer a potential replacement for conventional modeling methodologies to deal with the nonlinear, unpredictable, complex, and multivariate nature of biodiesel systems. Artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) are the most often utilized ML tools in biodiesel research. To effectively attain maximum biodiesel yield, suitable optimization techniques based on nature-inspired optimization algorithms need to be integrated with these tools to obtain the best possible combination of various operating variables. Future research should focus on utilizing ML approaches for monitoring and managing biodiesel production systems to increase their effectiveness and promote commercial feasibility. Thus, the review discusses the various ML techniques used in modeling and optimizing biodiesel production systems

Bioprocessing of underutilized Artocarpus altilis fruit to bioethanol by Saccharomyces cerevisiae: A fermentation condition improvement study

Raw materials availability needed for commercial bioethanol production is one of the challenges against its global adoption. Identifying rich, cheap, underused, and readily available starch sources for bioethanol production could help address the problem. Thus, this current study investigated the bioconversion of underutilized Artocarpus altilis (breadfruit) starch to bioethanol using Saccharomyces cerevisiae. The effects of the essential fermentation conditions (fermentation time, breadfruit starch hydrolysate (BSH) concentration, pH, and inoculum size) and their interactions on bioethanol production were investigated. The central composite design was used to generate twenty-one experiments conducted under batch fermentation conditions in the laboratory. The breadfruit starch hydrolysis led to a BSH concentration of 108.9 g/L under a starch concentration of 122 g/L, microwave output of 720 W, and an incubation time of 6 min. For the fermentation of BSH, maximum bioethanol production of 4.99% (V) was reached under the cultivation conditions of BSH concentration of 80 g/L, medium pH of 4.7, inoculum size of 2% (V), and fermentation time of 20.41 h. Except for pH, the impact of each parameter on the bioethanol production was in this order: BSH concentration, inoculum size, and fermentation time. While for the interactions amongst the parameters, the impact is in this order: BSH concentration and inoculum size; BSH concentration and fermentation time; and fermentation time and inoculum size. The results of this study indicated breadfruit starch could be hydrolyzed using acid hydrolysis and microwave irradiation in a relatively short time. The BSH obtained could potentially add to other substrates for bioethanol production

Appraisal of Artificial Neural Network and Response Surface Methodology in Modeling and Process Variable Optimization of Oxalic Acid Production from Cashew Apple Juice: A Case of Surface Fermentation

This study assessed the effects and interactions of cashew apple juice (CAJ) concentration, pH, time, methanol concentration, and NaNO3 concentration on oxalic acid fermentation in a central composite design. The efficacies of artificial neural network (ANN) and response surface methodology (RSM) in modeling and optimizing the process were evaluated using correlation coefficient (R), coefficient of determination (R2), and absolute average deviation (AAD). The highest oxalic acid production observed was 120.66 g/L under optimum values of a CAJ concentration of 291 g/L, pH of 6.9, time of 10.82 days, methanol concentration of 2.91% (v/v), and NaNO3 concentration of 1.05 g/L that were numerically predicted by the developed RSM quadratic model. Using the developed ANN model coupled with rotation inherit optimization, the highest oxalic acid production observed was 286.75 g/L under the following optimum values: CAJ of 291 g/L, pH of 6.5, time of 12.64 days, methanol concentration of 3.82% (v/v), and NaNO3 concentration of 2.41 g/L. The results showed that the ANN model (R = 0.9996, R2 = 0.9999, AAD = 0.21%) was better than the RSM model (R = 0.9986, R2 = 0.9973, AAD = 1.00%) for optimizing oxalic acid fermentation. The use of the ANN model led to a 2.4-fold increase in oxalic acid yield over the RSM model