Abstract: Bioremediation technology is now used for treatment instead of traditional metal removal methods. A strain was isolated from Marsa Alam, Red sea, Egypt showed high resistance to high lead concentration and was identified by the 16S rRNA gene sequencing technique as Halomonas sp. ES015. Medium optimization was carried out using Plackett-Burman design, and the most significant factors were yeast extract, casamino acid and inoculums size. The optimized media obtained by the statistical design raised the removal efficiency from 84% to 99% from initial concentration 250 ppm of lead. Moreover, Box-Behnken experimental design was applied to study the relationship between yeast extract concentration, casamino acid concentration and inoculums size. The optimized medium increased removal efficiency to 97% from initial concentration 500 ppm of lead. Immobilized Halomonas sp. ES015 cells on sponge cubes, using optimized medium in loop bioremediation column, showed relatively constant lead removal efficiency when reused six successive cycles over the range of time interval. Also metal removal efficiency was not affected by flow rate changes. Finally, the results of this research refer to the possibility of lead bioremediation by free or immobilized cells of Halomonas sp. ES015. Also, bioremediation can be done in batch cultures and semicontinuous cultures using column technology.
Abstract: It is practically significant to research the traffic flow of intersection because the capacity of intersection affects the efficiency of highway network directly. This paper analyzes the traffic conditions of an intersection in certain urban by the methods of queuing theory and statistical experiment, sets up a corresponding mathematical model and compares it with the actual values. The result shows that queuing theory is applied in the study of intersection traffic flow and it can provide references for the other similar designs.
Abstract: Immobilization of lipase enzyme produced from palm oil mill effluent (POME) by the activated carbon (AC) among the low cost support materials was optimized. The results indicated that immobilization of 94% was achieved by AC as the most suitable support material. A sequential optimization strategy based on a statistical experimental design, including one-factor-at-a-time (OFAT) method was used to determine the equilibrium time. Three components influencing lipase immobilization were optimized by the response surface methodology (RSM) based on the face-centered central composite design (FCCCD). On the statistical analysis of the results, the optimum enzyme concentration loading, agitation rate and carbon active dosage were found to be 30 U/ml, 300 rpm and 8 g/L respectively, with a maximum immobilization activity of 3732.9 U/g-AC after 2 hrs of immobilization. Analysis of variance (ANOVA) showed a high regression coefficient (R2) of 0.999, which indicated a satisfactory fit of the model with the experimental data. The parameters were statistically significant at p
Abstract: Pretreatment is an essential step in the conversion of
lignocellulosic biomass to fermentable sugar that used for biobutanol
production. Among pretreatment processes, microwave is considered
to improve pretreatment efficiency due to its high heating efficiency,
easy operation, and easily to combine with chemical reaction. The
main objectives of this work are to investigate the feasibility of
microwave pretreatment to enhance enzymatic hydrolysis of
corncobs and to determine the optimal conditions using response
surface methodology. Corncobs were pretreated via two-stage
pretreatment in dilute sodium hydroxide (2 %) followed by dilute
sulfuric acid 1 %. Pretreated corncobs were subjected to enzymatic
hydrolysis to produce reducing sugar. Statistical experimental design
was used to optimize pretreatment parameters including temperature,
residence time and solid-to-liquid ratio to achieve the highest amount
of glucose. The results revealed that solid-to-liquid ratio and
temperature had a significant effect on the amount of glucose.
Abstract: This paper presents an approach for the determination of the optimal cutting parameters (spindle speed, feed rate, depth of cut and engagement) leading to minimum surface roughness in face milling of high silicon stainless steel by coupling neural network (NN) and Electromagnetism-like Algorithm (EM). In this regard, the advantages of statistical experimental design technique, experimental measurements, artificial neural network, and Electromagnetism-like optimization method are exploited in an integrated manner. To this end, numerous experiments on this stainless steel were conducted to obtain surface roughness values. A predictive model for surface roughness is created by using a back propogation neural network, then the optimization problem was solved by using EM optimization. Additional experiments were performed to validate optimum surface roughness value predicted by EM algorithm. It is clearly seen that a good agreement is observed between the predicted values by EM coupled with feed forward neural network and experimental measurements. The obtained results show that the EM algorithm coupled with back propogation neural network is an efficient and accurate method in approaching the global minimum of surface roughness in face milling.
Abstract: Response surface methodology was used for
quantitative investigation of water and solids transfer during osmotic
dehydration of beetroot in aqueous solution of salt. Effects of
temperature (25 – 45oC), processing time (30–150 min), salt
concentration (5–25%, w/w) and solution to sample ratio (5:1 – 25:1)
on osmotic dehydration of beetroot were estimated. Quadratic
regression equations describing the effects of these factors on the
water loss and solids gain were developed. It was found that effects
of temperature and salt concentrations were more significant on the
water loss than the effects of processing time and solution to sample
ratio. As for solids gain processing time and salt concentration were
the most significant factors. The osmotic dehydration process was
optimized for water loss, solute gain, and weight reduction. The
optimum conditions were found to be: temperature – 35oC,
processing time – 90 min, salt concentration – 14.31% and solution
to sample ratio 8.5:1. At these optimum values, water loss, solid gain
and weight reduction were found to be 30.86 (g/100 g initial sample),
9.43 (g/100 g initial sample) and 21.43 (g/100 g initial sample)
respectively.
Abstract: Direct fermentation of 226 white rose tapioca stem to
ethanol by Fusarium oxysporum was studied in a batch reactor.
Fermentation of ethanol can be achieved by sequential pretreatment
using dilute acid and dilute alkali solutions using 100 mesh tapioca
stem particles. The quantitative effects of substrate concentration, pH
and temperature on ethanol concentration were optimized using a full
factorial central composite design experiment. The optimum process
conditions were then obtained using response surface methodology.
The quadratic model indicated that substrate concentration of 33g/l,
pH 5.52 and a temperature of 30.13oC were found to be optimum for
maximum ethanol concentration of 8.64g/l. The predicted optimum
process conditions obtained using response surface methodology was
verified through confirmatory experiments. Leudeking-piret model
was used to study the product formation kinetics for the production
of ethanol and the model parameters were evaluated using
experimental data.
Abstract: In this work, statistical experimental design was
applied for the optimization of medium constituents for Gentamicin
production by Micromsonospora echinospora subs pallida (MTCC
708) in a batch reactor and the results are compared with the ANN
predicted values. By central composite design, 50 experiments are
carried out for five test variables: Starch, Soya bean meal, K2HPO4,
CaCO3 and FeSO4. The optimum condition was found to be: Starch
(8.9,g/L), Soya bean meal (3.3 g/L), K2HPO4 (0.8 g/L), CaCO3 (4
g/L) and FeSO4 (0.03 g/L). At these optimized conditions, the yield
of gentamicin was found to be 1020 mg/L. The R2 values were found
to be 1 for ANN training set, 0.9953 for ANN test set, and 0.9286 for
RSM.