Develop a model that will control the error to achieve stability using DTC and fuzzy logic with duty ratio.

Figure 3.1 Simulink model for direct torque control of induction motor.

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A Simulink model above was developed to study the performance of the conventional DTC and fuzzy controller for 4 poles induction motor to reduce the high ripple torque in the motor. After the field work experiment, the error of the torque, flux linkage and position of stator flux linkage were used in the simulation and the data generated are in table 3.1 below.
To determine the error in the torque of the induction motor that causes vibration which lead to backlash that result in the production of less standard products.
The errors in the magnetic torque of the motor were determined using the torque ripple test apparatus.
Because we want to know the actual error in the induction motor that causes the high ripple torque in the motor.

Figure3.2Torque ripple test apparatus
A motor with torque ripple of 0.9N-m was connected to the shaft of the motor and with a load torque sensor that can measure the vibration or ripple of the shaft and will equally give the vibrational result of the motor then a DC voltage was supplied to the motor and observed a peak to peak torque equal to 0.9N-m. The formula for torque ripple calculation was used.
Tr = Torque ripple
Peak to peak value of the ripple = 0.9Nm, 0.15Nm
Average output of the ripple = 0.15Nm
In table 3.1 below, actual torque equal 0.15N-m, measured torque equal to 0.9N-m, error in torque is equal to 0.75N-m.
Tr = Peak – to -peak x 100
Average output

Tr = (0.9 – 0.15) x 100 = 5 ÷ 0.15 = 13.33%

To determine the stator flux linkage error in the induction motor that also causes vibration.
The errors in the stator flux linkage of the motor were determined.
To help us to know the actual flux linkage error that contributed to the high ripple torque in the motor.

Figure 3.3Stator motor
Themotor was dismantled and the flux meter was used to determine the coils in the slots of the stator of the motor, when the flux meter probe that have indicator at the end where it will indicate the amount of flux linkage at any instant were placed on top of the coil in the slot, it will indicate the amount of flux linkages.
At the end of the whole slot, we got approximately 170wb while the standard value is 150wb, as stated in table 3.1 below.
To determine the position of the stator flux linkage space vector in the poles of the induction motor.
The positions of the stator flux linkage space vector were determined.
Because we want to know the position of the flux linkage in the different poles of the induction motor.
In figure 2 above, the flux meter was used to measure the flux linkages in the different poles of the electric motor, in order to know the position of the flux linkage space vector of the motor. With the measurement, we observed that the flux linkage is varies per poles in the table 3.1 below.
Table 3.1 Result obtained after the analysis
Actual value Measured value Error
Torque 0.15Nm 0.9 Nm 0.75 Nm
Flux linkage 150wb 170wb 10wb
Position of the flux linkage 0.5? 5? 4.95?

Figure 3.4 Simulink model for fuzzy logic with duty ratio of induction motor.

The Simulink model were simulated and the result are in the table 4.1, 4.2, and 4.3, below.
Direct torque control (DTC) and fuzzy logic with duty ratio model were designed.
Because we want to control the induction motor drives in order to reduce the high ripple torque of the motor.
In the principles of direct torque control of induction motor, the ripples in the motor can be reduced if the errors of the torque and the flux linkage and the angular region of the flux linkage are sub-divided into several smaller sub-section then the errors should be pick and compared in other to select voltage vector with less ripples, in doing so, a more accurate voltage vector is being selected in the switching of the system hence the torque and flux linkage errors were reduced.
In the conventional DTC a voltage vector is applied for the entire switching period, and this causes the stator current and electromagnetic torque to increase over the whole switching period. Thus for small errors, the electromagnetic torque exceeds its reference value early during the switching period, and continues to increase, causing a high torque ripple. This is then followed by switching cycles in which the zero switching vectors are applied in order to reduce the electromagnetic torque to its reference value.
The ripple in the torque and flux can be minimize by applying the selected inverter vector for a complete switching period, as in the conventional DTC induction motor drive, but only for a part of the switching period. The time for which a non-zero voltage vector has to be applied is selected just to increase the electromagnetic torque to its reference value and the zero voltage vector was applied for the rest of the switching period.
During the application of the zero voltage vector, no power was consumed by the machine, and thus the electromagnetic flux is almost constant, it was only decreases slightly. The average input DC voltage to the motor during the application of each switching vector was ?Vdc. By adjusting the duty ratio between zero and one, it is possible to apply voltage to the motor with an average value between 0 and Vdc during each switching period. Thus, the
Torque ripple will be low compared to when the full DC link voltage was applying for the complete switching period. This increases the demand of the voltage vector, without an increase in the number of semiconductor switches in the inverter.
The duty ratio of each switching period is a non-linear function of the
Electromagnetic torque error, stator flux-linkage error, and the position of the stator flux linkage space vector. Therefore, by using a fuzzy-logic-based DTC system, it is possible to perform fuzzy-logic-based duty-ratio control, where the duty ratio is determined during each switching cycle. In such a fuzzy logic system, there are three inputs, the electromagnetic torque error, the stator flux-linkage space vector position (??) within each sector assigned with the voltage vectors and the flux error where the output of the fuzzy-logic controller is equal to the value of duty ratio.
There are various types of fuzzy logic controller for this particular application. A Mamdani-type fuzzy logic controller, which contains a rule base, a fuzzifier, and a defuzzifier, is selected. Fuzzification is performed using membership function. The inputs and the output of the fuzzy controller are assigned Gausian membership functions. The universe of discourse for the torque error and the duty ratio is varied using simulations to get acceptable torque ripple reduction.
The attention in the fuzzy rule is to reduce the torque ripple. Generally the duty ratio is proportional to the torque error, since the torque rate of change is proportional to the angle between the stator flux and the applied voltage vector, the duty ratio depends on the position of the flux within each sector. The use of two fuzzy sets is the fact that when the stator flux is greater than its reference value a voltage vector that advance the stator flux vector by two sectors is applied which result in a higher rate of change for the torque compared to the application of a voltage vector that advance the stator flux vector by one sector when the stator flux linkage is lower than its reference value.
The duty ratio is selected proportional to the magnitude of the torque error so that if the torque error is Small, Medium or Large THEN the duty ratio is Small, Medium orLarge respectively. The fuzzy rules are then adjusted to reflect the effects of the flux error, torque error and position of the space vector error. If the torque error is medium and the stator flux lies in sector with magnitude greater than its reference value then the voltage vector Vk+2 is selected. If the flux position is small, that means there is a large angle between the flux and the selected voltage vector that makes the selected vector more effective in increasing the torque so that the duty ratio is set as small rather than medium, the fuzzy rule is stated as IF (torque error is medium) AND (flux position is small) THEN (duty ratio is small)IF (torque error is large) AND (flux position is small) THEN (duty ratio is medium).
Using the above reasoning and simulation to find the fuzzy rules, the two sets of fuzzy rules are summarized in Table 3.2 below.

Table 3.2 Rules for the duty ratio fuzzy controller
Flux Torque error dT=k1 Small Medium Large
d?=0 Small Small Small Medium
Large Small Medium Large
?d=1 Small Small Medium Large
Medium Small Medium Large
Large Medium Large Large

Fuzzy logic toolbox was used in the implementation of the duty ratio fuzzy controller. The Graphic User Interface included in the toolbox was used to edit the membership functions for the inputs (the torque error and the flux position),the output (the duty ratio). The membership functions and the fuzzy rules were adjusted using the simulation until an acceptable torque ripple reduction was achieved.
Simulate the model above in the Simulink environment and validate the result.

The model that will reduced the high ripple torque in the induction motor were developed.
To enable us study the performance of the conventional direct torque control and fuzzy logic with duty ratio controller for four (4) pole induction motor torque control and also to simulate for the same and verified for the purpose of reducing the high torque ripple in the induction motor drive.
The motor parameters

Definition of terms
Pa = Active power per phase
Qa = Phase reactive power
Ia = Phase current
Va = phase voltage
Rs = Stator winding resistance
Rr = Rotor winding resistance
Lm = Magnetizing inductance per phase
Xis = Stator leakage reactance
Lis = Stator inductance per phase
Xir = Rotor leakage reactance
Lir = Rotor leakage inductance per phase
Dc = Direct current
Rdc = Resistance in direct current
X = Reactance
Xm = Magnetizing reactance
Xn = Total reactance

The motor is a three phase 158-W, 240-V induction motor (Model 295 Bodine Electric Co.)
The motor is Y-connected with no access to the neutral point.
DC Resistance Test:
To determine R1;
Connect any two stator leads to a variable voltage DC power supply.
Adjust the power supply to provide rated stator current.
Determine the resistance from the voltmeter and ammeter readings.
As shown in figure 3.7, a DC voltage VDC is applied so that the current IDC is close to the motor rating.
Because the machine is Y-connected: RS = Rdc/2 = (VDC/IDC)/2.
From measurement, VDC = 30.6V, IDC = 1.05A.
RS = RDC = (31.5/1.04) = 15.14?/phase.
2 2

Figure 3.7 Circuit for DC resistance test.
To determine X1 and X2
Determine R2 when combined with data from the DC test.
Block the rotor so that it will not turn.
Connect to a variable voltage supply and adjust until the blocked – rotor current is equal to the rated current.
To determine the magnetizing reactance, Xm and combined core, friction, and wind age losses.
Connect as in block rotor test below.
The rotor is unblocked and allowed to run unloaded at rated voltage and frequency.
The set up for no load test and blocked rotor test is shown in the figure below:

Figure 3.8 Circuit for no load and locked rotor test.
With the motor running at no load, measure V, I and P to find the machine reactance Xn =Xis+Xm
Table 4.3 Measured value
Frequency (Hz) 50
Voltage (V) 230
Current (A) 1.32
Real power (W) 158

At no load the per-unit slip is approximately zero, hence the equivalent circuit is as shown in figure 3.9 below.

Figure 3.9 Equivalent circuit of three phase induction motor under no load test.
The real power P represents,
Hysteresis and Eddy current losses (core losses)
Friction and wind age losses (rotational losses)
Copper losses in stator and rotor (usually small as no load)
Phase voltage:
Va =V = 220 = 132V
?3 ?3
Phase current:
la = 1.32A
Phase real power:
Pa = Pa/3 = 138.2 ÷3 = 46.1W
Phase reactive power:
Q_a = ??(VaIa)2-P2a= ?(((137 x 1.32)2)-(46.1)2)=174.86VAr?_
Xn = Qa=174.86 =100.36?
I2a 1.322
Since S ~ 0,
Xn~ Xls +Xm
3. Locked rotor test
With the rotor locked, the rotor speed is zero and per- unit slip is equal to unity. The equivalent circuit is as shown in Figure 3.10 or Figure 3.11.

Figure 3.10 Equivalent circuit of three phase induction motor under locked rotor test.

Figure 3.11 Simplified equivalent circuit of three phase induction motor under locked rotor rest.

Table 4.4 the tested value
Frequency (Hz) 50
Voltage (V) 68.52
Current (A) 1.3
Real power (W) 105.33

Phase voltage:
Va =V = 68.52 = 39.56V
?3 ?3
Phase current:
la = 1.3A
Active power per phase
Pa = P = 105.33=35.1W
3 3
Reactive power phase
Q_a = ??(VaIa)2-P^2 a= ?(((35.56 x1.3)2)-(35.1)2)=30.08VAr?_

For a class C motor.
Xls = 0.3 x Qa= 0.3 x 30.08 = 5.34?
I2a 1.32
Xlr = 0.7 xQa = 0.7 x 30.08 = 12.46?
12a 1.32
From the no – load test, Xn = 100.36?, so
Xm = Xn – Xls = 100.36 – 5.34 = 95.02?
R = Pa = 35.1 = 20.77?
12a 1.32
From figures 3.11,
R2 = R – Ris= 20.77 – 5.34 = 1 5.43?
Comparing figures 3.10 and 3.11,
R2 + jX2 = (Rr + jXir) x jXm
(Rr + jXir) + jXm
R2 =Rr X2m
Rr + (Xlr + Xm)2
Rr = R2 x (Xir + Xm)2 = 15.43 x (12.46 + 95.02)2 = 19.74?
Xm 95.02
Stator winding resistance Rs = 15.14?/phase
Rotor winding resistance Rr = 19.74?/phase
Magnetizing reactance Xm = 95.02?/phase
The magnetizing inductance per phase is
Lm = Xm = 95.02 = 0.3024H
2?f 2? x 50
Stator leakage reactance Xls= 5.34?/phase
The stator inductance per phase is
Lls = Xls= 5.34 = 0.0169H.
2?f 2nx50
Rotor leakage reactance Xlr = 12.46?/phase,
The rotor leakage inductance per phase is
Llr = Xlr =12.46 = 0.0396H.
2?f 2?x50
Table 4.5: Motor parameters
Rated voltage 240V
Maximum torque 1.5N-m
Poles 4
Rated speed 1440rpm
Stator resistance 15.14?
Rotor resistance 19.74?
Stator leakage inductance 0.0169H
Rotor leakage inductance 0.0396H
Mutual inductance 0.3024H

MATLAB fuzzy logic tool box was used in the implementation of the duty ratio fuzzy controller. The graphic user interface included in the tool box was used to edit the membership functions for the inputs (the torque error and the flux position), the output (the duty ratio). A Mamdani type fuzzy inference engine was used in the simulation. The membership functions and the fuzzy rules were adjusted using the simulation until a particular torque ripple reduction was achieved.
To know the performance of the duty ratio controller, the simulation was run at switching frequency of 5KHz. The difference between the conventional DTC and DTC with duty ratio fuzzy control was clearly realized by monitoring the switching behavior of the stator voltage and the electric torque. The selected voltage vector is applied for the complete sampling period and the torque keeps increasing for the complete period, then a zero voltage is applied and the torque keeps decreasing for the complete sampling period and these results in high torque ripple.
The selected voltage vector is applied for part of the sampling period and removed for the rest of the period. As a result, the electric torque increases for part of the sampling period and then starts to decrease. By adjustment of the duty ratio, the desired average torque may be continuously maintained. The duty ratio controller smoothly adjusts the average stator voltage.


Othello’s clown comes out and insults the musicians by comparing the noise from their instruments to farts since their using Wind instruments.The clown then tells the players Othello loves their music so much he wants them to stop playing.However, if they have any music that can’t be heard, they’re more than welcome to play that kind of music. The musicians say they don’t have that type of music, so he tells them to leave.
Cassio is falling right into the hands of Iago because he’s so determined to make his case to Othello by getting Desdemona to vouch for him. Iago promises to get Othello away from Desdemona so Cassio can speak with her. Cassio then says, “I never knew A Florentine more kind and honest.” Emilia tells Cassio that it looks like everything is going to be good with Othello and him, but Instead of Cassio taking her word, he says that he wants her to help him set up a private meeting so he can talk to Desdemona face to face.
Othello tells Iago to go and deliver his letters to the Senate. Othello wants to send his regards to the Senate. The purpose of this scene is to get Othello away from Desdemona, so Cassio can talk to her privately.
1. Desdemona is falling right into the web of Iago’s plans, because she promises Cassio that she’ll not rest until she has persuaded Othello to make Cassio his lieutenant once again. She wants them to start over and become friends once again. Desdemona tells Cassio that she would rather die than give up on fixing the tragic situation between Othello and Cassio
2.Othello says, “Excellent wretch! Perdition catch my soul But I do love thee! And when I love thee not, Chaos is come again..” Othello is saying that he loves Desdemona so much and that life without her would be chaotic.
3. Iago compares jealousy to a green-eyed monster. He says,”O, beware, my lord, of jealousy! It is the green-eyed monster which doth mock the meat it feeds on”. It reveals that the man who is jealous starts to make conclusions based on mere suspicion and will believe anything that will fuel those suspicions..
4. Othello says that if he does find that Desdemona is wild (haggard), then, by the strings that tie her to his heart, he would release her. Othello then concludes that Desdemona is lost to him, and the only way to relieve himself from his sorrows is by hating her. Right after he says this though, Emilia and Desdemona come in. Othello’s mind completely changes and he comes to his senses again. He says that he can’t believe that his wife would cheat on him and that he would need evidence to believe otherwise.
5. The scene starts with Desdemona noticing that her husband doesn’t seem well, and she tries to make him feel better by offering to bind his head with her handkerchief. Othello tells her that the handkerchief is too small and he pushes it away. It ends up dropping to the floor and they both don’t notice. Iago wants to put the handkerchief in Cassio’s room, so he can fuel Othello’s suspicions that Desdemona is in fact cheating on him with Cassio.
6.Othello’s allusion to Diana’s visage shows that he has lost faith in himself. He’s alluding to the fact that Desdemona’s name was pure like Diana, but now its black like his skin from all the “sins” that she has “committed”.

1. Othello asks Desdemona for her hand, and notices that it’s moist. This leads him to believe that she indeed has been unfaithful. He then says that her hand is “frank,” implying that she is too open. Othello is saying that she’s willing to give her hand to anyone. Desdemona gets fed up of talking about her hand and she changes the subject to remind Othello that he promised to see Cassio. However, this was a very bad move, because it only raises Othello’s suspicions and enrages him even more. This shows us that Othello is now indeed very jealous and that he’s not in control of himself anymore.
2. Othello tells Desdemona the story of the handkerchief. He says It’s a family heirloom that is very sacred. He said that a psychic told his mom that, as long as she had the handkerchief, Othello’s dad would love her, but, if she lost it or gave it away, Othello’s dad would lose his love for her and he would start to cheat on her. Othello’s mom gave him the handkerchief before she died, and she told him to give it to the woman he marries. Othello then says that losing the handkerchief would be very terrible.
Othello believes that the handkerchief has magical powers and this tells us that he indeed does believe in superstitions.
3. Emilia starts off by saying, “Is not this man jealous?” Desdemona says that she has never seen her husband act like this, and that she doesn’t know what’s going on. Emilia then says that this isn’t surprising and that she shouldn’t worry, since women are basically like food to men.

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3. The Working principle of Electronic Weight Scale
Electronic weight scales usually display a number on the LCD and equipped with electronic measuring components. The electronic weighing scales operate based on the following principle that is the force exerted by the load situated on the balance pan is transmitted to the load cell which in turn emits an electric signal whose intensity is proportional with the force. The electrical signal is picked up by the electronic balance block, processed , amplified and transmitted to a digital display system that is digital mass indicator, the result show will representing the weight of the mass located on the load pan. A very common solution is to use strain gauges (strain-sensitive transducers). The strain gauge is a passive transducer that use ` electrical resistance variation ` in wire to sense the strain produced by a force on a wire. These commercial weighing devices with low resolution were generally used. The strain gauges are wired as a Wheatstone-bridge to balance for temperature changes . When the pan is not loaded by any object, all four resistors are the same and the input of the amplifier will be zero. When an object is placed on the pan resistor 1 and resistor 4 are compacted and their resistance will decrease , resistor 2 and resistor 3 are strained and their resistance is increased. This because a voltage difference at the input of the amplifier, proportional to the weight of the object. The strain gauges are wired as a `Wheatstone-bridge` to compensate for temperature changes . The figure below shows the diagram of an electronic weight scale with a strain gauge.

1 : Spring body
2 : Weighing pan
3 : Mounting plate
4 : Placing and wiring of the strain gauges
Some of the advantages of electronic/digital weight scale are the plain fact that digital scales are considerably effortless to read. Besides, the electronic weight scale will give more precision of reading value. Here are some disadvantages to electronic/digital scales, most likely the very regular one being incorrect readings. Several people have criticised that when they stand on the scale at one spot and obtain a reading and when they get back after a few seconds they get a different reading altogether. Commonly , digital scales are good but it will be worn after a regular used. This instrument needs to calibrate after years to get better accuracy.

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3.1. Screening and identification of bacterial producing the methanol-tolerant lipase

Lipase producing bacteria were screened in enrichment culture medium supplemented with olive oil as a sole source of carbon. Furthermore, methanol (30%, v/v) was also used to acquire the methanol tolerant lipase. The clear area around the colonies on the tributyrin agar plate was evaluated as lipase production. The greatest lipolytic strains were also examined on the olive oil plate complemented with phenol red, as a pH indicator. Results showed that MG isolate was a strain which displayed the maximum pink halo around the colony. The 16S rDNA gene of MG isolate was amplified and sequenced (Genbank Accession No. MF927590.1) and compared by BLAST investigation to other bacteria in the NCBI database. The results proposed a near relationship between MG40 isolate and the other members of the Enterobacter genus with a maximum sequence homology (99%) to Enterobacter cloacae. The phylogenetic tree (Fig. 1) designated that the strain MG was associated with Enterobacter species and used for following study.

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3.2. Purification and immobilization of the lipase
Cell free supernatant of MG stain was exposed to ammonium sulfate precipitation (85% saturation) and Q-sepharose chromatography. Lipase MG was eluted from the Q-Sepharose column with a 19.5-fold purification and a 38.1 % yield, and it displayed a specific activity of 442.6 U/mg. This yield of MG lipase was analogous to that of lipase from S. maltophilia CGMCC 4254 (33.90%) (Li et al., 2013) and lower than lipase from P. aeruginosa PseA (51.6%) (Gaur et al., 2008), but higher than lipase from B. licheniformis strain SCD11501 (8.4 %) (Sharma and Kanwar, 2017). SDS–PAGE analysis of the purified MG40 lipase shown that it has a single band about 33 kDa, which it is different from the other Enterobacter cloacae.
Results of protein measurement with Bradford technique displayed that protein loading on these coated magnetite nanomaterials was succeeded. Moreover, the results of quantitative determination of protein loading on these nanomaterials shown that, immobilization efficiency was achieved about 73%. mGO-CLEAs lipase were dispersed in phosphate buffer. After a magnet was positioned sidewise, mGO-CLEAs Lipase showed fast response (60 seconds) to the peripheral magnetic field. It means that the magnetic CLEAs-Lip particles were shown suitable magnetic concern even though layers of CLEAs-Lipase were covered on their surfaces, in which it is significant in term of lipase immobilization.

3.3. Analytical characterization
Lipase MG40 was immobilized on the surface of magnetic functionalized graphene oxide, in which aldehyde groups of glutaraldehyde making linkage between amine of lipase and amino coated magnetite nanomaterials (Xie and Huang, 2018). Fig. 2a and b display SEM images of magnetic functionalized graphene oxide and mCLEAs-Lipase on magnetic graphene oxide, respectively. The SEM analysis of graphene oxide on Fig. 2a shown an irregular circular structure which was similar to the previous reports (Wang et al. 2015; Dwivedee et al. 2017), providing a bulky specific surface zone of the nanomaterials. Results of SEM image in Fig. 2b shown that lipase immobilization seem to reduce the construction of stacked GO structures. These results designated that the glutaraldehyde linkage successfully have been occurred between the amine surface of magnetic functionalized graphene oxide and amino groups of lipase.
Elemental EDX investigation from particular part of SEM image of magnetic CLEAs-Lipase for elemental plotting obviously specifies the existence of associated atoms of support including C, N, O, Si, P, S and Fe which displays the effective functionalization of APTES, particularly by noticing Si atom (Heidarizadeh et al., 2017). Furthermore, the remarkable attendance of phosphorous atom can intensely endorse the effective lipase immobilization (Fig. 3).
Presence of functional groups on surface of graphene and immobilization of lipase MG10 onto these nanoparticles were investigated by FTIR spectroscopy. FTIR spectra of graphene oxide (A), magnetic functionalized graphene oxide (B) and magnetic functionalized graphene oxide-CLEA lipase (C) have been shown in Fig. 4. The peak around 532-614 cm?1 could be evaluated to the stretching vibration of Fe–O in Fe3O4 nanoparticles (Fig. 5B, C), indicating the presence of Fe3O4 in the graphene oxide which directed that the preparation of Fe3O4-graphene oxide nanoparticles was effective (Thangaraj et al., 2016; Xie and Huang, 2018).
Moreover, peaks at 1635 and 1636 cm?1 resemble C=O vibrations of the present carbonyl and carboxyl groups on the mGO and presence of amide link between glutaraldehyde with Fe3O4 nanoparticles and CLEAs (Cui et al., 2015; Xie and Huang, 2018). Additionally, a characteristic adsorption band achieved at 3447 cm?1 equivalent to the adsorbed H2O and OH group on the mGO surface (Paludo N, 2015), which shown excessive absorbance in all of these nanoparticles and the magnetic functionalized graphene oxide-CLEA (Mehrasbi et al., 2017). FTIR spectrum of magnetic functionalized graphene oxide shows the presence of a peak in 2922 cm?1 spreads to aliphatic chain of functionalized APTES (Heidarizadeh, et al., 2017).
After lipase immobilization on the mGO (Fig. 5c), the 614 cm?1 band owing to the stretching vibration of Fe–O in Fe3O4 nanoparticles was practically vanished, which signifying the covering of Fe3O4 by lipase. Moreover, FTIR spectrum of magnetic functionalized graphene oxide-CLEA lipase also shown two absorption peaks at 2840 and 2922 cm?1 mentioning C-H stretching in -CH3 and -CH2-, which demonstrate the immobilization of enzyme on the support. In addition, the appearance two new FTIR absorption bands at 1404 and 1514 cm?1 because of the lipase were detected as associated with the mGO support, so indicating that the enzyme was covalently bound to the mGO composites via amide links.

3.4. Biochemical characterization of free and immobilized enzyme
3.4.1. Effect of temperature and pH on the lipase activity
As shown in Fig. 5A, the maximum activity of free and immobilized lipase was obtained at pH 8.0 and 9.0, respectively. Moreover, relative lipase activity of immobilized lipase was faintly lower than free enzyme in acidic pH, but slightly higher than in basic pH. Therefore, the immobilization process seems to expand the tolerance of the lipase in harsh basic conditions. Lipase activity in different temperatures was shown in Fig. 5B. The immobilized lipase showed a broad range of maximum temperature activity about 40-60 °C, compare to free enzyme. These results indicating the development of covalent links between protein and support, which may diminish conformational flexibility and result in preserve lid opening (Perez et al., 2011; Lu et al., 2009).
3.4.2. Thermal stability of free and immobilized lipase
Immobilization method is one of the most promising strategies to improve catalytic activity for the applied application. Consequently, to explore the thermal stability, free and immobilized enzyme were maintained in phosphate buffer (100 mM, pH 7.5) for 3h at 60 °C, and then the remaining activities were measured in the phosphate buffer (100 mM, pH 7.5) with pNPP as substrate. The lipase activity of both free and immobilized lipases was highest up to 45 min of incubation at 60 °C. The remaining activity of the free lipase is 50 % while the immobilized lipase reserved 85 % of its initial activity after 3h of incubation at 60 °C (Fig. 6a). These results evidently designate that the immobilization of lipases into mGO can avoid their conformation transition at high temperature, and improving their thermal stability.
3.4.3. Determination of Km and Vmax
Kinetic parameters of free and mGO-lipase were investigated by calculating initial reaction rates with different substrate concentrations. As shown in Fig. 6B and Table 1, Vmax values of mGO-CLEA-lipase was slightly higher than free enzyme about 0.1 µmol/min, which directed the rate of pNPP hydrolysis was not significantly changed after mGO-CLEAs-lipase preparation. The same results were also observed for magnetic CLEAs of the other enzyme. In the case of mGO-CLEAs-lipase, the detected lower Km value state a greater lipase affinity for the pNPP substrate, about 2.25 folds. It approves that conformational changes by reason of enzyme immobilization assistance the protein to appropriately turn its active site concerning the substrate (Aytar and Bakir, 2008; Sangeetha and Abraham, 2008; Talekar et al., 2012).

3.4.4. Reusability assay
Reusability of immobilized lipase preparation is a dominant factor for its commercial use in biotransformation reaction. The reusability of mGO-CLEAs lipase was measured up to 8 cycles. Enzyme activity of mGO-CLEAs lipase was the highest up to 5 cycles, but it continuously decrease over 5 cycles (Fig. 7a). Protein leaking was also investigated during reusability test of mGO-CLEAs lipase. Results exhibited no lipase activity was detected in reaction mixture up to 4 cycles of lipase reusability test. These results recommend that suitable cross-linking of enzyme and mGO nanomaterials produced stable MGO-CLEAs lipase (Talekar et al., 2012).
Storage stability of free and mGO-CLEAs lipase were also investigated by storing these enzyme in phosphate buffer at 4 °C and considering the lipase activity. Results displayed mGO-CLEAs-lipase retained about 75 % of its original activity after 30 days of incubation, in which free enzyme missed its initial activity at the same time (Fig. 7b). These results verified that mGO-CLEAs lipase had chief protection on the storage stability of lipase. These results designated that an active mGO-CLEAs lipase prevent protein leaking from mGO-CLEAs nanomaterials (Yong et al., 2008).

3.5. Biodiesel production from non-edible
Nowadays, non-edible oil resources as a favorable source for biodiesel synthesis have been admired for researchers. Ricinus communis is a small and fast-growing tree which is a highly productive and precocious maker of toxic seeds. In addition, it is very adjustable to diverse situations and has been broadly distributed. The highest biodiesel synthesis (26 %) from R. communis oil was gained at room temperature after 24 h of incubation by Entrobacter Lipase MG40 (10 mg) (Fig. 8). Mehrasbi and co-workers described using of free C. antarctica lipase B (100 mg) constructing 34% of biodiesel from waste cooking oil at 50 °C after 72 h of incubation (Mehrasbi et al., 2017). Some excellent properties of MG40 lipase such as methanol-tolerant, and short time transesterification make it capable as a latent enzyme for biodiesel synthesis from non-edible oils.
Remarkably, mGO-CLEAs lipase formed the highest biodiesel construction (78 %) from R. communis oil after 24 h (Fig. 5). Furthermore, the immobilized MG40 lipase improved biodiesel construction from R. communis oil about 3.1 folds at diverse time of incubation, compare to free lipase (Fig. 5). De los Ríos reported 42% of biodiesel production by using immobilized lipase of C. antarctica (De los Ríos et al., 2011).
As mentioned formerly, construction of several links between lipase and support, could reserve protein in open conformation and improved the enzyme rigidity with affiliated making of a protected micro-environment. Furthermore, it made a further active lipase cross-linking in mCLEAs lipase which evades enzyme leaking from composite and shield it against methanol solvent and the other by products (Talekar et al., 2012; Aytar and Bakir, 2008; Sangeetha and Abraham, 2008).

4. Conclusion
Lipase MG40 is a high potent lipase (thermostable, inducible, high methanol-tolerant, and short time reaction rate) which was isolated from local oil contaminated soils. Entrobacter lipase MG40 was immobilized on the magnetic graphene oxide nanocomposites. This nanobiocatalyst was characterized and employed for the production of biodiesel from non-edible oil feedstocks such as R. communis oil. The immobilization of lipase significantly increased the storage stability, the thermal stability and the reusability of the enzyme. Remarkably, lipase nanocomposite showed a shift to low temperatures and acidic pH, which is excellent properties for biodiesel production. Lipase-graphene nanocomposite was totally active after 5 cycle of enzyme activity. Biodiesel production was also achieved by 75% recovery from oil feedstock which would have potential in green and clean production processes.


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