2.1.1 Description of Project
Sugarcane Juice is a Marketing concept that deals in product. Our product is tetra pack packed sugarcane juice. Sugarcane juice is extensively consumed in summer season due its delectable flavor and low price. But the issue is that it can’t be preserved for prolonged period. In Pakistan no company has introduced sugar cane juice up to now in tetra pack. The methodology employed in Pakistan is kind of old and so many people don’t plump for this sector because of low shelf life threat. Therefore we have chosen to take a shot at sugar cane juice since sugar cane juice is naturally sweet, so it can be sold with a bit extra treatment.
2.1.1 Product Sketch: Sugarcane is the most imperative member of the plant kingdom with a metabolism leading to the accumulation of sucrose. It is transported as glucose and fructose inside the developing plant. The crop gives the least expensive of nourishing and health-giving food.
The sugarcane belongs to the monocot family. It is a perpetual plant which grows from 2.5 to 4.25 meters. With adequate care, it grows up to 7.5 meters. The measurement of steins differs from 2.5 to 8 cm. It has a few joints after each couple of centimeters.
2.1.2 Origin and Distribution: The names sugar and sugarcane have been gotten from the Sanskrit word, Shankar. Sugarcane is native to Pakistan. It was developed here from the old times. Sugarcane is presently cultivated everywhere throughout the globe. Pakistan stands fourth in sugarcane growth, trailed by Brazil, India, Cuba, China, Mexico, the U.S.A., South Africa and Columbia.
2.1.3 Food Value: The juice is extracted from the stems by squeezing it through iron rollers. It is nutritious and refreshing. It includes about 15 per cent natural sugar and is loaded with organic salts and vitamins.
In the beginning, sugarcane was cultivated exclusively to chew in the pacific and South Eastern Asia, a custom which has now spread all through the vast majority of the tropics. The juice can likewise be utilized for drinking or sweetening. In sweltering summer days, it becomes a relieving drink. A little lime juice might be blended in the juice to enhance its flavor. Food value of sugarcane juice is as per the following:
Moisture 90.2%
Calcium 10 mg
Protein -0.1%
Phosphorus 10 mg
Fat 0.2%
Iron 1.1 mg
Minerals 0.4%
Carbohydrates 9.1%

2.1.2 Business Potential
We have more clear and exclusive information about the market and we also know the customer demand and choices. In view of our Research we are strictly determined to serve consumer needs and wants with an absolutely new taste of juice like “Sugarcane Juice”. We are going to market our new product for five groups of people. They are demonstrated graphically below:
Our immediate geographic market will be Karachi city with a populace of around 21.2 million.

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Male & Female
Generation – kid, young, matured people, old people and jaundice patients. We know the following about the profile of the typical resident of Karachi:
22% children
18% mature
38% young
20% Jaundice Patients
2% of the old people
Social class: Middle Class, Upper Middle Class, and Upper Class.
Life style: Achiever
Benefits: Quality
User status: Potential client, first-time client, and consistent client.
2.1.3 Swot Analysis
1) Strengths:
• We have presented sugarcane juice in tetra pack for the very first time. So it is beneficial for us to have more consumer than other new competitors because the time they take to enter the market the buyer will become loyal to our product.
• Sugar cane produces good quality products. It cannot settle with low quality.
• We have introduced it the first time as there is no any sugarcane juice in tetra pack obtainable in market.
User status: Potential user, first-time user, and regular user
2) Weaknesses:
• Sugarcane juices cannot be store for long time.
• Customers are brand loyal toward competitor’s product so we have to persuade them. As there are already competitors who are dealing in juices and beverages, so it is not easy to shift them from other juices.
3) Opportunities
• Healthy organic and natural drinks oriented global and local culture.
• Limited alternative in regionally produced natural fruit juices.
• High trade potential.
• With excellent pre harvest planning, which is around half of the aggregate generation, could be transformed into potential business opportunity.
• Reduction in excise and import duties on food processing machinery.
4 Threats
• We confront trouble if government forces taxes on them which compel them to increase the rate of their product.
• There is massive competition in juices market.
• There are no many entry obstacles so a large numbers of local juices enter in juices market.
• Inaccessibility of necessary industries statistics.
• Single Product Company.
• High processing and packaging cost.
2.1.4 Marketing Strategy
1) Objectives:
We have set aggressive but accomplishable objectives for the first and second years of market entry.
? First-year Objectives: We are aiming for a 3 percent share of the Pakistan juice market through unit sales volume of 200,000.
? Second-year Objectives: Our second year objective is to reach break-even on the sugar cane juice and launch our new product.
2) Target Customers:
Our potential client will be all sorts of people. But we will fragment our market based on their need and properties towards safe product. Our client will be:
? Students at each level from school to universities.
? Employees who works whole day.
? Households and travelers. In summer session each sort of individuals with no age restrains utilizes this juice. Because of unhygienic products greater part of the general population avoids this. We will exceptionally focus on those sorts of people.
3) Positioning:
Brand strategy is at the core of marketing scheme. It is the demonstration of outlining the organization offer and image with the goal that it involves an esteemed place in the targeted consumer mind. Sugarcane juice is exceptionally useful for health and gives refreshment. As we are launching sugar cane squeeze in tetra pack, so we need to make positive and durable picture in the brains of clients that we are giving hygienic juice since we tend to introduce new flavors like mint and ginger etc.
Positioning Statement:
For every individual, who desires a 100% natural product that offers rejuvenation and freshness, our Sugarcane juice is a packaged drink that offers energy and refreshment at reasonable cost, best quality and value for your money.
Point of parity: Different flavor Tetra packing
Point of difference: 100% pure juice
4) Impact
It is quite a new idea which has not been yet commercialized due to the lengthy and uncertain methods of preserving this juice This juice is require not to be advertised so much since it is new thing which individuals will attempt and love a great deal. Simply we can place it on college cafeteria, school cafeteria, office cafeteria, grocery shops, the main thing we should do is to guarantee its timeframe of realistic usability and long preserving time.
2.1.5 Marketing Mix
Sugarcane Juice marketing mix is comprised of the following approaches:
Promotion product, price, place, marketing communication, marketing research(R ; D) and customer service.
1) Promotion:
The marketing of the product i.e. the advertisement, sales promotion and other promotional tool scan alter the purchasing conduct because some of the individuals greatly inspired by the publicity of the product. We have decided to use integrated marketing communication in which there will be mix of different marketing promotional tools that will pass on clear and stable idea of our product to the customers. These Promotional tools are given below:
• Television
• Newspaper
• Promotion vans
• Unique selling proposition
2) Pricing
In the present market of Pakistan, there are various juice items exist. Through the statistical surveying we have uncovered that a large percentage of these items’ price is in the range of Rs.25 to Rs.30.
As we are trying to launch similar products but with different test, it is smarter to set the cost in view of different products to which we will contend. We have experienced our whole production process and found that even if we keep the price of our product with respect to the cost of our competitor product, we can still cover our aggregate expense. So, the selling price we set for our product is Rs.25
Comparison with the other product *price:
Name of the company Price

2.1.6 Conclusion
The idea of sugarcane juice is new in Pakistan. Our Sugarcane juice is an innovative and new concept for marketing. Customers who are aware about their health must accept our product. In accordance to our marketing research, we see that our product as the best marketing product within a couple of years.

Like all life forms, new strains of E. coli evolve through the natural biological processes of mutation, gene duplication, and horizontal gene transfer; in particular, 18% of the genome of the laboratory strain MG1655 was horizontally acquired since the divergence from Salmonella. E. coli K-12 and E. coli B strains are the most frequently used varieties for laboratory purposes. Some strains develop traits that can be harmful to a host animal. These virulent strains typically cause a bout of diarrhea that is often self-limiting in healthy adults but is frequently lethal to children in the developing world. (Futadar et al., 2005). More virulent strains, such as O157:H7, cause serious illness or death in the elderly, the very young, or the immunocompromised.
The genera Escherichia and Salmonella diverged around 102 million years ago (credibility interval: 57–176 mya), which coincides with the divergence of their hosts: the former being found in mammals and the latter in birds and reptiles. (Wang et al., 2009). This was followed by a split of an Escherichia ancestor into five species (E. albertii, E. coli, E. fergusonii, E. hermannii, and E. vulneris). The last E. coli ancestor split between 20 and 30 million years ago.
The long-term evolution experiments using E. coli, begun by Richard Lenski in 1988, have allowed direct observation of genome evolution over more than 65,000 generations in the laboratory. For instance, E. coli typically do not have the ability to grow aerobically with citrate as a carbon source, which is used as a diagnostic criterion with which to differentiate E. coli from other, closely, related bacteria such as Salmonella. In this experiment, one population of E. coli unexpectedly evolved the ability to aerobically metabolize citrate, a major evolutionary shift with some hallmarks of microbial speciation.
The time between ingesting the STEC bacteria and feeling sick is called the “incubation period”. The incubation period is usually 3–4 days after the exposure, but may be as short as 1 day or as long as 10 days. The symptoms often begin slowly with mild belly pain or non-bloody diarrhea that worsens over several days. HUS, if it occurs, develops an average of 7 days after the first symptoms, when the diarrhea is improving.

• History of antibiotics – 1
19th century:Louis Pasteur & Robert Koch
• History of antibiotics – 2
Plant extracts
– Quinine (against malaria)
– Ipecacuanha root (emetic, e.g. in dysentery)
Toxic metals
– Mercury (against syphilis)
– Arsenic (Atoxyl, against Trypanosoma)
• Dyes
– Trypan Blue (Ehrlich)
– Prontosil (azo-dye, Domagk, 1936)
• History of antibiotics – 3
Paul Ehrlich
• started science of chemotherapy
• Systematic chemical modifications
(“Magic Bullet”) no. 606 compound = Salvarsan (1910)
• Selective toxicity.
• Developed the Chemotherapeutic Index
• History of antibiotics – 4
Penicillin- the first antibiotic – 1928• Alexander Fleming observed the
killing of staphylococci by a fungus (Penicillium notatum)
• observed by others – never exploited
• Florey & Chain purified it by freeze-drying (1940) – Nobel prize 1945
• First used in a patient: 1942
• World War II: penicillin saved 12-15% of lives
• History of antibiotics – 5
Selman Waksman – Streptomycin (1943), was the first scientist who discovered antibiotic active against all Gram-negatives for examples; Mycobacterium tuberculosis
– Most severe infections were caused by Gram-negatives and Mycobacterium
tuberculosis, extracted from Streptomyces – extracted from Streptomyces
– 20 other antibiotics include. neomycin, actinomycin
According to the Oxford Dictionary, the term Antibiotics encompasses medicines (such as penicillin or its derivatives) that inhibit the growth of or destroys microorganisms. Antibiotics are naturally occurring substances that exhibit inhibitory properties towards microbial growth at high concentrations. (Zaffiri, et al., 2012).
-Antibiotics are selective in their effect on different microorganisms, being specific in their action not only against genera and species but even against strains and individual cells. Some of these agents act mainly on gram-positive bacteria, while others inhibit only gram-negative ones.
-Some antibiotics are produced by some organism, from different strains of penicillin.
-Bacteria are sensitive to the antibiotic which enable them to developed resistance after contact, for several periods.

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Based on the clinical use of antibiotics, it may appear that these compounds play a similar role as microbial weapons in nature, yet this seems unlikely due to the fact that the concentrations used in the clinical setting are significantly higher than that produced in nature (Fajardo et al., 2008). Due to experimental evidence, it makes more sense to see antibiotics as small, secreted molecules involved in cell-to-cell communication within microbial communities.
(Martinez, 2008). Diverse Studies have been conducted in which different antibiotics and antibiotic-like structures were administered to different bacterial species at levels below the compounds minimum inhibitory concentrations (MIC). (Fajardo et al., 2008). that was

2.2 Principle of direct torque control of induction motor:
In a direct torque controlled (DTC) induction motor drive, it is possible to control directly the stator flux linkage (s?)or the rotor flux (r?)or the magnetizing flux (m?) and the electromagnetic torque by the selection of an optimal inverter voltage vector. The selection of the voltage vector of the voltage source inverter is made to restrict the flux and torque error within their respective flux and torque hysteresis bands and to get the fastest torque response and highest efficiency at every instant. DTC enables both quick torque response in the transient operation and reduction of the harmonic losses and acoustic noise.
The Benefits of using DTC include the following:
1 No need for motor speed or position feedback in 95% of applications. Thus, installation of costly encoders or other feedback devices can be avoided.
2DTC control is available for different types of motor including permanent magnet and synchronous reluctance motors.
3Accurate torque and speed control down to low speeds, as well as full startup torque down to zero speed.
4 Excellent torque linearity.
5 High static and dynamic speed accuracy.
6 No preset switching frequency optimal transistor switching is determined
2.2.1 Voltage Source Inverter
A six step voltage source inverter provides the variable frequency AC voltage input to the induction motor in DTC method. The DC supply to the inverter is provided either by a DC source like a battery, or a rectifier supplied from a three phase or single phase AC source. Fig. 2.2 shows a six step voltage source inverter. The inductor L is inserted to limit short circuit through fault current. A large electrolytic capacitor C is inserted to stiffen the DC link voltage.
The switching devices in the voltage source inverter bridge must be capable of being turned OFF and ON. Insulated gate bipolar transistors (IGBT) are used because they can offer high switching speed with enough power rating. Each IGBT has an inverse parallel-connected diode. This diode provide alternate path for the motor current after the IGBT, is turned off.

Figure 2.2 Voltage Source Inverter
Each leg of the inverter has two switches one connected to the high side (+) of the DC link and the other to the low side (-); only one of the two can be ON at any moment. When the high side gate signal is ON the phase is assigned the binary number 1, and assigned the binary number 0 when the low side gate signal is ON. Considering the combinations of status of phases a, b and c the inverter has eight switching modes(Va,Vb,Vc=000-111) V2 (000) are zero voltage vectors V0 (000) and V7 (111) where the motor terminals are short circuited and the others are nonzero voltage vectors V1 to V6
The six nonzero voltages space vectors will have the orientation, and also shows the possible dynamic locus of the stator flux, and its different variation depending on the VSI states chosen. The possible global locus is divided into six different sectors signaled by the discontinuous line. Each vector lies in the center of a sector of width named S1 to S6 according to the voltage vector it contains.
It can be seen that the inverter voltage directly force the stator flux, the required stator flux locus will be obtained by choosing the appropriate inverter switching state. Thus the stator flux linkage move in space in the direction of the stator voltage space vector at a speed that is proportional to the magnitude of the stator voltage space vector. By selecting one after another the appropriate stator voltage vector, is then possible to change the stator flux in the required method. If an increase of the torque is required then the torque is controlled by applying voltage vectors that advance the
same sector depending on the stator flux position.

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Figure 2.3.Stator flux vector locus and different possible switching Voltage vectors. FD: flux decrease. FI: flux increase. TD: torque decrease.
TI: torque increase.
Table 2.1.General Selection Table for Direct Torque Control, “k” being the sector number.
Voltage vector Increase Decrease
Stator flux Vk,Vk+1, Vk-1 Vk+2,Vk-2, Vk+3
Torque Vk+1, Vk-1 Vk+2, Vk-2

This can be tabulated in the look-up Table 2.1 (Takahashi look-up table).
Finally, the DTC classical look up table is as follows:
Table 2.2 conventional DTC look up table
Flux errorD? Torque error
DT S1 S2 S3 S4 S5 S6

1 1 V2 V3 V4 V5 V6 V¬1
0 V0 V7 V0 V7 V8 V7
-1 V6 V1 V2 V3 V4 V5

0 1 V3 V4 V5 V6 V1 V2
0 V0 V7 V0 V7 V0 V7
-1 V5 V6 V1 V2 V3 V4


Figure 2.4 Direct Torque control scheme
A schematic of Direct Torque Control is shown. As it can be seen, there are two different loops corresponding to the magnitudes of the stator flux and torque. The reference values for the flux stator modulus and the torque are compared with the actual values, and the resulting error values are supplied into the two level and three-level hysteresis blocks respectively. The outputs of the stator flux error and torque error hysteresis blocks, together with the position of the stator flux are used as inputs of the look up table. The inputs to the look up table are given in terms of 1,0,-1 depend on whether torque and flux errors within or beyond hysteresis bands and the sector number in which the flux sector presents at that particular moment. In accordance with the figure 1.2, the stator flux modulus and torque errors tend to be restricted within its respective hysteresis bands.
From the schematic of DTC it is cleared that, for the proper selection of voltage sector from lookup table, the DTC scheme require the flux and torque estimations.
2.3.1 Techniques for Quantifications of Stator Flux in DTC:
Accurate flux quantifications in Direct Torque controlled induction motor drives is necessary to ensure proper drive operation and stability. Most of the flux estimation methods proposed was based on voltage model, current model, or the combination of both. The estimation based on current model normally applied at low frequency, and stator current and rotor mechanical speed or position. In some industrial applications, the use of incremental encoder to get the speed or position of the rotor is undesirable since it reduces the robustness and reliability of the drive. It has been generally known that even though the current model has managed to remove the sensitivity to the stator resistance variation. The use of rotor parameters in the estimation introduced error at high rotor speed due to the rotor parameter variations. So in this present DTC control scheme the flux and torque are quantified by using voltage model which does not need a position sensor and the only motor parameter used is the stator resistance. (Oghanna, 2011)
Fuzzy logic has become one of the most successful of today’s technology for developing sophisticated control system. With it aid, complex requirement may be implemented in simply, easily and inexpensive controlling method. The application ranges from consumer products such as cameras, camcorder, washing machines and microwave ovens to industrial process control, medical instrumentation and decision support system .many decision-making and problem solving tasks are too complex to be understand quantitatively however, people succeed by using knowledge that is imprecise rather than precise. Fuzzy logic is all about the relative importance of precision. It has two different meanings. In a narrow sense, fuzzy logic is a logical system which is an extension of multi valued logic, but in wider sense fuzzy logic is synonymous with the theory of fuzzy sets. Fuzzy set theory is originally introduced by LotfiZadeh in the 1960s, resembles approximate reasoning in it use of approximate information and uncertainty to generate decisions.
Several studies shows, both in simulations and experimental results, that Fuzzy Logic control yields superior results with respect to those obtained by conventional control algorithms thus, in industrial electronics the FLC control has become an attractive solution in controlling the electrical motor drives with large parameter variations like machine tools and robots. However, the FL Controllers design and tuning process was often complex because several quantities, such as membership functions, control rules, input and output gains, etc. must be adjusted. The design process of a FLC can be simplified if some of the mentioned quantities are obtained from the parameters of a given Proportional-Integral controller (PIC)for the same application. (Lotfizabeh, 2011).
2.5 Why fuzzy logic controller (FLC)
• Fuzzy logic controller was used to design nonlinear systems in control applications. The design of conventional control system is normally based on the mathematical model. If an accurate mathematical model is available with known parameters it can be analyzed and controller can be designed for specific performances, such procedure is time consuming.
• Fuzzy logic controller has adaptive characteristics. The adaptive characteristics can achieve robust performance to system with uncertainty parameters variation and load disturbances.
The main principles of fuzzy logic controller.
The fuzzy logic system involves three steps fuzzification application of fuzzy rules and decision making and defuzzification. Fuzzification involves mapping input crisp values and decision is made based on these fuzzy rules. These fuzzy rules are applied to the fuzzified input values and fuzzy outputs are calculated in the last step, a defuzzifier coverts the fuzzy output back to the crisp values. The fuzzy controller in this thesis is designed to have three fuzzy input variables and one output variable for applying the fuzzy control to direct torque control of induction motor. There are three variable input fuzzy logic variables. The stator flux error, electromagnetic torque error, and angle of the flux in the stator.

Figure 2.5. Block Diagram of Fuzzy logic controller.
The membership functions of these Fuzzy sets are triangular with two membership function N and P for the flux-error, three membership functions N, Z, P for the torque-error, six membership variables for the stator flux position sector and eight membership functions for the output commanding the inverter. The inference system contains thirty six Fuzzy rules which is framed in order to reduce the torque and flux ripples. Each rule takes three inputs, and produces one output, which is a voltage vector. Each voltage vector corresponds to a switching state of the inverter. The switching state decides the pulse to be applied to the inverter. The Fuzzy inference uses MAMDANI’s procedure for applying Fuzzy rules which is based on minimum to maximum decision. Depending on the values of flux error, torque error and stator flux position, the output voltage vector is chosen based on the Fuzzy rules. Using Fuzzy Logic controller the voltage vector is selected such that the amplitude and flux linkage angle is controlled. Since the torque depends on the flux linkage angle the torque can be controlled and hence the torque error is very much reduced.
2.6. Fuzzy logic controller (FLC)
Fuzzy logic expressed operational laws in linguistics terms instead of mathematical equations. Many systems are too complex to model accurately, even with complex mathematical equations, therefore traditional methods become impracticable in these systems.
However fuzzy logics linguistic terms provide a possible method for defining the operational characteristics of such system.
Fuzzy logic controller can be considered as a special class of symbolic controller. The configuration of fuzzy logic controller block diagram is shown in Fig.2.6

Figure 2.6 Block diagram for Mamdani type Fuzzy Logic Controller
The fuzzy logic controller has three main components
1. Fuzzification.
2. Fuzzy inference.
3. Defuzzification.
2.6.1. Fuzzification
The following functions:
1. Multiple measured crisp inputs first must be mapped into fuzzy membership function this process is called fuzzification.
2. Performs a scale mapping that transfers the range of values of input variables into corresponding universes of discourse.
3. Performs the function of fuzzification that converts input data into suitable linguistic values which may be viewed as labels of fuzzy sets.
Fuzzy logic’s linguistic terms are often expressed in the form of logical implication, such as IF-THENrules. These rules define a range of values known as fuzzy membership functions.
Fuzzy membership function may be in the form of a triangle, a trapezoidal, and a bell as shown in Fig. 2.7

Triangle Trapezoid


Figure 2.7. (a) Triangle, (b) Trapezoid, and (c) BELL membership functions.
The inputs of the fuzzy controller are expressed in several linguist levels. As shown in Fig.2.8 these levels can be described as positive big (PB), positive medium (PM), positive small (PS), negative small (NS), negative medium (NM), and negative big (NB). Each level is described by fuzzy set below.

Figure.2.8.Seven levels of fuzzy membership function

2.6.2. Fuzzy inference
Fuzzy inference is the process of draw up the mapping from a given input to an output using fuzzy logic. The mapping then provides a basis from which decisions can be made. There are two types of fuzzy inference systems that can be implemented in the Fuzzy Logic Toolbox: Mamdani-type and Sugeno-type. These two types of inference systems vary to some extent in the way outputs are determined.
Fuzzy inference systems have been successfully applied in fields such as automatic control, data classification, decision analysis, expert systems, and computer vision. Because of its multi-disciplinary nature, fuzzy inference systems are associated with a number of names, such as fuzzy-rule-based systems, fuzzy expert systems, fuzzy modeling, fuzzy associative memory, fuzzy logic controllers, and simply, fuzzy Mamdani’s fuzzy inference method is the most commonly seen fuzzy methodology.
Mamdani’s method was among the first control systems built using fuzzy set theory. It was proposed in 1975 by Ebrahim Mamdani as an attempt to control a steam engine and boiler combination by arranging a set of linguistic control rules obtained from experienced human operators. Mamdani’s effort was based on LotfiZadeh’s 2011on fuzzy algorithms for complex systems and decision processes.
The second phase of the fuzzy logic controller is its fuzzy inference where the knowledge base and decision making logic reside .The rule base and data base from the knowledge base. The data base contains the description of the input and output variables. The decision making logic evaluates the control rules .the control-rule base can be developed to tolerate the output action of the controller to the inputs.
2.6.3. Defuzzification
The output of the inference mechanism is fuzzy output variables. The fuzzy logic controller must convert its internal fuzzy output variables into crisp values so that the actual system can use these variables. This conversion is called defuzzification.
2.7: Fuzzy Direct Torque Controller
The fuzzy direct torque control technique consists of inverter, induction motor, torque controller, flux controller, flux estimator, torque estimator and clarke’s transform. The fuzzy logic technique which is based on the language rules, is used to solve this nonlinear issue. In a three phase voltage source inverter, the switching commands of each inverter leg are matched. For each leg a logic state Ci (I = a,b,c) is defined, thatCi is 1 IF the upper switch turned ON and zero IF the lower switch turned OFF. IFCi is 0 THEN it means that the lower switch is ON and upper switch is turned OFF. Since three are independent there will be eight different states, so eight different voltages.
To study the performance of the developed DTC model, a closed loop torque control of the drive is simulated using MATLAB/Simulink simulation package. The torque error and flux errors were compared in their respective hysteresis band to generate their respective logic state as (ST) and (S?). The sector logic state (S?) is used as the third controlling signal for referring the DTC switching table. These three controlling signals are used to determine the instantaneous inverter switching voltage vector from three dimensional DTC switching lookup table. The simulation results are implemented for conventional DTC scheme and proposed fuzzy based DTC scheme. There are three non-zero voltage vectors and two voltage vectors.

Figure2.9Block Diagram of fuzzy logic DTC
The DTFC on induction motor drives is designed to have three fuzzy input variables and one output control variable to achieve fuzzy logic based DTC of the induction machine. Its functional block diagram is as shown in fig. 2.9 the three input variables are the stator flux error, electromagnetic torque error and angle of stator flux. The output was the voltage space vector. The DTF Cconsist of fuzzification, rule base, data base, decision making and defuzzification.
The input variable (?T) and (?) are fuzzified using fuzzy functions over the respective domains. The output of DTFC was also fuzzified, the all possible fuzzy rules are stored in fuzzy rule base.
DTFC takes the decision for the given input crisp variables by firing this rule base.

Figure2.10 DTC functional Block Diagram

With the principle of direct torque control (DTC)of induction motor, the high ripple torque in the motor have being reduced to above 65% in the reviewed work.
These controls have being one of the best controls for driving induction motor because of its principles. Though DTC strategy is popular and simpler to implement than the flux vector control method because voltage modulators and coordination transformations are not required.
Although, it introduces some drawbacks as follows:
1. High magnitude of torque ripple
2. Torque and small errors in flux and torque are not distinguished. In other word, the same vectors are used during start up and step changes and during steady state.
3. Sluggish response in both start up and step changes in either flux or torque.
In other to overcome the mentioned drawbacks, there are difference solution like fuzzy logic duty ratio control method. In this work fuzzy logic with duty ratio control is proposed to use with direct torque control to reduce this high ripple torque and realized the best DTC improvement.

2.5.1 Anaerobic ponds
These are the first ponds and are the recipients of the influent from homes and industries which is thick and dark in colour. These are responsible for anaerobic respiration so they are mainly concerned with the presence of anaerobic bacteria that digest the wastewater which is highly organic at this stage. They are the deepest of the ponds as anaerobic bacteria do not require oxygen and sunlight in order to digest. Mara (2004) concurred by suggesting that these ponds are usually deeper due to sludge accumulation and the main function is to remove biological oxygen demand in a relative short retention time of few days. Organic matter is removed by the sedimentation of settable solids and anaerobic digestion in the sludge layer. Theoretically, anaerobic systems should generate lesser amounts of sludge compared to aerobic systems, however, in practice in the Australian meat process industry, anaerobic ponds frequently fill rapidly with solids (Watson: 1999).
Anaerobic ponds are usually used for treatment of industrial and agricultural wastes which contain high organic matter and sulphate (Rhajbhandari: 2007). Anaerobic ponds provide a degree of pre-treatment, thereby enabling a reduction in the size requirements of the subsequent aerobic ponds. Anaerobic treatment is more suited to wastewater with high BOD (IETC-UNEP, 2002) and therefore useful at reducing high concentrations of BOD and suspended solids from agricultural and food processing wastewater.
A properly designed anaerobic pond can achieve around 60% BOD removal at 20° C and one-day hydraulic retention time is sufficient for wastewater with a BOD of up to 300 mg/l and temperatures higher than 20° C (Mara: 2003). The anaerobic ponds acts mostly like an uncovered tank that breaks down the organic matter in the effluent through the use of organisms, releasing methane and carbon dioxide( Quiroga: 2014).
The main operational problems of anaerobic ponds are the odour problems, mosquitoes and other insects and the possible reasons of odour problems are excessive loading rate, presence of toxic substances and inhibitors in influent, sudden drop of temperature and low influent pH value (Marinella: 2015). When considering the effects on climate change also, another disadvantage of anaerobic pond systems is the emission of greenhouse gases such as methane, carbon dioxide and nitrogen oxide that are normally released to the atmosphere since the areas are open and need sunlight and wind to operate (Glaz: 2016).

Figure 4

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Diagram showing different types wastewater ponds namely anaerobic, facultative and maturation ponds

2.5.2 Facultative Ponds
These are the largest of the waste stabilisation ponds and they harbour both aerobic and anaerobic bacteria and Shelton (2005) concurred that they are referred as fulcatative ponds because the term facultative refers to the fact that these ponds operate with both aerobic and anaerobic zones. Reed (1995) stated that the facultative pond is the most common type used in the United States of America and in different terms such as oxidation pond, sewage lagoon and photosynthetic pond.

According to Palacios (2014), the waste water treated in the aerated ponds is discharged into the facultative ponds which need to fulfill two fundamental requirements of fulcatative ponds that are to have an adequate organic load and an oxygen balance that keeps the aerobic conditions over the anaerobic layer situated in the bottom of the pond. The presence of algae in the aerobic and facultative zones is essential for the successful performance of facultative ponds (EPA, 2002). In sunlight conditions, the algal cells utilise carbon dioxide from the water and release oxygen produced during photosynthesis. The oxygen produced by algae and surface reaeration is then used by aerobic and facultative bacteria to stabilise organic material in the upper layer of water.

Fulcatative ponds can be broadly classified as primary or secondary based on the characteristics of the influent. If the facultative pond receives influent without pre-treatment, it is named as primary facultative pond whereas if the fulcatative pond receives pre-treated influent from anaerobic pond, septic tank or shallow sewerage systems, it is called a secondary facultative pond (Sperling: 2007).

According to Environmental Protection Agency (2002), facultative ponds are usually 1.2 to 2.4 m in depth and are not mechanically mixed or aerated. The wastewater is more greenish and there is the presence of algae in water. They are designed for BOD removal on the basis of a relatively low surface loading to permit the development of a healthy algal population as the oxygen for BOD removal by the pond bacteria is mostly generated by algal photosynthesis (Mara and Pearson: 1998). The algae is beneficial to the process as it uses the carbon dioxide produced by aerobic bacteria to grow and release more oxygen which is needed by the aerobic bacteria for survival. This interrelationship between algae and aerobic bacteria is called symbiosis and allows for the removal of nutrients, heavy metals and pathogens (Alamgir: 2016).

2.5.3 Maturation Ponds
The effluent from fulcatative pond is channelled into the maturation ponds. The main purpose of maturation ponds is to remove pathogens found in the wastewater. These are very shallow, usually 0.9 – 1 m depth to allow light penetration to the bottom and aerobic conditions throughout the whole depth (Dehgani: 2007). The maturation ponds have a similar purpose than the facultative ponds with the difference being that in these ponds, there is hardly any accumulation of solids and the increase of the pH due to the photosynthetic activity results in an important bacterial mortality. Kayombo (2015) also added that maturation ponds usually show less vertical biological and physicochemical stratification and are well-oxygenated throughout the day.

The number and size of maturation ponds is defined by the necessary retention time required for the removal of faecal coliform and it should also be noted that the above also performs the oxidation of a small amount of biological oxygen demand (Martinez 2014). Total nitrogen removal in a whole waste stabilization system depends on the number of maturation ponds included in the waste stabilisation ponds system Pena (2004).
Groundwater contamination
National centre for Groundwater defines groundwater as water that is found beneath the earth’s surface and it is an important source of drinking water especially in the rural areas (Rotatu, 2008). It is also fresh water from rain or melting snow and ice that soaks into the soil and is stored in the tiny pores between the rocks and particles of soil (EPA, 2018). The quality of groundwater is determined by various chemical constituents and their concentration, which are mostly derived from the geological data of the particular region through groundwater flows (Khumbar, 2011). Human activities can change the natural composition of groundwater through the disposal of chemicals and microbial matter on land or into the soils and this can lead to groundwater contamination which is the change of groundwater quality due to the activities of man (Harter, 2003).
The sources of groundwater contamination can be natural in nature whereby naturally occurring particulates of the soil such as iron, fluorides, manganese, arsenic, chlorides, or sulphates can become dissolved in ground water. Other naturally occurring substances, such as decaying organic matter, can move in ground water as particles (EPA, 2018). Man-made or human activities also promote groundwater contamination. Activities such as agricultural development, surface water irrigation, chemical use in agriculture, urban and industrial development affect the quality and quantity of groundwater as well ( USGS, 2016). In Jordan, Al Ramtha wastewater treatment plant was discovered to be the main cause of groundwater pollution and high levels of nitrates were found on nearby wells (Obeidat).

2.0 Literature Review
2.1 Introduction
Based on Joseph Schumpeter (1934), financial innovation can be understood as a factor which leads to growth in firms as well as in economies. It refers to new or improved ways of making more profits, either to decrease the production costs or to create and sustain a high demand level.
This chapter also looks at the main theories behind financial innovation, which forms the literature of this study. The six theories explained below are Schumpeter’s theory of innovation, Innovation diffusion theory, Constraint-induced theory, Transaction cost theory and Circumvention theory of innovation.
2.1.1 Schumpeter’s Theory of Innovation
In his work “Theory of economic development”, Schumpeter explained that innovation could be divided into five main types which are as follows:
1. Launching of new product or existing but improved model of the product
2. Applying new processes of production or sales
3. Launching of new market
4. Obtaining new raw material sources or unfinished goods
5. Creating new or destroying present market structures
The Schumpeter theory of profit holds that in order to get greater profits, one must innovate, which leads to competition in the sector. Accordingly, innovations can occur in 4 stages: invention, innovation, diffusion and imitation. He argues that a firm will be profitable when the new product or process will lower its cost of production or increase its demand level. Competitors will eventually imitate the new or improved product as it becomes more popular and its demand start to rise. The innovating firm no longer holds a monopoly power. As such, he concluded that innovation can cause creative destruction. insert footnote : in his book Capitalism, Socialism and Democracy. Hence, he points out that innovation continuously reshapes the economy internally and keeps on creating new structures while dismantling old ones.
2.1.2 Diffusion of Innovation theory
The diffusion of innovations theory, based on Everett Rogers (1962), has been contextualised in different fields namely sociology, marketing and communications among others. It relates to the motives and the circumstances under which innovation is spread over time among the different market participants. Tufano (1989) notes that innovation spreads when imitators copy successful and profitable banking innovations. Accordingly, it depends a lot on human capital such as knowledge and innovation is bound to reach to a saturation point over time. According to Rogers (2003), the extent to which a firm endorses a new idea is defined as innovativeness while the diffusion process is known as the innovation-decision making process (as illustrated below) has five steps (Knowledge, persuasion, decision, implementation and confirmation). Furthermore, four categories of adopters have been identified: early adopters, early majority, late majority and laggards. Insert diagram
Rogers suggested certain factors that affect the diffusion of innovation comprises of notably relative advantage (innovation brings improvement to existing ideas), compatibility (whether the innovation is suitable with present values and requirements of adopters), observability (how much is the innovation visible to others), trialability (if the innovation can be tested before being adopted) and complexity (level of difficulty experienced when using the innovation). Hence, he noted that innovations with the above attributes are more likely and quicker to be adopted than others.
Akhavein et al. found only a few quantitative studies insert footnote on the names of the studies on diffusion of innovations. However, diffusion is only successful when a group adopts innovation either through optional, collective or authority innovation decisions. Difficulty is also experienced when measuring the exact causes of innovation. Al-Jabri and Sohail (2012) observed that the factors which affected the adoption were relative advantage, compatibility, observability while trialability and complexity had no effect and perceived risk had a negative influence on adoption. Akhavein et al. conducted a study on diffusion of financial innovation and found that larger banks have adopted innovations before other banks and that adoption of innovation is positively influenced by the size of the banks and its network.
2.1.3 Constraint Induced theory
According to Merton (1992), financial innovation is considered the “engine” which leads the financial system to its aim of improving economic performance.
Silber, an American economist proposed the theory of constraint-induced innovation, which showed that financial innovation arises primarily because firms want to maximise their profits. (Cherotich et al., 2015) As such, small banks with the most constraints have higher incentives to innovate with new financial products or processes designed to reduce financial costs of the banks (Tufano, 2002). Certain factors such as particular leadership styles tend to ensure an unwavering management style, thereby decreasing the firm’s efficiency. The firm should eliminate these restrictions. Ben-Horim and Silber (1977) carried a ground-breaking study of the constraint-induced notion of innovation and examined the drivers of financial innovation. They discovered that the constraints caused by government regulations encourage firms to innovate. (pg 279)
Nonetheless, Silber’s approach disregards the role of institutions in innovation and since it considers innovation in its excessive adversity, it is not considered as a good incentive for innovation.
2.1.4 Transaction cost theory
The transaction cost theory is explained in a study carried out by Hicks and Niehans in 1983, (Cited in Cherotich et al., 2015), which demonstrated that a fall in transaction costs is a major influence behind financial innovation. The low costs are in turn, caused by technological advances. Therefore, a fall in transaction costs will lead to more innovation by firms and a general enhancement of financial and banking services. Tsuma et al.(2018) investigated the effects of innovation on financial performance which was based on this theory. They found that process innovations led to a fall in transaction costs and consequently led to positive financial performance. This theory is believed to be different from the others as it focuses on a firm’s perspective instead of a macro-economic level.
The transaction cost theory also suggests that product innovation has affects transaction value and as a result, with customized and improved products offered to the bank’s customers, transaction costs can be reduced. (Tahir et al., 2018)
2.1.5 Circumvention theory/ regulation theory
Proposed by Kane (Cited in Cherotich et al., 2015), the circumvention theory believes that financial innovation is driven mostly by the regulations imposed on the finance sector firms by the government. Indeed, over-regulation, imposition of high taxes and other types of sovereign controls tend to increase the operation costs of the firms due to compliance, which in turn leads to lower profit opportunities. Consequently, regulatory constraints imposed led the firms to engage in market and regulatory innovation (Achieng et al, 2015). This situation can be illustrated in the case of capital requirements in banks, where a bank has to comply with the regulations issued by the Central bank and hence, incurs certain compliance costs. As such, regulations stimulate innovation. Innovations such as Eurobonds and equity swaps have been created especially to bypass regulations (Tufano, 2002).
Khraisha and Arthur (2018) noted that innovative processes and products such as securitization were effectively created to avoid high costs due to legal compliance.
Despite regulation inducing financial innovation, critics argue that regulation requirements impede innovations. Nonetheless, regulations have been increased after the financial crash of 2007/2008, partially caused by the trend of deregulation which prevailed during this period. Furthermore, through regulation, many mechanisms have been designed and implemented to level the ‘playfield’ (Khraisha and Arthur, 2018).


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