A New Approach of Syrup Manufacturing Using Fuzzy Time Control Discrete Event System

Pharmaceutical industries of the world are manufacturing their most of the goods in syrup form. The proposed study relates with the designing of medicated syrup manufacturing, using the fuzzy time control discrete event system. The system is designed with three inputs; viscosity, specific gravity, and chemical selection. And eight outputs temperature, temperature time, mixing speed, mixing time, valve, valve opening time, PH at current liquid temperature, and PH time. System is controlled by controlling the four parameters; valve selection, temperature monitoring unit, mixing motor, and PH control unit. System takes feed back from four sensors and time control rules are formulated and simulated using MATLAB tool box..


I. INTRODUCTION
Syrup is the aqueous pharmaceutical preparation having the concentrated sugar solution with active and non-active ingredients.These preparations are manufactured by pharmaceutical industries following the standards of British and European pharmacopoeias in the whole world.The syrup solutions are characterized by parameters temperature, specific gravity, pH, and viscous consistency.[1]- [5] the proposed syrup manufacturing model is designed by using fuzzy time control discrete event system in which output variables are time dependent and discussed with non probabilistic uncertainty issues [1] and controller work on heuristic knowledge [6] controller operate the plant ON or OFF for a specific period of time.Design system has flexibility to easily adjust the input and output parameters according to the mixture requirement.

II. FUZZY TIME CSCRETONTROL DITCRETE EVENT SYSTEM
Fuzzy time control discrete event system is modified form of fuzzy logic controls system, which is basically combination of two different systems.One is discrete event system and the other is fuzzy time control system.This system contains Fuzzifiers, inference engine, knowledge base (which contains data base, rule base, and output membership functions), defuzzifiers, and discrete event system [7].
Three inputs; viscosity, specific gravity, and chemical selection are used for the designed syrup manufacturing system.Three numeric input values are given to three Fuzzifiers.After receiving numeric values of inputs these Fuzzifiers convert them into linguistic variables.These linguistic variables are then given to inference engine where the max-min composition is applied and gives eight values of R,s.Knowledge base provides eight singleton values according to the fuzzy rules designed for the proposed syrup manufacturing system after getting the crisp values.Defuzzifiers get eight values from inference engine and eight from knowledge base at its input and give the eight crisp values at its output.Here eight defuzzifiers are used, four for output variable; temperature, mixing speed, PH, valve selection and four for output time; temperature time, mixing time valve opening time, and PH time [7].The values of out variables are converted into binary codes using analog to digital converter (ADC) and decoder and crisp values of output time; temperature time, mixing time valve opening time, and PH time are provided to the pulse strobe units which provides the time pulses.These time pulses allow the binary codes to pass for a specific period of time.Then these binary codes are used to make active discrete event system under time constrain [8].Plot of input MF viscosity contains six MF, s F2 [1], F2 [2], F2 [3], F2 [4], F2 [5], F2 [6] and five regions input numeric value of viscosity lies in any one of the five region as shown in Fig. 3.    II.
To verify the design model of syrup manufacturing the input fuzzy variable value are taken; viscosity=1.4,specific gravity=1.7 and chemical selection=16 this value of viscosity lies in the first half of second region and maps with the fuzzy variable medium and small, medium is taken as F1 [3] and small is taken as F1 [2] the value of specific gravity lies in the second half of the second region and maps with the fuzzy variable medium and small, medium is taken as F2 [3], F2 [2].Selected value of chemical selection lies in the send half of second region and maps with the fuzzy variable medium and small, medium is taken as F3 [3], F3 [2].Fuzzifier result for this model are shown in Table III.Inference engine take values from the Fuzzifier and apply min-max composition and gives the eight values; R1, R2, R3, R4, R5, R6, R7, and R8.
In the output MF plot there are two overlapping regions and system takes three inputs so there are eight rules are design for this system [9] which are listed in the Table IV.
Singleton values obtained from knowledge base for applied rule are listed in Table V.
Output crisp values are obtained by defuzzification.There are many methods used for defuzzification e.g.(MOM) mean of maximum.(SOM) smallest of maximum (LOM) left of maximum etc.

Fig. 1 .
Fig. 1.Experimental arrangement of Syrup manufacturing fuzzy time control discrete event system

Fig. 3 .
Fig. 3. Input MF plot for viscosity Plot of input MF chemical selection contains six MF,s

Fig. 4 .
Fig. 4. Input MF plot of chemical selection Plot of output MF are shown in Fig. 5 for the convenience in calculation the range values of output membership function for mixing speed, mixing time, valve selection, valve opening time, PH at given temperature, PH time, Temperature, Temperature time are taken same, plot contains five output MF, s and four regions

Fig. 7 (
Fig. 7(a) Plot between specific gravity viscosity and temperature

Fig. 7 (
Fig. 7(b) Plot between specific gravity, viscosity and temperature time Fig. 7(c) Shows that mixing speed does not depend on viscosity depends on specific gravity.

Fig. 7 (
Fig. 7(f) plot between specific gravity, viscosity and the syrup PH

Fig. 7 (
Fig. 7(h) Shows that mixing speed depends on the specific gravity of the liquid

Fig. 7 (
Fig. 7(h) Plot between chemical selection, specific gravity and mixing speed

TABLE II :
OUTPUT MEMBERSHIP FUNCTIONS AND THEIR RANGES