Conventional Control of Continuous Fluidized Bed Dryers for Pharmaceutical Products

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1 Conventional Control of Continuous Fluidized Bed Dryers for Pharmaceutical Products Gurashi Abdullah Gasmelseed and Mahdi Mohammed 2 Department of Chemical Engineering, University of Science and Technology, Khartoum, Sudan gurashigasm@gmail.com 2 Department of Chemical Engineering, University of Science and Technology, Khartoum, Sudan biryab7@gmail.com Publishing Date: March 04, 207 Abstract The dynamic response studies with step disturbances in the manipulated and load variables are investigated. These studies are useful in control system identification schemes for fluidized bed dryer. Both conventional and digital control strategies were developed. Three loops were taken for comparison between the methods of tuning and stability analysis for conventional and digital control, each control loop was treated separately and its transfer functions were developed.in conventional control the characteristic equations were determined and used in Routh array to determine the ultimate gain (Ku), the ultimate period (Pu) was determined by using direct substitution. The OLTF's were used by Root Locus and Bode plot methods using MATLAB software to determine Ku and Pu. It is observed that the three methods of investigating the stability gave optimum and identical parameters Ku and Pu, and they were almost the same. The average values of the ultimate gains and periods were obtained and they were introduced in Zeigler Nichols table to get the adjustable parameters, however an average was taken to give more précised and correct results. Also the offset was investigated for P-controller, PI-controller and PID-controller. Keywords: Dynamic Response, Fluidization, Drying Control, Automatic Control of Dryers, System Stability and Tuning, Routh Hurwitz, Root Loucs Plot, Direct Substitution, Bode Plot and System Stability. Introduction Drying means the removal of relatively small amounts of water from wet material by the application of heat. Drying is an energy-intensive operation that accounts for up to 5% of the industrial energy usage. Moreover, conventional dryers often operate at low thermal efficiency, typically between 25% and 50%, but it may be as low as 0%. Fluidized bed dryer is used widely in food, metallurgical, chemical and pharmaceutical industry, because of the shorter drying time required and simple maintenance and operation. This type of dryers is based on the phenomena of fluidization. Fluidization is the operation by which solid particles are transformed into fluid-like state through suspension in gas or liquid. When a gas is passed through a layer of particles supported by a grid at low flow rate, the fluid percolates through the void spaces between stationary particles. As the fluid velocity increased, the void age increases, this resulting in an increase in pressure drop on the particles. The pressure drop across the particle layer will continue to increase in proportion to the gas velocity till the pressure drop reaches a constant value that is equivalent to the weight of the particles in the bed divided by the area of the bed, at this point the frictional force between particles and fluid counterbalances the weight of the particles. At this stage the bed is to be incipiently fluidized. Fluid velocity at this point is known as minimum fluidization velocity. With an increase in flow rates beyond minimum fluidization, large instabilities with bubbling, channeling of gas and decrease in pressure drop are observed. Fluidized bed dryers have some drawbacks. Material with a wide particle-size distribution cannot be handled satisfactorily, while at high temperatures the melting and fusing of the material on the grid plate can become a problem. To circumvent these difficulties, dryers, originally developed for grain drying, have been made with conical bottom sections which give a spouted bed rather than a fully fluidized one. Deriving mathematical models can be done by utilizing physical laws to derive a mathematical model, this model must be rigorous enough to give an accurate description of the process. In most cases they obtained models are set of ordinary differential equations with one or more partial differential equation, thus solving them requires powerful mathematical solvers. Drying control is defined as the ability to dry a product to a desired moisture content with acceptable variation. Poor moisture content produces a distribution with a wide moisture content variation, whereas, improved control results in narrow distribution. 7

2 Automatic control of dryers is probably one of the least studied areas of process control and has not progressed with improvements in drying and dryer design. This may be attributed to various factors, including:. The lack of direct, on-line and reliable methods for sensing product moisture content. 2. The complex and highly nonlinear dynamics of drying process, leading to difficulties in modeling process adequately.. The lack of emphasis on product quality in the past. 4. An apparent lack of knowledge of the important role that dryer control plays in product quality and drying efficiency. Prior to control system design, control synthesis must be performed. The synthesis of control configurations for multivariable system involves selection of controlled and manipulated variables, pairing manipulated inputs and controlled outputs (loop pairing), and selection of the best control configuration. Generally, input variables can be classified into manipulated variables and disturbances or load variables. In industrial drying systems the manipulated variables typically include inlet air temperature, superficial air velocity, inlet solids flow rate and dryer-wall temperature. Load variables include ambient air temperature, ambient air humidity, and feed moisture content. The controlled variables in dryers are Figure : Continuous Fluidized Bed Dryer dried product moisture content, exhaust air temperature and exhaust air humidity. The most desirable drying process output variable to control is product moisture content, but this is difficult to measure directly. Often, the moisture content of the dried product can be inferred from the temperature and humidity of the exhaust gas. However, due to the weak correlation between the temperature and the actual product moisture content, using indirect control usually results in poor control of the drying process. Multivariable control design must be considered for fluidized bed dryers in order to account for dynamic interactions between the control loops. 8

3 Research Objectives: / Design of fluidized bed dryer for drying pharmaceutical products. 2/ Design of conventional and digital control of fluidized bed dryer. / Comparison of performance between conventional and digital control of fluidized bed dryer. Methodology System Stability and Tuning: Stability: System have several properties such as controllability, stability, and invariability.which play a very decisive role in their behavior. From these characteristic, stability plays the most important role. The most basic practical control problem is the design of a closed - loop system such that its output follows its input as closely as possible, unstable system cannot guarantee such behavior and therefore are not useful in practice. Stability Test: The system is stable when all poles of the transfer function have negative real parts. If any pole has a positive real part, then the system is unstable. To ensure a good performance of the system, each of the control loops mentioned earlier should be analyzed for the stability, separately.for the present work, we have four methods used to check the stability of the system.such methods are:. Routh Hurwitz. 2. Root Loucs plot.. Direct Substitution. 4. Bode plot. Routh Hurwitz (Routh s Criterion): One absolute method of determining whether complex or real roots lie in the right hand plane is by use of Routh s criterion the method entails systematically generating a column of numbers that are then analyzed for sign variations.the first step is to arrange the denominator of transfer function into descending powers of S. All terms including those that are Zero should be included. The stability is determined from the system characteristic equation. Root Loucs Plot: It is one of the most powerful techniques in controller design and analysis when there is no time delay.root locus is a graphical representation of the roots of the closed loop characteristic polynomial.the analysis most commonly uses the proportional gain as the parameter.a Root locus plot is a figure that shows how the roots of the closed loop characteristic equation vary as the gain of the feedback controller changes from zero to infinity Direct Substitution: Determination of the ultimate period by direct substitution method (ωco). By steps below: G(s) = C(s) R(s) = πf +πl Set s= iω in the characteristic equation. Taking the real part. Taking the imaginary part. Substitute the value of cross over frequency. The ultimate period {Pu = 2π }. ωco Bode Plot and System Stability: The Bode diagram in honour of H.W.Bode gives a convenient method to represent the frequency response characteristics of a system. It represents the amplitude ratio and phase angle of the response of the system as the function of the frequency. It shows the variation of the logarithm of the amplitude ratios with the frequency and the variation of the phase shift with the frequency. To cover a large range of frequencies, the log scale is used for the frequency. Results and Discussion Based on operation conditions of the fluidized bed dryer shown in table (4.), control strategy was developed as shown in figure (4.). The block diagrams were constructed, the transfer functions of loop through loop were identified, and the characteristic equations were calculated. Stability analysis, tuning and simulation responses were obtained 9

4 Table (.): Operating conditions of fluidized bed dryer: Parameters Units Values Inlet air temperature Inlet air humidity % Outlet air temperature 9 40 Outlet air humidity % Height of dryer Cm 57 Fluidized bed height Cm 42 Diameter of the bed Cm 92 Thickness Mm 2 Material of construction 6 (product contact part) 04 (non-contact part) Air pressure Bar 6 Type of steam used Saturated steam 20

5 Control Strategy for Continuous System: Figure (.): Physical Diagram of the Fluidized Bed Dryer, Continuous system A. Control of the Furnace Temperature (Loop ): Transfer Functions Identification: Proportional controller: G (c)=kc Valve transfer function: G (v)= 0.s+ 2

6 Process transfer function: G (p)= (5s+) Sensor transfer function : G (m)= (0.2s+) Figure (.2): Block diagram of loop () with identified transfer functions Analysis of Stability and Tuning of Loop : Routh-Hurwtz Analysis: The characteristic equation: Kc + (0.s + )(5s + )(0.2s + ) =0 0.s +.52s 2 +5.s+(+ Kc)=0 The ultimate gain ku = 79.6 Determination of the ultimate period by direct substitution method (ω co): The ultimate period Root Locus Method: The OLTF of loop : OLTF = Kc (0.s+)(5s+)(0.2s+) Pu = 0.86 sec 22

7 Figure (.): Root Locus plot of loop The ultimate gain ku = 78.5 Pu = 2π = sec 7.24 Bode Plot Method: The OLTF of loop : OLTF = Kc (0.s+)(5s+)(0.2s+) 2

8 Figure (.4): Bode plot of loop At -80 Pu = sec Ku = 78. The Average of Ultimate Gains and Ultimate Periods: Ku(R) + Ku(R L) + Ku (B) Ku (average) = Ku (average) = = Pu (average) = Pu (average) = Pu(R) + Pu(R L) + Pu (B) = sec Table (.2): (Ziegler-Nichols) Tuning parameters by using Ku (average) and Pu (average) : Type of controller Kc τi τd P PI PID Simulation of the System for (loop ): System Response for P-Controller: The overall transfer function: G(s) = 7.874s s +.52s s

9 Offset investigation for P-controller: Figure (.5): System response of loop using P- Controller Offset = C - C id C id = magnitude of unit step change = C = lim s 0 [s C(s)] C = = offset = = System Response for PI-Controller: The overall transfer function and the system response for PI-controller were determined using MATLAB software. The overall transfer function: G(s) = 5.088s s s s +.805s s

10 Figure (.6): System response of loop using PI-Controller Offset investigation for PI-controller: G(s)= C(s) R(s) = Offset = C - C id offset = = s s s s +.805s s+5.4 System Response for PID-Controller: G(s) = The overall transfer function: 0.498s +6.27s s s s +4.48s s

11 Figure (.7): System response of loop using PID-Controller Offset investigation for PID-controller G(s)= C(s) R(s) = 0.498s +6.27s s s s +4.48s s r(t) = R(S) = S 0.498s C(s) = [ +6.27s s ]. 0.04s s +4.48s s s Offset = C - C id C id = magnitude of unit step change = C = lim s 0 [s C(s)] C = = offset = = 0 Table (.): Characteristics of closed loop Characteristic Over shoot(%) Rise time(sec) Settling time(sec) Decay ratio Value

12 Dampness coefficient (ξ) Final value The system is under damped. ƺ < Table (.4): Characteristics of closed loop response with PI-controller Characteristic Over shoot(%) Rise time(sec) Settling time(sec) Decay ratio Dampness coefficient (ξ) Final value Value ƺ < The system is underdamped overshoots. Table (.5): Characteristics of closed loop response with PID-controller Characteristic Value Over shoot(%) Rise time(sec) Settling time(sec) Decay ratio Dampness coefficient (ξ) Final value ƺ < The system is under damped overshoots 28

13 Figure (.8): The comparison between different type of controllers (P, PI and PID) Due to the minimum overshoot the PI-controller is selected, this because high overshoot in temperature will damage the products. Table (.6): The overshoot of different types of controllers Type of controller Overshoot (%) P 87. PI 26 PID 94.8 B. Control of the Fluidized Bed Pressure (Loop 2): Transfer Functions Identification: G (c)=kc G (v)= 0.s+ G (p)= (0s+) G (m)= (0.2s+) 29

14 The Average of Ultimate Gains and Ultimate Periods: Ku(R) + Ku(R L) + Ku (B) Ku (average) = Ku (average) = = 87.0 Pu (average) = Pu (average) = Pu(R) + Pu(R L) + Pu (B) =.506 sec Table (.7): (Ziegler-Nichols) Tuning parameters by using Ku and Pu (soourse) Type of controller Kc τi τd P PI PID Simulation of the System for (loop 2): Figure (.9): The comparison between the three types of controllers 0

15 Table (.8): The overshoot of different types of controllers Type of controller Overshoot(%) P 65.6 PI 0 PID 69.8 Due to the minimum overshoot the P-controller is selected. C. Control of the Outlet Air Humidity (Loop ): G (c)=kc G (v)= G (p)= s 2 +2s+ G (m)= (0.s+) The Average of Ultimate Gains and Ultimate Periods: Ku(R) + Ku(R L) + Ku (B) Ku (average) = Ku (average) = = 2.97 Pu(R) + Pu(R L) + Pu (B) Pu (average) = Pu (average) = =.8 sec Table (.9): (Ziegler-Nichols) Tuning parameters Type of controller Kc τi τd P PI PID

16 Simulation of the System for (loop ): Figure (.0): The comparison between the three types of controllers Table (.0): The overshoot of different types of controllers Type of controller Overshoot (%) P 68.5 PI 9.9 PID 58.7 Due to the maximum overshoot the PI-controller is selected. 2

17 Conclusions International Journal of Engineering, Applied and Management Sciences Paradigms, Vol. 45, Issue 0 The stability and tuning are different giving different parameters, the root locus and bode plots are also different with different parameters and stability limits. It may be concluded that the digital controller (PLC) itself tune to stable perform. Recommendations There for it is recommended that continuous control system should be replaced by discrete control system. Acknowledgement The authors wish to thank the graduate college of the Karary University for Help and registration of this work for PhD in chemical engineering. References [] Gasmelseed, G, A, A Text Book of Engineering Process Control, G. Town, Khartoum, (205). [2] Robinson, J. W., "Improved Moisture Content Control Saves Energy", Internet: (2000). [] Jumah, R. Y., Mujumdar, A. S. and Raghavan, G. S., "Control of Industrial Dryers", Handbook of Industrial Drying, 2nded, (A.S.Mujumdar, ed.), Marcel Dekker, New York, (995), pp [4] Strumillo, C., Jones, P., and Zulla, R., "Energy Aspects in Drying", Handbook of Industrial Drying, 2nded, (A.S.Mujumdar, ed.), Marcel Dekker, New York, (995), pp.4-68.