Producer gas from different fuels produced in different gasifier types may considerably vary in composition. Because of the presence of CO, producer gas is toxic in nature. In its raw form, the gas tends to be extremely dirty, containing significant quantities of tars, soot, ash and water. In downdraft gasifier the fuel slowly moves down by gravity. During this downward movement, the fuel reacts with air, which is supplied by the suction of a blower or an engine and is converted into combustible producer gas in a complex series of oxidation, reduction, and pyrolysis reactions [ 3 ].
Ash is removed from the bottom of the reactor. The simplified diagram of this electric power plant kW is shown in Figure 2 , where the following parts can be seen biomass and air feeding, ash removal, gas cleaning and conditioning. The gasifier is a cylindrical reactor of 0. The moving bed of biomass rests on a perforated eccentric rotating grate which is at the bottom of the gasifier.
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The grate is driven by an electric motor, which operates at programmable time intervals. The frequency of motion could be modified to control the biomass residence time inside the reactor. The ashes fall through the perforated grate to be collected in a lower chamber. From this chamber the ashes are extracted by a screw conveyor.
A roots blower supplies air into the gasifier through a circular pipe located in the reactor throat, which has three injectors with a radial distribution that enters 4cm inside the bed. Temperature is measured inside the reactor using four Type-K thermocouples located at different levels. An online gas analyzer allows continuous measurements of CO and CO 2 using infrared absorption.
The simple block diagram of the gasifier control system is shown in Figure.
Gasification of Coal and Biomass Char Using a Superheated Steam Flame - White Rose eTheses Online
In addition, there exist disturbance variables which cannot be adjusted by the controller. The set points are the desired values for the process variables. Manipulated variables were used through the controller to obtain the desired effect on the process variables. The throat temperature is very closely related to the quality of the gas being produced.
The heating value of the produced gas was calculated from the average gas composition during each run. Also investigations are in hand to make comparisons with measurements of CO alone.
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This is the reason for taking it as another process variable. The detailed block diagram of gasifier control system is shown in Figure 4. Experiment have been carried out on the development of models of the downdraft gasifier to investigate the effects of varying biomass moisture, amount of fluidizing agent, gasification temperature and gas composition, viewing that gasification temperature has the highest influence on the efficiency. For studying the dynamic response of the gasifier, only temperature T is considered as a controlled variable and air flow rate F A as a manipulated variable.
Multi input and multi output system MIMO of the gasifier has been proposed later in this chapter for developing fuzzy controller as shown in figure 4. The Step response method is based on momentary response tests. Many industrial processes have step responses of the system in which the step response is varied after an initial time. A system with step response can be approximated by the transfer function. From the data of temperature values at different times obtained from the gasifier plant. A steady state response is plotted as shown in Figure 5. Where K is static gain and T is time constant.
The step change from is optimum region for controlling the particular gasifier. Proportional-Integral-Derivative PID algorithm is the most common control algorithm used in industry presently. Often, people use PID to control processes that include heating and cooling systems, fluid level monitoring, flow control and pressure control.
PID controller is not an adaptive controller, hence the controller has to be tuned frequently and whenever load changes. Auto- tuning of these controllers becomes difficult for complex systems [ 5 , 6 ]. In order to prove the drawbacks of conventional controller in downdraft gasifier a little attempt is made to design a PID control which is designed to ensure the specifying desired nominal operating point for temperature control of gasifier and regulating it, so that it stays closer to the nominal operating point in the case of sudden disturbances, set point variations, and noise.
The proportional gain Kp , integral time constant Ti , and derivative time constant Td of the PID control settings are designed using Zeigler- Nichols tuning method. The simulink model of PID control is shown in Figure 6.
The conventional controller has not suitable for this type of highly non-linear and slow process. In order to improve the gasifier control process the intelligent control techniques are proposed further in this paper. The process of gasification is a highly non-linear and slow process, and hence the development of an accurate model is very difficult.
The model must be representing the non-linear dynamic characteristics of the process. A plant model for biomass gasification process of woody wastes is proposed for control purpose, based on the plant data in a typical biomass gasification process in the biomass gasifier [ 7 ].
In this paper, a steady state model is developed with the collected plant data. In order to fit the collected plant data to the steady state model of the plant, certain simple mathematical equations were developed by adjusting the mathematical relations between the variables with reference to the recorded data.
The recorded plant data are as shown in the Table 1.go to link
One of the objectives of the control model developed here is to tune the controller. It is the amount of biomass consumed for the gasification process being considered for monitoring. It depends upon the flow rate F A , frequency of rotation of the grate f g and moisture content H p. Figure 8 shows the simulink model for biomass consumption. The mathematical expression for biomass consumption is equation 1. It depends on as F hb , F A , H p and the type of material expressed as a function of a material factor m b which represents the amount of of air needed to obtain combustion of 1 kg of dry biomass.
Figure 9 shows the simulink model of the equivalence ratio. The expression for ER is as follows equation 2. It depends on ER and H p. When H p is low, the ratio increases with ER to reach a maximum. The expression derived for the ratio is eqution 3. Temperature is related to the quality of produced gas very closely.
The value of T first decreases with ER, then increases and finally, again decreases. Figure 11 shows the simulink model for the temperature. The expression for T is equation 4. The equations 1 - 4 some constant values are assumed in order to fit with experimental data. Using the simulink models of four subsystems, the complete steady state model of the biomass gasifier was developed.
The gasifier model for control is shown in figure. This model can be used to validate the rules and membership functions of the fuzzy model. A fuzzy system is a static nonlinear mapping between its inputs and outputs. Fuzzy system provides a formal methodology for representing, manipulating and implementing a human heuristic knowledge about how to control a system [ 8 , 9 - 10 , 11 ].
The block diagram of a Fuzzy system as shown in figure 13 includes fuzzification, inference mechanism, rule base and defuzzification. The inference mechanism evaluates which control rules are relevant at the current time and then decides what the input to the plant should be. Catalytic effects are also studied by changing the bed materials viz.
Different biomass samples such as rice husk, rice straw, saw dust, wood chips, sugarcane bagasse and coconut coir have been gasified in the present work with different bed materials. Temperature during gasification was varied with C. ER was varied within 0.
Gasification of Coal and Biomass Char Using a Superheated Steam Flame
Attempt is made to develop correlation for the yield of hydrogen on the basis of dimensional analysis by relating different system parameters for all the biomass feed samples. Experimental results show hydrogen yields to vary within gm per kg of feed sample for different biomass samples. The calculated values of H2yieldare compared against the experimentally observed data.
If you are citizen of an European Union member nation, you may not use this service unless you are at least 16 years old. Schneider, Martin. Technical Univeristy Dresden, Phd Thesis. Van der Hoeven, Dorus. Technische Universiteit Eindhoven, Phd Thesis Peters, Bernhard. Thermal Conversion of Solid Fuels. CFD modelling. Tip: To turn text into a link, highlight the text, then click on a page or file from the list above. I believe it is open source. Thanks Steve Unruh. Yes Jim, I apologize.
I found your Kalle paper listing after I had posted my comment.