CHAPTER 8 CONCLUSION AND FUTURE WORK

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1 196 CHAPTER 8 CONCLUSION AND FUTURE WORK 8.1 GENERAL The greatest challenge to researchers and gasification system manufacturers are automation and control of downdraft biomass gasifier systems. In this work controlling technique has been adapted to two different downdraft biomass gasifier systems with capacity 135Kg/hr. Automation and control of biomass gasifier process were more advantageous when compared to manual operation. Experiments were carried out on two different biomass gasifier systems in different circumstances. Experimental result shows that the gasification efficiency decreases with increase in moisture content of the biomass and it reduces the calorific value of producer gas. The tar fraction also increases with the increase in moisture. The moisture content below 15% by weight is desirable for trouble free and economical operation of the gasifier. Higher moisture contents reduce the thermal efficiency of the gasifier and results in low gas heating values. Igniting the fuel with higher moisture content becomes increasingly difficult, which leads to poor yield and gas quality.

2 197 A static model of the biomass gasifier process (135kg/hr) was obtained by empirical method. The model was tested for different set of input data and the results were compared with the results of actual system in which the static model resembles the actual process. A fuzzy based intelligent controller has been developed for downdraft biomass gasifier system with capacity (135kg/hr). Fuzzy inference system was implemented in a microcontroller. The following variables were used to design the fuzzy controller. i) Manipulated variables : Air flow rate (F A ) and frequency of rotation of the grate (f g ) ii) Disturbance: Moisture content (Hp) iii) Controlled variables: Temperature (T), and CO/CO 2 ratio The CO/CO 2 ratio and temperature were effectively controlled with fuzzy logic controller. The implementation results for the gasifier system were found to be better than conventional control. Experimental analyses were conducted on downdraft biomass gasifier system with capacity (6kg/hr). A dynamic first order model with dead time was obtained using process reaction curve method. This model was used to develop various controllers which were verified by simulation.

3 198 Experimentally it is observed that gasification temperature has the highest influence on the efficiency, hence temperature has been considered as a controlled variable with air flow rate as a manipulated variable. The PI and PID controllers were designed with the help of dynamic model.the Ziegler Nichols method was employed to find out the gain values Kp, Ki and Kd. The response of the PID had oscillations and finally settles at 100 seconds. Comparatively the response of PI controller to the process has faster settling time and reduced oscillations. The fuzzy based intelligent controller has been designed with obtained dynamic model of the gasifier process. The fuzzy logic controller offers better performance for the parameters like settling time, overshoot and undershoot. The self-tuning fuzzy logic controller was implemented with the help of dynamic model in LabVIEW. The controller performances have been investigated and found that settling time was still faster for a self tuning fuzzy controller compared to other controllers. The process reaches its desired value at about 25 seconds. An ash removal process of biomass gasifier system was considered. Ash handling system may be automated by regulating the pressure of the gasifier unit. Fuzzy control was designed to regulate the pressure without over shoot. A prototype was designed to implement the fuzzy controller using microcontroller in laboratory set up. Improved response

4 199 were obtained undershoot. for the process without overshoot and The biomass gasifier unit allows clean gas without particles. This permits (i) large range of maximum temperature of the gas at entry to the heat exchanger (ii) higher reduction in co 2 emissions because of higher biomass to natural gas ratio which means higher power production results in lesser co 2 emissions. Biomass gasification offers the most attractive alternative energy system for agricultural purposes. Most preferred fuels for gasification are charcoal and wood. However biomass residues are the most appropriate fuels for on-farm systems. 8.2 FUTURE WORK The prototype fuzzy logic controller for controlling the temperature and CO/CO 2 in downdraft biomass gasifier with capacity 135Kg/hr can be suggested to be implemented for the actual process. The pressure controller has been designed using fuzzy logic for ash removal process in biomass gasifier systems can be recommended for actual process Self tuning fuzzy logic controller is more suitable controller for biomass gasifier system because of variable time delay. Hence this controller may be implemented in real time.

5 200 The complete automation biomass based power plant. Very limited experience has been gained in gasification of biomass residues. Most extensively used and researched systems have been based on downdraft gasification. However it appears that for fuels with high ash content fluidized bed combustion may offer a solution. At present no reliable and economically feasible systems exist. Focus on syngas purification and system optimization. Initially the construction needs to be as light as possible in order not to reduce excessive hauling capacity of the vehicle. Later mobile applications can be operated with fairly large variations in engine (and gasifier) load.