Selection of biomass for efficient blend using mcdm technique and optimization of gasification process parameters using design of experiments

Abstract

Materials and Methods: Materials locally available Cotton stalk, groundnut shell, Sugarcane bagasse and Coconut shells were manually collected from the saurashtra, Gujarat region, and Proximate analysis was performed. This study presented comparative knowledge on biomass ranking analysis for the efficient blend, using three methods for MCDM Analytic Hierarchy Process (AHP), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and Grey Relation Analysis (GRA). Initially, the effect of various process parameters on the gas composition (H2, CO, and H2/CO) and the cold gas efand#64257;ciency was investigated using the one-factor-at-a-time (OFAT) method. This method involves changing the value of one parameter while keeping the others parameter constant. Then full factorial design and response surface methodology were used to design the biomass gasification experiment from the feed rate range of 6.1 10.1 kg/hr, temperature range of 600 1000 °C, and blend ratio 25-100%. CS and CNS were manually mixed into different blending ratios of CS25:CNS75, CS50:CNS50, CS75:CNS25, and CS100:CNS0. In this experimental study, In this research performed 36 tests run on the gasifier for different processing parameters. newlineResults and Discussion: In this research, the gasification of different biomass was carried out in gasiand#64257;er. This research is good conformity between the results of MCDM methods. The best material for the biomass was found to be Cotton Stalk and Coconut Shell that blend is efficient for biomass gasification. The entire test run is first evaluated using a full factorial design. The result shows excellent CO, H2, H2/CO, and CGE at a blend ratio of CS75:CNS25. The FFD optimization results are compared to the RSM optimization and desirability approach, which predicts more using CS70: CNS30 biomass blends with comparatively good responses. newlineConclusions: The experimental readings and results are reviewed in more detail by a statistical tool such as the response surface methodology using a complete factor design