Optimization of Ash and Energy Yields from the Combustion of Flamboyant Pod, Groundnut Shell and Additive (Kaolin) Composite

E. Dada, Blessing A. Adebayo, A. Alade, K. Oladosu
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Abstract

Aim: I-optimal design via the Combined Methodology of the design expert software was used to optimize the ash yield from the combustion of a biocomposite mixture of Flamboyant Pod and Groundnut Shell with additive (Kaolin) in a grate furnace. Study Design: Analysis of Variance, and Artificial Neural Network were used to predict the ash yield from the data generated from the design process, while the coefficient of determination between variables was determined using the Principal Coefficient Analysis. The resulting ash yield was characterized for ash composition using a Scanning Electron microscope. Place and Duration of Study: Biochemical Engineering Laboratory, Department of Chemical Engineering, Ladoke Akintola University of Technology, Ogbomoso. September-December 2020. Methodology: Proximate analysis was used to determine Moisture Content, Fixed Carbon Content, and Volatile Matter values used to determine the Higher Heating Value of the composite. Results: The sample with 53wt. % Flamboyant Pod, 37wt. % Groundnut Shell, and 10wt. % kaolin mixture at 825 °C gave the lowest ash yield of 5wt. %. The correlation coefficient (R2) of the model equation developed for ash yield (0.9992) validated the model due to its closeness to 1. The deviation between the experiment and prediction for ash yield indicated 3wt. %. The Higher Heating Value calculated shows that the lowest ash yield composition has a higher heating value of 15.14 and the highest yield mixture has a lower Higher Heating Value of 12.26. Conclusion: The reduction of ash yield from 56 to 5wt. % (as observed in previous studies shows a greater improvement in ash reduction during the combustion process.
优化燃烧香荚兰、花生壳和添加剂(高岭土)复合材料产生的灰分和能量
目的:通过设计专家软件的 "组合方法 "进行 I-优化设计,以优化在炉排炉中燃烧含添加剂(高岭土)的火棘荚和花生壳生物复合混合物的灰产量。研究设计:使用方差分析和人工神经网络从设计过程产生的数据中预测灰产量,同时使用主系数分析确定变量之间的决定系数。使用扫描电子显微镜对所得灰分产量的灰分成分进行表征。研究地点和时间:拉多克-阿金托拉理工大学化学工程系生化工程实验室,奥博莫索。2020 年 9 月至 12 月。研究方法:采用近似分析法确定水分含量、固定碳含量和挥发性物质值,用于确定复合材料的较高热值。结果:在 825 °C 温度下,含有 53 重量%花生荚果、37 重量%花生壳和 10 重量%高岭土混合物的样品灰分最低,仅为 5 重量%。灰分产率模型方程的相关系数(R2)(0.9992)接近 1,验证了该模型。计算得出的较高热值显示,灰分产量最低的成分具有 15.14 的较高热值,而灰分产量最高的混合物具有 12.26 的较低较高热值。结论灰分产率从 56% 降至 5%(与之前的研究结果一致),表明在燃烧过程中灰分的减少有了更大的改善。
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