Optimization of Ash Yield from Bicomposite Biomass (Terminalia catappa and Chrysophyllum albidium) Seed Barks with Additive upon Combustion

Abstract

The ash yield from the combustion of a mixture of Africa star apple and tropical almond seeds shells (biocomposite biomass) with ammonium dihydrogen phosphate as an additive in a furnace was optimized using I-Optimal Design under the Combined Methodology of the Design Expert Software. The data obtained were analysed statistically using Analysis of Variance (ANOVA), Artificial Neural Network (ANN) for the prediction of ash yield and Principal Component Analysis (PCA) to determine the coefficient of determination (R²) between variables. Proximate analysis was used to evaluate Moisture Content (MC), Fixed Carbon Content (FCC), and Volatile Matter (VM) values while the Higher Heating Value (HHV) of the mixtures that gave the highest and lowest ash yields was evaluated numerically. The optimum conditions of process variables for the compositions of tropical almond, African star apple, and ammonium dihydrogen phosphate, as well as the temperature, were 30%, 60%, 10% and 704 oC, respectively leading to a minimum ash yield of 24.8%. The mathematical models for the ash using the I-optimal design indicate a good fit to the Quadratic model with a R² of 0.9999. The ANN model agreed significantly with the experimental results with an R² of 0.9939. The VM, FCC, MC, AC and HHV of the highest ash yield were 11.00%, 2.34%, 3.20%, 33.80% and 4487.747 , respectively. The study established the suitability of optimisation tool to develop solid fuel mixtures for possible use in grate furnaces and its efficiencies.

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

“…Ash deposition, poor ignition and combustion temperature reduction have been identified as some of the challenges faced during biomass combustion in large-scale utilization of biomass for power/heat generation, thus the reduction of ash formation and aggregation during biomass combustion among others has become a point of interest in the biomass-based renewable energy industry [4] as this will ensure efficient and clean combustion of biomass. "The use of additives such as kaolin during the combustion process is one of the means of solving some of these challenges" [5]. "High moisture content can lead to poor ignition and reduction of combustion temperature, which in turn hinders the combustion of the reaction products and consequently affects the quality of combustion.…”

“…It additionally comprises of a group of techniques utilized in the empirical study of connections between at least one dependent variables and various independent variables. Significant procedures in optimization includes Response Surface, Factorial, and Mixture Methodologies" [5,11,12]. One of the most widely used techniques for classifying and forecasting behaviour is Artificial Neural Networks (ANN), which have been demonstrated to be significantly more accurate at forecasting than more conventional linear techniques like regression analysis.…”

“…One of the most widely used techniques for classifying and forecasting behaviour is Artificial Neural Networks (ANN), which have been demonstrated to be significantly more accurate at forecasting than more conventional linear techniques like regression analysis. This technique has been effectively applied in several engineering and scientific sectors during the past several years [5]. This is because it can be used to evaluate data that isn't enough.…”