Optimization of Calorific Value of Densified Bush Mango Shell and Palm Pressed Fibre Briquettes
Edeh John Chijioke,
Eze Nixson Nwakonam,
Ibeh Matthew Imagwuike,
Eboh Francis Chinweuba
Issue:
Volume 7, Issue 3, May 2022
Pages:
36-44
Received:
28 June 2022
Accepted:
18 July 2022
Published:
24 August 2022
Abstract: Energy value of biomass materials can be enhanced through composition, densification and process parameter manipulation. In this study, biomass briquettes of bush mango shell (BMS) and palm pressed fibre (PPF) compositions were evaluated and its calorific values optimized. The effects of biomass concentration, dwelling/compaction time and compression pressure on calorific value were investigated for briquette samples in the compositions of BMS: PPF ratios of 100:0, 75:25, 50:50, 25:75, and 0:100) as sample A, B, C, D and E respectively. An empirical prediction model of the combustion property of the briquettes was developed and optimized using response surface methodology. It was observed across the samples that as bush mango shell composition increased, the calorific value improved significantly from 12.4kJ/kg to 18.65kJ/kg. Increase in dwelling time and pressure also showed slight increase in calorific value of the briquette samples. An optimum calorific value of 19.03 kJ/kg for briquette sample B (75:25 biomass ratio) was realized at dwelling time of 40 minutes and pressure of 25MPa as adequately predicted by a reduced second order model. The model prediction accuracy was over 98% (Pred. R2 of 0.9858) with Coefficient of Variance of 0.64% and Adeq. Precision value of 63.936. Thus, Sample B briquettes possess improved combustion properties with burning rate of 0.472g/min at optimum conditions hence suitable for adoption by investors in renewable energy sector.
Abstract: Energy value of biomass materials can be enhanced through composition, densification and process parameter manipulation. In this study, biomass briquettes of bush mango shell (BMS) and palm pressed fibre (PPF) compositions were evaluated and its calorific values optimized. The effects of biomass concentration, dwelling/compaction time and compressi...
Show More
Flowing Water Algorithm: A New Approach for Combinatorial Optimization Problems
Xuepeng Liu,
Qing Wang,
An-Da Li
Issue:
Volume 7, Issue 3, May 2022
Pages:
45-52
Received:
11 July 2022
Accepted:
16 August 2022
Published:
24 August 2022
Abstract: Being greatly inspired by the natural flowing regulation of water, we propose a new meta-heuristic algorithm — Flowing Water Algorithm (FWA) for the solution of combinatorial optimization problems (COPs). Since the solution space of COPs is multidimensional, complex and has many local extreme values, according to our proposed method, it appears to be similar to an endless hilly area with mountains, valleys and plateaus. The downward-flowing water in such area finds its way to the lowest point in the hill. Water always flows downward and eventually converges at the lowest place without any outside intervention except for gravity. Such a flowing course can be deemed as a process for the water to seek for the lowest point. The proposed algorithm is derived from such a water flow process. This algorithm combines a local search strategy with a population-based search strategy to improve both local and global search abilities. Four operators, including the local search, water overflow, drilling water tunnel and evaporation-rain are included in FWA, making this algorithm successfully perform tabu search, positive feedback, “survival of the fittest”, and local optimum escape. Two examples of its application in the traveling salesman problem (TSP) show that FWA outperforms the benchmark methods for both solution quality and convergence speed.
Abstract: Being greatly inspired by the natural flowing regulation of water, we propose a new meta-heuristic algorithm — Flowing Water Algorithm (FWA) for the solution of combinatorial optimization problems (COPs). Since the solution space of COPs is multidimensional, complex and has many local extreme values, according to our proposed method, it appears to ...
Show More