Himawan Tri Bayu Murti Petrus
Department of Chemical Engineering (Sustainable Mineral Processing Research Group), Faculty of Engineering, Universitas Gadjah Mada, Jalan Grafika No. 2 Kampus UGM Bulaksumur, D. I. Yogyakarta 55281, Indonesia

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Densification process of merbau (Intsia bijuga) and matoa (Pometia pinnata J.R. Forster & J.G Forster) sawdust waste for biomass based solid fuel source in West Papua Indonesia: Optimization using Response Surface Methodology (RSM) Agus Prasetya; Halomoan Siagian; Felix Arie Setiawan; Himawan Tri Bayu Murti Petrus
Jurnal Rekayasa Proses Vol 15 No 1 (2021): Volume 15, Number 1, 2021
Publisher : Jurnal Rekayasa Proses

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jrekpros.66024

Abstract

Merbau (Intsia bijuga) and matoa (Pometia pinnata J.R. Forster & J.G Forster) are two amongst many prominent biomass sources from West Papua, Indonesia. With their versatile characteristics, merbau and matoa wood are used in many industries such as furniture, music instrument, and many other specialty products. However, wood processing industries can emit up to 60% of the residue. In this study, the usage of both merbau and matoa sawdust wastes as solid fuel was studied using response surface methodology (RSM). merbau and matoa sawdust are sieved to get the desired particle size (-20+50 mesh). Two kinds of the sawdust are then mixed in various compositions (70, 50, and 30% merbau). The mixed sawdust is then mixed with varied sticky starch solutions (1, 5, and 10%) to be formed in pellets with various moulding compression force (50, 100, and 150 kg/cm²). The pellets are then analyzed for its characteristics such as ash, moisture contents, and calorific value to be compared with its initial conditions. A full three-level factorial design under RSM was applied to explain the correlation between independent and dependent variables. The results show that statistically, merbau content, binder content, and compressive force showed relatively significant effects on the studied responses (ash content, moisture content, and calorific value). In addition, ANOVA analysis proved that each variable has significant effects on the responses that are confirmed by practically zero P-value. The coefficient of determinations (R²) are all above 0.96 and the normal probability plots confirm that the proposed models adequate the experimental results.