AMA, Agricultural Mechanization in Asia, Africa and Latin America (AMA) (issn: 00845841) is a peer reviewed journal first published online after indexing scopus in 1982. AMA is published by Farm Machinery Industrial Research Corp and Shin-Norinsha Co. AMA publishes every subjects of general engineering and agricultural engineering.
AMA, Agricultural Mechanization in Asia, Africa and Latin America (ISSN: 00845841) is a peer-reviewed journal. The journal covers Agricultural and Biological Sciences and all sort of engineering topic. the journal's scopes are in the following fields but not limited to:Azerbaijan Medical Journal Gongcheng Kexue Yu Jishu/Advanced Engineering Science Zhonghua er bi yan hou tou jing wai ke za zhi = Chinese journal of otorhinolaryngology head and neck surgery Interventional Pulmonology
The dark brownish color effluent from palm oil mill effluent (POME) has continued to receive considerable interest and posed widespread concern due to its repercussions on water bodies. This study examines the performance of hydrophilic composite hollow fibre poly (vinylidene fluoride) (PVDF)-polyvinylpyrrolidone(PVP) membrane embedded with titanium dioxide (TiO2) nanoparticles as a robust antifouling intermediary for the decolourization of palm oil mill effluent (POME). The nanocomposite hollow fibre membrane with varied TiO2-nanoparticles loadings (0-2.0 wt.%) were swirled through phase inversion procedure to provide a negatively charged surface. The developed nanocomposite membranes were analyzed and compared according to SEM, contact angle, EDX, permeability flux, COD and color removal, and fouling resistance from (POME). Color removal, fouling resistance, and flux permeability enhanced with a rise in TiO2 loading. The flux efficiency of the membranes was assessed using pure water and POME as a feed liquid. Results show that the membrane-embedded with 1.0 wt% concentration of TiO2 exhibited higher performance showing pure water flux and POME flux, COD and color removal of 120.74 L/m2h, 74.3 L/m2h, 90.1%, and 94.7% from POME respectively. Furthermore, an excellent fouling hindrance was attained, thereby allowing the percentage of flux recovery ratio (FRR) recorded was 95.46%, 92.49%, and 87.49%, after three cycles of uninterrupted filtrations for a period of 540 min. Yet, declination in flux and fouling resistance was observed at supplemental loading of 1.5 and 2.0 wt%-TiO2 owing to the accumulation of nanofillers in the membrane matrix at the higher concentrations.
This study provides the use of a near-infrared (NIR) sensor with an appropriate gimbal mounted on a lightweight Unmanned Aerial Vehicle (UAV) to operate remote sensing acquisitions of oil palm trees between the age of 9 to 15 years, at low altitudes. UAVs and NIR sensor calibrations and corrections were used in this study to develop a correction approach for obtaining accurate remote sensing images that may be used to monitor oil palms. The major aspects of calibration required for quality control of the flight data are UAV attitude flight information such as heading, pitch, and yaw. Compared to the previous system, which is based on a gyroscopic instrument, this flight information delivers superior precision and reliability. Geometric and radiometric sensor corrections and calibrations were carried out. After correcting undesired and sensor fault features, reliability image data analysis was created. As a new approach, the Improved Normalized Difference Vegetation Index (INDVI) algorithm uses the red and green channels as reflectance and the blue channel as absorption (400-1100 nm region), where healthy plants normally reflect green light in NIR and visible light, as opposed to the traditional NDVI, which only uses the near-infrared and red channels where the processing image is converted using ImageJ software. The results only discuss whether an area is rich in biomass or has less vegetation, which influences the results because INDVI and NDVI are two different indices.
Agricultural wastes such as palm oil waste, corn stover, rice straw and wheat straw are some of the wastes that are popular to become feedstocks for anaerobic co-digestion. This is because of their plentiful supply, potential to utilize as a source of biogas, high potential for biogas yields, and low costs. In this study, decanter cake from palm oil mill (OPDC) and sewage sludge were used as substrate to determine the potentiality to produce methane gas from anaerobic co-digestion. Methane yield was analyzed using Excel solver between theoretical yield and experimental yield by using the modified Gompertz Equation. 6400 mL of anaerobic digester was used to conduct the fermentation at mesophilic temperature of 38±1 ⁰C for 30 days. The digester containing sewage sludge and decanter cake mixed at inoculum to substrate (I/S) ratio of 2:1 at 25% of total solid content. Biogas produced was collected and measured using a syringe and transferred into Hungate tube by water displacement method. The biogas composition was measured by gas chromatography (GC). The maximum biogas yield that was produced in anaerobic digester was 5848 mL with the highest methane yield of 581 mL CH4/g-VS at the 14th day. The co-digestion of sewage sludge and decanter cake was converted to methane corresponding to cumulative methane production of 10451 mL CH4/g VS. The experimental yield was compared with theoretical yield by using modified Gompertz. Based on the experimental data, 16748.5 mL of cumulative methane yield was obtained and compared to the cumulative predicted data of 32811.1 mL respectively. In conclusion, decanter cake and sewage sludge have a potential to become substrate in anaerobic co-digestion.
Recently, the glut of residues generated from the oil palm mill sector has become a topical global issue due to poor management strategies that constitute environmental risks. This work focused on the characterization of palm oil mill ashes to improve the bearing strength of weak soil. Palm oil mill ashes, namely boiler bottom ash (POMBBA), boiler retained fly ash (POMBFA), and collector fly ash (POMFA) from the incineration process of oil palm biomass were characterized. The morphology of palm oil mill ashes was observed under scanning electron microscopy (SEM). Scanning electron microscope-energy dispersive X-ray analysis was used to analyze the chemical and physical properties of the ashes, with mineral coal fly ash as a control in comparison to palm oil mill ashes. Results of the chemical composition of coal fly ash (CoFA) revealed that it was rich in silicon dioxide (SiO2) with over 38.55%, POMBBA 39.53%, POMBFA 34.77%, and POMFA 15.08% respectively. Yet, enhancement of soft soil requires the presence of Al2O3, Fe2O3, and CaO, to produce a composite with adequate strength. All the chemical constituents of the studied ashes exhibit pozzolanic and cementitious properties, which could reinforce soil bearing capacity and a low-cost binder in the road construction sector.
The performance of reflectance spectral measurement using a spectroscopic method could be affected by scanning distances. Therefore, this study was undertaken to investigate the effect of scanning distances on the performance of a spectroscopic method to predict oil content of oil palm fruitlets. A total of 216 fruitlet samples from six bunches at different maturity stages were used. Spectral data was collected using a high-resolution fibre optic spectrometer with wavelengths ranged between 500 and 900 nm. The samples were scanned at five different scanning distances namely 0, 1, 2, 3, 4 and 5 cm. Soxhlet extraction analysis was performed to determine the oil content of the samples. Partial least square (PLS) regression method was used to develop calibration and prediction models to correlate the spectral data with the respective oil content of the samples. For prediction models, the coefficient of determination (R2) for 0, 1, 2, 3, 4 and 5 cm scanning distances were 0.95, 0.93, 0.86, 0.83, 0.80 and 0.80, respectively. Artificial neural network (ANN) was used to classify fruitlets samples into the maturity stages and yielded good classification performance with an average of 94%. These results indicate that the oil content of fruitlet samples can be predicted by using the spectroscopic method. However, the scanning distances could affect the prediction accuracy of the models. This study has also demonstrated that the spectroscopic method coupled with ANN algorithm could be applied to classify the maturity stages of oil palm fruitlets.