Optimization of syngas production from co-gasification of palm oil decanter cake and alum sludge: An RSM approach with char characterization.

The study explores co-gasification of palm oil decanter cake and alum sludge, investigating the correlation between input variables and syngas production. Operating variables, including temperature (700-900 °C), air flow rate (10-30 mL/min), and particle size (0.25-2 mm), were optimized to maximize syngas production using air as the gasification agent in a fixed bed horizontal tube furnace reactor. Response Surface Methodology with the Box-Behnken design was used employed for optimization. Fourier Transformed Infra-Red (FTIR) and Field Emission Scanning Electron Microscopic (FESEM) analyses were used to analyze the char residue. The results showed that temperature and particle size have positive effects, while air flow rate has a negative effect on the syngas yield. The optimal CO + H2 composition of 39.48 vol% was achieved at 900 °C, 10 mL/min air flow rate, and 2 mm particle size. FTIR analysis confirmed the absence of C─Cl bonds and the emergence of Si─O bonds in the optimized char residue, distinguishing it from the raw sample. FESEM analysis revealed a rich porous structure in the optimized char residue, with the presence of calcium carbonate (CaCO3) and aluminosilicates. These findings provide valuable insights for sustainable energy production from biomass wastes.

Changes in the microbiota during biological treatment of palm oil mill waste: A critical review.

Palm oil mill waste has a complex cellulosic structure, is rich in nutrients, and provides a habitat for diverse microbial communities. Current research focuses on how the microbiota and organic components interact during the degradation of this type of waste. Some recent studies have described the microbial communities present in different biodegradation processes of palm oil mill waste, identifying the dominant bacteria/fungi responsible for breaking down the cellulosic components. However, understanding the degradation process's mechanisms is vital to eliminating the need for further pretreatment of lignocellulosic compounds in the waste mixture and facilitating the commercialization of palm oil mill waste treatment technology. Thus, the present work aims to review microbial community dynamics via three biological treatment systems comprehensively: composting, vermicomposting, and dark fermentation, to understand how inspiration from nature can further enhance existing degradation processes. The information presented could be used as an umbrella to current research on biological treatment processes and specific research on the bioaugmentation of indigenous microbial consortia isolated during the biological degradation of palm oil mill waste.

Conversion of waste frying oil into biodiesel using recoverable nanocatalyst based on magnetic graphene oxide supported ternary mixed metal oxide nanoparticles.

The magnetic graphene oxide (GO) supported with heterogeneous ternary mixed metal oxide (MMO) was used as nanocatalyst to enhance the conversion of waste frying oil (WFO) triglycerides to biodiesel via esterification process. In this regard, acidic MGO was modified with three basic metal cations of cerium, zirconium, and strontium oxides to produce heterogeneous MGO@MMO nanocatalyst. The nanocatalyst was characterized by FESEM, TEM, EDX and FTIR. The influence of different parameters such as catalyst material ratio, methanol to oil ratio, contact time, and reaction temperature was studied. Based on the results of effecting parameters, the MGO@MMO nanocatalyst converted WFO to biodiesel with a yield 94%, a reaction time of 90 min, methanol to oil ratio (8:1), and a temperature of 60 °C. Esterification mechanism indicated the MGO@MMO nanocatalyst having both binary Brønsted acid-base sites that increased the conversion yields as compared to MGO and MMO at low temperatures.

Non-Carcinogenic Health Risk Assessment due to Fluoride Exposure from Tea Consumption in Iran Using Monte Carlo Simulation.

Excessive intake of fluoride can cause adverse health effects. Consumption of tea as a popular drink could be a potential source of fluoride exposure to humans. This research aimed to evaluate the fluoride concentration in tea among the Iranian people using the available data in the literature and to assess the health risk related to the consumption of tea in men, women, and children. The health risk assessment was conducted using the chronic daily intake and hazard quotient according to the approach suggested by the Environmental Protection Agency. The fluoride content in published studies varied noticeably, ranging from 0.13 to 3.27 mg/L. The results revealed that the hazard quotient (HQ) in age groups of women (21-72 years) and children (0-11 years) was within the safe zone (HQ < 1) which showed that there was no potential of non-carcinogenic risk associated with drinking tea in these groups. However, in one case of the men (21-72 years), the HQ > 1 which shows a probable risk of fluorosis. The order of non-carcinogenic health risks in the studied groups was in the order of men > women > children. The results of this can be useful for organizations with the responsibility of human health promotion.

Phytoremediation of palm oil mill secondary effluent (POMSE) by Chrysopogon zizanioides (L.) using artificial neural networks.

Artificial neural networks (ANNs) have been widely used to solve the problems because of their reliable, robust, and salient characteristics in capturing the nonlinear relationships between variables in complex systems. In this study, ANN was applied for modeling of Chemical Oxygen Demand (COD) and biodegradable organic matter (BOD) removal from palm oil mill secondary effluent (POMSE) by vetiver system. The independent variable, including POMSE concentration, vetiver slips density, and removal time, has been considered as input parameters to optimize the network, while the removal percentage of COD and BOD were selected as output. To determine the number of hidden layer nodes, the root mean squared error of testing set was minimized, and the topologies of the algorithms were compared by coefficient of determination and absolute average deviation. The comparison indicated that the quick propagation (QP) algorithm had minimum root mean squared error and absolute average deviation, and maximum coefficient of determination. The importance values of the variables was included vetiver slips density with 42.41%, time with 29.8%, and the POMSE concentration with 27.79%, which showed none of them, is negligible. Results show that the ANN has great potential ability in prediction of COD and BOD removal from POMSE with residual standard error (RSE) of less than 0.45%.