Combustion of palm oil decanter cake (PODC) is a propitious alternative waste to energy means. However, the mono-combustion of PODC prompt severe ash slagging behavior which give rise to reduction in heat transfer and also shorten the lifespan of combustion reactors. In this study, alum sludge (AS) was introduced at different proportion of 30%, 50% and 70% to revamp the slagging characteristics of PODC during combustion. The addition of AS improved ash fusion temperature of PODC during co-combustion as ash fusion temperature increased significantly under high AS dosage. Slagging and fouling indices showed that at 50% AS addition, slagging tendency of the co-combustion ashes can be ignored. The predictive model for PODC-AS combustion showed good correlation coefficient with 0.89. Overall, co-combustion of PODC and AS is an ideal ash related problem-solving route. The proposed PODC slagging preventive method by AS was based on: (1) limited amount of aluminum content in PODC-AS system resulted in development of refractory ash (2) reduction in proportion of basic oxide which act as ash bonding glue played important role in the regulation of slagging (3) reduction of cohesive bond by formation of spongy and porous structure which prevented ash slagging.
Development of strategies for removing heavy metals from aquatic environments is in high demand. Cadmium is one of the most dangerous metals in the environment, even under extremely low quantities. In this study, kenaf and magnetic biochar composite were prepared for the adsorption of Cd2+. The synthesized biochar was characterized using (a vibrating-sample magnetometer VSM), Scanning electron microscopy (SEM), X-ray powder diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), and X-ray photoelectron spectroscopy (XPS). The adsorption batch study was carried out to investigate the influence of pH, kinetics, isotherm, and thermodynamics on Cd2+ adsorption. The characterization results demonstrated that the biochar contained iron particles that help in improving the textural properties (i.e., surface area and pore volume), increasing the number of oxygen-containing groups, and forming inner-sphere complexes with oxygen-containing groups. The adsorption study results show that optimum adsorption was achieved under pH 5-6. An increase in initial ion concentration and solution temperature resulted in increased adsorption capacity. Surface modification of biochar using iron oxide for imposing magnetic property allowed for easy separation by external magnet and regeneration. The magnetic biochar composite also showed a higher affinity to Cd2+ than the pristine biochar. The adsorption data fit well with the pseudo-second-order and the Langmuir isotherm, with the maximum adsorption capacity of 47.90 mg/g.
Membrane fouling is a critical bottleneck to the widespread adoption of membrane separation processes. It diminishes the membrane permeability and results in high operational energy costs. The current study presents optimizing the operating parameters of a novel rotating biological contactor (RBC) integrated with an external membrane (RBC + ME) that combines membrane technology with an RBC. In the RBC + ME, the membrane panel is placed external to the bioreactor. Response surface methodology (RSM) is applied to optimize the membrane permeability through three operating parameters (hydraulic retention time (HRT), rotational disk speed, and sludge retention time (SRT)). The artificial neural networks (ANN) and support vector machine (SVM) are implemented to depict the statistical modelling approach using experimental data sets. The results showed that all three operating parameters contribute significantly to the performance of the bioreactor. RSM revealed an optimum value of 40.7 rpm disk rotational speed, 18 h HRT and 12.4 d SRT, respectively. An ANN model with ten hidden layers provides the highest R2 value, while the SVM model with the Bayesian optimizer provides the highest R2. RSM, ANN, and SVM models reveal the highest R-square values of 0.97, 0.99, and 0.99, respectively. Machine learning techniques help predict the model based on the experimental results and training data sets.
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.