Markov chain quasi-Monte Carlo method for forecasting fire hotspots in Sarawak, Malaysia.

Stochastic modeling approaches have attracted many researchers to the field. However, fire hotspot detection suffers from not using a Markov chain quasi-Monte Carlo (MCQMC) as a forecasting methodology. This paper proposes improvements to the computational time by combining the strengths of the Markov chain Monte Carlo (MCMC) and quasi-Monte Carlo (QMC) methods. The proposed method can lead to more precise and stable results, particularly in problems with high-dimensional integration or complex probability distributions. The proposed method is applied to a case study of fire hotspot detection in Sarawak, Malaysia. The outcome of this study reveals that the MCQMC method is more computationally efficient, taking only 0.0746 seconds compared to MCMC's 0.0914 seconds and QMC's 0.0994 seconds. It is shown that the best option derived by the proposed method is effective in predicting fire hotspots and providing quick solutions to protect the environment and communities from forest fires.

Optimization and characterization of magnetite-reduced graphene oxide nanocomposites for demulsification of crude oil in water emulsion.

Oily wastewater from the oil and gas industry negatively affects the environment. Oily wastewater typically exists in the form of an oil-in-water emulsion. Conventional methods to treat oily wastewater have low separation efficiency and long separation time and use large equipment. Therefore, a simple but effective method must be developed to separate oil-in-water emulsions with high separation efficiency and short separation times. Magnetite-reduced graphene oxide (M-RGO) nanocomposites were used as a demulsifier in this work. Magnetite nanoparticles (Fe3O4) were coated on reduced graphene oxide (rGO) nanosheets via an in situ chemical synthesis method. The synthesized M-RGO nanocomposites are environmentally friendly and can be recovered after demulsification by an external magnetic field. M-RGO characterization was performed using X-ray diffraction, Fourier transform infrared spectroscopy, X-ray photoelectron spectroscopy, field emission scanning microscopy, Raman spectroscopy, and vibrating sample magnetometry. Demulsification performance was evaluated in terms of M-RGO dosage, effects of pH, and brine concentration. The demulsification capability of M-RGO was determined based on the residual oil content of the emulsion, which was measured with a UV-vis spectrometer. The response surface method was used to determine the optimum conditions of the input variables. The optimum demulsification efficiency achieved at pH 4 and M-RGO dosage of 29 g L-1 was approximately 96%. This finding demonstrates that M-RGO nanocomposites are potential magnetic demulsifiers for oily wastewater that contains oil-in-water emulsions. Also, the recyclability of this nanocomposite has been tested and the results shown that it is a good recyclable demulsifier.