Optimization of Operational Parameters Using Artificial Neural Network and Support Vector Machine for Bio-oil Extracted from Rice Husk.

Bio-oil production from rice husk, an abundant agricultural residue, has gained significant attention as a sustainable and renewable energy source. The current research aims to employ artificial neural network (ANN) and support vector machine (SVM) modeling techniques for the optimization of operating parameters for bio-oil extracted from rice husk ash (RHA) through pyrolysis. ANN and SVM methods are employed to model and optimize the operational conditions, including temperature, heating rate, and feedstock particle size, to enhance the yield and quality of bio-oil. Additionally, ANN modeling is utilized to create a predictive model for bio-oil properties, allowing for the efficient optimization of pyrolysis conditions. This research provides valuable insights into the production and properties of bio-oil from RHA. By harnessing the capabilities of ANN and SVM, this research not only aids in understanding the intricate relationships between process variables and bio-oil properties but also provides a means to systematically enhance the production process. The predictive results obtained from the ANN were found to be good when compared with the SVM. Several models with different numbers of neurons have been trained with different transfer functions. R values for the training, validation, and test phases are around 1.0, i.e., 0.9981, 0.9976, and 0.9978, respectively. The overall R-value was 0.9960 for the proposed network. The findings were considered acceptable, as the overall R-value was close to 1.0. The optimized operational parameters contribute to the efficient conversion of RHA into bio-oil, thereby promoting the use of this sustainable resource for renewable energy production. This approach aligns with the growing emphasis on reducing the environmental impact of traditional fossil fuels and advancing the utilization of alternative and environmentally friendly energy sources.

Novel Activated Carbon Nanofibers Composited with Cost-Effective Graphene-Based Materials for Enhanced Adsorption Performance toward Methane.

Various types of activated carbon nanofibers' (ACNFs) composites have been extensively studied and reported recently due to their extraordinary properties and applications. This study reports the fabrication and assessments of ACNFs incorporated with graphene-based materials, known as gACNFs, via simple electrospinning and subsequent physical activation process. TGA analysis proved graphene-derived rice husk ashes (GRHA)/ACNFs possess twice the carbon yield and thermally stable properties compared to other samples. Raman spectra, XRD, and FTIR analyses explained the chemical structures in all resultant gACNFs samples. The SEM and EDX results revealed the average fiber diameters of the gACNFs, ranging from 250 to 400 nm, and the successful incorporation of both GRHA and reduced graphene oxide (rGO) into the ACNFs' structures. The results revealed that ACNFs incorporated with GRHA possesses the highest specific surface area (SSA), of 384 m2/g, with high micropore volume, of 0.1580 cm3/g, which is up to 88% of the total pore volume. The GRHA/ACNF was found to be a better adsorbent for CH4 compared to pristine ACNFs and reduced graphene oxide (rGO/ACNF) as it showed sorption up to 66.40 mmol/g at 25 °C and 12 bar. The sorption capacity of the GRHA/ACNF was impressively higher than earlier reported studies on ACNFs and ACNF composites. Interestingly, the CH4 adsorption of all ACNF samples obeyed the pseudo-second-order kinetic model at low pressure (4 bar), indicating the chemisorption behaviors. However, it obeyed the pseudo-first order at higher pressures (8 and 12 bar), indicating the physisorption behaviors. These results correspond to the textural properties that describe that the high adsorption capacity of CH4 at high pressure is mainly dependent upon the specific surface area (SSA), pore size distribution, and the suitable range of pore size.

Two-Step Dopamine-to-Polydopamine Modification of Polyethersulfone Ultrafiltration Membrane for Enhancing Anti-Fouling and Ultraviolet Resistant Properties.

Polydopamine has been widely used as an additive to enhance membrane fouling resistance. This study reports the effects of two-step dopamine-to-polydopamine modification on the permeation, antifouling, and potential anti-UV properties of polyethersulfone (PES)-based ultrafiltration membranes. The modification was performed through a two-step mechanism: adding the dopamine additive followed by immersion into Tris-HCl solution to allow polymerization of dopamine into polydopamine (PDA). The results reveal that the step of treatment, the concentration of dopamine in the first step, and the duration of dipping in the Tris solution in the second step affect the properties of the resulting membranes. Higher dopamine loadings improve the pure water flux (PWF) by more than threefold (15 vs. 50 L/m2·h). The extended dipping period in the Tris alkaline buffer leads to an overgrowth of the PDA layer that partly covers the surface pores which lowers the PWF. The presence of dopamine or polydopamine enhances the hydrophilicity due to the enrichment of hydrophilic catechol moieties which leads to better anti-fouling. Moreover, the polydopamine film also improves the membrane resistance to UV irradiation by minimizing photodegradation's occurrence.

Effect of a Different Number of Amine-Functional Groups on the Gas Sorption and Permeation Behavior of a Hybrid Membrane Comprising of Impregnated Linde T and 4,4'- (Hexafluoroisopropylidene) Diphthalic Anhydride-Derived Polyimide.

The bottleneck of conventional polymeric membranes applied in industry has a tradeoff between permeability and selectivity that deters its widespread expansion. This can be circumvented through a hybrid membrane that utilizes the advantages of inorganic and polymer materials to improve the gas separation performance. The approach can be further enhanced through the incorporation of amine-impregnated fillers that has the potential to minimize defects while simultaneously enhancing gas affinity. An innovative combination between impregnated Linde T with different numbers of amine-functional groups (i.e., monoamine, diamine, and triamine) and 4,4'-(hexafluoroisopropylidene) diphthalic anhydride (6FDA)-derived polyimide has been elucidated to explore its potential in CO2/CH4 separation. Detailed physical properties (i.e., free volume and glass transition temperature) and gas transport behavior (i.e., solubility, permeability, and diffusivity) of the fabricated membranes have been examined to unveil the effect of different numbers of amine-functional groups in Linde T fillers. It was found that a hybrid membrane impregnated with Linde T using a diamine functional group demonstrated the highest improvement compared to a pristine polyimide with 3.75- and 1.75-fold enhancements in CO2/CH4 selectivities and CO2 permeability, respectively, which successfully lies on the 2008 Robeson's upper bound. The novel coupling of diamine-impregnated Linde T and 6FDA-derived polyimide is a promising candidate for application in large-scale CO2 removal processes.