A review of hydrogen production optimization from the reforming of C1 and C2 alcohols via artificial neural networks

Hydrogen production from different fuels has received extensive study interest owing to its environmental sustainability, renewability, and lack of carbon emission. This research aims to investigate how artificial neural networks (ANNs) are employed to optimize operating parameters for the catalytic thermochemical conversion of methanol and ethanol and their impact on hydrogen production. According to the ANN model, peak methanol conversion (99%) occurs at lower temperatures of 300 °C with a maximum hydrogen yield of 2.905 mol, whereas peak ethanol conversion (85%) occurs at 500 °C owing to dehydrogenation and the C-C bond-breaking process. A steam-to-carbon (S/C) ratio of (3.5) was advantageous for methanol steam reforming (MSR), and a high ethanol concentration of 10–15 vol% was favorable for ethanol steam reforming (ESR). Ni (10 wt%), and Co (10 wt%) were the optimum metal combinations in the catalyst for ethanol reformation at a reforming temperature of 450 °C. The optimum metal catalysts for producing hydrogen and converting ethanol were those synthesized through co-precipitation. The peak hydrogen yield was attained at the sintering temperature of 560–570 °C. ANN technique is cost-effective, quick, and precise, with vast potential to produce hydrogen energy, and may give significant benefits for industrial applications

Influence of hydrodynamic shear stress on activated algae granulation process for wastewater treatment

In this study, activated algae granule was formed by cultivating a mixed culture of the microalga Chlorella vulgaris and activated sludge in batch photobioreactors (PBRs). Without aeration, the granulation process was investigated through four different agitation speeds of 80, 120, 160, 200 rpm (shear stress of 0.04, 0.15, 0.35, 0.69 Pa). Hydrodynamic shear stress created by agitation affected the activated algae granulation through microeddies. The largest granule was achieved in the PBR with agitation speed of 200 rpm (R200) with shear stress of 0.69 Pa (size of 339 µm). In the other hand, lower shear stress in R160 (shear stress of 0.35 Pa) gave optimal results in terms of wastewater treatment efficiency and granule formation time (165 days of operation). Shear stress below 0.15 Pa (120 rpm) showed no granule formation during 220 days operation. While shear stress from 0.04–0.15 Pa could guarantee mass transfer for the entire co-culture without aeration, shear stress from 0.15–0.69 Pa was a suitable range for the granulation process of activated algae. This study reveals that activated algae granule formation through agitation mechanism is a promissing solution for wastewater treatment and microalgae biomass recovery to substantially increase the share of renewable energy in the global energy mix

Synthesis of mesoporous MFI zeolite via bacterial cellulose-derived carbon templating for fast adsorption of formaldehyde

Mesoporous ZSM-5 (MFI) zeolite was synthesized by using bacterial cellulose-derived activated carbon (BC-AC500) with a high surface area as a hard template. Different ratios of BC-AC500 and zeolite precursor gel were prepared in a Teflon-lined autoclave and crystallized at 180 ºC for 48 h in a rotating oven. The physicochemical properties of the samples were characterized by x-ray diffraction (XRD), scanning/transmission electron microscopies (SEM/TEM), and N2 physisorption techniques. It was found that the mesoporous ZSM-5 zeolites have a specific surface area of 184-190 m2/g, a high mesopore volume of 0.120-0.956 ml/g and a wide pore size distribution ranging from 5 to 100 nm with a maximum at approximately 25.3 nm. The successfully made mesoporous ZSM-5 was tested as an adsorbent for formaldehyde adsorption in batch mode. The mesoporous ZSM-5 zeolite made from bacterial cellulose-derived activated carbon showed significantly faster adsorption kinetics than conventional ZSM-5 (0.0081 vs. 0.0007 g/mg min, respectively). The prepared material has an adsorption capacity of 98 mg/g and is highly reusable. The reported mesoporous ZSM-5 zeolites can be deployed for the rapid removal of toxic organics from wastewater when urgently needed, e.g., under breakthrough conditions

Cobalt-impregnated biochar produced from CO2-mediated pyrolysis of Co/lignin as an enhanced catalyst for activating peroxymonosulfate to degrade acetaminophen

While sulfate radical (SO4−)-based processes are useful to degrade acetaminophen (ACE), studies of using peroxymonosulfate (PMS) to degrade ACE are quite limited. In addition, although Co is validated as the most effective metal for activating PMS, very few Co catalysts have been developed and investigated for activating PMS to degrade ACE. Since carbon is a promising substrate to support Co nanoparticles (NPs) to form Co/carbon composite catalysts, most existing carbon substrates require delicate fabrications. As biochar is an easy-to-obtain but versatile carbon material, pyrolysis of Co/lignin affords an advantageous Co-impregnated biochar (CoIB) as an attractive catalyst for PMS activation. Specifically, as CO2 substitutes N2 as a reaction medium for pyrolysis of Co/lignin, the syngas production from pyrolysis can be substantially improved and a magnetic CoIB is afforded. This CoIB consists of evenly-distributed Co nanoparticles (NPs) impregnated in carbon matrices of biochar, and possesses several superior characteristics, such as high porosity, large surface area and magnetism, enabling CoIB a promising catalyst for activating PMS to degrade ACE. CoIB also shows a much higher catalytic activity of PMS activation than CoIBN2, and Co3O4 for degrading ACE. CoIB is also recyclable for activating PMS to effectively degrade ACE for multiple cycles. The ACE degradation pathway by this CoIB-activated PMS is proposed according to the degradation products. These findings validate that CoIB is assuredly an advantageous heterogeneous catalyst, which can be easily prepared from pyrolysis of Co/lignin in CO2 with concomitant enhanced syngas production for effectively activating PMS to degrade ACE

Modeling of hydrogen separation through Pd membrane with vacuum pressure using Taguchi and machine learning methods

The performance of hydrogen purification using palladium (Pd) membrane is analyzed by Taguchi and machine learning (ML) methods. Three system factors are considered: the feed gas mixture composition (i.e., H2, CO2, and H2O), retentate-side total pressure, and vacuum pressure. The Taguchi method designs the experimental cases based on the constructed orthogonal array. In addition, the hydrogen flux is investigated by the analyses of variance (ANOVA) and artificial neural networks (ANN) methods. The results show that the effects of the considered factors on the hydrogen permeation performance can be ranked as feed gas mixture composition > retentate-side total pressure > vacuum pressure. Both the retentate-side pressure and vacuum pressure present a positive effect on hydrogen flux. The average relative error of hydrogen flux between the experimental results and predictions by ANN is 2.1%, which proves that the ANN method is an effective technique for predicting the hydrogen flux through the Pd membrane. The ML classification test is then performed for hydrogen purity by three machine learning methods: decision tree (DT), support vector machine (SVM), and ensemble method. Among the various classification models, the Quadratic SVM model exhibits a relatively high average training accuracy but shows overfitting. However, the bagged model of the ensemble method can achieve an impressive training accuracy of 91.4% and a prediction accuracy of 85.7%

Cobalt impregnated pillared montmorillonite in the peroxymonosulfate induced catalytic oxidation of tartrazine

Aluminum pillared montmorillonite impregnated with cobalt (CoAP) was synthe-sized and characterized using chemical analysis, XRD and N-2-physisorption. CoAP was tested as a catalyst in the peroxymonosulfate (Oxone (R)) induced catalytic degradation of tartrazine. The influence of Oxone (R)/catalyst ratio and temperature on CoAP catalytic performance was investigated. The UV-Vis spectra obtained after predetermined periods of time of reaction were analyzed in order for tartrazine solution composition to be monitored. The reaction was more efficient at 50 degrees C than at 30 degrees C and the presence of new peaks for the reaction at 50 degrees C was observed. The peaks were deconvoluted and further analyzed. The intensity of two characteristic peaks gradually decreased during the investigated reaction following the first order kinetics. Newly formed peaks indicated the formation of degradation products. The initial increase of the intensity of some of them was followed by certain decrease as the reaction proceeded. CoAP was found to be efficient catalyst in Oxone (R) induced catalytic decolorization of tartrazine. The degradation of different products formed in tartrazine oxidation was evidenced

Tailoring Acidity and Porosity of Alumina Catalysts via Transition Metal Doping for Glucose Conversion in Biorefinery

Efficient conversion of food waste to value-added products necessitates the development of high-performance heterogeneous catalysts. This study evaluated the use of Al2O3 as a low-cost and abundant support material for fabricating Lewis acid catalysts, i.e., through the in-situ doping of Cu, Ni, Co, and Zr into Al2O3 followed by calcination. The characterisation results show that all catalysts were mainly amorphous. In particular, adding the transition metals to the Al2O3 matrix resulted in the increase of acidity and meso-/micro-pores. The catalysts were evaluated in the conversion of glucose, which can be easily derived from starch-rich food waste (e.g., bread waste) via hydrolysis, to fructose in biorefinery. The results indicate that the Ni-doped Al2O3 (Al-Ni-C) achieved the highest fructose yield (19 mol%) and selectivity (59 mol%) under heating at 170 oC for 20 min, of which the performance falls into the range reported in literature. In contrast, the Zr-doped Al2O3 (Al-Zr-C) presented the lowest fructose selectivity despite the highest glucose conversion, meaning that the catalyst was relatively active towards the side reactions of glucose and intermediates. The porosity and acidity, modified via metal impregnation, were deduced as the determinants of the catalytic performance. It is noteworthy that the importance of these parameters may vary in a relative sense and the limiting factor could shift from one parameter to another. Therefore, evaluating physicochemical properties as a whole, instead of the unilateral improvement of a single parameter, is encouraged to leverage each functionality for cost-effectiveness. This study provides insights into the structure-performance relationships to promote advance in catalyst design serving a sustainable food waste biorefinery