Effect of Process Parameters on the Photocatalytic Degradation of Phenol in Oilfield Produced Wastewater using ZnO/Fe2O3 Nanocomposites.

The upstream processing of crude oil is often associated with the presence of phenolic compounds when not properly treated could result in adverse effects on human health. The objective of the study was to investigate the effect of process parameters on the photocatalytic degradation of phenol. The ZnO/Fe2O3 nanocomposite photocatalyst was prepared by sol-gel method and characterized using various instrument techniques. The characterized ZnO/Fe2O3 nanocomposite displayed suitable physicochemical properties for the photocatalytic reaction. The ZnO/Fe2O3 nanocomposite was employed for the phenol degradation in a cylindrical batch reactor under solar radiation. The photocatalytic runs show that calcination temperature of the ZnO/Fe2O3 nanocomposite, catalyst loading, initial phenol concentration and pH of the wastewater significantly influence the photocatalytic degradation of phenol. After 180 min of solar radiation, the highest phenol degradation of 92.7% was obtained using the ZnO/Fe2O3 photocatalyst calcined at 400 ºC. This study has demonstrated that phenol degradation is significantly influenced by parameters such as calcination temperature of the ZnO/Fe2O3 nanocomposite, catalyst loading, initial phenol concentration and pH of the wastewater resulting in highest phenol degradation using the ZnO/Fe2O3 nanocomposite calcined at 400 ºC, initial phenol concentration of 0.5 mg/L, catalyst loading of 3 mg/L and pH of 3. Copyright © 2020 by Authors, Published by BCREC Group. This is an open access article under the CC BY-SA License (https://creativecommons.org/licenses/by-sa/4.0).

Effect of activation function in modeling the nexus between carbon tax, CO2 emissions, and gas-fired power plant parameters

Huge emissions of carbon dioxide (CO2) from the utilization of fossil fuel for power generation has significantly contributed to global warming. In view of this, technological pathways have been initiated to mitigate the effect of CO2 emissions through capture, storage, and utilization. Besides, there is an increasing acceptance of carbon tax which is levied in the proportion of carbon emissions from the utilization of fossil fuel. In this study, the nexus between carbon tax, equivalent CO2 emissions from the gas-fired power plant, natural gas flow rate, and air-to-fuel ratio was modeled using a perceptron neural network. The effect of various combinations of identity, hyperbolic tangent, and sigmoid activation functions at the hidden and outer layer of the neural network on the performance of the models was investigated. The various network configurations were trained using the Levenberg-Marquardt algorithm with the network errors backpropagated to enhance the performance. The optimized networks consist of three input units, 15 hidden neurons, and one output unit. The network performance in modeling the carbon tax prediction resulted in R2 of 0.999, 0.999, 0.999, 0.998, and 0.999 for model 1, model 2, model 3, model 4, and model 5, respectively which is an indication that the calculated carbon tax was strongly correlated with the predicted values. The prediction errors of 0.019, 0.009, 0.002, 0.016, 0.002 obtained from model 1, model 2, model 3, model 4, and model 5, respectively revealed the robustness of the models in predicting the carbon tax with minimum error. Among the various configurations investigated, the perceptron neural network configured with hyperbolic tangent and sigmoid activation function at the hidden and outer layers, as well as the configuration with sigmoid activation functions at the hidden and outer layers, offer the best performance. The sensitivity analysis shows that the flow rate of the natural gas had the most significant effect on the predicted carbon tax

Effect of incorporating TiO2 photocatalyst in PVDF hollow fibre membrane for photo-assisted degradation of methylene blue

A rapid growth in populations, living standards and industries has become a key contribution to water pollution. Clean water is an important resource for life, sustainable development and ecosystems. This study therefore investigates the photocatalytic degradation of an organic pollutant (methylene blue) using PVDF/TiO2 membrane. The main objective of the study is to determine the synergistic effect of incorporating TiO2 photocatalyst into the PVDF membrane on the mineralization of the organic pollutants. The TiO2 photocatalyst was characterized using Ultraviolet Visible Spectroscopy (UV-Vis), Scanning Electron Microscopy (SEM), Brunauer, Emmettt, and Teller (BET), and X-ray Diffraction (XRD) techniques. While the fabricated PVDF/TiO2 hollow fibre membranes were then characterized by scanning electron microscopy (SEM) and contact angle. The performance of the membrane was evaluated by photodegradation of methylene blue. The degradation study revealed that both the undoped PVDF and the TIO2 doped PVDF membrane were capable of degrading methylene blue. The performance of the membrane can be ranked as follows 9 wt% TiO2/PVDF > 6 wt% TiO2/PVDF > 3 wt% TiO2/PVDF > undoped PVDF showing the synergistic effect of incorporating the TiO2 photocatalyst into the PVDF membrane. The kinetics data of obtained from the rate of degradation of the methylene blue fitted well into first order kinetic data with apparent kinetic constants of 0.0591, 0.0295, 0.0188, and 0.0100 obtained using pure membrane, undoped PVDF, 3 wt% TiO2/PVDF, 6 wt% TiO2/PVDF, and 9 wt% TiO2/PVDF, respectively

Effect of Incorporating TiO2 Photocatalyst in PVDF Hollow Fibre Membrane for Photo-Assisted Degradation of Methylene Blue

A rapid growth in populations, living standards and industries has become a key contribution to water pollution. Clean water is an important resource for life, sustainable development and ecosystems. This study therefore investigates the photocatalytic degradation of an organic pollutant (methylene blue) using PVDF/TiO2 membrane. The main objective of the study is to determine the synergistic effect of incorporating TiO2 photocatalyst into the PVDF membrane on the mineralization of the organic pollutants. The TiO2 photocatalyst was characterized using Ultraviolet Visible Spectroscopy (UV-Vis), Scanning Electron Microscopy (SEM), Brunauer, Emmettt, and Teller (BET), and X-ray Diffraction (XRD) techniques. While the fabricated PVDF/TiO2 hollow fibre membranes were then characterized by scanning electron microscopy (SEM) and contact angle. The performance of the membrane was evaluated by photodegradation of methylene blue. The degradation study revealed that both the undoped PVDF and the TIO2 doped PVDF membrane were capable of degrading methylene blue. The performance of the membrane can be ranked as follows 9 wt% TiO2/PVDF > 6 wt% TiO2/PVDF > 3 wt% TiO2/PVDF > undoped PVDF showing the synergistic effect of incorporating the TiO2 photocatalyst into the PVDF membrane.  The kinetics data of obtained from the rate of degradation of the methylene blue fitted well into first order kinetic data with apparent kinetic constants of 0.0591, 0.0295, 0.0188, and 0.0100 obtained using pure membrane, undoped PVDF, 3 wt% TiO2/PVDF, 6 wt% TiO2/PVDF, and 9 wt% TiO2/PVDF, respectively. Received: 8th July 2018; Revised: 30th July 2018; Accepted: 5th August 2018 How to Cite: Abdullah, N., Ayodelea, B.V., Mansor, W.N.W., Abdullah, S. (2018). Effect of Incorporating TiO2 Photocatalyst in PVDF Hollow Fibre Membrane for Photo-Assisted Degradation of Methylene Blue. Bulletin of Chemical Reaction Engineering & Catalysis, 13 (3): 588-591 (doi:10.9767/bcrec.13.3.2909.588-591) Permalink/DOI: https://doi.org/10.9767/bcrec.13.3.2909.588-59

ayesian Regularization-Trained Multi-layer Perceptron Neural Network Predictive Modelling of Phenol Degradation using ZnO/Fe2O3 photocatalyst

The processing of crude oil in the onshore platform often results in the generation of produce water containing harmful organic pollutants such as phenol. If the produce water is not properly treated to get rid of the organic pollutants, human exposure when discharged could be detrimental to health. Photocatalytic degradation of the organic pollutant has been a proven, non-expensive techniques of removing these harmful organic compounds from the produce water. However, the detail experimentation is often tedious and costly. One way to investigate the non-linear relationship between the parameters for effective performance of the photodegradation is by artificial neural network modelling. This study investigates the predictive modelling of photocatalytic phenol degradation from crude oil wastewater using Bayesian regularization-trained multilayer perceptron neural network (MLPNN). The ZnO/Fe2O3 photocatalyst used for the photodegradation was prepared using sol-gel method and employed for the phenol degradation study in a batch reactor under solar irradiation. Twenty-six datasets generated by Box-Behken experimental design was used for the training of the MLPNN with input variables as irradiation time, initial phenol concentration, photocatalyst dosage and the pH of the solution while the output layer consist of phenol degradation. Several MLPNN architecture was tested to obtain an optimized 4 5 1 configuration with the least mean standard error (MSE) of 1.27. The MLPNN with the 4 5 1 architecture resulted in robust prediction of phenol degradation from the wastewater with coefficient of determination (R) of 0.999

Dehydrogenation of Cyclohexanol to Cyclohexanone Over Nitrogen-doped Graphene supported Cu catalyst

In this study, the dehydrogenation of cyclohexanol to cyclohexanone over nitrogen-doped reduced graphene oxide (N-rGO) Cu catalyst has been reported. The N-rGO support was synthesized by chemical reduction of graphite oxide (GO). The synthesized N-rGO was used as a support to prepare the Cu/N-rGO catalyst via an incipient wet impregnation method. The as-prepared support and the Cu/N-rGO catalyst were characterized by FESEM, EDX, XRD, TEM, TGA, and Raman spectroscopy. The various characterization analysis revealed the suitability of the Cu/N-rGO as a heterogeneous catalyst that can be employed for the dehydrogenation of cyclohexanol to cyclohexanone. The catalytic activity of the Cu/N-rGO catalyst was tested in non-oxidative dehydrogenation of cyclohexanol to cyclohexanone using a stainless-steel fixed bed reactor. The effects of temperature, reactant flow rate, and time-on-stream on the activity of the Cu/N-rGO catalyst were examined. The Cu/N-rGO nanosheets show excellent catalytic activity and selectivity to cyclohexanone. The formation of stable Cu nanoparticles on N-rGO support interaction and segregation of Cu were crucial factors for the catalytic activity. The highest cyclohexanol conversion and selectivity of 93.3% and 82.7%, respectively, were obtained at a reaction temperature of 270 °C and cyclohexanol feed rate of 0.1 ml/min. Copyright © 2020 by Authors, Published by BCREC Group. This is an open access article under the CC BY-SA License (https://creativecommons.org/licenses/by-sa/4.0).

CO2 capture for dry reforming of natural gas: Performance and process modeling of calcium carbonate looping using acid based CaCO3 sorbent

Several industrial activities often result in the emissions of greenhouse gases such as carbon dioxide and methane (a principal component of natural gas). In order to mitigate the effects of these greenhouse gases, CO2 can be captured, stored and utilized for the dry reforming of methane. Various CO2 capture techniques have been investigated in the past decades. This study investigated the performance and process modeling of CO2 capture through calcium carbonate looping (CCL) using local (Malaysia) limestone as the sorbent. The original limestone was compared with two types of oxalic acid-treated limestone, with and without aluminum oxide (Al2O3) as supporting material. The comparison was in terms of CO2 uptake capacity and performance in a fluidized bed reactor system. From the results, it was shown that the oxalic acid-treated limestone without Al2O3 had the largest surface area, highest CO2 uptake capacity and highest mass attrition resistance, compared with other sorbents. The sorbent kinetic study was used to design, using an Aspen Plus simulator, a CCL process that was integrated with a 700 MWe coal-fired power plant from Malaysia. The findings showed that, with added capital and operation costs due to the CCL process, the specific CO2 emission of the existing plant was significantly reduced from 909 to 99.7 kg/MWh

Catalytic performance of bimetallic cobalt–nickel/graphene oxide for carbon dioxide reforming of methane

The design of economical and robust catalysts is a substantial challenge for the dry reforming of methane (DRM). Monometallic nickel-based catalysts used for DRM reactions had comparable activity to noble metals. However, they turned out to be less stable during the reactions. As a continuation of the interest in synthesizing catalysts for DRM, this paper evaluates the catalytic performance of bimetallic Co–Ni catalysts regarding their synergy effect, with graphene oxide (GO) as support for the first time. The synthesized bimetallic catalysts prepared via the wet-impregnation method were characterized using N2 physisorption analysis, scanning electron microscopy (SEM), thermogravimetric analysis (TGA), and X-ray diffraction (XRD). The catalytic test was performed in a stainless-steel tubular reactor in atmospheric conditions with a reaction temperature of 800 °C, time-on-stream (TOS) of 300 min and CH4: CO2 being fed with a ratio of 1:1. The bimetallic 10 wt%Co–10 wt%Ni/GO and 20 wt%Co–10 wt%Ni/GO catalysts had a similar BET specific surface area in N2 physisorption analysis. The XRD pattern displayed a homogeneous distribution of the Co and Ni on the GO support, which was further validated through SEM–EDX. The conversion of CO2, CH4, and H2 yield decreased with reaction time due to the massive occurrence of side reactions. High conversions for CO2 and CH4 were 94.26% and 95.24%, respectively, attained by the bimetallic 20 wt%Co–10 wt%Ni/GO catalyst after 300 min TOS, meaning it displayed the best performance in terms of activity among all the tested catalysts

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries