Microwave assisted hydrothermal treatment (MHTC) was compared with torrefaction in terms of carbonization efficiency and physicochemical characteristics of char products. The utilization of produced char was optimized for composite solid biofuel production. The results show that MHTC significantly improved the binding capability of the microwave hydrochar (MHC) particles during co-densification with unprocessed biomass and coal. One possible contributor to the improved binding is the pseudo lignin formed during the MHTC, which led to a better interlocking of the feedstock particles and promoted the solid bridge formation. Composite pellet prepared with 80 wt% of torrefaction char (TC-120), 10 wt% of microwave hydrochar (MHC-30), and 10 wt% of Coal-04 showed a higher heating value of 24.54 MJ/kg and energy density of 26.43 GJ/m3, which is significantly higher than that of the raw cotton stalk pellet (16.77 MJ/kg and 18.76 GJ/m3, respectively), showing great promise as a solid biofuel. The moisture resistance and oxidation reactivity are also significantly improved. The results demonstrate that MHCs provides dual functionalities in acting as binder and fuel promoter in the production of composite biofuel. This study can provide new insight into the unique functions of MHC during fuel application, which demonstrates the great potential of applying MHTC in energy recovery from lignocellulosic biomass.
This study investigated the interactions between volatile and char during biomass pyrolysis at 400 °C, employing a β-5 lignin dimer and amino-modified graphitized carbon nanotube (CNT-NH2) as their models, respectively. The results demonstrated that both -NH2 and its carrier (CNT) facilitated the conversion of the β-5 dimer, which significantly increased from 9.7% (blank run), to 61.6% (with CNT), and to 96.6% (with CNT-NH2). CNT mainly favored the breakage of C-O bond in the feedstock to produce dimers with a yield of 55.5%, while CNT-NH2 promoted the cleavage of both C-O and C-C bonds to yield monomers with a yield up to 63.4%. Such significant changes in the pyrolysis behaviors of the β-5 lignin dimer after the introduction of CNT-NH2 were considered to be mainly caused by hydrogen-bond formations between -NH2 and the dimeric feedstock/products, in addition to the π-π stacking between CNT and aromatic rings.
The effects of the annealing temperatures and thicknesses on the shapes, sizes and arrangement of platinum (Pt) nanoparticles (NPs) on graphene and their sensing performance for hydrogen (H2) detection were investigated. It shows strong dependency of the annealing temperatures and thicknesses on the properties of NPs. It was found that the proposed technique is able to form the NPs with good size controllability and uniformity even for thick deposited layer, thus eliminating the requirement of very thin layer of below 5 nm for the direct NP synthesis by evaporation or sputtering. The transport properties of Pt NPs/graphene structure and its sensing performance on H2 at room temperature under various H2 concentration were evaluated. The results showed an acceptable sensing response, indicating an innovative approach to fabricate Pt NPs embedded graphene for gas sensing application.
Metallic foams are a new class of materials that have a great potential to be used in various functional and structural applications. Due to their competitive price compared to aluminium, metallic foams are anticipated to become an alternative material for light-weight structures. In this study, stainless steel foams are fabricated using a powder space holder method. The materials used include stainless steel powder, a novel space holder glycine and binders consisting of palm stearin and of polyethylene (PE). The stainless steel foams are sintered at 1100o C, 1200o C and 1300o C with sintering times of 1, 2 and 3 h, respectively, to investigate the effects of the sintering parameters on the compressive yield strength of the stainless steel foams. The results showed that all of the stainless steel foams produced exhibit the general behaviours of metal foams. The sintering time is the most significant parameter that influences the compressive yield strength of stainless steel foams. Increasing the sintering temperature and sintering time will increase the compressive yield strength. The interaction between the sintering temperature and sintering time is found to be not statistically significant.
In order to understand the characteristics of acoustic wave propagation in rocks within seismic frequency band (<100
Hz), the velocities of longitudinal and transverse waves of four different types of rocks were tested using low-frequency
stress-strain method by means of the physical testing system of rock at low frequency and the experimental data of acoustic
velocities of four different types of rocks at this frequency band were obtained. The experimental results showed that the
acoustic velocities of four different types of rocks increased with the increase of temperature and pressure within the
temperature and pressure ranges set by the experiment. The acoustic velocity of fine sandstone at 50% water saturation
was smaller than that of dry sample. The acoustic velocities of four different types of rocks were different and the velocities
of longitudinal waves of gritstone, fine sandstone, argillaceous siltstone and mudstone increased in turn under similar
conditions and were smaller than those at ultrasonic frequency. Few of existing studies focus on the acoustic velocity at
seismic frequency band, thus, further understanding of the acoustic characteristics at this seismic frequency band still
requires more experimental data.
The prime objective of the present research work was to evaluate the efficiency of bio-machine for the removal of Cadmium (Cd) from aquatic systems. Aspergillus niger fungus was used as bio-machine to remove Cd from aquatic systems. Twenty three different strains (IIB-1 to IIB-23) were isolated from industrial effluents and the Langmuir and Freundlich models were applied to the best Cadmium removal strain IIB-23 in order to obtain the adsorption parameters. Different parameters such as pH, temperature, contact time, initial metal concentratio, and biomass dosage on the biosorption of Cd were studied. The percent removal of Cd initially increased with an increase in pH ranging from 5.5-6.5 and then decreased by increasing pH from 7.0-7.5. An optimized pH used for Cd removal from aquatic systems was found to be 6.5. Additionally, an optimum amount of biomass was 1.33 g for the maximum removal of Cd from the aqueous solutions with initial metal concentration of 75 mg/L. The results obtained thus indicated that Langmuir model is the best suited for the removal of Cd from aquatic systems.
Biochar production from invasive species biomass discarded as waste was studied in a fixed bed reactor pyrolysis system under different temperature conditions for value-added applications. Prior to pyrolysis, the biomass feedstock was characterized by proximate, ultimate, and heating value analyses, while the biomass decomposition behavior was examined by thermogravimetric analysis. The heating values of the feedstock biomass ranged from 18.65 to 20.65 MJ/kg, whereas the volatile matter, fixed carbon, and ash content were 61.54-72.04 wt %, 19.27-26.61 wt % and 1.51-1.86 wt %, respectively. The elemental composition of carbon, hydrogen, and oxygen in the samples was reported to be in the range of 47.41-48.47 wt %, 5.50-5.88 wt % and 46.10-45.18 wt %, respectively, while the nitrogen and sulphur content in the biomass samples were at very low concentrations, making it more useful for valorization from environmental aspects. The biochar yields were reported in the range of 45.36-58.35 wt %, 28.63-44.38 wt % and 22.68-29.42 wt % at a pyrolysis temperature of 400 °C, 500 °C, and 600 °C, respectively. The biochars were characterized from ultimate analysis, heating value, energy densification ratio, energy yield, pH, Fourier transform infrared spectroscopy (FTIR), and scanning electron microscopy and energy dispersive X-ray spectrometry (SEM and EDX), to evaluate their potential for value-added applications. The carbon content, heating value, energy densification ratio, and the porosity of the biochars improved with the increase in pyrolysis temperature, while the energy yield, hydrogen, oxygen, and nitrogen content of the biochars decreased. This study revealed the potential of the valorization of underutilized discarded biomass of invasive species via a pyrolysis process to produce biochar for value-added applications.
This study is aimed to rationalise and demonstrate the efficacy of utilising laser cutting technique in the fabrication of glulam mortise & tenon joints in timber frame. Trial-and-error experiments aided by laser cutter were conducted to produce 3D timber mortise & tenon joints models. The two main instruments used were 3D modelling software and the laser cutter TH 1390/6090. Plywood was chosen because it could produce smooth and accurate cut edges whereby the surface could remain crack-free, and it could increase stability due to its laminated nature. Google SketchUp was used for modelling and Laser CAD v7.52 was used to transfer the 3D models to the laser cutter because it is compatible with AI, BMP, PLT, DXF and DST templates. Four models were designed and fabricated in which the trial-and-error experiments proved laser cutting could speed up the manufacturing process with superb quality and high uniformity. Precision laser cutting supports easy automation, produces small heat-affected zone, minimises deformity, relatively quiet and produces low amount of waste. The LaserCAD could not process 3D images directly but needed 2D images to be transferred, so layering and unfolding works were therefore needed. This study revealed a significant potential of rapid manufacturability of mortise & tenon joints with high-quality and high-uniformity through computer-aided laser cutting technique for wide applications in the built environment.
Biomass is considered as the largest renewable energy source in the world. However, some of its inherent properties such as hygroscopicity, lower energy content, low mass density and bio-degradation on storage hinder its extensive application in energy generation processes. Torrefaction, a thermochemical process carried out at 200-300°C in a non-oxidative environment, can address these inherent problems of the biomass. In this work, torrefaction of bagasse was performed in a bench-scale tubular reactor at 250°C and 275°C with residence times of 30, 60 and 90 mins. The effects of torrefaction conditions on the elemental composition, mass yield, energy yield, oxygen/carbon (O/C) and hydrogen/carbon (H/C) ratios, higher heating values and structural composition were investigated and compared with the commercially available 'Thar 6' and 'Tunnel C' coal. Based on the targeted mass and energy yields of 80% and 90% respectively, the optimal process conditions turned out to be 250°C and 30 mins. Torrefaction of the bagasse conducted at 275°C and 90 min raised the carbon content in bagasse to 58.14% and resulted in a high heating value of 23.84 MJ/kg. The structural and thermal analysis of the torrefied bagasse indicates that the moisture, non-structural carbohydrates and hemicellulose were reduced, which induced the hydrophobicity in the bagasse and enhanced its energy value. These findings showed that torrefaction can be a sustainable pre-treatment process to improve the fuel and structural properties of biomass as a feedstock for energy generation processes.
Recently, natural fiber-reinforced polymers (NFRPs) have become important materials in many engineering applications; thus, to employ these materials some final industrial processes are needed, such as cutting, trimming, and drilling. Because of the heterogeneous nature of NFRPs, which differs from homogeneous materials such as metals and polymers, several defects have emerged when processing the NFRPs through traditional cutting methods such as high surface roughness and material damage at cutting zone. In order to overcome these challenges, unconventional cutting methods were considered. Unconventional cutting methods did not take into account the effects of cutting forces, which are the main cause of cutting defects in traditional cutting processes. The most prominent unconventional cutting processes are abrasive waterjet (AWJM) and laser beam (LBM) cutting technologies, which are actually applied for cutting various NFRPs. In this study, previously significant studies on cutting NFRPs by AWJM and LBM are discussed. The surface roughness, kerf taper, and heat-affected zone (HAZ) represent the target output parameters that are influenced and controlled by the input parameters of each process. However, this topic requires further studies on widening the range of material thickness and input parameter values.
Temperature is known to influence the operational efficiency of enhanced biological phosphorus removal (EBPR) systems. This study investigated the impact of thermal stress above 30°C on the properties of an EBPR community established with tropical inoculum. The results confirmed the stability of the 30°C EBPR system with high P-removal efficiency over 210 days. Accumulibacter was abundant in the community. When the EBPR sludge was subjected to a sudden temperature increase to 35°C under multiple cycles of anaerobic-aerobic phases, each lasting 4 h, high P-removal was maintained over 2 days, before gradually failing when the Competibacter appeared to outcompete Accumulibacter. These data suggested that the EBPR capacity is robust when subjected to occasional thermal stress. However, it could not be maintained even for a short time under temperature stress at 40°C. Thus, the threshold temperature for tropical EBPR failure is between 35°C and 40°C. PRACTITIONER POINTS: EBPR was stably maintained at 30°C with Accumulibacter being dominant. Good EBPR activities persisted for a short period at 35°C. EBPR was deteriorated at 40°C. The threshold temperature for tropical EBPR failure is between 35°C and 40°C.
Background - Recently, there have been attempts to develop mHealth applications for asthma self-management. However, there is a lack of applications that can offer accurate predictions of asthma exacerbation using the weather triggers and demographic characteristics to give tailored response to users. This paper proposes an optimised Deep Neural Network Regression (DNNR) model to predict asthma exacerbation based on personalised weather triggers. Methods - With the aim of integrating weather, demography, and asthma tracking, an mHealth application was developed where users conduct the Asthma Control Test (ACT) to identify the chances of their asthma exacerbation. The asthma dataset consists of panel data from 10 users that includes 1010 ACT scores as the target output. Moreover, the dataset contains 10 input features which include five weather features (temperature, humidity, air-pressure, UV-index, wind-speed) and five demography features (age, gender, outdoor-job, outdoor-activities, location). Results - Using the DNNR model on the asthma dataset, a score of 0.83 was achieved with Mean Absolute Error (MAE)=1.44 and Mean Squared Error (MSE)=3.62. It was recognised that, for effective asthma self-management, the prediction errors must be in the acceptable loss range (error<0.5). Therefore, an optimisation process was proposed to reduce the error rates and increase the accuracy by applying standardisation and fragmented-grid-search. Consequently, the optimised-DNNR model (with 2 hidden-layers and 50 hidden-nodes) using the Adam optimiser achieved a 94% accuracy with MAE=0.20 and MSE=0.09. Conclusions - This study is the first of its kind that recognises the potentials of DNNR to identify the correlation patterns among asthma, weather, and demographic variables. The optimised-DNNR model provides predictions with a significantly higher accuracy rate than the existing predictive models and using less computing time. Thus, the optimisation process is useful to build an enhanced model that can be integrated into the asthma self-management for mHealth application.
The experimental determination of thermophysical properties of nanofluid (NF) is time-consuming and costly, leading to the use of soft computing methods such as response surface methodology (RSM) and artificial neural network (ANN) to estimate these properties. The present study involves modelling and optimization of thermal conductivity and viscosity of NF, which comprises multi-walled carbon nanotubes (MWCNTs) and thermal oil. The modelling is performed to predict the thermal conductivity and viscosity of NF by using Response Surface Methodology (RSM) and Artificial Neural Network (ANN). Both models were tested and validated, which showed promising results. In addition, a detailed optimization study was conducted to investigate the optimum thermal conductivity and viscosity by varying temperature and NF weight per cent. Four case studies were explored using different objective functions based on NF application in various industries. The first case study aimed to maximize thermal conductivity (0.15985 W/m oC) while minimizing viscosity (0.03501 Pa s) obtained at 57.86 °C and 0.85 NF wt%. The goal of the second case study was to minimize thermal conductivity (0.13949 W/m °C) and viscosity (0.02526 Pa s) obtained at 55.88 °C and 0.15 NF wt%. The third case study targeted maximizing thermal conductivity (0.15797 W/m °C) and viscosity (0.07611 Pa s), and the optimum temperature and NF wt% were 30.64 °C and 0.0.85,' respectively. The last case study explored the minimum thermal conductivity (0.13735) and maximum viscosity (0.05263 Pa s) obtained at 30.64 °C and 0.15 NF wt%.
Atmospheric air temperature is the most crucial metrological parameter. Despite its influence on multiple fields such as hydrology, the environment, irrigation, and agriculture, this parameter describes climate change and global warming quite well. Thus, accurate and timely air temperature forecasting is essential because it provides more important information that can be relied on for future planning. In this study, four Data-Driven Approaches, Support Vector Regression (SVR), Regression Tree (RT), Quantile Regression Tree (QRT), ARIMA, Random Forest (RF), and Gradient Boosting Regression (GBR), have been applied to forecast short-, and mid-term air temperature (daily, and weekly) over North America under continental climatic conditions. The time-series data is relatively long (2000 to 2021), 70% of the data are used for model calibration (2000 to 2015), and the rest are used for validation. The autocorrelation and partial autocorrelation functions have been used to select the best input combination for the forecasting models. The quality of predicting models is evaluated using several statistical measures and graphical comparisons. For daily scale, the SVR has generated more accurate estimates than other models, Root Mean Square Error (RMSE = 3.592°C), Correlation Coefficient (R = 0.964), Mean Absolute Error (MAE = 2.745°C), and Thiels' U-statistics (U = 0.127). Besides, the study found that both RT and SVR performed very well in predicting weekly temperature. This study discovered that the duration of the employed data and its dispersion and volatility from month to month substantially influence the predictive models' efficacy. Furthermore, the second scenario is conducted using the randomization method to divide the data into training and testing phases. The study found the performance of the models in the second scenario to be much better than the first one, indicating that climate change affects the temperature pattern of the studied station. The findings offered technical support for generating high-resolution daily and weekly temperature forecasts using Data-Driven Methodologies.
The thermochemical processes such as gasification and co-gasification of biomass and coal are promising route for producing hydrogen-rich syngas. However, the process is characterized with complex reactions that pose a tremendous challenge in terms of controlling the process variables. This challenge can be overcome using appropriate machine learning algorithm to model the nonlinear complex relationship between the predictors and the targeted response. Hence, this study aimed to employ various machine learning algorithms such as regression models, support vector machine regression (SVM), gaussian processing regression (GPR), and artificial neural networks (ANN) for modeling hydrogen-rich syngas production by gasification and co-gasification of biomass and coal. A total of 12 machine learning algorithms which comprises the regression models, SVM, GPR, and ANN were configured, trained using 124 datasets. The performances of the algorithms were evaluated using the coefficient of determination (R2), root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE). In all cases, the ANN algorithms offer superior performances and displayed robust predictions of the hydrogen-rich syngas from the co-gasification processes. The R2 of both the Levenberg-Marquardt- and Bayesian Regularization-trained ANN obtained from the prediction of the hydrogen-rich syngas was found to be within 0.857-0.998 with low prediction errors. The sensitivity analysis to determine the effect of the process parameters on the model output revealed that all the parameters showed a varying level of influence. In most of the processes, the gasification temperature was found to have the most significant influence on the model output.
The introduction of nucleic acid amplification techniques has revolutionized the field of medical diagnostics in the last decade. The advent of PCR catalyzed the increasing application of DNA, not just for molecular cloning but also for molecular based diagnostics. Since the introduction of PCR, a deeper understanding of molecular mechanisms and enzymes involved in DNA/RNA replication has spurred the development of novel methods devoid of temperature cycling. Isothermal amplification methods have since been introduced utilizing different mechanisms, enzymes, and conditions. The ease with which isothermal amplification methods have allowed nucleic acid amplification to be carried out has had a profound impact on the way molecular diagnostics are being designed after the turn of the millennium. With all the advantages isothermal amplification brings, the issues or complications surrounding each method are heterogeneous making it difficult to identify the best approach for an end-user. This review pays special attention to the various isothermal amplification methods by classifying them based on the mechanistic characteristics which include reaction formats, amplification information, promoter, strand break, and refolding mechanisms. We would also compare the efficiencies and usefulness of each method while highlighting the potential applications and detection methods involved. This review will serve as an overall outlook on the journey and development of isothermal amplification methods as a whole.
Biochar derived from banana peels can be used as an alternative nutrient in the soil that can promote crop growth while reducing fertiliser usage. Biochar stability has proportional relationship to biochar residence time in the soil and potassium is one of the vital nutrients needed for plant growth. This research aims at providing optimum pyrolysis operating conditions like temperature, residence time, and heating rate using banana peels as feedstock. An electrical tubular furnace was used to conduct the pyrolysis process to convert banana peels into biochar. The elemental compositions of biochar are potassium, oxygen (O), and carbon (C) content. The O:C ratio was used as the biochar stability indicator. Analysis of results showed that operating temperature has the most remarkable effect on biochar yield, biochar stability, and biochar's potassium content. In addition, a multilayer feedforward artificial neural network model was developed for the pyrolysis process. Eleven training algorithms were selected to model the multi-input multi-output neural network (MIMO). The most suitable training algorithm was identified through four performance criterions which are root mean square error (RMSE), mean absolute error (MSE), mean absolute percentage error (MAPE), and regression (R2). The results show that the Levenberg-Marquardt backpropagation training algorithm has the lowest error. From the chosen training algorithm, neural network was trained, and optimum operating parameters for banana peel were predicted at 490 °C, 110 min, and 11 °C/min with a high yield of 47.78%, O/C ratio of 0.2393, and 14.04 wt. % of potassium.
The application of waste oils as pyrolysis feedstocks to produce high-grade biofuels is receiving extensive attention, which will diversify energy supplies and address environmental challenges caused by waste oils treatment and fossil fuel combustion. Waste oils are the optimal raw materials to produce biofuels due to their high hydrogen and volatile matter content. However, traditional disposal methods such as gasification, transesterification, hydrotreating, solvent extraction, and membrane technology are difficult to achieve satisfactory effects owing to shortcomings like enormous energy demand, long process time, high operational cost, and hazardous material pollution. The usage of clean and safe pyrolysis technology can break through the current predicament. The bio-oil produced by the conventional pyrolysis of waste oils has a high yield and HHV with great potential to replace fossil fuel, but contains a high acid value of about 120 mg KOH/g. Nevertheless, the application of CaO and NaOH can significantly decrease the acid value of bio-oil to close to zero. Additionally, the addition of coexisting bifunctional catalyst, SBA-15@MgO@Zn in particular, can simultaneously reduce the acid value and positively influence the yield and quality of bio-oil. Moreover, co-pyrolysis with plastic waste can effectively save energy and time, and improve bio-oil yield and quality. Consequently, this paper presents a critical and comprehensive review of the production of biofuels using conventional and advanced pyrolysis of waste oils.
One of the most significant chemical operations in the past century was the Haber-Bosch catalytic synthesis of ammonia, a fertilizer vital to human life. Many catalysts are developed for effective route of ammonia synthesis. The major challenges are to reduce temperature and pressure of process and to improve conversion of reactants produce green ammonia. The present review, briefly discusses the evolution of ammonia synthesis and current advances in nanocatalyst development. There are promising new ammonia synthesis catalysts of different morphology as well as magnetic nanoparticles and nanowires that could replace conventional Fused-Fe and Promoted-Ru catalysts in existing ammonia synthesis plants. These magnetic nanocatalyst could be basis for the production of magnetically induced one-step green ammonia and urea synthesis processes in future.
Understanding crystallization behaviors is of utmost importance for developing robust amorphous pharmaceutical solids. Herein, the crystal growth behaviors of amorphous anti-inflammatory drug nimesulide (NIME) are systemically investigated in the glassy and supercooled liquid state as a function of temperature. A sudden over-tenfold increase is observed in the bulk crystal growth of NIME on cooling below its glass transition temperature (Tg). This fast growth behavior is known as a glass-to-crystal (GC) mode and has been reported in some molecular glasses. Fast surface crystal growth of NIME can persist up to Tg + 57°C with a weak jump in its growth rates at 30-40°C. In addition, surface crystal growth and GC growth of NIME exhibit an almost identical temperature dependence, supporting the view that GC growth is indeed a surface-facilitated process. Moreover, the bubble-induced fast crystal growth of NIME is observed in the interior of its supercooled liquid with approximately the same growth kinetics as surface crystal growth. These findings are relevant for a full understanding of the surface-related crystallization behaviors and physical stability of amorphous pharmaceutical formulations.