Modeling the heat of vaporization of petroleum fractions and pure hydrocarbons

The present contribution was aimed to estimate the vaporization enthalpy of petroleum fractions and pure hydrocarbons by using the combination of adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithm (GA) called GA-ANFIS. This tool can approximate the vaporization enthalpy as a function of the specific gravity, molecular weight, and boiling point temperature with high accuracy based on 122 data gathered from the previously published literature. Furthermore, results from the proposed model have been compared with different correlations and its acceptable predictive ability against other correlations was proved in order to the estimation of the vaporization enthalpy. The percentage of absolute relative deviation and R-squared (R²) was 1.64% and 0.9967%, respectively. This tool is simple to use and can be of considerable help for petroleum engineers to have an accurate estimation of vaporization enthalpy of hydrocarbon fractions of pure hydrocarbons.     

Fault isolation in manufacturing systems based on learning algorithm and fuzzy rule selection

Neural Computing and Applications - This paper addresses the fault detection and isolation problem in manufacturing systems. Some of these systems can be affected by several faults, a first way of...     

Feasibility of Ni/Ti and Ni/GF cathodes in microbial electrolysis cells for hydrogen production from fermentation effluent: A step toward real application

The low cost, low over-potential loss, good catalytic properties for hydrogen evolution reaction (HER), high corrosion stability, commercially available, and could be applied in pH-neutral solution and ambient temperature are important properties for the cathode materials when it is applied in microbial electrolysis cell (MEC) technology. This study has two-pronged objectives: the first is to investigate the feasibility of titanium (Ti) and graphite felt (GF) coated with nickel (Ni), and the second is to generate hydrogen from the fermentation effluent (FE). The electrodeposition (ED) method was used to deposit Ni catalyst onto Ti (Ni/Ti) and GF (Ni/GF) surfaces. The scanning electron microscopy (SEM) and energy dispersive X-ray (EDX) spectroscopy were used to characterize the cathode morphology and element composition. The catalytic properties of Ni/Ti and Ni/GF could be evaluated using the linear sweep voltammetry tests. The maximum volumetric H₂ production rates of MEC using Ni/Ti and Ni/GF cathodes were obtained at 0.39 ± 0.01 and 0.33 ± 0.03 m³ H₂ m⁻³ d⁻¹ respectively. The Ni/Ti and Ni/GF cathodes could be used as alternative cathodes while producing hydrogen from FE.     

IoMT-Based Smart Monitoring Hierarchical Fuzzy Inference System for Diagnosis of COVID-19

The prediction of human diseases, particularly COVID-19, is an extremely challenging task not only for medical experts but also for the technologists supporting them in diagnosis and treatment. To deal with the prediction and diagnosis of COVID-19, we propose an Internet of Medical Things-based Smart Monitoring Hierarchical Mamdani Fuzzy Inference System (IoMTSM-HMFIS). The proposed system determines the various factors like fever, cough, complete blood count, respiratory rate, Ct-chest, Erythrocyte sedimentation rate and C-reactive protein, family history, and antibody detection (lgG) that are directly involved in COVID-19. The expert system has two input variables in layer 1, and seven input variables in layer 2. In layer 1, the initial identification for COVID-19 is considered, whereas in layer 2, the different factors involved are studied. Finally, advanced lab tests are conducted to identify the actual current status of the disease. The major focus of this study is to build an IoMT-based smart monitoring system that can be used by anyone exposed to COVID-19; the system would evaluate the user’s health condition and inform them if they need consultation with a specialist for quarantining. MATLAB-2019a tool is used to conduct the simulation. The COVID-19 IoMTSM-HMFIS system has an overall accuracy of approximately 83%. Finally, to achieve improved performance, the analysis results of the system were shared with experts of the Lahore General Hospital, Lahore, Pakistan.     

Prediction of Cloud Ranking in a Hyperconverged Cloud Ecosystem Using Machine Learning

Cloud computing is becoming popular technology due to its functional properties and variety of customer-oriented services over the Internet. The design of reliable and high-quality cloud applications requires a strong Quality of Service QoS parameter metric. In a hyperconverged cloud ecosystem environment, building high-reliability cloud applications is a challenging job. The selection of cloud services is based on the QoS parameters that play essential roles in optimizing and improving cloud rankings. The emergence of cloud computing is significantly reshaping the digital ecosystem, and the numerous services offered by cloud service providers are playing a vital role in this transformation. Hyperconverged software-based unified utilities combine storage virtualization, compute virtualization, and network virtualization. The availability of the latter has also raised the demand for QoS. Due to the diversity of services, the respective quality parameters are also in abundance and need a carefully designed mechanism to compare and identify the critical, common, and impactful parameters. It is also necessary to reconsider the market needs in terms of service requirements and the QoS provided by various CSPs. This research provides a machine learning-based mechanism to monitor the QoS in a hyperconverged environment with three core service parameters: service quality, downtime of servers, and outage of cloud services.     

Novel Nanoencapsulation Technology and its Potential Role in Bile Acid-Based Targeted Gene Delivery to the Inner Ear

Hearing loss impacts a large proportion of the global population. Damage to the inner ear, in particular the sensitive hair cells, can impact individuals for the rest of their lives. There are very limited options for interventions after damage to these cells has occurred. Targeted gene delivery may provide an effective means to trigger appropriate differentiation of progenitor cells for effective replacement of these sensitive hair cells. There are several hurdles that need to be overcome to effectively deliver these genes. Nanoencapsulation technology has previously been used for the delivery of pharmaceuticals, proteins and nucleic acids, and may provide an effective means of delivering genes to trigger appropriate differentiation. This review investigates the background of hearing loss, current advancements and pitfalls of gene delivery, and how nanoencapsulation may be useful.     

CO2 sorption and diffusion in polymethyl methacrylate–clay nanocomposites

This study reports the glass transition temperature (T_g), and sorption and diffusion of subcritical CO₂ gas in polymethyl methacrylate (PMMA) nanocomposites containing organically modified smectite clay, Cloisite 20A (C20A). A range of methods for preparing the PMMA‐clay nanocomposites was investigated and a solution coprecipitation method was selected as the most appropriate. Using this method, PMMA nanocomposite containing 2, 4, 6, and 10 wt% nanoclay loadings were prepared. Wide‐angle X‐ray diffraction (XRD) analysis and scanning electron microscopy (SEM) indicated that the 2 wt% nanocomposite materials had a well‐dispersed intercalated clay structure. The T_g for PMMA‐C20A nanocomposites, as measured by differential scanning calorimetry (DSC), was found to be independent of the clay loading. CO₂ solubility studies from 0 to 65°C and pressures up to 5.5 MPa using an in situ gravimetric technique were performed on compression‐molded films. The organoclay was found to have no effect on the solubility of CO₂ in PMMA, and therefore the solubility of CO₂ in the nanocomposite can be determined from the solubility of CO₂ in the matrix polymer alone. Diffusion coefficients were determined using the appropriate transport models for these test conditions and the diffusion coefficients for CO₂ in PMMA‐C20A composites were found to increase with organoclay loading. It is believed that the processing path taken to prepare the nanocomposites may have resulted in the agglomeration of the C20A organoclay, thereby preventing the polymer chains from fully wetting and intercalating a large number of clay particles. These agglomerations are responsible for the formation of large‐scale holes within the glassy nanocomposite, which behave as low resistance pathways for gas transport within the PMMA matrix. POLYM. ENG. SCI., 45:904–914, 2005. © 2005 Society of Plastics Engineers