Model-driven engineering of process control software – beyond device-centric abstractions

This paper presents a new, two-level, model-driven engineering approach to industrial process control software. The first level (infrastructure engineering) is concerned with the following: the definition of the development process and guidelines, the definition of a domain-specific modeling language, the specification of the model transformations, and the development of a tool suite. This tool suite enables modeling of the process control software and the automatic code generation for programmable logic controllers. In the second level (application engineering), the process control software is engineered using the results of the infrastructure level. The approach is demonstrated on excerpts from an industrial project.     

The Life Assessment of API 5L Grade B Geothermal Pipeline in Correlation with Corrosion under Insulation

The corrosion under insulation dominates metal loss in the case of API （American Petroleum Institute） 5L Grade B geothermal pipeline, whereas, the other possible corrosion causes, such as stress corrosion and thermal cracking, did not serve a role as critical factors regarding to pipeline degradation during 26-years working operation. Actually, the header pipe diameter 32 inches and 40 inches require priority for inspection due to higher corrosion rate compared to smaller pipe diameters. By simulation method on similar heavy duty service condition in which the working pressure of 15 bars and temperature of 183 ℃, it was proved that corrosion rate will severely occur at corrosion rate more than 10 mpy in the pipe diameters of 20 inches, 32 inches and 40 inches. Further,condensation factor may contribute more significant in the case of corrosion under insulation. Practically, the metal loss at a half initial thickness requires priority for inspection, intensive maintenance or possible partial replacement.     

Graphene synthesis,fabrication, characterization based on bottom-up and top-down approaches: An overview

This study presents an overview on graphene synthesis, fabrication and different characterization techniques utilized in the production. Since its discovery in 2004 by Andre Geim and Kostya Novoselov several research articles have been published globally to this effect, owing to graphene’s extraordinary, and exclusive characteristics which include optical transparency, excellent thermal, and mechanical properties. The properties and applications of this two-dimensional carbon crystal composed of single-layered material have created new avenues for the development of high-performance future electronics and technologies in energy storage and conversion for the sustainable energy. However, despite its potential and current status globally the difficulty in the production of monolayer graphene sheet still persists. Therefore, this review highlighted two approaches in the synthesis of graphene, which are the top-down and bottom-up approaches and examined the advantages and failings of the methods involved. In addition, the prospects and failings of these methods are investigated, as they are essential in optimizing the production method of graphene vital for expanding the yield, and producing high-quality graphene.     

Energy-Efficient Low-Complexity Algorithm in 5G Massive MIMO Systems

Energy efficiency (EE) is a critical design when taking into account circuit power consumption (CPC) in fifth-generation cellular networks. These problems arise because of the increasing number of antennas in massive multiple-input multiple-output (MIMO) systems, attributable to inter-cell interference for channel state information. Apart from that, a higher number of radio frequency (RF) chains at the base station and active users consume more power due to the processing activities in digital-to-analogue converters and power amplifiers. Therefore, antenna selection, user selection, optimal transmission power, and pilot reuse power are important aspects in improving energy efficiency in massive MIMO systems. This work aims to investigate joint antenna selection, optimal transmit power and joint user selection based on deriving the closed-form of the maximal EE, with complete knowledge of large-scale fading with maximum ratio transmission. It also accounts for channel estimation and eliminating pilot contamination as antennas M → ∞. This formulates the optimization problem of joint optimal antenna selection, transmits power allocation and joint user selection to mitigate inter-cell-interference in downlink multi-cell massive MIMO systems under minimized reuse of pilot sequences based on a novel iterative low-complexity algorithm (LCA) for Newton’s methods and Lagrange multipliers. To analyze the precise power consumption, a novel power consumption scheme is proposed for each individual antenna, based on the transmit power amplifier and CPC. Simulation results demonstrate that the maximal EE was achieved using the iterative LCA based on reasonable maximum transmit power, in the case the noise power is less than the received power pilot. The maximum EE was achieved with the desired maximum transmit power threshold by minimizing pilot reuse, in the case the transmit power allocation ρ_d = 40 dBm, and the optimal EE = 71.232 Mb/j.     

Identifying mortality risk factors amongst acute coronary syndrome patients admitted to Arabian Gulf hospitals using machine‐learning methods

Acute coronary syndrome (ACS) is a leading cause of mortality and morbidity in the Arabian Gulf. In this study, the in‐hospital mortality amongst patients admitted with ACS to Arabian Gulf hospitals is predicted using a comprehensive modelling framework that combines powerful machine‐learning methods such as support‐vector machine (SVM), Naïve Bayes (NB), artificial neural networks (NN), and decision trees (DT). The performance of the machine‐learning methods is compared with that of the performance of a commonly used statistical method, namely, logistic regression (LR). The study follows the current practise of computing mortality risk using risk scores such as the Global Registry of Acute Coronary Events (GRACE) score, which has not been validated for Arabian Gulf patients. Cardiac registry data of 7,000 patients from 65 hospitals located in Arabian Gulf countries are used for the study. This study is unique as it uses a contemporary data analytics framework. A k‐fold (k = 10) cross‐validation is utilized to generate training and validation samples from the GRACE dataset. The machine‐learning‐based predictive models often incur prejudgments for imbalanced training data patterns. To mitigate the data imbalance due to scarce observations for in‐hospital mortalities, we have utilized specialized methods such as random undersampling (RUS) and synthetic minority over sampling technique (SMOTE). A detailed simulation experimentation is carried out to build models with each of the five predictive methods (LR, NN, NB, SVM, and DT) for the each of the three datasets k‐fold subsamples generated. The predictive models are developed under three schemes of the k‐fold samples that include no data imbalance, RUS, and SMOTE. We have implemented an information fusion method rooted in computing weighted impact scores obtain for an individual medical history attributes from each of the predictive models simulated for a collective recommendation based on an impact score specific to a predictor. Finally, we grouped the predictors using fuzzy c‐mean clustering method into three categories, high‐, medium‐, and low‐risk factors for in‐hospital mortality due to ACS. Our study revealed that patients with medical history related to the presences of peripheral artery disease, congestive heart failure, cardiovascular transient ischemic attack valvular disease, and coronary artery bypass grafting amongst others have the most risk for in‐hospital mortality.     

Removal of lead ions from aqueous solutions using powdered corn cobs

This paper is a report on an investigation conducted on adsorption of lead ions (Pb²⁺) onto powdered corn cobs (PCC). Corn cobs were collected from a selected location in Nigeria. The corn cobs were crushed and pulverised into different particle sizes. Its compositions and adsorption properties of Pb²⁺ onto PCC were studied. The effects of pH, particle size of PCC, and initial concentration of Pb²⁺ on the adsorption properties were monitored. The adsorption capacities were analysed by using standard adsorption models. The models were evaluated statistically (total error, coefficient of determination (CD), model of selection criterion (MSC), and root mean square error). The study revealed that PCC contained 2.33%, 86.89%, 10.78%, 0.52%, and 4.56% ash, volatile, moisture, solubility in water, and solubility in 0.25 M of HCl by mass, respectively. Acid digestion of a gram of PCC indicated that PCC contained no chromium and lead, but contained 131 mg Fe, 54.79% carbon, 8.03% hydrogen, 0.41% nitrogen, 0.010 mg of Al, and 1.70 mg calcium. The isotherm models parameters were 28.509 L/mg and 0.141 mg/g; 0.138 mg/g and 11.494 L/mg; 0.142 L/mg and 0.013 mg/g; 0.129 mg/g and 25.641 L/mg, 28.509 L/mg, 3.795 mg/g and 2.336 for Langmuir, Freundlich, Temkin, activated sludge, and Redlich–Peterson. The PCC particle size, initial pH, and initial Pb²⁺ concentration had effects on the adsorption parameters. The statistical evaluations showed that the best model for adsorption of lead ions from raw water onto PCC based on lower errors, high CD (0.88), reliability (97.5%), and MSC (1.86) was Freundlich and followed by activated sludge model. It was concluded that PCC is a good adsorbent like powdered eggshell and other carbon‐based materials.     

Mechanism of Soret‐Dufour,magnetohydrodynamics, heat and mass transfer flow with buoyancy force,and viscous dissipation effects

This paper examined the mechanism of both positive and negative effects of Soret‐Dufour with heat and mass transfer processes over an accelerating permeable surface. The partial differential flow equations were simplified using similarity variables, and the resulting equations were solved numerically using the spectral homotopy analysis method (SHAM). The SHAM is used in separating nonlinear equations into linear and nonlinear. The physics of each pertinent flow parameters was used to examine their influence on velocity, temperature, and concentration fields. The effect of Soret‐Dufour was examined separately, and its negative effect was used to determine its influence on velocity, temperature, and concentration fields. The result revealed that positive Soret‐Dufour enhances the boundary layer, whereas negative Soret‐Dufour parameter decreases the boundary layer. The result presented in this paper is in good agreement with existing works in literature.     

Measurement and correlation of physical properties of aqueous solutions of tetrabutylammonium hydroxide,piperazine and their aqueous blends

The density, viscosity and refractive index of aqueous solutions of tetrabutylammonium hydroxide (TBAOH), pi-perazine (PZ) and their aqueous blends are determined at several temperatures (303.15 to 333.15 K). All these measured physicochemical properties decreases with an increase in temperature. The density data is used to cal-culate the coefficient of thermal expansion and excess molar volume of al aqueous binary and ternary solutions. The coefficient of thermal expansion increases with increase in temperatures and concentrations. The negativity of excess molar volume for al the aqueous solution decreased with increase in temperature. Each physical prop-erty is correlated with temperature by least square method and the corresponding coefficients for each property are presented. The prediction values from correlations for the physical properties are in good agreement with the experimental values.