The purpose of this article is to point out the need to use software simulation tools in industrial practice to optimize the production process and assess the economic effectiveness of investment, including risk. The goal of the research is to find an optimal investment variant to ensure an increase in the production volume of at least 50% and to achieve the maximum economic efficiency of the investment, even considering the risk. The article presents a comprehensive approach that enables the achievement of the set research goal. The selection of the optimal version of the investment is carried out in three steps. Firstly, the versions of the investment variants are assessed from the production point of view using the program Tecnomatix Plant Simulation. Subsequently, the versions of the investment variants are assessed from an economic point of view and from a risk point of view. Economic efficiency is assessed using the financial criteria net present value (NPV), profitability index... [more]

Because of its slow rate of disintegration, plastic debris has steadily risen over time and contributed to a host of environmental issues. Recycling the world’s increasing debris has taken on critical importance. Pyrolysis is one of the most practical techniques for recycling plastic because of its intrinsic qualities and environmental friendliness. For scale-up and reactor design, an understanding of the degradation process is essential. Using one model-free kinetic approach (Friedman) and two model-fitting kinetic methods (Arrhenius and Coats-Redfern), the thermal degradation of Polyethylene Terephthalate (PET) microplastics at heating rates of 10, 20, and 30 °C/min was examined in this work. Additionally, a powerful artificial neural network (ANN) model was created to forecast the heat deterioration of PET MPs. At various heating rates, the TG and DTG thermograms from the PET MPs degradation revealed the same patterns and trends. This showed that the heating rates do not impact the... [more]

Multi-attribute decision-making (MADM) refers to making preference decisions via assessing a finite number of pre-specified alternatives under multiple and usually conflicting attributes. Many problems in the field of additive manufacturing (AM) are essentially MADM problems or can be converted into MADM problems. Recently, a variety of MADM methods have been applied to solve MADM problems in AM. This generates a series of interesting questions: What is the general trend of this research topic from the perspective of published articles every year? Which journals published the most articles on the research topic? Which articles on the research topic are the most cited? What MADM methods have been applied to the field of AM? What are the main strengths and weaknesses of each MADM method used? Which MADM method is the most used one in this field? What specific problems in AM have been tackled via using MADM methods? What are the main issues in existing MADM methods for AM that need to be... [more]

Flood disasters are a natural occurrence around the world, resulting in numerous casualties. It is vital to develop an accurate flood forecasting and prediction model in order to curb damages and limit the number of victims. Water resource allocation, management, planning, flood warning and forecasting, and flood damage mitigation all benefit from rain forecasting. Prior to recent decades’ worth of research, this domain demonstrated to be promising prospects in time series prediction tasks. Therefore, the main aim of this study is to build a forecasting model based on the exponential smoothing-long-short term memory (ES-LSTM) structure and recurrent neural networks (RNNs) for predicting hourly precipitation seasons; and classify the precipitation using an artificial neural network (ANN) model and decision tree (DT) algorithm. We employ the dataset from the Australian commonwealth office of meteorology named Historical Daily Weather dataset to test the effectiveness of the proposed mode... [more]

The accelerated advancement of industrialization, urbanization, and technology produces an enormous amount of waste materials that are channelled into the environment, contaminating the soil, water and air. This exceedingly large volume of waste in the planet’s environment has made it challenging and difficult to handle; thus, it is urgent to facilitate alternative methods of waste disposal. Moreover, the consumption of concrete raw materials increases as a consequence of a sudden increase in concrete usage. In this study, printed circuit boards (PCB), cutting waste (e-waste) (0%, 5%, 10%, 15%, 20%) and recycled concrete aggregate (construction and demolition waste) (0%, 20%, 40%, 60%, 80%, 100%) replace the fine and coarse aggregate; this is utilised in the making of self-compacting concrete (SCC). To mitigate the impact of shrinkage and micro-cracks produced during loading, synthetic fibres (polypropylene fibres) (0%, 0.25%, 0.5%, 0.75%, 1%) are incorporated into the dense matrix of... [more]

This research paper presents novel condensed CNN architecture for the recognition of multispectral images, which has been developed to address the lack of attention paid to neural network designs for multispectral and hyperspectral photography in comparison to RGB photographs. The proposed architecture is able to recognize 10-band multispectral images and has fewer parameters than popular deep designs, such as ResNet and DenseNet, thanks to recent advancements in more efficient smaller CNNs. The proposed architecture is trained from scratch, and it outperforms a comparable network that was trained on RGB images in terms of accuracy and efficiency. The study also demonstrates the use of a Bayesian variant of CNN architecture to show that a network able to process multispectral information greatly reduces the uncertainty associated with class predictions in comparison to standard RGB images. The results of the study are demonstrated by comparing the accuracy of the network’s predictions... [more]

Friction stir welding is a material processing technique used to combine dissimilar and similar materials. Ultimate tensile strength (UTS) is one of the most common objectives of welding, especially friction stir welding (FSW). Typically, destructive testing is utilized to measure the UTS of a welded seam. Testing for the UTS of a weld seam typically involves cutting the specimen and utilizing a machine capable of testing for UTS. In this study, an ensemble deep learning model was developed to classify the UTS of the FSW weld seam. Consequently, the model could classify the quality of the weld seam in relation to its UTS using only an image of the weld seam. Five distinct convolutional neural networks (CNNs) were employed to form the heterogeneous ensemble deep learning model in the proposed model. In addition, image segmentation, image augmentation, and an efficient decision fusion approach were implemented in the proposed model. To test the model, 1664 pictures of weld seams were cre... [more]

The exactitude of petroleum fluid molecular weight correlations affects significantly the precision of petroleum engineering calculations and can make process design and trouble-shooting inaccurate. Some of the methods in the literature to predict petroleum fluid molecular weight are used in commercial software process simulators. According to statements made in the literature, the correlations of Lee−Kesler and Twu are the most used in petroleum engineering, and the other methods do not exhibit any significant advantages over the Lee−Kesler and Twu correlations. In order to verify which of the proposed in the literature correlations are the most appropriate for petroleum fluids with molecular weight variation between 70 and 1685 g/mol, 430 data points for boiling point, specific gravity, and molecular weight of petroleum fluids and individual hydrocarbons were extracted from 17 literature sources. Besides the existing correlations in the literature, two different techniques, nonlinear... [more]

To ensure the ecological security of the basin, data on the heavy metal content in the sediments of the Weihe River Basin were collected to analyze the spatial distribution characteristics of heavy metals based on descriptive statistics and geostatistics. The geoaccumulation index and potential ecological risk assessment were conducted, and the number of pollution sources and the contribution rate were determined by combining correlation analysis, principal component analysis, and APCS-MLR. The results showed that the mean values of As, Pb, Zn, Ni, Cr, Hg, Cu, and Cd were 15.42, 27.27, 88.05, 31.05, 75, 0.13, 29.47, and 1.05 mg·kg−1, respectively. Igeo is in the order of Cd > Hg > As > Cu > Pb > Zn > Cr > Ni. Cd had the highest potential ecological risk factor, followed by Hg with a critical risk proportion of 12.6%. The traceability analysis showed that the heavy metals come from mixed, industrial, and agricultural sources, among which Pb, Zn, Ni, Cr, and Cu are affected by soil-formi... [more]

Quality control is considered a critical aspect of plastic materials in the injection molding process. Two types of deformations occur during the injection molding process, namely, volumetric shrinkage and warpage. This study aims to optimize the warpage of the polyethylene terephthalate preform (PET) used for the packing of carbonated drinks. PET warpage results in an uneven distribution of material over the wall surface of the preform and causes variation in wall thickness. During the filling operation of carbonated drinks, the preforms are subjected to high pressure at the points where the wall thickness is at a minimum, which induces a high-stress concentration. Under high pressure, the preforms are ruptured at the points where the warpage is at a maximum (stress concentration area), causing wastage of the beverage as well as the preform. In this study, the Taguchi method and analysis of variance (ANOVA) are used to determine the most significant parameters to induce warpage during... [more]

Enzyme inhibitors are frequently used to treat viral illnesses. Protease inhibitors are a promising class for combating novel and life-threatening viral infections. This research aimed to evaluate the efficacy and safety of lopinavir/ritonavir monotherapy or lopinavir/ritonavir plus interferon for the treatment of COVID-19. The PubMed, Scopus, Web of Science, and Cochrane Library databases were searched for English articles with full texts available online. ReviewManager software was used to conduct a meta-analysis, subgroup analysis, and sensitivity analysis. Following the creation of the protocol, the collected sources were sorted into categories and evaluated for quality. Risk and hazard ratios and the random effects model were implemented, with statistical heterogeneity assigned using the Higgins I2 statistic. Lopinavir/ritonavir, with or without interferon, was associated with a nonsignificant higher mortality rate (odds ratio [OR] 1.29; 95% confidence interval [CI] 0.95 to 1.761;... [more]

Multi-point forming (MPF) is an advanced and flexible method to form sheet metal workpieces. Although there are studies investigating different aspects of this method, the studies on the effects of pin diameter on sheet and pin contact on MPF are insufficient. In this study, pins with diameters of 10, 12, and 14 mm were used to investigate the damage factor, effective stress distribution, and required forming loads of three forms of aluminum 1100 parts in finite element simulations. In addition, experimental works were conducted for the 12 mm pin and the forming loads and the thinning on the contact points of pin and formed sheet metal parts were compared with the simulations. The 14 mm pin forming provided the highest effective stress distributions and the damage factors of 0.448, 0.770, and 0.329 were obtained for form1, form2, and form3, respectively. The percentage errors between experimental works and simulations using 12 mm pin forming were calculated as 7.4, 5.1, and 2.4% for al... [more]

In this work, the emulsified liquid membrane (ELM) extraction process was studied as a technique for separating different pollutants from an aqueous solution. The emulsified liquid membrane used consisted of Sorbitan mono-oleate (Span 80) as a surfactant with n-hexane (C6H14) as a diluent; the internal phase used was nitric acid (HNO3). The major constraint in the implementation of the extraction process by an emulsified liquid membrane (ELM) is the stability of the emulsion. However, this study focused first on controlling the stability of the emulsion by optimizing many operational factors, which have a direct impact on the stability of the membrane. Among the important parameters that cause membrane breakage, the surfactant concentration, the emulsification time, and the stirring speed were demonstrated. The optimization results obtained showed that the rupture rate (Tr) decreased until reaching a minimum value of 0.07% at 2% of weight/weight of Span 80 concentration with an emulsif... [more]

NOx concentration is an important indicator of the response to ammonia dosage and nitrogen emissions, and its accurate prediction allows for efficient and rational optimal control of ammonia dosage. Due to the large external noise, time lag and non-linearity of the cement denitrification process, it is difficult to derive accurate mathematical prediction models. Therefore, a new machine learning model, namely EEMD-MImRMR-BASBP, is developed. Firstly, Ensemble Empirical Mode Decomposition (EEMD) and median-averaged filtering is used to process the data and remove the noise. In order to handle the large time lags, non-linearity and non-smoothness among the variables, mutual information (MI) based on the entropy principle is proposed to calculate the lag time of the non-linear system; furthermore, according to the feature variable selection method of Max-Relevance and Min-Redundancy (mRMR), the factors with strong influence are selected as the input variables of the prediction model in co... [more]

The conventional denoising method in Rayleigh imaging in a general sense requires an additional hardware investment and the use of the underlying physics. This work demonstrates an alternative image denoising reconstruction model based on unsupervised learning that aims to remove Mie scattering and shot noise interference from two-dimensional (2D) Rayleigh images. The model has two generators and two discriminators whose parameters can be trained with either feature-paired or feature-unpaired data independently. The proposed network was extensively evaluated with a qualitative examination and quantitative metrics, such as PSNR, ER, and SSIM. The results demonstrate that the feature-paired training network exhibits a better performance compared with several other networks reported in the literature. Moreover, when the flame features are not paired, the feature-unpaired training network still yields a good agreement with ground truth data. Specific indicators of the quantitative evaluati... [more]

Smart grids are the latest technology to generate and dispatch an optimal amount of power. Thus, there is a need for stability analysis in smart grid systems. If the smart grid is incorporated into the power system, then the phasor measurement unit (PMU) is used to measure the voltage, current, and frequency. Additionally, the central control unit monitors and controls the power. However, there is a possibility of inserting wrong data into the smart grid as the PMUs are transmitting the data through the Internet and other wireless protocols. There is a need to find solutions to this threat to make the power flow safe and secure in the future. In this paper, two-area load frequency control (LFC) is used for testing the game-theory based security treatment and improving the system’s stability by using an artificial neural network. The two-area system and five-area system are used to test the stability of the power system.

This paper presents the modelling of heat and moisture transfer in a clothes-conditioning unit with the aim of improving the moisture content distribution to the clothes. A multicomponent, non-reacting, two-phase Eulerian−Eulerian model was utilised to solve the computational model. The clothes inside the conditioning unit were modeled as retangular towels (porous medium) of uniform thickness. Mass flow distribution of air and steam through the clothes was studied by systematically varying the steam nozzle angle (30° to 75°) and air inflow grill angle (45° to 105°). The simulation results were studied to identify the impact of design parameters on the mass flow distribution inside the clothes-conditioning unit. The mass flow of steam and the air−steam mixture were calculated through each towel in the forward and reverse direction. Response surface analysis was conducted to correlate the total mass flow rate and steam mass flow rate through each towel with the design variables. Moreover... [more]

Wind power generation highly depends on the determination of wind power potential, which drives the design and feasibility of the wind energy production investment. This gives an important role to wind power estimation, which creates the need for an accurate wind data analysis and wind energy potential assessments for a given location. Such assessments require the implementation of an accurate and suitable wind distribution model. Therefore, in the quest for a well-fitted model, eight methods for estimating the Weibull parameters are investigated in this paper. The methods were then investigated by employing statistical tools, and their performances have been discussed in terms of various error indicators such as root mean squared error (RMSE), regression error (R2), chi-square (X2), and mean absolute error (MAE). Meteorological data for diverse terrain from 14 provinces with 30 sites scattered across Iran were employed to examine the performance of the investigated methods. The result... [more]

Because the linear motor feeding system always runs in complex working conditions for a long time, its performance and state transition have great randomness. Therefore, abnormal detection is particularly significant for predictive maintenance to promptly discover the running state degradation trend. Aiming at the problem that the abnormal samples of linear motor feed system are few and the samples have time-series features, a method of abnormal operation state detection of a linear motor feed system based on normal sample training was proposed, named GANomaly-LSTM. The method constructs an encoding-decoding-reconstructed encoding network model. Firstly, the time-series features of vibration, current and composite data samples are extracted by the long short-term memory (LSTM) network; Secondly, the three-layer fully connected layer is employed to extract potential feature vectors; Finally, anomaly detection of the system is completed by comparing the potential feature vectors of the t... [more]

Quantitative estimates of reservoir parameters and elastic anisotropy using seismic methods is essential for characterizing shale oil reservoirs. Rock physics models were established to quantify elastic anisotropy associated with clay properties, laminated microstructures, and bedding fractures at different scales in shale. The inversion schemes based on the built rock physics models were proposed to estimate reservoir parameters and elastic anisotropy using well log data. Based on the back propagation neural network framework, the obtained rock physical inversion results were used to establish the nonlinear models between elastic properties and reservoir parameters and elastic anisotropy of shale. The established correlations were applied for quantitative seismic interpretation, converting seismic inversion results to the reservoir parameters and elastic anisotropy to characterize the shale oil reservoir comprehensively. The predicted elastic anisotropy of the shale matrix reflects th... [more]

Groundwater flow and transport are crucial for performance and safety assessment in the radioactive waste geological disposal. This study presents the groundwater flow and advective transport simulations for assessing the performance of a reference repository placed in fractured rocks. The study involves the concept of radionuclides migrating into the mobile water in fractures surrounding the deposition hole and calculates two specific quantitative indicators in the field of radioactive waste geological disposal. The indicators equivalent flow rate (Qeq) and flow-related transport resistance (F) are used to express the groundwater flow and transport resistance in the host rock. Based on the hydrogeological conceptual model, the study employs DarcyTools to model the groundwater flow and advective transport of a base case. This study then conducts sensitivity analyses by varying the hydraulic conductivity of the key hydrogeological unit and the excavation damage zone. The uncertainty ana... [more]

A proton exchange membrane fuel cell (PEMFC) is a more environmentally friendly alternative to deliver electric power in various applications, including in the transportation industry. As PEMFC performance characteristics are inherently nonlinear and involved, the prediction of the performance in a given application for different operating conditions is important in order to optimize the efficiency of the system. Thus, modelling using artificial neural networks (ANNs) to predict its performance can significantly improve the capabilities of handling the multi-variable nonlinear performance of the PEMFC. However, further investigation is needed to develop a dynamic model using ANNs to predict the transient behavior of a PEMFC. This paper predicts the dynamic electrical and thermal performance of a PEMFC stack under various operating conditions. The input variables of the PEMFC stack for the analysis consist of the cathode inlet temperature, anode inlet pressure, anode and cathode inlet f... [more]

The focus of this work is on developing a nonlinear grey model for the laminar flow regime of carrier fluid across the flat plate collector. The trust region reflective algorithm was used to solve the nonlinear laminar flow problem and handle the sparse matrix. The stream function, dimensionless velocity, gradients of velocity components, carrier fluid temperature, and the absorber plate temperature were estimated for a flat plate collector operated with and without a circular chimney. Similarly, the same technique was adopted to determine a model for heat transfer across the absorber plate. The numerically obtained solution was also compared with the parameters obtained through the instrumental measurement. The relative tolerance was kept at 10−5 for the white and grey box solutions. The developed model was noticed to have a smaller deviation than the conventional analytical model to predict the experimental values. The slip condition was observed for the proposed scheme. The uncertai... [more]

In 2021, the residential sector had an electricity consumption of around 39% in México. Householders influence the quantity of energy they manage in a home due to their preferences, culture, and economy. Hence, profiling the householders’ behavior in communities allows designers or engineers to build strategies that promote energy reductions. The household socially connected products ease routine tasks and help profile the householder. Furthermore, gamification strategies model householders’ habits by enhancing services through ludic experiences. Therefore, a gamified smart community concept emerged during this research as an understanding that this type of community does not need a physical location but has similar characteristics. Thus, this paper proposes a three-step framework to tailor interfaces. During the first step, the householder type and consumption level were analyzed using available online databases for Mexico. Then, two artificial neural networks were built, trained, and... [more]

This paper presents a framework for photovoltaic (PV) fault detection based on statistical, supervised, and unsupervised machine learning (ML) approaches. The research is motivated by a need to develop a cost-effective solution that detects the fault types within PV systems based on a real dataset with a minimum number of input features. We discover the appropriate conditions for method selection and establish how to minimize computational demand from different ML approaches. Subsequently, the PV dataset is labeled as a result of clustering and classification. The labelled dataset is then trained using various ML models before evaluating each based on accuracy, precision, and a confusion matrix. Notably, an accuracy ranging from 94% to 100% is achieved with datasets from two different PV systems. The model robustness is affirmed by performing the approach on an additional real-world dataset that exhibits noise and missing values.