Both anthropogenic and natural sources of pollution are regionally significant. Therefore, in order to monitor and protect the quality of Langat River from deterioration, we use Artificial Intelligence (AI) to model the river water quality. This study has applied several machine learning models (two support vector machines (SVMs), six regression models, and artificial neural network (ANN)) to predict total suspended solids (TSS), total solids (TS), and dissolved solids (DS)) in Langat River, Malaysia. All of the models have been assessed using root mean square error (RMSE), mean square error (MSE) as well as the determination of coefficient (R2). Based on the model performance metrics, the ANN model outperformed all models, while the GPR and SVM models exhibited the characteristic of over-fitting. The remaining machine learning models exhibited fair to poor performances. Although there are a few researches conducted to predict TDS using ANN, however, there are less to no research condu... [more]

A three-dimensional computational study of double-diffusive natural convection was conducted to explore the impact of tilt on the thermal and solutal performance of a floating pyramidal solar still filled with an air-steam mixture. In the present work, the still is cooled from the upper walls and is maintained at a low vapor concentration. The bottom wall of the still is maintained at a hot temperature and high concentration. Four different models of baffles placed in the upper region of the solar still have been studied. The mathematical formulation of the equations governing the problem is based on the vector current potential -vorticity formalism. The numerical method of finite volumes is used. The effect of Rayleigh and tilt angle of the floating solar still on the flow structure, iso-temperatures, iso-concentrations, and heat and mass transfer rates were examined. The most relevant results of this study are (i) an uncooled air-vapor mixture outlet was observed during tilting for t... [more]

This paper proposes a metallic fracture estimation method that combines digital image correlation and convolutional neural networks, based on a proven theory that the strain distribution of a component changes when a crack occurs in a structure. By using digital image correlation, the method achieves noncontact and nondestructive sensing, as well as high interference immunity. We utilize a digital image correlation system to produce strain distribution graphs that reflect occurrences and propagations of fractures during fatigue processes. A deep residual network (ResNet) regression model is trained by correlating strain distribution graphs with the corresponding fracture lengths, so that the fracture propagation condition can be estimated by data from digital image correlation. In the experiment, according to the American Society for Testing Materials (ASTM) standards, we fabricate a set of aluminum specimens and perform fatigue tests with data acquisition by digital image correlation.... [more]

To obtain quality variables that cannot be measured in real time during the production process but reflect information on the quality of the final product, the batch production process has the characteristics of a strong time-varying nature, non-Gaussian data distribution and high nonlinearity. A locally weighted partial least squares regression quality prediction model (KICA-JITL-LWPLS), based on wavelet kernel function independent meta-analysis with immediate learning, is proposed. The model first measures the similarity between the normalized input data and the historical data and assigns the input data to the group of historical data with high similarity to it, based on the posterior probability of the Bayesian classifier; subsequently, wavelet kernel functions are selected and kernel learning methods are introduced into the independent meta-analysis algorithm. An independent meta-analysis, based on the wavelet kernel function, is performed on the classified input data to obtain pr... [more]

The massive increase in disposable plastic globally can be addressed through effective recovery methods, and one of these methods is pyrolysis. R software may be used to statistically model the composition and yield of pyrolysis products, such as oil, gas, and waxes to deduce an effective pyrolysis mechanism. To date, no research reports have been documented employing the Arrhenius equation in R software to statistically forecast the kinetic rate constants for the pyrolysis of high-density plastics. We used the Arrhenius equation in R software to assume two series of activation energies (Ea) and pre-exponential factors (Ao) to statistically predict the rate constants at different temperatures to explore their impact on the final pyrolysis products. In line with this, MATLAB (R2020a) was used to predict the pyrolysis products of plastic in the temperature range of 370−410 °C. The value of the rate constant increased with the temperature by expediting the pyrolysis reaction due to the re... [more]

This paper presents the multi-objective optimization of a crude oil hydrotreating (HDT) process with a crude atmospheric distillation unit using data-driven models based on bootstrap aggregated neural networks. Hydrotreating of the whole crude oil has economic benefit compared to the conventional hydrotreating of individual oil products. In order to overcome the difficulty in developing accurate mechanistic models and the computational burden of utilizing such models in optimization, bootstrap aggregated neural networks are utilized to develop reliable data-driven models for this process. Reliable optimal process operating conditions are derived by solving a multi-objective optimization problem incorporating minimization of the widths of model prediction confidence bounds as additional objectives. The multi-objective optimization problem is solved using the goal-attainment method. The proposed method is demonstrated on the HDT of crude oil with crude distillation unit simulated using A... [more]

Sour water stripping can treat the sour water produced by crude oil processing, which has the effect of environmental protection, energy saving and emission reduction. This paper aims to reduce energy consumption of the unit by strengthening process parameter optimization. Firstly, the basic model is established by utilizing Aspen Plus, and the optimal model is determined by comparative analysis of back propagation neural network (BPNN), radial basis function neural network (RBFNN) and generalized regression neural network (GRNN) models. Then, the sensitivity analysis of Sobol is used to select the operating variables that have a significant influence on the energy consumption of the sour water stripping system. Finally, the particle swarm optimization (PSO) algorithm is used to optimize the operating conditions of the sour water stripping unit. The results show that the RBFNN model is more accurate than other models. Its network structure is 5-66-1, and the expected value has an appro... [more]

The color of wine is an important quality parameter essential for the first impression of consumers. The International Organization of Vine and Wine (OIV) recommends two methods to describe wine color: color calculation according to Glories and the determination of coordinates in the CIE L*a*b* color space. The measurement of wine color is often not feasible for winemakers because the required instrumentation is expensive and bulky. In this study, the influence of photometer settings on the calculated color was investigated based on 14 wines. Furthermore, the CIE L*a*b* and Glories system were compared using 56 red and 56 white wines. Photometer settings were found to influence the reproducibility of color determination. In addition, CIE L*a*b* system do not correlate in all wines with the Glories system and Glories probably provides less information about wine color. Using interpolation, CIE L*a*b* coordinates were calculated from single wavelength measurements taken by a small-sized... [more]

This article deals with the regression analysis of the ultrasonic signal amplitude when the character of the reflection surface has been changed from a planar case to a sharp corner case. The experiment was performed at a measurement distance within the interval from 100 mm to 215 mm. A nonlinear correlation between the amplitude of the ultrasound signal and the measured distance was demonstrated. By analyzing the frequency spectra, a poor nonlinear correlation between the maximum frequency component and the distance vector was found for the sharp corner case versus the planar case, which proved similar nonlinear characteristics as the signal amplitude marker. The strong linear correlation in the distance difference vectors in the amplitude analysis of the ultrasound signal confirmed the hypothesis of a direct relationship between the reflection surface geometric characteristic and the polarity of the difference. The ultrasound signal was identified as a 3rd-order dynamic system. The n... [more]

Multiple graph and semi-supervision techniques have been successfully introduced into the nonnegative matrix factorization (NMF) model for taking full advantage of the manifold structure and priori information of data to capture excellent low-dimensional data representation. However, the existing methods do not consider the sparse constraint, which can enhance the local learning ability and improve the performance in practical applications. To overcome this limitation, a novel NMF-based data representation method, namely, the multiple graph adaptive regularized semi-supervised nonnegative matrix factorization with sparse constraint (MSNMFSC) is developed in this paper for obtaining the sparse and discriminative data representation and increasing the quality of decomposition of NMF. Particularly, based on the standard NMF, the proposed MSNMFSC method combines the multiple graph adaptive regularization, the limited supervised information and the sparse constraint together to learn the mo... [more]

In the tobacco redrying process, process parameter settings are greatly influenced by ambient temperature and humidity, and the moisture content of the tobacco leaf. In the face of complex and variable tobacco leaf characteristics, it is difficult to accurately adapt the process parameters to fluctuations in the incoming material characteristics by manual experience alone. Therefore, an improved optimization method combining an improved particle swarm optimization algorithm (IPSO) and an adaptive penalty function is proposed, which can adaptively recommend the best combination of process parameters according to the dynamic incoming characteristics of the tobacco leaf, to reduce the deviation in the outlet moisture and temperature of the roaster under different processing standards of the tobacco leaf. Firstly, the Radial Basis Function (RBF) Neural Network is used to fit the relationship between process parameters and roaster exit moisture content and temperature. Then, taking the stan... [more]

The study’s goal is to assess the flexural strength of concrete that includes waste marble powder using machine learning methods, i.e., ANFIS, Support vector machines, and Gaussian processes approaches. Flexural strength has also been studied by using the most reliable approach of sensitivity analysis in order to determine the influential independent variable to predict the dependent variable. The entire dataset consists of 202 observations, of which 120 were experimental and 82 were readings from previous research projects. The dataset was then arbitrarily split into two subsets, referred to as the training dataset and the testing dataset, each of which contained a weighted percentage of the total observations (70−30). Output was concrete mix flexural strength, whereas inputs comprised cement, fine and coarse aggregates, water, waste marble powder, and curing days. Using statistical criteria, an evaluation of the efficacy of the approaches was carried out. In comparison to other algor... [more]

This study implemented a bidirectional artificial neuro-fuzzy inference system (ANFIS) to solve the problem of system resilience in synchronized and islanded grid mode/operation (during normal operation and in the event of a catastrophic disaster, respectively). Included in this setup are photovoltaics, wind turbines, batteries, and smart load management. Solar panels, wind turbines, and battery-charging supercapacitors are just a few of the sustainable energy sources ANFIS coordinates. The first step in the process was the development of a mode-specific control algorithm to address the system’s current behavior. Relative ANFIS will take over to greatly boost resilience during times of crisis, power savings, and routine operations. A bidirectional converter connects the battery in order to keep the DC link stable and allow energy displacement due to changes in generation and consumption. When combined with the ANFIS algorithm, PV can be used to meet precise power needs. This means it c... [more]

Mechanical damage is a threat to both food security and sustainability. Bruising is the most common type of mechanical damage, and it causes a huge economic loss due to rejection of fresh produce and downgrading of the appearance quality by consumers. Therefore, this study aims to examine the effect of bruising during postharvest handling using a pendulum test technique. Pomegranate fruit were bruised once at two impact levels (1.189 ± 0.109 and 2.298 ± 0.239 J) and then stored (at 5 °C ± 1 °C and 22 °C ± 1 °C) for 28 days. The study evaluated the effect of impact bruising, storage temperature, and duration on the bruise magnitude and quality attributes of the bruised and non-bruised pomegranates. The results showed that the investigated factors affect the bruise size of bruised pomegranates. Increasing storage temperature from 5 to 22 °C and impact level from 1.189 to 2.298 J increased the bruise area, bruise volume, and bruise susceptibility over time. Alterations in total soluble so... [more]

The glass industry is highly energy-intensive, consuming approximately 500−700 million GJ each year. Replacing inefficient equipment with better-performing equipment is a good strategy to reduce the energy consumption of a glass plant. Since there are many alternative solutions, the choice of which technological improvement to implement is usually difficult. Therefore, a review of solutions to reduce energy consumption in a glass plant is pivotal. The literature offers similar studies, but they are not sufficiently up-to-date and do not represent the actual state of the art, which should be updated. Thus, this paper aims to provide an updated list of alternative solutions, clustering them into different categories (e.g., the process stage). Moreover, this paper investigates the current applicability of energy-saving solutions in Italy. Specifically, a sample of 103 Italian companies is considered and the type of interventions that the companies recently implemented or that they intend... [more]

Additives are widely used to enhance the rheological and performance properties of asphalt binder to satisfy the demands of extreme loading and climatic conditions. Meanwhile, adding to the complexity of asphalt binder behaviour that requires more time, effort, and material resources during laboratory work. The purpose of this research was to use Artificial Neural Networks (ANNs) to predict the recovery (R) and nonrecoverable compliance (Jnr) behaviour of asphalt binder based on mechanical test parameters and rheological properties of asphalt binder. A comprehensive experimental database consisting of the results of the frequency sweep and Multiple Stress Creep Recovery (MSCR) test using a dynamic shear rheometer (DSR) at five test temperatures (46 ∘C, 52 ∘C, 58 ∘C, 64 ∘C, and 70 ∘C). Prediction models for R and Jnr of asphalt binder modified with different contents of fly ash, fly ash-based geopolymer, glass powder/fly ash-based geopolymer, and styrene−butadiene styrene (SBS) were dev... [more]

The distribution and evolution of air in airliner cabins is an important basis for the study of cabin thermal environment and pollutant propagation. Due to the complex heat and mass transfer problems caused by forced and natural convection in a large-scale space, the accurate prediction of air distribution in airliner cabins still faces huge challenges. This study takes the cabin of the Airbus A320 as the research object. The accurate numerical simulation of the flow and heat transfer process in an airliner cabin under mixing ventilation mode was carried out using the Hybrid Thermal Lattice Boltzmann Method (HTLBM) combined with GPU (Graphics Processing Unit) acceleration technology, and the influence of human thermal plumes on air distribution and evolution characteristics in an airliner cabin was analyzed. The research shows that the human thermal plume changes the air distribution in the passenger cabin. The thermal plume slows down the jet attenuation, increases the energy exchange... [more]

Dry dust collectors are a typical dust and gas coexistence space. Dust explosion risk assessments should be performed for effective prevention and control of dust explosion accidents. In this paper, a dust explosion risk assessment index system for dust removal systems was constructed following the dust characteristics and the actual operation of a dry dust collector. The proposed system consisted of three first-level indexes (dust explosion characteristic parameters, environmental parameters in the dust collector box, use state of explosion prevention and control device) and seven second-level indexes (dust explosion sensitivity, dust explosion severity, temperature in the dust collector box, pressure difference between inlet and outlet, operating state of spark detection, operating the explosion venting disc, and operating state of the lock gas ash discharge valve). The analytic hierarchy process was adapted to calculate the weight of each index. Additionally, a dust explosion risk a... [more]

Oxidation-reduction potential (ORP) sensors would constitute a robust surveillance and control solution for aeration and external carbon dosing in wastewater biological treatment processes if a clear correlation exists between the ORP values and process variables (e.g., dissolved oxygen (DO), nitrate, and chemical oxygen demand (COD). In this study, ORP values and other water quality variables were analyzed, and principal component analysis (PCA) and analysis of variance were used to study the relationships between ORP and main reactive substances under anoxic conditions. Mathematical models were then established using multiple regression analysis. The results showed that under anoxic conditions, ORP was positively correlated with nitrate, DO, and COD and negatively correlated with ammonia nitrogen, phosphate, and pH. COD had a low correlation with the ORP value change. PCA showed that the mathematical model of ORP can be established by using DO, nitrate, and phosphate, for which the a... [more]

Shale gas reservoirs are one of the most rapidly growing forms of natural gas worldwide. Gas production from such reservoirs is possible by using extensive and deep well fracturing to contact bulky fractions of the shale formation. In addition, the main mechanisms of the shale gas production process are the gas desorption that takes place by diffusion of gas in the shale matrix and by Darcy’s type through the fractures. This study presents a finite element model to simulate the gas flow including desorption and diffusion in shale gas reservoirs. A finite element model is used incorporated with a quadrilateral element mesh for gas pressure solution. In the presented model, the absorbed gas content is described by Langmuir’s isotherm equation. The non-linear iterative method is incorporated with the finite element technique to solve for gas property changes and pressure distribution. The model is verified against an analytical solution for methane depletion and the results show the robus... [more]

Due to multiple factors influencing the construction safety of TBM hydraulic tunnels, risk assessment is a critical point of a construction management plan to avoid possible risks. In this paper, a safety-risk evaluation index system of TBM construction for hydraulic tunnels is built based on the identification and analysis of possible sources of risk in techniques, geologic, equipment, management, and accidents. Considering the influence of factors such as the experience level and the expertise of decision makers, a combination assignment method of index weights is proposed based on binary semantics. On the basis of a fuzzy normal distribution used as the subordinate function distribution of fuzzy evaluation levels, the subordinate function distribution of fuzzy evaluation levels under multi-level intersection situations is introduced, and a comprehensive evaluation model of safety risks for TBM tunnel construction is built. The validity and practicality of the evaluation model is exa... [more]

The estimation of the formation permeability is considered a vital process in assessing reservoir deliverability. The prediction of such a rock property with the use of the minimum number of inputs is mandatory. In general, porosity and permeability are independent rock petrophysical properties. Despite these observations, theoretical relationships have been proposed, such as that by the Kozeny−Carmen theory. This theory, however, treats a highly complex porous medium in a very simple manner. Hence, this study proposes a comprehensive ANN model based on the back propagation learning algorithm using the FORTRAN language to predict the formation permeability from available well logs. The proposed ANN model uses a weight visualization curve technique to optimize the number of hidden neurons and layers. Approximately 500 core data points were collected to generate the model. These data, including gamma ray, sonic travel time, and bulk density, were collected from numerous wells drilled in... [more]

Artificial neural networks (ANNs) have achieved great success in performing machine learning tasks, including classification, regression, prediction, image processing, image recognition, etc., due to their outstanding training, learning, and organizing of data. Conventionally, a gradient-based algorithm known as backpropagation (BP) is frequently used to train the parameters’ value of ANN. However, this method has inherent drawbacks of slow convergence speed, sensitivity to initial solutions, and high tendency to be trapped into local optima. This paper proposes a modified particle swarm optimization (PSO) variant with two-level learning phases to train ANN for image classification. A multi-swarm approach and a social learning scheme are designed into the primary learning phase to enhance the population diversity and the solution quality, respectively. Two modified search operators with different search characteristics are incorporated into the secondary learning phase to improve the a... [more]

A hierarchical structure based on a Deep LSTM Supervised Autoencoder Neural Network (Deep LSTM-SAE NN) is presented for the detection and classification of faults in industrial plants. The proposed methodology has the ability to classify incipient faults that are difficult to detect and diagnose with traditional and many recent methods. Faults are grouped into different subsets according to the degree of difficulty to classify them accurately in the proposed hierarchical structure. External pseudo-random binary signals (PRBS) are injected in the system to enhance the identification of incipient faults. The approach is illustrated on the benchmark process (Tennessee Eastman Process) in order to compare across different methodologies. The efficacy of the proposed method is shown by a comprehensive comparison between many recent and traditional fault detection and diagnosis methods in the literature for Tennessee Eastman Process. The proposed work results in significant improvements in th... [more]

For locating the source of leaking gas in various engineering fields, several issues remain in the immediate estimation of the location of diffusion sources from limited observation data, because of the nonlinearity of turbulence. This study investigated the practical applicability of diffusion source-location prediction using a convolutional neural network (CNN) from leaking gas instantaneous distribution images captured by infrared cameras. We performed direct numerical simulation of a turbulent flow past a cylinder to provide training and test images, which are scalar concentration distribution fields integrated along the view direction, mimicking actual camera images. We discussed the effects of the direction in which the leaking gas flows into the camera’s view and the distance between the camera and the leaking gas on the accuracy of inference. A single learner created by all images provided an inference accuracy exceeding 85%, regardless of the inflow direction or the distance b... [more]