Prediction of air-overpressure induced by blasting using an ANFIS-PNN model optimized by GA

Application of artificial intelligence tools in wastewater and waste gas treatment systems: Recent advances and prospects

The non-linear complex relationships among the process variables in wastewater and waste gas treatment systems possess a significant challenge for real-time systems modelling. Data driven artificial intelligence (AI) tools are increasingly being adopted to predict the process performance, cost-effective process monitoring, and the control of different waste treatment systems, including those involving resource recovery. This review presents an in-depth analysis of the applications of emerging AI tools in physico-chemical and biological processes for the treatment of air pollutants, water and wastewater, and resource recovery processes. Additionally, the successful implementation of AI-controlled wastewater and waste gas treatment systems, along with real-time monitoring at the industrial scale are discussed.

Empirical relation to evaluate blast induced crack development zone while using explosives of different detonation pressure in opencast bench blasting

The optimum utilisation of explosive energy in the rock blasting operation is a prime challenge for the blast designers. The explosive energy in this operation is used for movement of burden. The optimum fracturing of the rock mass to meet the production demand takes place along tension. In the process of blasting, the detonation pressure of the explosives in the blasthole induces shock wave to the rock mass. The propagating shock wave is initially compressive in nature and becomes tensile after being reflected from the free face. The extent of tensile damage zone would give the optimum burden for blasting. The explosive properties along with the rock mass properties and charge configuration influences the extent of tensile damage zone. In this study, an empirical relation has been developed for estimation of blast induced tensile damage zone. The experimental trials were conducted at a coal mine using two different types of explosives for the validation of the developed empirical relation. The ground vibration predictors were developed using the data of experimental trials. The induced damage zone was computed using empirical relation proposed by Forsyth (1993) and developed ground vibration predictors. The estimated damage zone using developed empirical predictor and Forsyth relation were compared. The difference in the induced damage zone using two approaches is within 10%. The predicted values using developed empirical relation are accurate with RMSE value of 0.227 m. Hence, the developed empirical relation would be beneficial for estimation of blast induced crack zone.

Predicting Sliding Angles on Random Pit-Distributed Textures Using Probabilistic Neural Networks

The three-phase contact line best reflects the sliding ability of droplets on solid surfaces. Most studies on the sliding angle (SA) of superhydrophobic surfaces are limited to regularly arranged microtextured surfaces, lacking definite models and effective methods for a complex surface of a random texture. In this study, random pits with an area ratio of 19% were generated on 1 mm × 1 mm subregions, and the subregions formed arrays on a sample surface of 10 mm × 10 mm to obtain a randomly distributed microtexture surface with no pit overlaps. Although the contact angle (CA) of randomly pitted texture was the same, the SA was different. The SA of surfaces was affected by the pit location. The location of random pits increased the complexity of the three-phase contact line movement. The continuity of the three-phase contact angle (T) can reveal the rolling mechanism of the random pit texture and predict the SA, but the relationship between the T and SA is a relatively poor linear relation (R² = 74%), and the SA of the random pit texture can only be roughly estimated. The quantized pit coordinates and SA were used as the input and output labels for the PNN model, respectively, and the accuracy of the model convergence was 90.2%.

Evaluation of adaptive neuro-fuzzy inference system-genetic algorithm in the prediction and optimization of NOx emission in cement precalcining kiln

The increasing demand for cement due to urbanization growth in Africa countries may result in an upsurge of pollutants associated with its production. One major air pollutant in cement production is nitrogen oxides (NOx) and reported to cause serious damage to human health and the ecosystem. The operation of a cement rotary kiln NOx emission was studied with plant data using the ASPEN Plus software. It is essential to understand the effects of calciner temperature, tertiary air pressure, fuel gas, raw feed material, and fan damper on NOx emissions from a precalcining kiln. In addition, the performance capability of adaptive neuro-fuzzy inference systems and genetic algorithms (ANFIS-GA) to predict and optimize NOx emissions from a precalcining cement kiln is evaluated. The simulation results were in good agreement with the experimental results, with root mean square error of 2.05, variance account (VAF) of 96.0%, average absolute deviation (AAE) of 0.4097, and correlation coefficient of 0.963. Further, the optimal NOx emission was 273.0 mg/m³, with the parameters as determined by the algorithm were calciner temperature at 845 °C, tertiary air pressure - 4.50 mbar, fuel gas of 8550 m³/h, raw feed material 200 t/h, and damper opening of 60%. Consequently, it is recommended that ANFIS should be combined with GA for effective prediction, and optimization of NOx emission in cement plants.

Vaccination and isolation based control design of the COVID-19 pandemic based on adaptive neuro fuzzy inference system optimized with the genetic algorithm

The study of the COVID-19 pandemic is of pivotal importance due to its tremendous global impacts. This paper aims to control this disease using an optimal strategy comprising two methods: isolation and vaccination. In this regard, an optimized Adaptive Neuro-Fuzzy Inference System (ANFIS) is developed using the Genetic Algorithm (GA) to control the dynamic model of the COVID-19 termed SIDARTHE (Susceptible, Infected, Diagnosed, Ailing, Recognized, Threatened, Healed, and Extinct). The number of diagnosed and recognized people is reduced by isolation, and the number of susceptible people is reduced by vaccination. The GA generates optimal control efforts related to the random initial number of each chosen group as the input data for ANFIS to train Takagi-Sugeno (T-S) fuzzy structure coefficients. Also, three theorems are presented to indicate the positivity, boundedness, and existence of the solutions in the presence of the controller. The performance of the proposed system is evaluated through the mean squared error (MSE) and the root-mean-square error (RMSE). The simulation results show a significant decrease in the number of diagnosed, recognized, and susceptible individuals by employing the proposed controller, even with a 70% increase in transmissibility caused by various variants.

A Novel Combination of PCA and Machine Learning Techniques to Select the Most Important Factors for Predicting Tunnel Construction Performance

Numerous studies have reported the effective use of artificial intelligence approaches, particularly artificial neural networks (ANNs)-based models, to tackle tunnelling issues. However, having a high number of model inputs increases the running time and related mistakes of ANNs. The principal component analysis (PCA) approach was used in this work to select input factors for predicting tunnel boring machine (TBM) performance, specifically advance rate (AR). A reliable and precise forecast of TBM AR is desirable and critical for mitigating risk throughout the tunnel building phase. The developed PCAs (a total of four PCAs) were used with the artificial bee colony (ABC) method to predict TBM AR. To assess the created PCA-ANN-ABC model’s capabilities, an imperialist competitive algorithm-ANN and regression-based methods for estimating TBM AR were also suggested. To evaluate the artificial intelligence and statistical models, many statistical evaluation metrics were evaluated and generated, including the coefficient of determination (R2). The findings indicate that the PCA-ANN-ABC model (with R2 values of 0.9641 for training and 0.9558 for testing) is capable of predicting AR values with a high degree of accuracy, precision, and flexibility. The modelling approach utilized in this study may be used to other comparable studies involving the solution of engineering challenges.

The Sustainable Island Tourism Evaluation Model Using the FDM-DEMATEL-ANP Method

The purpose of this study is first to propose a comprehensive evaluation model for sustainable island tourism, and then to provide guidelines and suggestions for the development thereof. Based on the advantages of using fuzzy set theory, this study’s method included the fuzzy Delphi method (FDM), the decision-making trial and evaluation laboratory (DEMATEL), the analytic network process (ANP), and FDM- DEMATEL-ANP (FDANP). From the literature review results and experts’ surveys, the dimensions of the evaluation criteria for sustainable island tourism are governance, economy and finance, socio-culture, and the environment. Compared with other studies, its major contributions and differences are the governance and finance dimensions, and the evaluation criteria for the marine industry, marine cultures, and marine environments. The findings show that the relative importance of the dimensions from high to low are economy and finance, governance, the environment, and socio-culture. The top five key criteria begin with having an official administration organization, having a tourism industry, and revenue uncertainties based on public health events. These, along with policies and regulations, and local food and drink, are thought to provide the necessary conditions for sustainable island tourism. The implications for theory and practice and future research directions are discussed.

An advanced computational intelligent framework to predict shear sonic velocity with application to mechanical rock classification

Shear sonic wave velocity (Vs) has a wide variety of implications, from reservoir management and development to geomechanical and geophysical studies. In the current study, two approaches were adopted to predict shear sonic wave velocities (Vs) from several petrophysical well logs, including gamma ray (GR), density (RHOB), neutron (NPHI), and compressional sonic wave velocity (Vp). For this purpose, five intelligent models of random forest (RF), extra tree (ET), Gaussian process regression (GPR), and the integration of adaptive neuro fuzzy inference system (ANFIS) with differential evolution (DE) and imperialist competitive algorithm (ICA) optimizers were implemented. In the first approach, the target was estimated based only on Vp, and the second scenario predicted Vs from the integration of Vp, GR, RHOB, and NPHI inputs. In each scenario, 8061 data points belonging to an oilfield located in the southwest of Iran were investigated. The ET model showed a lower average absolute percent relative error (AAPRE) compared to other models for both approaches. Considering the first approach in which the Vp was the only input, the obtained AAPRE values for RF, ET, GPR, ANFIS + DE, and ANFIS + ICA models are 1.54%, 1.34%, 1.54%, 1.56%, and 1.57%, respectively. In the second scenario, the achieved AAPRE values for RF, ET, GPR, ANFIS + DE, and ANFIS + ICA models are 1.25%, 1.03%, 1.16%, 1.63%, and 1.49%, respectively. The Williams plot proved the validity of both one-input and four-inputs ET model. Regarding the ET model constructed based on only one variable,Williams plot interestingly showed that all 8061 data points are valid data. Also, the outcome of the Leverage approach for the ET model designed with four inputs highlighted that there are only 240 “out of leverage” data sets. In addition, only 169 data are suspected. Also, the sensitivity analysis results typified that the Vp has a higher effect on the target parameter (Vs) than other implemented inputs. Overall, the second scenario demonstrated more satisfactory Vs predictions due to the lower obtained errors of its developed models. Finally, the two ET models with the linear regression model, which is of high interest to the industry, were applied to diagnose candidate layers along the formation for hydraulic fracturing. While the linear regression model fails to accurately trace variations of rock properties, the intelligent models successfully detect brittle intervals consistent with field measurements.

Machine Learning-Based Intelligent Prediction of Elastic Modulus of Rocks at Thar Coalfield

Elastic modulus (E) is a key parameter in predicting the ability of a material to withstand pressure and plays a critical role in the design of rock engineering projects. E has broad applications in the stability of structures in mining, petroleum, geotechnical engineering, etc. E can be determined directly by conducting laboratory tests, which are time consuming, and require high-quality core samples and costly modern instruments. Thus, devising an indirect estimation method of E has promising prospects. In this study, six novel machine learning (ML)-based intelligent regression models, namely, light gradient boosting machine (LightGBM), support vector machine (SVM), Catboost, gradient boosted tree regressor (GBRT), random forest (RF), and extreme gradient boosting (XGBoost), were developed to predict the impacts of four input parameters, namely, wet density (ρwet) in gm/cm3, moisture (%), dry density (ρd) in gm/cm3, and Brazilian tensile strength (BTS) in MPa on output E (GPa). The associated strengths of every input and output were systematically measured employing a series of fundamental statistical investigation tools to categorize the most dominant and important input parameters. The actual dataset of E was split as 70% for the training and 30% for the testing for each model. In order to enhance the performance of each developed model, an iterative 5-fold cross-validation method was used. Therefore, based on the results of the study, the XGBoost model outperformed the other developed models with a higher accuracy, coefficient of determination (R2 = 0.999), mean absolute error (MAE = 0.0015), mean square error (MSE = 0.0008), root mean square error (RMSE = 0.0089), and a20-index = 0.996 of the test data. In addition, GBRT and RF have also shown high accuracy in predicting E with R2 values of 0.988 and 0.989, respectively, but they can be used conditionally. Based on sensitivity analysis, all parameters were positively correlated, while BTS was the most influential parameter in predicting E. Using an ML-based intelligent approach, this study was able to provide alternative elucidations for predicting E with appropriate accuracy and run time at Thar coalfield, Pakistan.

The Effectiveness of Ensemble-Neural Network Techniques to Predict Peak Uplift Resistance of Buried Pipes in Reinforced Sand

Buried pipes are extensively used for oil transportation from offshore platforms. Under unfavorable loading combinations, the pipe’s uplift resistance may be exceeded, which may result in excessive deformations and significant disruptions. This paper presents findings from a series of small-scale tests performed on pipes buried in geogrid-reinforced sands, with the measured peak uplift resistance being used to calibrate advanced numerical models employing neural networks. Multilayer perceptron (MLP) and Radial Basis Function (RBF) primary structure types have been used to train two neural network models, which were then further developed using bagging and boosting ensemble techniques. Correlation coefficients in excess of 0.954 between the measured and predicted peak uplift resistance have been achieved. The results show that the design of pipelines can be significantly improved using the proposed novel, reliable and robust soft computing models.