An Environment-Friendly Rock Excavation Method

Blasting is used as an economical tool for rock excavation in mines. However, part of the explosive energy is converted into elastic waves, resulting in ground vibration and excessive vibration, which may cause damage to nearby buildings. Meanwhile, toxic gases are also produced during the explosion. In this paper, an environment-friendly method for rock excavation is proposed. A series of vibration tests were conducted, and the peak particle velocity was monitored. The results showed that the proposed method can replace the conventional blasting method in mines. Besides that, the vibration caused by the proposed method is much smaller than by the conventional method. By adjusting the direction of the high-pressure gas injection, buildings around the mine can be protected well from vibration. Also, the production of toxic gases during excavation will no longer be a problem. Thus, a milder environmental impact can be achieved. However, the rocks excavated by the proposed method are relatively large, which still need to be broken further. On this issue, further study is required

An Environment-Friendly Rock Excavation Method

Blasting is used as an economical tool for rock excavation in mines. However, part of the explosive energy is converted into elastic waves, resulting in ground vibration and excessive vibration, which may cause damage to nearby buildings. Meanwhile, toxic gases are also produced during the explosion. In this paper, an environment-friendly method for rock excavation is proposed. A series of vibration tests were conducted, and the peak particle velocity was monitored. The results showed that the proposed method can replace the conventional blasting method in mines. Besides that, the vibration caused by the proposed method is much smaller than by the conventional method. By adjusting the direction of the high-pressure gas injection, buildings around the mine can be protected well from vibration. Also, the production of toxic gases during excavation will no longer be a problem. Thus, a milder environmental impact can be achieved. However, the rocks excavated by the proposed method are relatively large, which still need to be broken further. On this issue, further study is required

Prediction of blast-induced air overpressure using a hybrid machine learning model and gene expression programming (GEP) : a case study from an iron ore mine

Mine blasting can have a destructive effect on the environment. Among these effects, air overpressure (AOp) is a major concern. Therefore, a careful assessment of the AOp intensity should be conducted before any blasting operation in order to minimize the associated environmental detriment. Several empirical models have been established to predict and control AOp. However, the current empirical methods have many limitations, including low accuracy, poor generalizability, consideration only of linear relationships among influencing parameters, and investigation of only a few influencing parameters. Thus, the current research presents a hybrid model which combines an extreme gradient boosting algorithm (XGB) with grey wolf optimization (GWO) for accurately predicting AOp. Furthermore, an empirical model and gene expression programming (GEP) were used to assess the validity of the hybrid model (XGB-GWO). An analysis of 66 blastings with their corresponding AOp values and influential parameters was conducted to achieve the goals of this research. The efficiency of AOp prediction methods was evaluated in terms of mean absolute error (MAE), coefficient of determination (R 2 ), and root mean square error (RMSE). Based on the calculations, the XGB-GWO model has performed as well as the empirical and GEP models. Next, the most significant parameters for predicting AOp were determined using a sensitivity analysis. Based on the analysis results, stemming length and rock quality designation (RQD) were identified as two variables with the greatest influence. This study showed that the proposed XGB-GWO method was robust and applicable for predicting AOp driven by blasting operations

A combination of expert-based system and advanced decision-tree algorithms to predict air-overpressure resulting from quarry blasting

This study combined a fuzzy Delphi method (FDM) and two advanced decision-tree algorithms to predict air-overpressure (AOp) caused by mine blasting. The FDM was used for input selection. Thus, the panel of experts selected four inputs, including powder factor, max charge per delay, stemming length, and distance from the blast face. Once the input selection was completed, two decision-tree algorithms, namely extreme gradient boosting tree (XGBoost-tree) and random forest (RF), were applied using the inputs selected by the experts. The models are evaluated with the following criteria: correlation coefficient, mean absolute error, gains chart, and Taylor diagram. The applied models were compared with the XGBoost-tree and RF models using the full set of data without input selection results. The results of hybridization showed that the XGBoost-tree model outperformed the RF model. Concerning the gains, the XGBoost-tree again outperformed the RF model. In comparison with the single decision-tree models, the single models had slightly better correlation coefficients; however, the hybridized models were simpler and easier to understand, analyze and implement. In addition, the Taylor diagram showed that the models applied outperformed some other conventional machine learning models, including support vector machine, k-nearest neighbors, and artificial neural network. Overall, the findings of this study suggest that combining expert opinion and advanced decision-tree algorithms can result in accurate and easy to understand predictions of AOp resulting from blasting in quarry sites. © 2020, International Association for Mathematical Geosciences

Optimización de los hiperparámetros de una máquina de regresión de soporte vectorial utilizando enjambre de partículas para el pronóstico de casos de COVID-19

En este trabajo se propone un método para la optimización de los hiperparámetros de una máquina de regresión de soporte vectorial mediante la adaptación de la metaheurística de enjambre de partículas. El método se utiliza para pronosticar la serie de tiempo del total de casos positivos acumulados de la reciente enfermedad COVID-19 en la ciudad de Bogotá, Colombia. Para validar el rendimiento del método se establece una comparación con la máquina de regresión de soporte vectorial sin hiperparámetros optimizados, en términos de métricas de medición del rendimiento como lo son el error cuadrático medio, error absoluto medio y el coeficiente de determinación. Con un valor en el error cuadrático medio de 0,000045, un coeficiente de determinación de 0,998884 y el valor-p de 0,0015, para la prueba no paramétrica de Wilcoxon, el método propuesto presenta un mejor desempeño en el pronóstico. Finalmente se pone a discusión la aplicabilidad de este tipo de métodos en el pronóstico de casos en las epidemias.In the present article a hyperparameter optimization of a vectorial-support regression machine via adaptation of metaheuristics of a particle swarm is proposed. This method will be used so that a forecasting of the time series of the total amount of positive accumulated cases of COVID-19 in Bogotá, Colombia. In order to validate the performance of the method, a comparison with a regression vectorial-support machine whose hyperparameters have not been optimized will be made, being the metrics those of performance measurement like mean square error, mean absolute error, and determination coefficient. The proposed method finds itself at a greater level of performance when the mean square error value is that of 0,000045, the determination coefficient corresponds with the value of 0,998884 and the p-value of 0,0015, for the nonparametric Wilcoxon test. Finally, applicability of these sorts of methods for forecasting of cases-behavior amidst epidemics is discussed

Effects of a proper feature selection on prediction and optimization of drilling rate using intelligent techniques

One of the important factors during drilling times is the rate of penetration (ROP), which is controlled based on different variables. Factors affecting different drillings are of paramount importance. In the current research, an attempt was made to better recognize drilling parameters and optimize them based on an optimization algorithm. For this purpose, 618 data sets, including RPM, flushing media, and compressive strength parameters, were measured and collected. After an initial investigation, the compressive strength feature of samples, which is an important parameter from the rocks, was used as a proper criterion for classification. Then using intelligent systems, three different levels of the rock strength and all data were modeled. The results showed that systems which were classified based on compressive strength showed a better performance for ROP assessment due to the proximity of features. Therefore, these three levels were used for classification. A new artificial bee colony algorithm was used to solve this problem. Optimizations were applied to the selected models under different optimization conditions, and optimal states were determined. As determining drilling machine parameters is important, these parameters were determined based on optimal conditions. The obtained results showed that this intelligent system can well improve drilling conditions and increase the ROP value for three strength levels of the rocks. This modeling system can be used in different drilling operations

Development of data intelligent models for electricity demand forecasting: case studies in the state of Queensland, Australia

Electricity demand (G) forecasting is a sustainability management and evaluation task for all energy industries, required to implement effective energy security measures and determine forward planning processes in electricity production and management of consumer demands. Predictive models for G forecasting are utilized as scientific stratagems for such decision-making. The information generated from forecast models can be used to provide the right decisions regarding the operation of National Electricity Markets (NEMs) through a more sustainable electricity pricing system, energy policy, and an evaluation of the feasibility of future energy distribution networks. Data intelligent models are considered as potential forecasting tools, although challenges related to issues of non-stationarity, periodicity, trends, stochastic behaviours in G data and selecting the most relevant model inputs remain a key challenge. This doctoral thesis presents a novel study on the development of G forecasting models implemented at multiple lead-time forecast horizons utilizing data-intelligent techniques. The study develops predictive models using real G data from Queensland (second largest State in Australia) where the electricity demand continues to elevate. This research is therefore, divided into four primary objectives designed to produce a G forecasting system with data-intelligent models. In first objective, the development and evaluation of a multivariate adaptive regression splines (MARS), support vector regression (SVR) and autoregressive integrated moving average (ARIMA) model was presented for short-term (30 minutes, hourly and daily) forecasting using Queensland’s aggregated G data. MARS outperformed SVR and ARIMA models at 30-minute and hourly horizon, while SVR was the best model for daily G forecasting. The second objective reported the successful design of SVR model for daily period, including short-term periods (e.g., weekends, working days, and public holidays), and the long-term (monthly) period. Subsequently, the hybrid SVR, with particle swarm optimization (i.e., PSO-SVR) integrated with improved empirical mode decomposition with adaptive noise (ICEEMDAN) tool was constructed where PSO is adopted to optimize SVR parameters and ICEEMDAN was adopted to address non-linearity and non-stationary in G data. The capability of ICEEMDAN-PSO-SVR to forecast G was benchmarked against ICEEMDAN-MARS and ICEEMDAN-M5 Tree, including traditional PSO-SVR, MARS and M5 model tree methods. As G is subjected to the influence of exogenous factors (e.g., climate variables), the third objective established a G forecasting model utilizing atmospheric inputs from the Scientific Information for Land Owners (SILO) observed data fields and the European Centre for Medium Range Weather Forecasting outputs. These models were developed using G extracted from the Energex database for eight stations in southeast Queensland for an artificial neural network (ANN) model over 6-hourly and daily forecast horizons. The final objective was to advance the methods in previous objectives, by applying wavelet transformation (WT) as a decomposition tool to model daily G. Using real data from the University of Sothern Queensland (Toowoomba, Ipswich, and Springfield), the maximum overlap discrete wavelet transform (MODWT) was adopted to construct the MODWT-PACF-online sequential extreme learning machine (OS-ELM) model. The results revealed that newly developed MODWT-PACF-OSELM (MPOE) model attained superior performance compared to the models without the WT algorithm. In synopsis, the predictive models developed in this doctoral thesis will to provide significant benefits to National Electricity Markets in respect to energy distribution and security, through new and improved energy demand forecasting tools. Energy forecasters can therefore adopt these novel methods, to address the issues of nonlinearity and non-stationary in energy usage whilst constructing a real-time forecasting system tailored for energy industries, consumers, governments and other stakeholders