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Enzyme induced carbonate precipitation (EICP) is an emerging soil improvement method using free urease enzyme for urea hydrolysis. This method has advantages over the commonly used microbially induced carbonate precipitation (MICP) process as it does not involve issues related to bio-safety. However, in terms of efficiency of calcium carbonate production, EICP is considered lower than that of MICP. In this paper, a high efficiency EICP method is proposed. The key of this new method is to adopt a one-phase injection of low pH solution strategy. In this so-called one-phase-low-pH method, EICP solution consisting of a mixture of urease solution of pH?=?6.5, urea and calcium chloride is injected into soil. The test results have shown that the one-phase-low-pH method can improve significantly the calcium conversion efficiency and the uniformity of calcium carbonate distribution in the sand samples as compared with the conventional two-phase EICP method. Furthermore, the unconfined compressive strength of sand treated using the one-phase-low-pH method is much higher than that using the two-phase method and the one-phase-low-pH method is also simpler and more efficient as it involves less number of injections.
相似文献Despite being a simple and inexpensive pretreatment technology, the cost-effectiveness of riverbank filtration (RBF) depends on complex hydrogeological and hydrogeochemical variables. One of the most important issues for decision makers regarding RBF is optimal site selection. Therefore, a methodology for multicriteria site evaluation for large-scale RBF schemes is offered. The methodology is primarily designed as a prescreening method, applied over a wide area, but can also serve as a guide for evaluating individual RBF sites. To facilitate further discussion about improvements on the methodology, the reasoning behind each relevant factor and its weight in the evaluation is presented. The methodology is divided into three sequential steps through which a site can be assessed. The first step is to establish the existence of connectivity between the river and aquifer. This is termed the essential criterion, and is a binary determination of site suitability. If the site is determined to be suitable, it is then assessed via a set of quantity criteria, which measure the aquifer capacity and amount of bank filtrate that can be effectively abstracted. Lastly, water quality criteria are assessed by means of surface-water and groundwater quality. The quantity and quality criteria form a result expressed as the site suitability index (SSI), which ranges from 0 to 1, where higher scores represent increased suitability. Finally, the methodology is applied to evaluate existing sites of large-scale RBF application as a demonstration of its applicability. The success of these existing sites is compared to the calculated SSI value and discussed.
相似文献Innovation efforts in developing soft computing models (SCMs) of researchers and scholars are significant in recent years, especially for problems in the mining industry. So far, many SCMs have been proposed and applied to practical engineering to predict ground vibration intensity (BIGV) induced by mine blasting with high accuracy and reliability. These models significantly contributed to mitigate the adverse effects of blasting operations in mines. Despite the fact that many SCMs have been introduced with promising results, but ambitious goals of researchers are still novel SCMs with the accuracy improved. They aim to prevent the damages caused by blasting operations to the surrounding environment. This study, therefore, proposed a novel SCM based on a robust meta-heuristic algorithm, namely Hunger Games Search (HGS) and artificial neural network (ANN), abbreviated as HGS–ANN model, for predicting BIGV. Three benchmark models based on three other meta-heuristic algorithms (i.e., particle swarm optimization (PSO), firefly algorithm (FFA), and grasshopper optimization algorithm (GOA)) and ANN, named as PSO–ANN, FFA–ANN, and GOA–ANN, were also examined to have a comprehensive evaluation of the HGS–ANN model. A set of data with 252 blasting operations was collected to evaluate the effects of BIGV through the mentioned models. The data were then preprocessed and normalized before splitting into individual parts for training and validating the models. In the training phase, the HGS algorithm with the optimal parameters was fine-tuned to train the ANN model to optimize the ANN model's weights. Based on the statistical criteria, the HGS–ANN model showed its best performance with an MAE of 1.153, RMSE of 1.761, R2 of 0.922, and MAPE of 0.156, followed by the GOA–ANN, FFA–ANN and PSO–ANN models with the lower performances (i.e., MAE?=?1.186, 1.528, 1.505; RMSE?=?1.772, 2.085, 2.153; R2?=?0.921, 0.899, 0.893; MAPE?=?0.231, 0.215, 0.225, respectively). Based on the outstanding performance, the HGS–ANN model should be applied broadly and across a swath of open-pit mines to predict BIGV, aiming to optimize blast patterns and reduce the environmental effects.
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