Applying emergy and decoupling analysis to assess the sustainability of China’s coal mining area
استفاده از تحلیل اضطراری و جداسازی برای ارزیابی پایداری منطقه استخراج زغال سنگ چین-2020
The sustainable development of coal mining area continues to be one of the most topical issues in the world. Taking Shainxi Province as a case, this study applies emergy and decoupling analysis to build a multi-index sustainability evaluation system and constructs an emergy decoupling index to investigate the sustainability of a coal mining area in China during 2006e2015. It overcomes the problem of the unification of the traditional evaluation index system and integrates the influence of economic development, resources, the environment, and energy. The study finds that the coal mining area still depends on its coal resources. The sustainability of the coal mining area is still at a low level, and it is not sustainable in the long term. The economic growth still has a strong negative decoupling from the environmental loss. Energy management system and circular economic system should be built to improve the coal mining area’s sustainability. In the long run, the coal mining industry should gradually be abandoned. Based on China’s growing energy consumption, the findings of this study may not only serve as a reference for management to improve the sustainability of the coal mining areas but also to address China’s energy shortage problem.
Keywords: Sustainability | Emergy analysis | Decoupling | Coal mining area
Risk-constrained stochastic power procurement of storage-based large electricity consumer
منبع تغذیه تصادفی محدود شده در معرض خطر مصرف کننده بزرگ برق مستقر در انبار-2020
Large electricity consumers can be either a large industrial consumer or a coalition of small electricity consumers. Large consumers (LCs) confront with various uncertainties due to the use of various power resources in the power procurement process, such as renewable resources, self-generation units, forward contracts, and pool market. These uncertainties can be lead to many financial risks for LCs. In this paper, the stochastic power procurement problem of large consumers is solved, and the new risk-measurement method is used to analyze the large consumer risks in power procurement process. The mentioned risk-measurement method is called downside risk constraints (DRC) method, which is used to model the financial risk imposed from uncertain parameters along with the stochastic problems. According to obtained results, it can be concluded that DRC method is a nonequilibrium method, which is applied clearly as a constraint to the optimization problem. In addition by using the DRC, LC can experience lower-risk strategy in the power procurement problem. Also, using DRC can make the total cost of large consumer independent of scenarios, which led to the lower-risk experiencing by the large consumer. Finally, results are expressed that lower-risk cost in DRC is less than the cost of the worst scenario in stochastic programming.
Keywords: Large electricity consumer | Risk-measuring | Energy trading and business | Energy storage management | Stochastic programming | Downside risk constraints
A hierarchical energy management system for multiple home energy hubs in neighborhood grids
یک سیستم مدیریت سلسله مراتبی انرژی برای مراکز چندگانه انرژی خانگی در شبکه های محلی-2020
This paper presents a hierarchical energy management system (HEMS) for multiple home energy hubs in the neighborhood grid (MHEHNG). The main objectives are maximizing financial profit and shaving the peak of upstream grid. This way, the proposed HEMS manages the energy generation and energy storing, as well as energy purchase/sale of each home energy hub (HEH) under the two levels including lower and upper levels. The lower level is responsible for supplying the internal load and reducing the energy cost in each HEH. The upper level is the central energy management system (CEMS) which is focused on forming a coalition between the local HEHs, as well as giving the tempting offers to increase the financial profit through a heuristic bidding strategy. The principle of proposed bidding strategy is based on weighted distributing of excess power among consumers that is one of the contribution of this paper. It leads to trading more energy at the lowest possible price. Determining the most appropriate operational scenario in each HEH requires the investigation of both technical and financial aspects. A novel scenario selector method has been proposed based on SOC-tariff plane. This is another contribution of this work. A simulator has been implemented in the MATLAB/GUI software environment to facilitate the evaluation of proposed HEMS performance. The simulation results indicate the effectiveness of the proposed HEMS. They show a decrease in the total energy cost of the CBs by almost 9.4%, and an increase in the total profit of the HEHs by 4.55%. Also, it was found out that the energy can be purchased from HEHs at varying rates and can be sold to the consumers at almost constant rates by using the proposed bidding strategy. This motivates HEHs to submit more power at lower tariffs to the CEMS.
Keywords: Hierarchical energy management system | Home energy hub | Renewable energy | Simulator | Several home energy hubs in a neighborhood | grid
New insights on ground control in intelligent mining with Internet of Things
بینش جدید در زمینه کنترل زمین در معادن هوشمند با اینترنت اشیاء-2020
The conception of Smart city has been gaining momentum in recent years. Coal mines as a part of city should be characterized with smart or intelligent features. Production and safety are two major themes in coal mining. With the development of automation, Internet of Things (IoT), big data, artificial intelligence, and cloud computing in Fourth Industrial Revolution, Intelligence Mining has been put forward by Chinese Academy of Engineering to achieve the goal of unmanned workface production. However, safety is not highlighted in the novel idea. In this paper, ground control in intelligent mining with IoT is studied. An architecture of ground control with IoT is proposed. The previous research on theoretical modeling and on-site monitoring methods are reviewed. Then the IoT based ground control method is proposed. An on-going dynamic platform on ground control are proposed based on our research of nondestructive testing (NDT) on rock bolt anchorage quality assessment. The research progress is introduced with equipment introduction, principles, and an onsite experiment. Future developments on combination of NDT and IoT of ground control is discussed. The ideas, frameworks, and results in this paper can make efforts on safety control and spark new ideas in the much-anticipated Intelligence Mining.
Keywords: Ground control | Intelligence Mining | Mine safety | Internet of Things
A laboratory approach to CO2 and CO emission factors from underground coal fires
رویکرد آزمایشگاهی به عوامل انتشار CO2 و CO از آتش سوزی زغال سنگ زیرزمینی-2020
Carbon emissions from underground coal fires (UCF) have become an emerging research topic and their role in global climate warming has been widely debated. Currently, one big uncertainty for assessing UCFs carbon emission is the hypothesized carbon emission factors (EF) from the complete combustion of coal, while the EF of smoldering combustion of coal in the context of UCF is still unknown yet. In this work, a 1/20 scale laboratory experimental framework was proposed to characterize transient carbon emissions and quantify EFCO2 and EFCO. Effects of fire depth, ventilation area (aperture size), and coal rank on carbon emissions were explored with the extrapolation to the full-scale UCF. Results showed that total carbon emissions increase with the carbon content of coal. Volatile content is an important factor impacting the burning behavior and gas emission. Stable EFCO2 and EFCO of UCF, independent of the fire depth and aperture size, were estimated as 2006 ± 36 g kg−1 and 345 ± 132 g kg−1, respectively; its combustion efficiency was 85% ± 3%. The extrapolation of experimental data estimates the CO2 emission of coal fires in China and the USA as 2.34 × 107–4.61 × 107 t yr−1, which accounted for 0.4% - 0.9% of total CO2 emissions in the world in 2016.
Keywords: Greenhouse gas (CO2) | Carbon monoxide (CO) | Incomplete combustion | Smoldering fires
Adsorption characteristics of supercritical CO2/CH4 on different types of coal and a machine learning approach
ویژگی های جذب CO2 / CH4 فوق بحرانی در انواع مختلف ذغال سنگ و رویکرد یادگیری ماشین-2019
The injection of CO2 into deep coal beds can not only improve the recovery of CH4, but also contribute to the geological sequestration of CO2. The adsorption characteristics of coal determine the amount of the greenhouse gas that deep coal seams can store in place. Using self-developed adsorption facility of supercritical fluids, this paper studied the adsorption behavior of supercritical CO2 and CH4 on three types of coal (anthracite, bituminous coal A, bituminous coal B) under different temperatures of 35 °C, 45 °C and 55 °C. The influence of temperature, pressure, and coal rank on the Gibbs excess and absolute/real adsorption amount of supercritical CO2/CH4 on coal samples has been analyzed. Several traditional isotherm models are applied to interpret the experimental data and Langmuir related models are verified to provide good performances. However, these models are limited to isothermal conditions and are highly depended on extensive experiments. To overcome these deficiencies, one innovative adsorption model is proposed based on machine learning methods. This model is applied to the adsorption data of both this paper and four early publications. It was proved to be highly effective in predicting adsorption behavior of a certain type of coal. To further break the limit of coal type, the second optimization model is provided based on published data. Using the second model, one can predict the adsorption behavior of coal based on the fundamental physicochemical parameters of coal. Overall, working directly with the real data, the machine learning technique makes the unified adsorption model become possible, avoiding tedious theoretical assumptions, derivations and strong limitations of the traditional model.
Keywords: Supercritical CO2 | Supercritical CH4 | Coal | Adsorption model | Machine learning
ISeeU: Visually interpretable deep learning for mortality prediction inside the ICU
ISeeU: یادگیری عمیق قابل تفسیر برای پیش بینی مرگ و میر در بخش مراقبت های ویژه-2019
To improve the performance of Intensive Care Units (ICUs), the field of bio-statistics has developed scores which try to predict the likelihood of negative outcomes. These help evaluate the effectiveness of treatments and clinical practice, and also help to identify patients with unexpected outcomes. However, they have been shown by several studies to offer sub-optimal performance. Alternatively, Deep Learning offers state of the art capabilities in certain prediction tasks and research suggests deep neural networks are able to outperform traditional techniques. Nevertheless, a main impediment for the adoption of Deep Learning in healthcare is its reduced interpretability, for in this field it is crucial to gain insight into the why of predictions, to assure that models are actually learning relevant features instead of spurious correlations. To address this, we propose a deep multiscale convolutional architecture trained on the Medical Information Mart for Intensive Care III (MIMIC-III) for mortality prediction, and the use of concepts from coalitional game theory to construct visual explanations aimed to show how important these inputs are deemed by the network. Results show our model attains a ROC AUC of 0.8735 (± 0.0025) which is competitive with the state of the art of Deep Learning mortality models trained on MIMIC-III data, while remaining interpretable. Supporting code can be found at https://github.com/ williamcaicedo/ISeeU.
Keywords: Deep learning | MIMIC-III | ICU | Shapley Values
Assessing the eco-efficiency of a circular economy system in Chinas coal mining areas: Emergy and data envelopment analysis
ارزیابی کارآیی سازگار با محیط زیست یک سیستم اقتصاد دایره ای در مناطق استخراج زغال سنگ چین: تجزیه و تحلیل اضطراری پوششی داده ها-2019
Improving the eco-efficiency of the circular economy system in mining areas has been recognized as the most effective way to reduce the greenhouse effect and achieve sustainable development. Based on the emergy theory and on data envelopment analysis (DEA), this paper adopts the SBM-Undesirable model to evaluate the eco-efficiency of the circular economy system in Chinas largest coal mining area, Shanxi Province, during the period 2006e2015. Emergy flow indices are treated as input and output indices. Eco-efficiency is factorized into economic efficiency and environmental efficiency. The potential for improvement of the circular economy system is analyzed based on input redundancy and output deficiency. The results for the period 2006e2015 indicate the following: (1) both the input and the output of the circular system increase during this period; (2) the increased input relies on mostly imported emergy, and the increase inwaste emergy is lower than the exported emergy; (3) eco-efficiency is invalid except for 2011 and 2012 and exhibits a decreasing trend beginning in 2013; (4) environmental efficiency is invalid over the entire period, and the eco-efficiency level is positively related to the economic efficiency score; and (5) the circular economy system has a larger energy saving space, and the key to achieving sustainability of the circular economy system is output growth.
Keywords: Emergy analysis | DEA | Eco-efficiency | Circular economy | Coal mining area
Machine learning for measurement-based bandwidth estimation
یادگیری ماشین برای تخمین پهنای باند مبتنی بر اندازه گیری-2019
The dispersion that arises when packets traverse a network carries information that can reveal relevant network characteristics. Using a fluid-flow model of a bottleneck link with first-in first-out multiplexing, accepted probing tools measure the packet dispersion to estimate the available bandwidth, i.e., the residual capacity that is left over by other traffic. Difficulties arise, however, if the dispersion is distorted compared to the model, e.g., by non-fluid traffic, multiple bottlenecks, clustering of packets due to interrupt coalescing, and inaccurate time-stamping in general. It is recognized that modeling these effects is cumbersome if not intractable. This motivates us to explore the use of machine learning in bandwidth estimation. We train a neural network using vectors of the packet dispersion that is characteristic of the available bandwidth. Our testing results reveal that even a shallow neural network identifies the available bandwidth with high precision. We also apply the neural network under a variety of notoriously difficult conditions that have not been included in the training, such as randomly generated networks with the multiple bottleneck links and heavy cross traffic burstiness. Compared to two state-of-the-art model-based techniques as well as a recent machine learning-based technique (Yin et al., 2016), our neural network approach shows improved performance. Further, our neural network can effectively control the estimation procedure in an iterative implementation. We also evaluate our method with other supervised machine learning techniques.
Keywords: Available bandwidth estimation | Machine learning
Assessment of eco-geo-environment quality using multivariate data: A case study in a coal mining area of Western China
ارزیابی کیفیت محیط زیست با استفاده از داده های چند متغیره: یک مطالعه موردی در یک منطقه استخراج زغال سنگ در غرب چین-2019
Coal resource extensive development and utilization seriously threatens the region fragile ecological environment in Western China. Using the Yushenfu coal mining area as a study site, the ecological and geological environment (eco-geo-environment) factors affected by coal mining were analyzed. Spatiotemporal data extracted from remote sensing images was combined with survey data to establish an evaluation and prediction model. Integrated eco-geo-environmental quality was established by calculating the combined weight of each factor using the fuzzy delphi analytic hierarchy process method to develop a comprehensive multi-factor ecogeo- environment quality evaluation map. Results shows that the eco-geo-environment quality was divided in five grades of worse, bad, medium, good and better, the overall condition of the study region is moderate and it is apparent that regions with less intensive mining activities (Yushen sub coal mining area) are in better condition as compared to those regions where intensive mining is well established (Shenfu sub coal mining area). Comparisons with the classified eco-geo-environment categories revealed that different eco-geo-environment quality grades were affected different eco-geo-environment categories after a period of coal resources exploitation, but eco-geo-environment quality generally has been declining since the development and utilization of coal resources. The model provides a more scientific and accurate method to evaluate regional eco-geoenvironmental quality, which is important for the coordinated development between coal mining and the fragile eco-environment.
Keywords: Ecologically vulnerable coal mining area | Multi-criteria decision analysis | Eco-geo-environment type | Eco-geo-environment quality assessment