Mapping coal fires using Normalized Difference Coal Fire Index (NDCFI): case study at Khanh Hoa coal mine, Vietnam

Khanh Hoa coal mine (Thai Nguyen province) is one of the largest coal mines in the north of Vietnam. For many years, this area suffered from underground fires at coal mine waste dumps, seriously affecting production activities and the environment. This paper presents the results of classification of underground fire areas at Khanh Hoa coal mine using Normalized Diference Coal Fire Index (NDCFI). 03 Landsat 8 OLI_TIRS images taken on December 2, 2013, December 10, 2016, and December 3, 2019 were used to calculate NDCFI index, and then classify the underground fire areas by thresholding method. In the study, the land surface temperature was also calculated from Landsat 8 thermal infrared bands data, and then compared with the results of underground coal fire classification at Khanh Hoa coal mine. The obtained results showed that the NDCFI index can be used effectively in detecting and monitoring underground fire areas at coal mines. The use of the NDCFI index also has many advantages due to its calculation simplicity and rapidness compared to other methods for classifying underground coal fire areas.

Coal Refinery Process Absorbability Index Assessment against Foot Print of Air Pollution by Usage of Robust Optimization Algorithms: A Novel Green Environment Initiative

Coals are employed as fiery substance, and every day, millions of ton coal are consumed by coal users around the world. It is investigated that the millions of coal’s transportation/logistic till the coal user plants via road route and also inside the coal user plants (known as twice factors) not only enhance the air pollution but also cause the global warming. It is earlier known that coals emit the toxic pollutants and offensive gases such as sulfur dioxide, SO2; nitrogen oxides, NOX; hydrogen chloride arsenic; carbon monoxide, CO; methane; CH4; and CO2 on reacting with environmental O2 due to said twice factors, i.e., during the transportation from coal refinery spot to entry gate of coal user plants (another spot) and in process logistic/movement inside the coal user plants (loading to conveyor to coal fire tubes “attached with coal crushers”). Therefore, the coal refinery technique/process is found as the best practice to control air pollution under concerns of twice factors. The reliable and trustworthy coal refining technology improves the quality of coal by eradicating or eliminating the coating or layers of toxic particles from coal’s surface, which speedily crumble or decompose in reacting with environmental O2 under twice factors. As results, coal refining technology adds the green supply chain value into proposed twice factors and also save the world from breeding of ills and viruses. It is understood that the best coal refinery technique/process helps to overcome and reduce air pollution by responding discussed twice factors (accepted as research challenge and motivation of research). In the presented research work, the authors developed and proposed a dynamic multidimension Coal Refinery Process Absorbability Index (CRPAI) structure (consisted of coal refinery core dimension and subdimensions correspond to CRPA alternative techniques/processes) appended with Robust Optimization Algorithm (ROA) to be explored for opting the best CRP from available options. But due to inherent ambiguity, vagueness, and inconsistency involve in both dimensions of proposed structure, the assessment of expert’s panel is gathered in the terms of linguistic variable “appropriateness ratings” against the subdimensions of CRPAI structure corresponding to preferred CRP options. Next, assigned appropriateness ratings against the subdimensions are substituted by GIVFN. To arrive to core dimensions from subdimensions of CRPAI structure, a GITFN-OWGO (Ordered Weighted Geometric Operator) is investigated and modified as a Ordered Weighted Geometric Average Operator (OWGAO) to be applied for estimating the weights of subdimensions (core novelty of work). Finally, a ROA (consisted of MULTI-MOORA with dominance theory) is applied on the output of OWGAO for opting the viable and best CRP option. The positive effect of the dynamic multidimension CRPAI structure is that it helps the coal refinery companies to assess measure and evaluate the best and feasible coal refinery process under concern of twice factors using expert information. The research can be used to control the air pollution by responding aforesaid twice factors by single practice (the best coal refinery process/technique assessment and evaluation).

Land Subsidence Prediction using Recurrent Neural Networks

In an environment, one of the natural geological hazards is land surface subsidence. There are several reasons for land subsidence among them are underground coal mining and coal fire in subsurface or deformation is primarily measured in terms of change in ground elevation values ( Z-dimension) at different time intervals at identified ground locations. All the conventional and exiting techniques have certain limitations in monitoring and predicting land surface subsidence. In this work, we predict the land subsidence for one year in the interval of twelve days on the datasets collected through a monitoring technique called Modified PSInSAR. The sample datasets contains 14 locations and 67 previous land subsidence value calculated from each location. We train and test predictive models and perform the prediction of the land subsidence using Vanilla and Stacked long short-term memories (LSTMs). Finally, we demonstrates the predicted deformation values of the 14 locations for one year.