ROCK DUST SURFACE CHEMISTRY MODIFICATIONS FOR ELIMINATING CAKE FORMATION AND IMPROVING DISPERSION IN COAL DUST EXPLOSION MITIGATION APPLICATIONS

Rock dust has been historically applied to mitigate the coal dust explosion in either dry or wet form. Dry rock dust performs best in inerting the potential coal dust explosion due to the greatest dispersive properties. However, it undesirably exposes underground coal miners to respirable dust particles which imposes a severe health danger. Wet dust application is able to significantly reduce the floatable dust particles but another problem associated with caking is predominant. Caking phenomenon is usually used to describe the change of free-flowing bulk solids into agglomerated chunks. Unfortunately, the environmental conditions of an underground mine have the potential to cause caking of the rock dust, especially for wet dust application, which reduces the dispersive characteristics needed for effective explosion mitigation and is also the focus of the present study. Surface modification of rock dust to generate a hydrophobic surface is believed to alleviate the caking problem by allowing instant water drainage and eliminating the formation of water and solid bridges. Surface modification of rock dust was evaluated in the present study with a series of potential modifying reagents including oleic acid, sodium oleate and stearic acid. The modification efficiency in terms of measured contact angle, zeta potential and dispersibility values was investigated with sodium oleate generating the best modification effect. Dispersants were investigated as well in the present work aiming to further increase the particles dispersibility in addition to the excellent hydrophobization effect generated by sodium oleate. However, dispersibility test results indicated that the adsorption of dispersant and sodium oleate was competitive. The preferential adsorption of dispersants over oleate deteriorated the surface hydrophobicity of particles and the dispersibility was decreased as a result. As anticipated, dry rock dust always provided the best dispersibility with almost 95% of the dust remaining in suspension at a dispersion time of 30 seconds. The percentage dust dispersion values of sodium oleate treated rock dust was increased to as high as 71% in contrast to 47% of untreated wet rock dust and the explosion potential was correspondingly reduced by almost 83%. The effect of sodium oleate was further studied as a function of reagent concentration to determine the optimum application condition. The dispersibility of rock dust particles was initially increased until the application of 0.1 wt% sodium oleate, after which it slightly decreased up to 0.5 wt% oleate. When the concentration was above 0.5 wt%, the dispersibility of dust particles substantially decreased to a value lower than the value obtained for regular wet dust. The optimum sodium oleate concentration was thus determined at approximately 0.1 by weight of rock dust particles with a corresponding contact angle of around 110 degrees. The pivotal of rock dust modification efficiency is its long-term stability which can be corroborated by irreversible chemical adsorption rather than the short-term physical adsorption. Therefore, the fundamental adsorption mechanism of sodium oleate on rock dust surface was continuously investigated by means of using surface tension measurements, FTIR, Thermogravimetric, SEM analyses and constructing the species distribution diagram. Based on the surface tension measurements and calculated apparent surface area occupied by per oleate molecule, the monolayer adsorption of oleate on dust surface was proposed with oleate concentration falling between 0.1-0.15 wt% which guarantees the long-term surface modification efficiency. Calcium oleate started precipitating out of bulk solution and depositing on the dust surface when the oleate concentration was above 0.15 wt% which became more predominant under high oleate concentration. Super hydrophobic particles together with nucleated calcium oleate nanoparticles tend to increase particles aggregation significantly through attractive hydrophobic particle-particle interactive force, which renders the particles more agglomerated instead of dispersed. Systematic and economic evaluation of the wet form rock dusting process in underground coal mine applications was conducted at the end to determine the safety effects, potential benefits and improvement for future implementation. Suggestions for future work were given as well to shed light on the dusting process together with rock dust surface chemistry modification

Recovering Structured Probability Matrices

We consider the problem of accurately recovering a matrix B of size M by M , which represents a probability distribution over M2 outcomes, given access to an observed matrix of "counts" generated by taking independent samples from the distribution B. How can structural properties of the underlying matrix B be leveraged to yield computationally efficient and information theoretically optimal reconstruction algorithms? When can accurate reconstruction be accomplished in the sparse data regime? This basic problem lies at the core of a number of questions that are currently being considered by different communities, including building recommendation systems and collaborative filtering in the sparse data regime, community detection in sparse random graphs, learning structured models such as topic models or hidden Markov models, and the efforts from the natural language processing community to compute "word embeddings". Our results apply to the setting where B has a low rank structure. For this setting, we propose an efficient algorithm that accurately recovers the underlying M by M matrix using Theta(M) samples. This result easily translates to Theta(M) sample algorithms for learning topic models and learning hidden Markov Models. These linear sample complexities are optimal, up to constant factors, in an extremely strong sense: even testing basic properties of the underlying matrix (such as whether it has rank 1 or 2) requires Omega(M) samples. We provide an even stronger lower bound where distinguishing whether a sequence of observations were drawn from the uniform distribution over M observations versus being generated by an HMM with two hidden states requires Omega(M) observations. This precludes sublinear-sample hypothesis tests for basic properties, such as identity or uniformity, as well as sublinear sample estimators for quantities such as the entropy rate of HMMs

Separation of Radionuclides from a Rare Earth-Containing Solution by Zeolite Adsorption

The increasing industrial demand for rare earths requires new or alternative sources to be found. Within this context, there have been studies validating the technical feasibility of coal and coal byproducts as alternative sources for rare earth elements. Nonetheless, radioactive materials, such as thorium and uranium, are frequently seen in the rare earths’ mineralization, and causes environmental and health concerns. Consequently, there exists an urgent need to remove these radionuclides in order to produce high purity rare earths to diversify the supply chain, as well as maintain an environmentally-favorable extraction process for the surroundings. In this study, an experimental design was generated to examine the effect of zeolite particle size, feed solution pH, zeolite amount, and contact time of solid and aqueous phases on the removal of thorium and uranium from the solution. The best separation performance was achieved using 2.50 g of 12-µm zeolite sample at a pH value of 3 with a contact time of 2 h. Under these conditions, the adsorption recovery of rare earths, thorium, and uranium into the solid phase was found to be 20.43 wt%, 99.20 wt%, and 89.60 wt%, respectively. The Freundlich adsorption isotherm was determined to be the best-fit model, and the adsorption mechanism of rare earths and thorium was identified as multilayer physisorption. Further, the separation efficiency was assessed using the response surface methodology based on the development of a statistically significant model

Synaesthesia in Chinese: A corpus-based study on gustatory adjectives in Mandarin

This study adopted a corpus-based approach to examine the synaesthetic metaphors of gustatory adjectives in Mandarin. Based on the distribution of synaesthetic uses in the corpus, we found that: (1) the synaesthetic metaphors of Mandarin gustatory adjectives exhibited directionality; (2) the directionality of Mandarin synaesthetic gustatory adjectives showed both commonality and specificity when compared with the attested directionality of gustatory adjectives in English, which calls for a closer re-examination of the claim of cross-lingual universality of synaesthetic tendencies; and (3) the distribution and directionality of Mandarin synaesthetic gustatory adjectives could not be predicted by a single hypothesis, such as the embodiment-driven approach or the biological association-driven approach. Thus, linguistic synaesthesia was constrained by both the embodiment principle and the biological association mechanism

Evaluation of Frother Types for Improved Flotation Recovery and Selectivity

A laboratory study was conducted to evaluate and compare the effectiveness of nine different frother types when used in a three-phase, continuously operating froth flotation system. The frothers included several that are commonly used in the industry (e.g., MIBC, 2EH, and F-1) as well as unique frother types (e.g., F-3). The tests were conducted in a 5-cm diameter laboratory flotation column that provided near plug-flow mixing conditions due to a length-to-diameter ratio of around 50:1. Test results indicate that F-1, MIBC, and MPC (in order of decreasing effectiveness) provided the weakest performance in terms of combustible recovery while F-2, MAC, and 2EH were the top three generating the highest separation efficiencies. When processing ultrafine coal, the ash content of the flotation concentrate ranged from 10% to 15% while recovering over 80% of the combustible material. F-3, F-4, and DIBC provided over 80% recovery of combustibles at the expense in the amount of hydraulic entrainment. The flotation performances were also closely examined in accordance with the fundamental properties of the nine tested frothers, and their correlations were addressed in detail

Auditory Synaesthesia and Near Synonyms: A Corpus-Based Analysis of sheng1 and yin1 in Mandarin Chinese

This paper explores the nature of linguistic synaesthesia in the auditory domain through a corpus-based lexical semantic study of near synonyms. It has been established that the near synonyms 聲 sheng “sound ” and 音 yin “sound ” in Mandarin Chinese have different semantic functions in representing auditory production and auditory perception respec-tively. Thus, our study is devoted to test-ing whether linguistic synaesthesia is sensi-tive to this semantic dichotomy of cognition in particular, and to examining the relation-ship between linguistic synaesthesia and cog-nitive modelling in general. Based on the cor-pus, we find that the near synonyms exhibit both similarities and differences on synaesthe-sia. The similarities lie in that both 聲 and音 are productive recipients of synaesthetic trans-fers, and vision acts as the source domain most frequently. Besides, the differences exist in se-lective constraints for 聲 and 音 with synaes-thetic modifiers as well as syntactic functions of the whole combinations. We propose that the similarities can be explained by the cogni-tive characteristics of the sound, while the dif-ferences are determined by the influence of the semantic dichotomy of production/perception on synaesthesia. Therefore, linguistic synaes-thesia is not a random association, but can be motivated and predicted by cognition.

Gradient-based Optimization for Bayesian Preference Elicitation

Effective techniques for eliciting user preferences have taken on added importance as recommender systems (RSs) become increasingly interactive and conversational. A common and conceptually appealing Bayesian criterion for selecting queries is expected value of information (EVOI). Unfortunately, it is computationally prohibitive to construct queries with maximum EVOI in RSs with large item spaces. We tackle this issue by introducing a continuous formulation of EVOI as a differentiable network that can be optimized using gradient methods available in modern machine learning (ML) computational frameworks (e.g., TensorFlow, PyTorch). We exploit this to develop a novel, scalable Monte Carlo method for EVOI optimization, which is more scalable for large item spaces than methods requiring explicit enumeration of items. While we emphasize the use of this approach for pairwise (or k-wise) comparisons of items, we also demonstrate how our method can be adapted to queries involving subsets of item attributes or "partial items," which are often more cognitively manageable for users. Experiments show that our gradient-based EVOI technique achieves state-of-the-art performance across several domains while scaling to large item spaces.Comment: To appear in the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20

Queue-Aware Dynamic Clustering and Power Allocation for Network MIMO Systems via Distributive Stochastic Learning

In this paper, we propose a two-timescale delay-optimal dynamic clustering and power allocation design for downlink network MIMO systems. The dynamic clustering control is adaptive to the global queue state information (GQSI) only and computed at the base station controller (BSC) over a longer time scale. On the other hand, the power allocations of all the BSs in one cluster are adaptive to both intra-cluster channel state information (CCSI) and intra-cluster queue state information (CQSI), and computed at the cluster manager (CM) over a shorter time scale. We show that the two-timescale delay-optimal control can be formulated as an infinite-horizon average cost Constrained Partially Observed Markov Decision Process (CPOMDP). By exploiting the special problem structure, we shall derive an equivalent Bellman equation in terms of Pattern Selection Q-factor to solve the CPOMDP. To address the distributive requirement and the issue of exponential memory requirement and computational complexity, we approximate the Pattern Selection Q-factor by the sum of Per-cluster Potential functions and propose a novel distributive online learning algorithm to estimate the Per-cluster Potential functions (at each CM) as well as the Lagrange multipliers (LM) (at each BS). We show that the proposed distributive online learning algorithm converges almost surely (with probability 1). By exploiting the birth-death structure of the queue dynamics, we further decompose the Per-cluster Potential function into sum of Per-cluster Per-user Potential functions and formulate the instantaneous power allocation as a Per-stage QSI-aware Interference Game played among all the CMs. We also propose a QSI-aware Simultaneous Iterative Water-filling Algorithm (QSIWFA) and show that it can achieve the Nash Equilibrium (NE)