The formation of ore mineral deposits on the Moon: A feasibility study

Most of the ore deposits on Earth are the direct result of formation by hydrothermal solutions. Analogous mineral concentrations do not occur on the Moon, however, because of the absence of water. Stratified ore deposits form in layered instrusives on Earth due to fractional crystallization of magma and crystal settling of high-density minerals, particularly chromium in the mineral chromite. We have evaluated the possibility of such mineral deposition on the Moon, based upon considerations of 'particle settling velocities' in lunar vs. terrestrial magmas. A first approximation of Stoke's Law would seem to indicate that the lower lunar gravity (1/6 terrestrial) would result in slower crystal settling on the Moon. However, the viscosity of the silicate melt is the most important factor affecting the settling velocity. The viscosities of typical lunar basaltic melts are 10-100 times less than their terrestrial analogs. These lower viscosities result from two factors: (1) lunar basaltic melts are typically higher in FeO and lower in Al2O3, Na2O, and K2O than terrestrial melts; and (2) lunar igneous melts and phase equilibria tend to be 100-150 C higher than terrestrial, largely because of the general paucity of water and other volatile phases on the Moon. Therefore, particle settling velocities on the Moon are 5-10 times greater than those on Earth. It is highly probable that stratiform ore deposits similar to those on Earth exist on the Moon. The most likely ore minerals involved are chromite, ilmenite, and native FeNi metal. In addition, the greater settling velocities of periodotite in lunar magmas indicate that the buoyancy effects of the melt are less than on Earth. Consequently, the possibility is considerably less than on Earth of deep-seated volcanism transporting upper mantle/lower crustal xenoliths to the surface of the Moon, such as occurs in kimberlites on Earth

A Study of the Prediction of Ammonium Bisulfate Formation Temperature by Artificial Intelligence

Ammonium bisulfate (ABS) is an acidic deposit that can form on the metal elements of air preheaters in power boilers, leading to unit operational issues. As a byproduct of the Selective Catalytic Reduction (SCR) systems for nitrogen oxide (NOx) emissions control, ABS could result in unit efficiency deterioration, even unit outage. ABS formation temperature is an important factor in controlling the issues associated with ABS fouling problems. If the ABS formation temperature could be monitored, the ABS deposition location could be identified. Subsequently, preventative actions could be taken to avoid ABS fouling to develop into a serious operational problem, such as air preheater plugging. This study deals with indirect predictive models of ABS formation temperature. Five models were developed based on data mining technologies, using actual power plant data. Data composed of 14,230 samples, from 49 variables were used in the study. In the modeling, Principal Component Analysis (PCA) and Sensitivity Analysis (SA) were used to reduce the number of variables in the data set. K-Means Clustering (KMC) was also employed to compress training samples. Neural Networks (NN) and Support Vector Machine (SVM) were used for data modeling. Model results were validated with ABS formation temperatures measured with an ABS dew-point probe. A SA was performed to determine the impact of individual variables on the ABS formation process. It was found that four unit variables: SO2 stack concentration, SCR gas outlet temperature, SCR inlet NOx concentration and dilution skid ammonia flow, can provide a good representation of the data set for ABS formation temperature prediction. The most accurate predictive model consists of a sequence of KMC and SVM. This approach can predict ABS formation temperature within a 9% error from the physical measurement