Value of Information Introduced by a Resource Model Updating Framework

A primary concern of the mining industry is to meet production targets, which are required and defined by customers. Deviations from these targets, in terms of quality and quantity, highly affect the economical aspect. Recently, an efficient resource model updating framework concept has been proposed aiming for the improvement of raw material quality control and process efficiency in any type of mining operation. The concept integrates online sensor measurements, obtained during production, into the resource model. In this way, due to the spatial variability, quality attributes of the blocks that will be produced in the next days or weeks are being updated based on real-time measurements. The concept has been applied in a lignite field with the aim of identifying local impurities in a lignite seam and to improve the prediction of coal quality attributes in neighbouring blocks. This paper investigates the added value of using the resource model updating framework by using the value of information analysis. The expected benefit of additional information (integration of the online sensor measurements into the resource model) is compared to a case where there is no additional information integrated into the process. These benefits are evaluated based on the economic impact determined by applying the resource model updating framework in mine planning.

     

Optimized scheduling for electric lift trucks in a sugarcane agro-industry based on thermal,biomass and solar resources

Resource scheduling for both cost and pollution minimization in the power system is so crucial. To reduce the greenhouse gas emission, employing renewable energy resources, especially solar and wind energy, and beside them plug-in hybrid electric vehicles are effective solutions. In industrial factories, using biomass resources for power generation is both economic and environmental approach. In sugarcane company, bagasse is plant fiber residue which is used as fuel. Electric lift trucks, capable of being connected to power grid, could decrease the pollution in industrial transportations. In this paper, scheduling problem for a large-scale sugarcane factory including solar resources, a thermal unit, and electric lift trucks is presented and solved by CPLEX solver in GAMS software. In order to consider uncertainties, different scenarios are noticed. To contribute better understanding of optimization problem, cost, pollution, and charging regime of electric lift trucks are carefully analyzed. The results show that implementation of the biomass electric power generation is effective for reducing cost and amount of emission.     

Optimal operation of a micro-grid containing energy resources and demand response program

In profit-based unit commitment, the objective of programming is to maximize profit and optimize generation. Practically, the gross profit depends not only on the revenue but also on the total expenditures. In this article, an efficient algorithm is suggested to assess the effect of uncertainties in determining 24-hour optimal strategy of a microgrid (MG) containing wind farms, photovoltaic, fuel cell, combined heat and power units, boiler, and energy storage devices (ESDs). The optimization problem is presented to determine the optimal points for the energy resources generation and to maximize the expected profit considering demand response (DR) programs and uncertainties. The uncertainties include wind speed, photovoltaic power generation (PVPG), market price, power, and thermal load demand. For modeling uncertainties, an effort has been made to predict uncertainties through the hybrid method of wavelet transform (WT)-artificial neural network (ANN)-imperialist competitive algorithm (ICA). In this study, three cases are assessed to confirm the performance of the proposed method. In the first case study, programing MG is isolated from the grid. In the second case study, which is grid-connected mode the WT-ANN-ICA and WT-ANN uncertainties predictions methods are compered. In the third case, which is grid-connected mode the effect of DR programs on the expected profit of energy resources is assessed.     

Watershed-wide trend analysis of temperature characteristics in Karun-Dez watershed, southwestern Iran

Trend estimation of climatic characteristics for a watershed is required to determine developing compatible strategies related to design, development, and management of water resources. In this study, the trends of the annual maximum (T _max), minimum (T _min), and mean (T _mean) air temperature; temperature anomaly (T _anomaly); and diurnal temperature range (DTR) time series at 13 meteorological stations located in the Karun-Dez watershed were analyzed using the Mann–Kendall and linear regression trend tests. The pre-whitening method was used to eliminate the influence of serial correlation on the Mann–Kendall test. The result showed increasing trends in the T _min, T _mean, and T _anomaly series at the majority of stations and decreasing trend in the T _max and DTR series. A geographical analysis of the trends revealed a broad warming trend in most of the watershed, and the cooling trends were observed only in the southern parts. Furthermore, the geographical pattern of the trends in the T _mean and T _anomaly series was similar, and the T _max data did not show any dominant trend for the whole watershed. This study provides temperature change scenarios that may be used for the design of future water resource projects in the watershed.     

Biodegradation of diazinon by the <Emphasis Type="Italic">Stenotrophomonas maltophilia</Emphasis> PS: pesticide dissipation kinetics and breakdown characterization using FTIR

A diazinon-degrading bacterium was isolated from paddy soils under flooded conditions. Biochemical characterization and 16S rRNA sequencing showed the isolate was an oxidase-positive Stenotrophomonas maltophilia. The isolate could grow on a mineral salt medium (MSM) supplemented with diazinon as the main carbon source (50 µg ml^?1) and dissipate the pesticide in a simple first-order manner with DT₅₀ and DT₉₀ of 4.54 and 15.09 days, respectively. The addition of glucose favored the bacterial growth and reduced the pesticide’s DT₅₀ and DT₉₀ to 3.39 and 11.27 days, respectively. The dissipation of diazinon in MSM was accompanied by a slight reduction of the pH which was more significant in the glucose-treated media. FTIR analysis proved the separation of the heterocyclic leaving group by hydrolyzing the ester bond and aerobic cleavage of the aromatic ring as the main pathways of diazinon degradation in the MSM. The dissipation of diazinon (150 µg g^?1) in the inoculated sterilized flooded paddy soils was biphasic, and based on this, only 31.55 µg g^?1 of the initial concentration declined with a faster rate while the rest of the residue dissipated slowly as the adsorbed phase. In the non-inoculated non-sterilized soils, diazinon dissipated more slowly with an initial lag phase.     

Neuro-fuzzy modeling based genetic algorithms for identification of geochemical anomalies in mining geochemistry

A genetic algorithm (GA)-based neuro-fuzzy approach is used for identification of geochemical anomalies by implementing a Takagi, Sugeno and Kang (TSK) type fuzzy inference system in a 5-layered feed-forward adaptive artificial neural network. This paper investigates the effectiveness of GA-based neuro-fuzzy for separating zone dispersed mineralization (ZDM) from blind mineralization, and its application for identification of geochemical anomalies in the arid landscape of the Lut metallogenic province in eastern Iran. Other classification algorithms such as metallometry, zonality, criteria, and back-propagation artificial neural network classifiers are also used for comparison. The genetic operators are carefully designed to optimize the artificial neural network, avoiding premature convergence and permutation problems. The results show that the GA-based hybrid neuro-fuzzy model can provide accurate results in comparison with those results obtained by other techniques. Neuro-fuzzy and GA-based neuro-fuzzy techniques appear to be well-suited for routine exploration geochemistry applications. In conjunction with statistics and conventional mathematical methods, hybrid approaches can be developed and may prove a step forward in the practice of applied geochemistry.