Risk quantification in grade variability of gold deposits using sequential Gaussian simulation

Risk quantification in grade is critical for mine design and planning.Grade uncertainty is assessed using multiple grade realizations,from geostatistical conditional simulations,which are effective to evaluate local or global uncertainty by honouring spatial correlation structures.The sequential Gaussian conditional simulation was used to assess uncertainty of grade estimates and illustrate simulated models in Sivas gold deposit,Turkey.In situ variability and risk quantification of the gold grade were assessed by probabilistic approach based on the sequential Gaussian simulations to yield a series of conditional maps characterized by equally probable spatial distribution of the gold grade for the study area.The simulation results were validated by a number of tests such as descriptive statistics,histogram,variogram and contour map reproductions.The case study demonstrates the efficiency of the method in assessing risk associated with geological and engineering variable such as the gold grade variability and distribution.The simulated models can be incorporated into exploration,exploitation and scheduling of the gold deposit.

Quantification and assessment of fault uncertainty and risk using stochastic conditional simulations

The effect of geological uncertainty on the development and mining of underground coal deposits is a key issue for longwall mining, as the presence of faults generates substantial monetary losses. This paper develops a method for the conditional simulation of fault systems and uses the method to quantify and assess fault uncertainty. The method is based on the statistical modelling of fault attributes and the simulation of the locations of the centres of the fault traces. Fault locations are generated from the thinning of a Poisson process using a spatially correlated probability field. The proposed algorithm for simulating fault traces takes into account soft data such as geological interpretations and geomechanical data. The simulations generate realisations of fault populations that reproduce observed faults, honour the statistics of the fault attributes, and respect the constraints of soft data, providing the means to thereby model and assess the related fault uncertainty.

Risk Assessment Based on Fault Tree Analysis for Damaged Pipe Repair During Operation in Petrochemical Plant

In petrochemical plant, the in-operation repairing is usually a repairing strategy with pressured inoperation repairing for avoiding huge economic losses caused by unplanned shutdown when some slight local leakage happens in pipes. This paper studies the effects of repairing strategies on the failure probability of the pipe systems in process industries based on the time-average fault tree approach, especially the in-operation repairing strategies including pressured in-operation repairing activities. The fault tree model can predict the effect of different repairing plans on the pipe failure probability, which is significant to the optimization of the repairing plans. At first pipes are distinguished into four states in this model, i.e., successive state, flaw state, leakage state and failure state. Then the fault tree approach, which is usually applied in the studies of dynamic equipment, is adopted to model the pipe failure. Moreover, the effect of pressured in-operation repairing is also considered in the model. In addition, this paper proposes a series of time-average parameters of the fault tree model, all of which are used to calculate node parameters of the fault tree model. At last, a practical case is calculated based on the fault tree model in a repairing activity of pipe thinning.

DCEFM Model for Emergency Risk Assessment of Ship Inflow

This paper proposes a risk assessment model considering danger zone,capsizing time,and evaluation time factors(DCEFM)to quantify the emergency risk of ship inflow and calculate the degree of different factors to the emergency risk of water inflow.The DCEFM model divides the water inflow risk factors into danger zone,capsizing time,and evacuation time factors.The danger zone,capsizing time,and evacuation factors are calculated on the basis of damage stability probability,the numerical simulation of water inflow,and personnel evacuation simulation,respectively.The risk of a capsizing scenario is quantified by risk loss.The functional relationship between the location of the danger zone and the probability of damage,the information of breach and the water inflow time,the inclination angle and the evacuation time,and the contribution of different factors to the risk model of ship water inflow are obtained.Results of the DCEFM show that the longitudinal position of the damaged zone and the area of the breach have the greatest impact on the risk.A simple local watertight plate adjustment in the high-risk area can improve the safety of the ship.

Dynamic risk assessment of gas pipeline operation process by fusing visual and olfactory monitoring

With the rapid increase in urban gas consumption,the frequency of maintenance and repair of gas pipelines has escalated,leading to a rise in safety accidents during these processes.The traditional manual supervision model presents challenges such as inaccurate monitoring results,incomplete risk factor analysis,and a lack of quantitative risk assessment.This research focuses on developing a dynamic risk assessment technology for gas emergency repair operations by integrating the monitoring outcomes of artificial olfactory for gas leakage information and video object recognition for visual safety factor monitoring data.To quantitatively evaluate the risk of the operation process,a three-dimensional risk assessment model combining gas leakage with riskcorrelated sensitivity was established as well as a separate three-dimensional risk assessment model integrating visual risk factors with predictable risk disposition.Furthermore,a visual risk quantification expression mode based on the risk matrix-radar map method was introduced.Additionally,a risk quantification model based on the fusion of visual and olfactory results was formulated.The verification results of simulation scenarios based on field data indicate that the visual-olfactory fusion risk assessment method can more accurately reflect the dynamic risk level of the operation process compared to simple visual safety factor monitoring.The outcomes of this research can contribute to the identification of safety status and early warning of risks related to personnel,equipment,and environmental factors in emergency repair operations.Moreover,these results can be extended to other operational scenarios,such as oil and gas production stations and long-distance pipeline operations.

An Economic Optimization Method for Demand-side Energy-storage Accident Backup Assisted Deep Peaking of Thermal Power Units

With the large-scale penetration of new energy such as wind power,its anti-modulation peak characteristics have increased challenges in power systems.Therefore,an economic optimization method for depth peak regulation and the depth of the emergency of the Energy storage(ES)accident on the demand side is proposed.First,a quantitative model of unplanned disconnection risk caused by weather state,load level,and fault type is constructed to obtain the spare and available ES capacity.Therefore,a deep peak regulation(DPR)economic optimization model is established to minimize the fuel injection cost of thermal power units,including ES accident standby,unit damage,and fuel demand.The particle swarm optimization algorithm is used to simulate the modified IEEE 30-node system.Based on the results,the DPR of ES accident standby can reduce the wind abandonment rate by 1.1%and the total peak adjustment cost by 33.5%under class-A weather.In class-C weather,the wind abandonment ratio can be reduced by 4.19%,reducing the cost of the total adjustment peak by 31.4%.The multiple purposes of improving the power grid modulation,wind power,and the standby utilization of ES accidents can be achieved.