scholarly journals Digital Twins with Distributed Particle Simulation for Mine-to-Mill Material Tracking

Minerals ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 524
Author(s):  
Martin Servin ◽  
Folke Vesterlund ◽  
Erik Wallin
Keyword(s):  
Material Properties ◽  
Granular Media ◽  
Simulation Models ◽  
Particle Simulation ◽  
Digital Twins ◽  
Processed Material ◽  
Real Material ◽  
Using Data ◽  
Downstream Processes ◽  
Combined Data

Systems for transport and processing of granular media are challenging to analyse, operate and optimise. In the mining and mineral processing industries, these systems are chains of processes with a complex interplay among the equipment, control and processed material. The material properties have natural variations that are usually only known at certain locations. Therefore, we explored a material-oriented approach to digital twins with a particle representation of the granular media. In digital form, the material is treated as pseudo-particles, each representing a large collection of real particles of various sizes, shapes and mineral properties. Movements and changes in the state of the material are determined by the combined data from control systems, sensors, vehicle telematics and simulation models at locations where no real sensors could see. The particle-based representation enables material tracking along the chain of processes. Each digital particle can act as a carrier of observational data generated by the equipment as it interacts with the real material. This make it possible to better learn the material properties from process observations and to predict the effect on downstream processes. We tested the technique on a mining simulator and demonstrated the analysis that can be performed using data from cross-system material tracking.

scholarly journals Reduction of Dynamic Impacts in Block Made of Concrete - Rubber Composites

2018 ◽  
Vol 14 (1) ◽  
pp. 61-68 ◽  
Author(s):  
Maciej Major ◽  
Izabela Major ◽  
Daniela Kuchárová ◽  
Krzysztof Kuliński
Keyword(s):  
Numerical Analysis ◽  
Dynamic Load ◽  
Material Properties ◽  
Material Model ◽  
Damping Factor ◽  
Rubber Composites ◽  
Elastic Deformations ◽  
Real Material ◽  
Additional Protection ◽  
Rubber Concrete

AbstractThe paper presents numerical analysis of block made of three layers: concrete with I-shape rubber pads, space filled with air and concrete with embedded cross rubber pads, respectively. The block is subjected to the dynamic load. To the analysis as rubber the hyperelastic incompressible Zahorski material model was assumed. This material well describes the real material properties in the range of large elastic deformations. Embedded rubber pads provide an additional protection against the transversal dynamic load. ADINA software was utilized to perform numerical analysis of determining the percentage damping factor of rubber-concrete composite in comparison with block made of concrete.

Experimental Investigation of Mechanical Behavior of a Polyamide before and after Constrained Groove Pressing Process

2018 ◽  
Vol 36 ◽  
pp. 25-36
Author(s):  
Mohammed Salah Bennouna ◽  
Benaoumeur Aour ◽  
Fatiha Bouaksa ◽  
Saad Hamzaoui
Keyword(s):  
Experimental Investigation ◽  
Mechanical Behavior ◽  
Tensile Properties ◽  
Material Properties ◽  
Hold Time ◽  
Thermoplastic Polymer ◽  
Processed Material ◽  
Before And After ◽  
Number Of Cycles ◽  
Number Of Passes

In this paper an experimental investigation of mechanical behavior of a thermoplastic polymer (polyamide PA 66) processed by constrained groove pressing (CGP) using several passes is presented. To this end, corrugating and straightening tools are designed and manufactured. The effects of the number of passes and the hold time on the mechanical behavior of the polyamide have been highlighted. The obtained results show that the material properties and the microstructure are significantly altered under CGP process. It has been found that the microhardness and the tensile properties have been progressed accordingly to the number of cycles, especially when the samples are processed using a hold time of five minutes. Hence, it can be concluded that this latter plays a very important role on the reorientation and stabilization of the microstructure when the processed material is a polymer.

A new approach for determining rice critical nitrogen concentration

2011 ◽  
Vol 149 (5) ◽  
pp. 633-638 ◽  
Author(s):  
R. CONFALONIERI ◽  
C. DEBELLINI ◽  
M. PIRONDINI ◽  
P. POSSENTI ◽  
L. BERGAMINI ◽  
...  
Keyword(s):  
Nutritional Status ◽  
Cropping Systems ◽  
Nitrogen Concentration ◽  
Simulation Models ◽  
Crop Models ◽  
N Concentration ◽  
Alternative Approach ◽  
Using Data ◽  
The University ◽  
Critical N Concentration

SUMMARYA reliable evaluation of crop nutritional status is crucial for supporting fertilization aiming at maximizing qualitative and quantitative aspects of production and reducing the environmental impact of cropping systems. Most of the available simulation models evaluate crop nutritional status according to the nitrogen (N) dilution law, which derives critical N concentration as a function of above-ground biomass. An alternative approach, developed during a project carried out with students of the Cropping Systems Masters course at the University of Milan, was tested and compared with existing models (N dilution law and approaches implemented in EPIC and DAISY models). The new model (MAZINGA) reproduces the effect of leaf self-shading in lowering plant N concentration (PNC) through an inverse of the fraction of radiation intercepted by the canopy. The models were tested using data collected in four rice (Oryza sativaL.) experiments carried out in Northern Italy under potential and N-limited conditions. MAZINGA was the most accurate in identifying the critical N concentration, and therefore in discriminating PNC of plants growing under N-limited and non-limited conditions, respectively. In addition, the present work proved the effectiveness of crop models when used as tools for supporting education.

scholarly journals Modeling Approaches to Predicting Persistent Hotspots in SCORE Studies for Gaining Control of Schistosomiasis Mansoni in Kenya and Tanzania

2019 ◽  
Vol 221 (5) ◽  
pp. 796-803 ◽  
Author(s):  
Ye Shen ◽  
Meng-Hsuan Sung ◽  
Charles H King ◽  
Sue Binder ◽  
Nupur Kittur ◽  
...  
Keyword(s):  
Linear Models ◽  
Predictive Accuracy ◽  
Infection Intensity ◽  
Intensity Of Infection ◽  
Satisfactory Performance ◽  
Total Prevalence ◽  
Stable Performance ◽  
Using Data ◽  
Predictive Approaches ◽  
Combined Data

Abstract Background Some villages, labeled “persistent hotspots (PHS),” fail to respond adequately in regard to prevalence and intensity of infection to mass drug administration (MDA) for schistosomiasis. Early identification of PHS, for example, before initiating or after 1 or 2 years of MDA could help guide programmatic decision making. Methods In a study with multiple rounds of MDA, data collected before the third MDA were used to predict PHS. We assessed 6 predictive approaches using data from before MDA and after 2 rounds of annual MDA from Kenya and Tanzania. Results Generalized linear models with variable selection possessed relatively stable performance compared with tree-based methods. Models applied to Kenya data alone or combined data from Kenya and Tanzania could reach over 80% predictive accuracy, whereas predicting PHS for Tanzania was challenging. Models developed from one country and validated in another failed to achieve satisfactory performance. Several Year-3 variables were identified as key predictors. Conclusions Statistical models applied to Year-3 data could help predict PHS and guide program decisions, with infection intensity, prevalence of heavy infections (≥400 eggs/gram of feces), and total prevalence being particularly important factors. Additional studies including more variables and locations could help in developing generalizable models.

scholarly journals Spatial and Seasonal Variations and Inter-Relationship in Fitted Model Parameters for Rainfall Totals across Australia at Various Timescales

Climate ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 4 ◽  
Author(s):  
Md Masud Hasan ◽  
Barry F. W. Croke ◽  
Fazlul Karim
Keyword(s):  
Probabilistic Models ◽  
Dispersion Model ◽  
Synthetic Data ◽  
Daily Rainfall ◽  
Seasonal Rainfall ◽  
Model Parameters ◽  
Exponential Dispersion Model ◽  
Gamma Distributions ◽  
Using Data ◽  
Combined Data

Probabilistic models are useful tools in understanding rainfall characteristics, generating synthetic data and predicting future events. This study describes the results from an analysis on comparing the probabilistic nature of daily, monthly and seasonal rainfall totals using data from 1327 rainfall stations across Australia. The main objective of this research is to develop a relationship between parameters obtained from models fitted to daily, monthly and seasonal rainfall totals. The study also examined the possibility of estimating the parameters for daily data using fitted parameters to monthly rainfall. Three distributions within the Exponential Dispersion Model (EDM) family (Normal, Gamma and Poisson-Gamma) were found to be optimal for modelling the daily, monthly and seasonal rainfall total. Within the EDM family, Poisson-Gamma distributions were found optimal in most cases, whereas the normal distribution was rarely optimal except for the stations from the wet region. Results showed large differences between regional and seasonal ϕ-index values (dispersion parameter), indicating the necessity of fitting separate models for each season. However, strong correlations were found between the parameters of combined data and those derived from individual seasons (0.70–0.81). This indicates the possibility of estimating parameters of individual season from the parameters of combined data. Such relationship has also been noticed for the parameters obtained through monthly and daily models. Findings of this research could be useful in understanding the probabilistic features of daily, monthly and seasonal rainfall and generating daily rainfall from monthly data for rainfall stations elsewhere.

Use of sensor-determined behaviours to develop algorithms for pasture intake by individual grazing cattle

2017 ◽  
Vol 68 (12) ◽  
pp. 1091 ◽  
Author(s):  
P. L. Greenwood ◽  
D. R. Paull ◽  
J. McNally ◽  
T. Kalinowski ◽  
D. Ebert ◽  
...  
Keyword(s):  
Simulation Models ◽  
Classification Model ◽  
Pasture Management ◽  
Concentrate Supplementation ◽  
Grazing Cattle ◽  
Sensor Devices ◽  
Measurement Units ◽  
Using Data ◽  
Prediction And Simulation ◽  
Further Development

Practical and reliable measurement of pasture intake by individual animals will enable improved precision in livestock and pasture management, provide input data for prediction and simulation models, and allow animals to be ranked on grazing efficiency for genetic improvement. In this study, we assessed whether pasture intake of individual grazing cattle could be estimated from time spent exhibiting behaviours as determined from data generated by on-animal sensor devices. Variation in pasture intake was created by providing Angus steers (n = 10, mean ± s.d. liveweight 650 ± 77 kg) with differing amounts of concentrate supplementation during grazing within individual ryegrass plots (≤0.22 ha). Pasture dry matter intake (DMI) for the steers was estimated from the slope (kg DM day–1) of the regression of total pasture DM per plot on intake over an 11-day period. Pasture DM in each plot, commencing with ≤2 t DM ha–1, was determined by using repeatedly calibrated pasture height and electronic rising plate meters. The amounts of time spent grazing, ruminating, walking and resting were determined for the 10 steers by using data from collar-mounted, inertial measurement units and a previously developed, highly accurate, behaviour classification model. An initial pasture intake algorithm was established for time spent grazing: pasture DMI (kg day–1) = –4.13 + 2.325 × hours spent grazing (P = 0.010, r2 = 0.53, RSD = 1.65 kg DM day–1). Intake algorithms require further development, validation and refinement under varying pasture conditions by using sensor devices to determine specific pasture intake behaviours coupled with established methods for measuring pasture characteristics and grazing intake and selectivity.

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