Real-time reconciliation of mineral processing plant data using bilinear material balance equations coupled to empirical dynamic models

Conventional UDS models are mechanistic which though appropriate for design purposes are less well suited to real-time control because they are slow running, difficult to calibrate, difficult to re-calibrate in real time and have trouble handling noisy data. At Salford University a novel hybrid of dynamic and empirical modelling has been developed, to combine the speed of the empirical model with the ability to simulate complex and non-linear systems of the mechanistic/dynamic models. This paper details the ‘knowledge acquisition module’ software and how it has been applied to construct a model of a large urban drainage system. The paper goes on to detail how the model has been linked with real-time radar data inputs from the MARS c-band radar.

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Towards real-time fire data synthesis using numerical simulations

Journal of Fire Sciences ◽

10.1177/0734904121993449 ◽

2021 ◽

pp. 073490412199344

Author(s):

Wolfram Jahn ◽

Frane Sazunic ◽

Carlos Sing-Long

Keyword(s):

Real Time ◽

Computational Fluid Dynamic ◽

Dynamic Models ◽

Fluid Dynamic ◽

Near Field ◽

Computing Time ◽

Temperature Correction ◽

Dynamic Simulations ◽

Conservation Of Energy ◽

Fire Scenarios

Synthesising data from fire scenarios using fire simulations requires iterative running of these simulations. For real-time synthesising, faster-than-real-time simulations are thus necessary. In this article, different model types are assessed according to their complexity to determine the trade-off between the accuracy of the output and the required computing time. A threshold grid size for real-time computational fluid dynamic simulations is identified, and the implications of simplifying existing field fire models by turning off sub-models are assessed. In addition, a temperature correction for two zone models based on the conservation of energy of the hot layer is introduced, to account for spatial variations of temperature in the near field of the fire. The main conclusions are that real-time fire simulations with spatial resolution are possible and that it is not necessary to solve all fine-scale physics to reproduce temperature measurements accurately. There remains, however, a gap in performance between computational fluid dynamic models and zone models that must be explored to achieve faster-than-real-time fire simulations.

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Optimal control of a novel uninterrupted multi-speed transmission for hybrid electric mining trucks

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering ◽

10.1177/0954407018821524 ◽

2019 ◽

Vol 233 (12) ◽

pp. 3235-3245 ◽

Cited By ~ 2

Author(s):

Weiwei Yang ◽

Jiejunyi Liang ◽

Jue Yang ◽

Nong Zhang

Keyword(s):

Dynamic Programming ◽

Real Time ◽

Control Strategy ◽

Dynamic Models ◽

Fuel Efficiency ◽

Real Time Control ◽

Traction Motor ◽

Time Control ◽

Power Split ◽

Shift Strategy

Considering the energy consumption and specific performance requirements of mining trucks, a novel uninterrupted multi-speed transmission is proposed in this paper, which is composed of a power-split device, and a three-speed lay-shaft transmission with a traction motor. The power-split device is adapted to enhance the efficiency of the engine by adjusting the gear ratio continuously. The three-speed lay-shaft transmission is designed based on the efficiency map of traction motor to guarantee the drivability. The combination of the power-split device and three-speed lay-shaft transmission can realize uninterrupted gear shifting with the proposed shift strategy, which benefits from the proposed adjunct function by adequately compensating the torque hole. The detailed dynamic models of the system are built to verify the effectiveness of the proposed shift strategy. To evaluate the maximum fuel efficiency that the proposed uninterrupted multi-speed transmission could achieve, dynamic programming is implemented as the baseline. Due to the “dimension curse” of dynamic programming, a real-time control strategy is designed, which can significantly improve the computing efficiency. The simulation results demonstrate that the proposed uninterrupted multi-speed transmission with dynamic programming and real-time control strategy can improve fuel efficiency by 11.63% and 8.51% compared with conventional automated manual transmission system, respectively.

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Short Term Injection Re-Distribution STIR: Real-Time Waterflood Optimization Technique Using Advanced Data Analytics

10.2118/205593-ms ◽

2021 ◽

Author(s):

Gaurav Modi ◽

Manu Ujjwal ◽

Srungeer Simha

Keyword(s):

Real Time ◽

Data Analytics ◽

Optimization Technique ◽

Material Balance ◽

Oil Production ◽

Data Driven ◽

Petroleum Engineering ◽

Short Term ◽

Injection Water ◽

Facility Level

Abstract Short Term Injection Re-distribution (STIR) is a python based real-time WaterFlood optimization technique for brownfield assets that uses advanced data analytics. The objective of this technique is to generate recommendations for injection water re-distribution to maximize oil production at the facility level. Even though this is a data driven technique, it is tightly bounded by Petroleum Engineering principles such as material balance etc. The workflow integrates and analyse short term data (last 3-6 months) at reservoir, wells and facility level. STIR workflow is divided into three modules: Injector-producer connectivity Injector efficiency Injection water optimization First module uses four major data types to estimate the connectivity between each injector-producer pair in the reservoir: Producers data (pressure, WC, GOR, salinity) Faults presence Subsurface distance Perforation similarity – layers and kh Second module uses connectivity and watercut data to establish the injector efficiency. Higher efficiency injectors contribute most to production while poor efficiency injectors contribute to water recycling. Third module has a mathematical optimizer to maximize the oil production by re-distributing the injection water amongst injectors while honoring the constraints at each node (well, facility etc.) of the production system. The STIR workflow has been applied to 6 reservoirs across different assets and an annual increase of 3-7% in oil production is predicted. Each recommendation is verified using an independent source of data and hence, the generated recommendations align very well with the reservoir understanding. The benefits of this technique can be seen in 3-6 months of implementation in terms of increased oil production and better support (pressure increase) to low watercut producers. The inherent flexibility in the workflow allows for easy replication in any Waterflooded Reservoir and works best when the injector well count in the reservoir is relatively high. Geological features are well represented in the workflow which is one of the unique functionalities of this technique. This method also generates producers bean-up and injector stimulation candidates opportunities. This low cost (no CAPEX) technique offers the advantages of conventional petroleum engineering techniques and Data driven approach. This technique provides a great alternative for WaterFlood management in brownfield where performing a reliable conventional analysis is challenging or at times impossible. STIR can be implemented in a reservoir from scratch in 3-6 weeks timeframe.

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Comparison of operator performance in a mineral processing plant

2010 IEEE International Conference on Systems, Man and Cybernetics ◽

10.1109/icsmc.2010.5641792 ◽

2010 ◽

Cited By ~ 1

Author(s):

Janne Pietila ◽

Olli Haavisto

Keyword(s):

Mineral Processing ◽

Processing Plant ◽

Operator Performance

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Integrated Digital Production Platform with the Reservoir Dynamic Models for a Giant Gas Condensate Reservoir in Real Time

10.2118/203406-ms ◽

2020 ◽

Author(s):

Ayesha Ahmed Abdulla Salem Alsaeedi ◽

Fahed Ahmed AlHarethi ◽

Eduard Latypov ◽

Muhammad Ali Arianto ◽

Nagaraju Reddicharla ◽

...

Keyword(s):

Real Time ◽

Dynamic Models ◽

Gas Condensate ◽

Production Platform ◽

Digital Production ◽

Condensate Reservoir

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Multi-model forecasts of the ongoing Ebola epidemic in the Democratic Republic of Congo, March–October 2019

Journal of The Royal Society Interface ◽

10.1098/rsif.2020.0447 ◽

2020 ◽

Vol 17 (169) ◽

pp. 20200447

Author(s):

Kimberlyn Roosa ◽

Amna Tariq ◽

Ping Yan ◽

James M. Hyman ◽

Gerardo Chowell

Keyword(s):

Real Time ◽

Dynamic Models ◽

Democratic Republic ◽

Democratic Republic Of Congo ◽

Epidemiological Data ◽

World Health ◽

Logistic Growth ◽

Richards Model ◽

Wide Range ◽

Model Approach

The 2018–2020 Ebola outbreak in the Democratic Republic of the Congo is the first to occur in an armed conflict zone. The resulting impact on population movement, treatment centres and surveillance has created an unprecedented challenge for real-time epidemic forecasting. Most standard mathematical models cannot capture the observed incidence trajectory when it deviates from a traditional epidemic logistic curve. We fit seven dynamic models of increasing complexity to the incidence data published in the World Health Organization Situation Reports, after adjusting for reporting delays. These models include a simple logistic model, a Richards model, an endemic Richards model, a double logistic growth model, a multi-model approach and two sub-epidemic models. We analyse model fit to the data and compare real-time forecasts throughout the ongoing epidemic across 29 weeks from 11 March to 23 September 2019. We observe that the modest extensions presented allow for capturing a wide range of epidemic behaviour. The multi-model approach yields the most reliable forecasts on average for this application, and the presented extensions improve model flexibility and forecasting accuracy, even in the context of limited epidemiological data.

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