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By B. Erik Ydstie, Tor Lindstad, Vianey Garcia-Osorio

"As a result of the high temperatures present in a Carbothermic Aluminum Process the content of aluminum and aluminum sub-oxide in the gases leaving this process is high. These gases as well as energy must be recovered. In a Vapor Recovery Unit were the aluminum compounds react in a series of Heterogeneous Non-catalytic Reactions forming solid, liquid and gas products. The different product phases depend on the temperature present in the system. In this work we present dynamic model of the Vapor Recovery Unit of a conceptual Carbothermic Aluminum Process. Because of the complexity of the system several models are developed and coupled to capture the essential physics of the multiphase reaction problem. A Shrinking Core Model describes the reaction mechanisms. Then material and energy balances are coupled with the thermodynamics properties from the FACT database of thermodynamic properties, resulting in a system of nonlinear Partial Differential Equations. In the paper we describe the model development and governing equations for the reaction mechanisms and fluid flow. Finally we review the numerical schemes created for the solution of the model system. The model can be used for control and design purposes.IntroductionThe Carbothennic Processes for producing Aluminum can be divided into two groups (Motzfeldt et al. (1989)) In the first approach pure alumina is produced and reduced to pure aluminum. In the second approach an alloy (e.g. aluminum and silicon) is made and then aluminum is produced from the alloy. The history of both processes has been described by Ginsberg et al. (1965). At least three aluminum producers began largescale testing of this process in the 60's Alcan, Pechey-Ugine and Reynolds. Alcoa did some smaller scale experiments.Alcoa was one of the first companies to produce aluminum by Carbothennic Reduction (Foster et al. (1956)) and US Patent 2,829,961 April 8, 1958. In this process a carbon containing molten aluminum phase was a floated on top of an oxy-carbide melt. During the cooling the dissolved carbon deposited as aluminum carbide. The carbide was then separated from the molten aluminum by a fluxing agent. This process was successful but economic unfeasible."

Jan 1, 2001

By V. Srivastava, T. Ghosh

"Modeling and Simulation of SAG Mill has been widely used in design and optimization of mill performance in terms of its power draw, processing capacity and product size distribution. However, these models are solved under steady approximation and do not provide any information on mill charge distribution in real time. This paper attempts to characterize mill contents by solving Whiten’s first order contents based model in Matlab/Simulink environment. The parameters in the model such as breakage rate (S), discharge rate (D) and appearance function (A) were estimated utilizing process and design data collected from a gold mine operating in Alaska coupled with nonlinear parameter estimation scheme. This model is then utilized to predict dynamic response of other key operational variables such as mill power, bearing pressure, charge level and product size distribution. The transient response of mill behavior with respect to changes in feed size distribution, tonnage and mill feed water is also presented. This dynamic simulation approach can be used for practicing different control strategy and training purposes. The observed response of mentioned variables was validated utilizing dynamic response data from the plant to encompass operational characteristics of SAG Mill behavior. Keywords: SAG Mill, Dynamic Modeling, Simulation, Nonlinear Parameter Estimation. INTRODUCTION Grinding in mineral industry is one the most important steps in the process of size reduction and plays a crucial role in determining the overall economics of the milling plant. Much of the efforts focused on improving the process efficiency using modeling and simulation have been practiced by researcher and engineers. However, up till now these models were simulated under steady state approximation because of their complex nature and difficulty in programming them in a dynamic framework. Steady state simulations have been successfully utilized in circuit designing, capacity planning and pre-flowsheet optimization. The maturity in steady state models is because of its applicability and regular use in steady state simulation packages such as JKSimMet, Limn, ModSim, etc. With the advent of equipment such as SAG and AG mill has been a recent shift in the simulating these models using dynamic approach. Because of the economic benefits of these devices and ability to act both as a primary grinding device and as secondary or tertiary crushing device they have gained a large popularity and are now universal to any milling plant. However, these device poses a real challenge to engineers and researchers in terms of operation and control. The major reason attributed to problems faced is because the ore constitutes the major portion of the charge in these devices when compared to ball and rod mill. This rock charge is subjected to disturbance such as changes in feed size distribution and ore hardness which results in unstable operation and frequent mill stoppages. One way to prevent this is to measure and control these external variables, however, lack of available instrumentation and unavailability of sensors to measure these variables has foiled the understanding of complete process occurring inside the mill thereby, inhibiting development of proper control system."

Jan 1, 2017

By V. Srivastava, T. Ghosh

The modeling and simulation of semiautogenous (SAG) mills have been widely used in the design and optimization of mill performance in terms of its power draw, processing capacity and product size distribution. However, these models are solved under steady approximation and do not provide any information on mill charge distribution in real time. 7KLVSDSHUDWWHPSWVWRFKDUDFWHUL]HPLOOFRQWHQWVE\VROYLQJ:KLWHQ¶V¿UVW RUGHUFRQWHQW EDVHGPRGHOLQWKH0$7/$% Simulink environment. The parameters in the model, such as breakage rate constant, discharge rate and appearance function, were estimated using process and design data collected from a gold mine operating in Alaska coupled with a nonlinear parameter estimation scheme. This model was then used to predict the dynamic responses of other key operational variables, such as mill power, bearing pressure, charge level and product size distribution. The transient response of mill behavior with respect to changes in feed size distribution, tonnage and mill feed water is also presented. This dynamic simulation approach can be used for practicing different control strategy and training purposes. The observed response of mentioned variables was validated using dynamic response data from the plant to encompass operational characteristics of SAG mill behavior.

By Magnus Evertsson, Haijie Li, Erik Hulthen, Mats Lindqvist

"The static and dynamic models for autogenous (AG) and semi-autogenous (SAG) mills have been studied for decades. Though the effects of mixing soft and hard ores in a mill and their interactions are not widely investigated. To develop a general numerical model for AG/SAG mills, a dynamic model has been introduced, and its capability to simulate multi component ore breakage has been examined and compared with previous publications. The simulation results show that the dynamic model can cover a range of milling conditions and is able to simulate complex scenarios with soft and hard ores. The dynamic model discussed here could provide substantial potential for the controlling and optimization of the comminution circuits. INTRODUCTION In the last decades, efforts have been devoted to studying the comminution circuit due to their high energy consumption. The energy directly spent in minerals comminution was about 2% globally in the year 2000 (Maurice et al, 2003). As one important part of the comminution process, AG/SAG mills are widely used for primary grinding in minerals processing. AG/SAG mill models include static and dynamic models (Kojovic et al, 2012; Yu et al, 2014; Yu et al, 2017; Liu and Spencer, 2004; Salazar et al, 2009). Many static models have been well validated over the past decades, but they are all based on steady state assumptions. Even though plants experience a steady-state condition under certain circumstances, it can be inaccurate to simulate the system by static models under all circumstances (Asbjörnsson et al, 2013). The advantages of a dynamic model are apparent that it provides more information and insights both inside the mill and through interactions with other machines in terms of pebble crushers, conveyor belts, screens and so on. In many mines, the multi-component grinding is inevitable, and the ratio of hard and soft components in the mill strongly affects mills operation and performance. Bueno (Bueno et al, 2010; Bueno et al, 2013) studied the effects of blending hard and soft ores in AG/SAG mills and used them to update the JKSimMet toolkit which was a steady state plant simulation software. However, due to the complexity of the particle interactions such as multi-component breakage function, appearance function, in mill transportation, there has been no dynamic AG/SAG mill models developed yet for simulating multi-component."

Jan 1, 2018

By Samuel Frimpong

The interaction of oil sands with a ground articulating pipeline (GAP) system has been simulated with the dynamic model established in this work by combining the multi-body and soil dynamics with the Automatic Dynamic Analysis Mechanical Systems (ADAMS) software. Mathematical models governing the interaction of the GAP system with the oil sand terrain have been modeled based on the theory of multi-body and soil dynamics. These models consist of GAP track-oil sand contact model for generating the contact force between oil sands and track and load-deformation oil sand model for capturing the dynamic behavior of oil sands. A virtual prototype has been created and simulated in the ADAMS environment. The simulation result of the effect of load on oil sand deformation indicates that maximum deformation value increases non-linearly with load varying from 0 to 60t. This study provides a solid foundation for further research and the GAP technology development.

May 1, 2004

By Khosrow Nikkhah

Increasingly ion exchange systems are being included in design of hydrometallurgical plants for metal extraction or impurity removal. Because such systems operate sequentially and are based on resins that exchange one ion for another, store it temporarily and release it to a regenerating solution, the usual steady state heat and mass balancing tools and spreadsheets cannot be used to design them successfully. This paper outlines the role of dynamic simulation in design of ion exchange systems. Examples are given for use of dynamic simulation in design and investigation of equipment based on consideration of the effect of input criteria such as expected ion loading and elution profiles. The methodology for dynamic modeling using IDEAS in design of multi column ion exchange trains operating in parallel is presented. Results shown include the non-steady state distribution of extracted metal and limitations imposed on operating conditions. These conditions include proposed sequencing of columns for loading and elution cycles, loading, elution and recycle flow rates as well as size and choice of related ion exchange plant equipment.

Jan 1, 2003

By J. W. Matousek

Copper losses in smelter slags are :typically normalized with some form of the distribution coefficient, written as the ratio of the weight percent assays of metal in slag to the metal in matte or its inverse: L(s/m) = %(Cu)/%[Cu] or L(m/s) = %[Cu]/%(Cu) However, this simple approach ignores the dynamics of the smelting process. It overlooks the effect of the chemical work done in producing a given matte grade from concentrates of different grades -- or from a single concentrate blended with varying quantities of copper bearing precipitates or reverts. This paper examines the combined influences of matte grade and the work done in smelting on the copper content of smelting furnace slags. A simple model for the relationship is proposed.

Jan 1, 1999

By A. Chaisantikulwat

Most solid-oxide fuel cell (SOFC) modeling emphasizes steady state cell operation. However, understanding the dynamic behavior is essential to predict the performance and limitations of SOFC power systems. In this work, we demonstrate a dynamic model of a SOFC, and we simulate a feedback control scheme that can maintain output voltage despite load changes. Dynamic response is determined as the solution of coupled partial differential equations derived from conservation laws for energy, mass and electric charge. From such a model, the step responses to system input variables can be determined. Low order dynamic models that are sufficient for feedback control design can be derived from the step responses. The development of the partial differential equation model is outlined, and the limitations of the control system are discussed.

Jan 1, 2005

By C. Steyn, J. D. le Roux

A dynamic model is used to represent the single-stage closed grinding mill circuit at Mogalakwena North Concentrator. The circuit consists of a ball mill closed by a hydrocyclone. The circuit is modelled according to a semi-mechanistic non-linear dynamic model available in literature. An algebraic routine is used to fit model parameters to steady-state process data. Preliminary validation results against dynamic data indicate that the model is capable of capturing the main dynamic trends of the plant. Since classical system identification techniques proved insufficient to produce accurate models; the non-linear model can be used as a reliable source for model-predictive controller design. Keywords: Grinding, modelling, process control, parameter estimation

Jan 1, 2020

By Helen Shang, Jianjun He

In nickel and copper production, sulfur dioxide (SO2) is generated during mineral smelting. Emission of sulfur dioxide into the atmosphere causes serious health and environment issues. Sulfuric acid plants, which convert the harmful SO2 gas to commercially useful sulfuric acid product, ensures safe and environmentally sound smelter operations. The sulfuric acid plant is mainly composed of a SO2 to SO3 converter, a series of heat exchangers and SO3 absorption towers. The SO2 converter is crucial for minimizing SO2 emission in a smelter. In this paper, steady state and dynamic modelling are carried out for the converter using mole and energy conservations. Simulations are performed to investigate the effect of feed conditions on SO2 conversion. The developed model represents an important component for modelling the acid plant in a smelter and is expected to provide a fundamental tool for the future optimization and control development.

Jan 1, 2014

By Wood CJ, Davis JJ, Askew H

A dynamic model of a dense medium circuit has been developed and tested against plant data. The model shows the response of the circuit to both controlled (changes in specific gravity set point or changes in circuit coal feed rate) and uncontrolled changes (changes in raw coal moisture, loss of raw coal feed) in operating conditions. The model is built up from dynamic models of the individual sumps, pumps, pipes, splitters, magnetic separators and automatic controllers in the circuit. The model can be used for design of automatic control loops and for investigation of possible process modifications such as changes in water addition points. This paper describes the model construction, the testing of the model against actual plant data and gives some examples of its use in investigating plant response to changes in operating conditions.

Jan 1, 1978

By R. Q. Honaker

Although the fundamental principles of hindered bed classifiers are relatively simple, the complex interactions between the operating parameters provide a degree of difficulty that hinders the ability for on-line plant optimization. Thus, a dynamic population balance model was developed for determining the hindered settling velocity of particles varying in density and size. The steady-state results of the model include partition curves as a function of particle size and density as well as metallurgical data. A statistical comparison of the simulation results with those obtained from industrial units reveal a high level of accuracy with adjusted R2 values greater than 0.95.

Jan 1, 1999

By Ali Nadir Khan

Depleting copper resources and advancing technologies have challenged industries to develop more viable, adaptable and cost efficient processes using also secondary raw materials in copper production. This study is targeting to that goal by dynamic modelling of flow and heat transfer coupled with chemical kinetics in an industrial scale flash smelting furnace settler using commercial CFD software ANSYS Fluent. First, different physical phenomena occurring inside the settler, for example, settling and separation of the matte/slag phases, and heat transfer between slag/matte phases and settler walls are studied. Secondly, reaction kinetics between matte and slag, and between slag/matte and settler walls, and impurity element distribution will be studied. This would also include phase changes phenomena due to these reactions and the flow of the reaction gases inside the settler. Settling of polydispersed droplets, their coagulation, breakage, and WEEE particle behavior are further targets of the modelling work.

By M. Karamoozian

Modelling and optimisation of the power draw of large SAG mills are important due to the large power draw which modern mills require (5~10 MW). The cost of grinding is the single biggest cost within the entire process of mineral processing. Traditionally, modelling of the mill power draw has been done using empirical models. Although these models are reliable, they cannot model mills and operating conditions, which are not within the model database boundaries. Also, due to their static nature, the impact of the changing conditions on the power draw, within the mill, cannot be deter-mined using such models. Despite advances in computing power, on-line modelling of large plant scale mills could be a time consuming task. On the other hand, using on-line parameters for model-ling, result in more reliable control of SAG mill without performing any more costly tests. In this paper, SAG mill power draw is modelled using neural network approach. Six inputs, namely: solid percent, two bearing pressures, noise, recycle and feed rate were used to model the power draw. The produced model shows an acceptable correlation coefficient, R2 =0.9623.

Jan 1, 2014

By Markus A. Reuter, Antoinette van Schaik

"INTRODUCTIONGoods and products, such as passenger vehicles, are manufactured by using a wide spectrum of different raw materials to meet highest consumer requirements during use. Due to the time-varying composition and complexity of multi-component products such as cars, recycling is complicated. It involves a network of interconnected processes and material and energy flows. Improving the efficiency of the material cycle of the car to comply with the recycling targets as imposed by EU legislation, requires insight into all aspects influencing the closing of the material cycle (dynamics, technology, economy, legislation, etc.) and their mutual relation. In order to define the link between recycling end-of-life products, their changing composition and design a dynamic model was developed for the resource cycle of cars. The model integrates the optimisation of the material cycle with the dynamic behaviour of recycling systems."

Jan 1, 2003

By K. Nikkhah, N. Dhadli, G. Freeman

Ammoniacal pressure leaching operations have been in commercial use at the Fort Saskatchewan refinery of Sherritt International Corporation since 1954. Recent improvements have significantly increased the capacity of this circuit. The computer modeling of this process has proved to be a valuable tool in facilitating the evaluation of different operating scenarios in an effective manner. Reagent response in the ammonia leach of nickel and cobalt sulphides is examined using the dynamic process simulation capabilities of IDEAS(TM) software. The dynamic simulation enables accurate measurement of the sensitivity of the process to variables such as temperature, feed rate and ammonia concentration as well as compartmental analysis of metals concentration, pulp density, vent gas composition, and the formation of specific complexes. The complexity of the ammonia leach process makes it a logical choice for evaluation by computational analysis. The methodology for dynamic modeling of a pressure leach process is presented.

Jan 1, 2005

By H. W. Smith, D. Guerin

"This paper describes the development of a dynamic model for a full- scale (64 tonnes/hr) grinding circuit consisting of an open-circuit rod mill feeding a ball mill operating in closed circuit. The object of constructing such a model is to investigate the static and dynamic behaviour of potential control schemes. Model selection, data processing and parameter estimation are discussed, and static and dynamic results of simulations are presented. Conclusions are drawn with regard to the adequacy of the model for such studies, and likely limitations on control of such circuits.IntroductionThe model developed here is intended to represent the grinding behaviour of the grinding circuit at the Lake Dufault division of Falconbridge Copper Mines Ltd. This circuit has been fully described elsewhere. At the time of the work described, the ball mill discharge was fed to a four-cell flotation bank , the tailings of which are combined with the rod mill discharge in the cyclone feed pump sump. As the emphasis in this study is on grinding behaviour, and as only I per cent of cyclone feed material is removed in these cells, no attempt has been made to include flotation behaviour in the simulation. One effect of the presence of flotation cells in the grinding circuit is, however, to necessitate somewhat finer grinding than might otherwise be needed: this will have effects on mill parameters.The paper is organized as follows. First the selection of models is discussed, paying particular attention to the problem of selecting simple, yet adequate, flow models for the mills. Then data gathering is described. The method of deriving mill model parameters is explained, and the results presented. Mass balancing of cyclone data and the determination of cyclone model parameters are described. The linking of the models into a full simulation is outlined, and sta tic and dynamic results presented. Finally, the results are discussed and conclusions drawn ."

Jan 1, 1980

By D. Bahrami, W. K. Asante, G. L. Danko

It is prudent to interpret atmospheric monitoring signals in real time for checking the safe limits of the air conditions in underground mines. In gassy mines, real-time evaluation increases the safety of operations. In all mines, continuous monitoring and evaluation contributes to maintaining air conditions within healthy and safe limits. Signal interpretation for safety conditions in mines is difficult for many reasons. An increase in hazardous contaminant concentrations can be predicted by signal pattern recognition, root cause analysis of rapid changes toward deterioration, and forward prediction in time using algorithms and numerical models. The paper describes an early warning system for analyzing monitored signal patterns continuously in real time as well as forward predicting the various environmental and working conditions to recognize dangerous trends that may affect safety and health in underground mines. A dynamic, numerical ventilation model with heat and gas contaminant simulation components is used for the analysis of atmospheric data. Methods and test results are discussed with numerical examples for signal propagation prediction. Several mine examples are studied using controlled, synthetic data for malfunction simulations to evaluate time delays between the detection time of suspicious signal trends and the time of dangerous threshold crossing marking an accident scenario. Delay time is found in the order of 20 min in the examples, signifying the useful time period for preventive intervention between EWS warning and the likely breakout of a following accident.

By D. Bahrami, W. K. Asante, G. L. Danko

It is prudent to interpret atmospheric monitoring signals in real time for checking the safe limits of the air conditions in underground mines. In gassy mines, real-time evaluation increases the safety of operations. In all mines, continuous monitoring and evaluation contributes to maintaining air conditions within healthy and safe limits. Signal interpretation for safety conditions in mines is difficult for many reasons. An increase in hazardous contaminant concentrations can be predicted by signal pattern recognition, root cause analysis of rapid changes toward deterioration, and forward prediction in time using algorithms and numerical models. The paper describes an early warning system for analyzing monitored signal patterns continuously in real time as well as forward predicting the various environmental and working conditions to recognize dangerous trends that may affect safety and health in underground mines. A dynamic, numerical ventilation model with heat and gas contaminant simulation components is used for the analysis of atmospheric data. Methods and test results are discussed with numerical examples for signal propagation prediction. Several mine examples are studied using controlled, synthetic data for malfunction simulations to evaluate time delays between the detection time of suspicious signal trends and the time of dangerous threshold crossing marking an accident scenario. Delay time is found in the order of 20 min in the examples, signifying the useful time period for preventive intervention between EWS warning and the likely breakout of a following accident.

By Patrick J. Hannigan

The pile foundation designer typically performs a subsurface exploration program and static pile capacity calculations before selecting a pile foundation type, length, and capacity. During construction, a pile installation criterion must be established to meet these design requirements. This installation criterion usually consists of a minimum penetration resistance or blow count to confirm pile capacity and may also include a minimum embedment requirement. This text will focus on use of wave equation analysis and dynamic measurements to meet design requirements. BEFORE THE WAVE EQUATION Since the mid-1800s, over 450 dynamic formulas for pile driving control have proliferated. In their simplest form, these formulas relate hammer energy to the work of soil resistance, or simply: W ? h = R ? s (1) Where W = ram weight h = ram drop height or stroke R = pile capacity s = pile penetration per blow

Jan 1, 1990